nils-herrmann/clean_scirepeval huggingface dataset:
MT-001 TUTORIAL Taking the Mystery out of the Infamous Formula , " SNR = 6 . 02 N + 1 . 76 dB , " and Why You Should Care by

You don't have to deal with ADCs or DACs for long before running across this often quoted formula for the theoretical signal-to-noise ratio (SNR) of a converter. Rather than blindly accepting it on face value, a fundamental knowledge of its origin is important, because the formula encompasses some subtleties which if not understood can lead to significant misinterpretation of both data sheet specifications and converter performance. Remember that this formula represents the theoretical performance of a perfect N-bit ADC. You can compare the actual ADC SNR with the theoretical SNR and get an idea of how the ADC stacks up.
3D printed tablets loaded with polymeric nanocapsules: An innovative approach to produce customized drug delivery systems.

The generation of multi-functional drug delivery systems, namely solid dosage forms loaded with nano-sized carriers, remains little explored and is still a challenge for formulators. For the first time, the coupling of two important technologies, 3D printing and nanotechnology, to produce innovative solid dosage forms containing drug-loaded nanocapsules was evaluated here. Drug delivery devices were prepared by fused deposition modelling (FDM) from poly(ε-caprolactone) (PCL) and Eudragit® RL100 (ERL) filaments with or without a channelling agent (mannitol). They were soaked in deflazacort-loaded nanocapsules (particle size: 138nm) to produce 3D printed tablets (printlets) loaded with them, as observed by SEM. Drug loading was improved by the presence of the channelling agent and a linear correlation was obtained between the soaking time and the drug loading (r2=0.9739). Moreover, drug release profiles were dependent on the polymeric material of tablets and the presence of the channelling agent. In particular, tablets prepared with a partially hollow core (50% infill) had a higher drug loading (0.27% w/w) and faster drug release rate. This study represents an original approach to convert nanocapsules suspensions into solid dosage forms as well as an efficient 3D printing method to produce novel drug delivery systems, as personalised nanomedicines.
Korean Wave as Tool for Korea ’ s New Cultural Diplomacy

In recent years, there has been an influx of Korean popular culture throughout the world, including East Asia, Southeast Asia, Europe, and the Americas. Korean popular culture, also known as the “Korean Wave” (Hallyu in Korean) ranges from television dramas, movies, popular music (K-pop), dance (B-boys), video game, food, fashion, tourism, and language (Hangul). The main focus of this paper is to examine the essence of the Korean Wave and its impact on the world. In particular, this paper aims to explore the relationships between the spread of the Korean Wave and political and social changes in a global perspective. That is, does the Korean Wave affect the political position and diplomatic leverage of Korea in any meaningful way? Toward this objective, this paper first examines the relevant literature of international relations for policy and culture change, especially with regards to globalization, interdependence, soft power and world value change. Then, recent developments of the Korean Wave are reviewed and critically analyzed in order to ascertain political and policy implications for Korean diplomatic and practical directives. Finally, we will draw an interpretive conclusion and recommendations toward the plausibility of the Korean Wave as a policy tool for Korea’s cultural diplomacy.
Inter- and intrarater reliability of the Modified Modified Ashworth Scale in patients with knee extensor poststroke spasticity.

Muscle spasticity is a common motor disorder following upper motor neuron syndrome. A reliable and valid clinical tool is essential to document the effect of therapeutic interventions aimed to improve function by reducing spasticity. The Modified Ashworth Scale (MAS) is the most widely used and accepted clinical scale of spasticity. The MAS has been recently modified. The aim of this investigation was to determine the interrater and intrarater reliability of clinical test of knee extensor post-stroke spasticity graded on a Modified Modified Ashworth Scale (MMAS). Two raters scored the muscle spasticity of 15 patients with ischaemic stroke. For the inter- and intrarater reliability, two raters agreed on 80.1% and 86.6%, respectively. The Kappa values were good (kappa=0.72, SE=0.14, p<0.001) between raters and very good (kappa=0.82, SE=0.12, p<0.001) within one rater. The values of Kendall tau-b correlation were acceptable for clinical use with 0.87 (SE=0.06, p<0.001) between raters and 0.92 (SE=0.05, p<0.001) within one rater. The MMAS demonstrated reliable measurements for a single rater and between raters for measuring knee extensor post-stroke spasticity. The results encourage further study on the reliability and the validity of the scale.
Information centric networking in the IoT: experiments with NDN in the wild

This paper explores the feasibility, advantages, and challenges of an ICN-based approach in the Internet of Things. We report on the first NDN experiments in a life-size IoT deployment, spread over tens of rooms on several floors of a building. Based on the insights gained with these experiments, the paper analyses the shortcomings of CCN applied to IoT. Several interoperable CCN enhancements are then proposed and evaluated. We significantly decreased control traffic (i.e., interest messages) and leverage data path and caching to match IoT requirements in terms of energy and bandwidth constraints. Our optimizations increase content availability in case of IoT nodes with intermittent activity. This paper also provides the first experimental comparison of CCN with the common IoT standards 6LoWPAN/RPL/UDP.
Dynamic Query Modeling for Related Content Finding

While watching television, people increasingly consume additional content related to what they are watching. We consider the task of finding video content related to a live television broadcast for which we leverage the textual stream of subtitles associated with the broadcast. We model this task as a Markov decision process and propose a method that uses reinforcement learning to directly optimize the retrieval effectiveness of queries generated from the stream of subtitles. Our dynamic query modeling approach significantly outperforms state-of-the-art baselines for stationary query modeling and for text-based retrieval in a television setting. In particular we find that carefully weighting terms and decaying these weights based on recency significantly improves effectiveness. Moreover, our method is highly efficient and can be used in a live television setting, i.e., in near real time.
Designing Simple Nonlinear Filters Using Hysteresis of Single Recurrent Neurons for Acoustic Signal Recognition in Robots

In this article we exploit the discrete-time dynamics of a single neuron with self-connection to systematically design simple signal filters. Due to hysteresis effects and transient dynamics, this single neuron behaves as an adjustable low-pass filter for specific parameter configurations. Extending this neuro-module by two more recurrent neurons leads to versatile highand band-pass filters. The approach presented here helps to understand how the dynamical properties of recurrent neural networks can be used for filter design. Furthermore, it gives guidance to a new way of implementing sensory preprocessing for acoustic signal recognition in autonomous robots.
Grouped Multi-Task CNN for Facial Attribute Recognition

The main goal of facial attribute recognition is to determine various attributes of human faces, e.g. facial expressions, shapes of mouth and nose, headwears, age and race, by extracting features from the images of human faces. Facial attribute recognition has a wide range of potential application, including security surveillance and social networking. The available approaches, however, fail to consider the correlations and heterogeneities between different attributes. This paper proposes that by utilizing these correlations properly, an improvement can be achieved on the recognition of different attributes. Therefore, we propose a facial attribute recognition approach based on the grouping of different facial attribute tasks and a multi-task CNN structure. Our approach can fully utilize the correlations between attributes, and achieve a satisfactory recognition result on a large number of attributes with limited amount of parameters. Several modifications to the traditional architecture have been tested in the paper, and experiments have been conducted to examine the effectiveness of our approach.
Recurrent Autoregressive Networks for Online Multi-object Tracking

The main challenge of online multi-object tracking is to reliably associate object trajectories with detections in each video frame based on their tracking history. In this work, we propose the Recurrent Autoregressive Network (RAN), a temporal generative modeling framework to characterize the appearance and motion dynamics of multiple objects over time. The RAN couples an external memory and an internal memory. The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory. We conduct experiments on the MOT 2015 and 2016 datasets to demonstrate the robustness of our tracking method in highly crowded and occluded scenes. Our method achieves top-ranked results on the two benchmarks.
Learning Curve of Thoracoscopic Repair of Esophageal Atresia

Thoracoscopic repair of esophageal atresia is considered to be one of the more advanced pediatric surgical procedures, and it undoubtedly has a learning curve. This is a single-center study that was designed to determine the learning curve of thoracoscopic repair of esophageal atresia. The study involved comparison of the first and second five-year outcomes of thoracoscopic esophageal atresia repair. The demographics of the two groups were comparable. There was a remarkable reduction of postoperative leakage or stenosis, and recurrence of fistulae, in spite of the fact that nowadays the procedure is mainly performed by young staff members and fellows. There is a considerable learning curve for thoracoscopic repair of esophageal atresia. Centers with the ambition to start up a program for thoracoscopic repair of esophageal atresia should do so with the guidance of experienced centers.
Conceptual framework of sustainable corporate entrepreneurship

Recently corporate entrepreneurship (CE) is getting considerable attention amongst researchers, especially in the developed economies. Looking at the significance of CE, research communities from the developing countries are also paying substantial attention towards corporate entrepreneurship. The literature has evolved from entrepreneurship to corporate entrepreneurship and is now moving towards sustainable corporate entrepreneurship. Initially, it was considered that corporate entrepreneurship had three main dimensions, such as innovativeness, proactiveness and risk taking. Later, competitive aggressiveness and autonomy were also introduced as corporate entrepreneurship dimensions. It has been observed that these dimensions play an important role in the economic growth for large businesses. Similarly, sustainability has also been getting attention, after it had been emphasised in modern literature and by various international organisations. Sustainability covers social, environmental and economic issues, thus recommending the importance of both corporate entrepreneurship and sustainability. This study aims to propose a framework of sustainable corporate entrepreneurship and firm performance for Malaysian public listed firms. This proposed framework can be generalised to apply to other countries. Sustainability has been incorporated with corporate entrepreneurship. In order to measure the entrepreneurial activities, this framework proposes firm performance as a dependent variable for a sustainable corporate entrepreneurship framework.
Speculative Markov blanket discovery for optimal feature selection

In this paper we address the problem of learning the Markov blanket of a quantity from data in an efficient manner Markov blanket discovery can be used in the feature selection problem to find an optimal set of features for classification tasks, and is a frequently-used preprocessing phase in data mining, especially for high-dimensional domains. Our contribution is a novel algorithm for the induction of Markov blankets from data, called Fast-IAMB, that employs a heuristic to quickly recover the Markov blanket. Empirical results show that Fast-IAMB performs in many cases faster and more reliably than existing algorithms without adversely affecting the accuracy of the recovered Markov blankets.
CoCoSpot: Clustering and recognizing botnet command and control channels using traffic analysis

We present CoCoSpot, a novel approach to recognize botnet command and control channels solely based on traffic analysis features, namely carrier protocol distinction, message length sequences and encoding differences. Thus, CoCoSpot can deal with obfuscated and encrypted C&C protocols and complements current methods to fingerprint and recognize botnet C&C channels. Using average-linkage hierarchical clustering of labeled C&C flows, we show that for more than 20 recent botnets and over 87,000 C&C flows, CoCoSpot can recognize more than 88% of the C&C flows at a false positive rate below 0.1%.
Discovering Basic Emotion Sets via Semantic Clustering on a Twitter Corpus

A plethora of words are used to describe the spectrum of human emotions, but how many emotions are there really, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each based around the existence of a set of basic emotions, and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we present research based on a theory that people transmit their understanding of emotions through the language they use surrounding emotion keywords. Using a labelled corpus of over 21,000 tweets, six of the basic emotion sets proposed in existing literature were analysed using Latent Semantic Clustering (LSC), evaluating the distinctiveness of the semantic meaning attached to the emotional label. We hypothesise that the more distinct the language is used to express a certain emotion, then the more distinct the perception (including proprioception) of that emotion is, and thus more basic. This allows us to select the dimensions best representing the entire spectrum of emotion. We find that Ekman’s set, arguably the most frequently used for classifying emotions, is in fact the most semantically distinct overall. Next, taking all analysed (that is, previously proposed) emotion terms into account, we determine the optimal semantically irreducible basic emotion set using an iterative LSC algorithm. Our newly-derived set (Accepting, Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a 6.1% increase in distinctiveness over Ekman’s set (Angry, Disgusted, Joyful, Sad, Scared). We also demonstrate how using LSC data can help visualise emotions. We introduce the concept of an Emotion Profile and briefly analyse compound emotions both visually and mathematically.
Analysis of Cesarean Section Rate - According to Robson's 10-group Classification.

High cesarean birth rates are an issue of international public health  Worries over such increases have led the World Health Organization to advise that Cesarean Section (CS) rates should not be more than 15%,2 with some evidence that CS rates above 15% are not associated with additional reduction in maternal and neonatal mortality and morbidity.3 Analyzing CS rates in different countries, including primary vs. repeat CS and potential reasons of these, provide important insights into the solution for reducing the overall CS rate. Robson,4 proposed a new classification system, the Robson Ten-Group Classification System to allow critical analysis according to characteristics of pregnancy (Table 1). The characteristics used are: (i) single or multiple pregnancy (ii) nulliparous, multiparous, or multiparous with a previous CS (iii) cephalic, breech presentation or other malpresentation (iv) spontaneous or induced labor (v) term or preterm births.
Estimation of an N-L-N Hammerstein-Wiener model

Estimation of a single-input single-output block-oriented model is studied. The model consists of a linear block embedded between two static nonlinear gains. Hence it is called N-L-N Hammerstein-Wiener model. First the model structure is motivated and the disturbance model is discussed. The paper then concentrates on parameter estimation. A relaxation iteration scheme is proposed by making use of a model structure in which the error is bilinear-in-parameters. This leads to a simple algorithm which minimizes the original loss function. The convergence and consistency of the algorithm are studied. In order to reduce the variance error, the obtained linear model is further reduced using frequency weighted model reduction. Simulation study will be used to illustrate the method.
Design of a Ku-band broadband U-slot microstrip antenna

In this paper, a U-slot rectangular microstrip patch antenna is designed in order to overcome the narrowband characteristic and gain broader band. The antenna has dual-band characteristics, so it has wider operating bandwidth. The antenna works at Ku-band and the center frequency is 16GHz. The characteristics are analyzed and optimized with Ansoft HFSS, the simulation results show that the absolute bandwidth and gain of the antenna unit are 2.7GHz and 8.1dB, and of the antenna array are 3.1GHz and 14.4dB. The relative bandwidth reaches to 19.4%, which is much wider than the general bandwidth of about 1% to 7%.
Word Alignment for Online Adaptive Machine Translation

A hot task in the Computer Assisted Translation scenario is the integration of Machine Translation (MT) systems that adapt sentence after sentence to the postedits made by the translators. A main role in the MT online adaptation process is played by the information extracted from source and post-edited sentences, which in turn depends on the quality of the word alignment between them. In fact, this step is particularly crucial when the user corrects the MT output with words for which the system has no prior information. In this paper, we first discuss the application of popular state-of-the-art word aligners to this scenario and reveal their poor performance in aligning unknown words. Then, we propose a fast procedure to refine their outputs and to get more reliable and accurate alignments for unknown words. We evaluate our enhanced word-aligner on three language pairs, namely English-Italian, EnglishFrench, and English-Spanish, showing a consistent improvement in aligning unknown words up to 10% absolute Fmeasure.
A General Analysis of the Convergence of ADMM

We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex. Our proof is based on a framework for analyzing optimization algorithms introduced in Lessard et al. , reducing algorithm convergence to verifying the stability of a dynamical system. This approach generalizes a number of existing results and obviates any assumptions about specific choices of algorithm parameters. On a numerical example, we demonstrate that minimizing the derived bound on the convergence rate provides a practical approach to selecting algorithm parameters for particular ADMM instances. We complement our upper bound by constructing a nearly-matching lower bound on the worst-case rate of convergence.
Touch Panel-type Dementia Assessment Scale: a new computer-based rating scale for Alzheimer's disease.

The Alzheimer's Disease Assessment Scale (ADAS) was designed as a rating scale for the severity of dysfunction in the cognitive and non-cognitive behaviours that are characteristic of persons with Alzheimer's disease. Its subscale, the ADAS-cog, is a cognitive testing instrument most widely used to measure the impact of the disease. However, the ADAS-cog takes more than 45 min to administer and requires a qualified clinical psychologist as the rater. A more comprehensive rating battery is therefore required. In the present study, we developed a computerized test battery named the Touch Panel-type Dementia Assessment Scale (TDAS), which was intended to substitute for the ADAS-Cog, and was specifically designed to rate cognitive dysfunction quickly and without the need of a specialist rater.



The hardware for the TDAS comprises a 14-inch touch panel display and computer devices built into one case. The TDAS runs on Windows OS and was bundled with a custom program made with reference to the ADAS-cog. Participants in the present study were 34 patients with Alzheimer's disease. Each participant was administered the ADAS-cog and the TDAS. The test scores for each patient were compared to determine whether the severity of cognitive dysfunction of the patients could be rated equally as well by both tests.



Pearson's correlation coefficient showed a significant correlation between the total scores (r= 0.69, P < 0.01) on the two scales for each patient. The Kendall coefficients of concordance obtained for the three corresponding pairs of tasks (word recognition, orientation, and naming object and fingers) showed the three TDAS tasks can rate symptoms of cognitive decline equally as well as the corresponding items on the ADAS-cog.



The TDAS appears to be a sensitive and comprehensive assessment battery for rating the symptoms of Alzheimer's disease, and can be substituted for the ADAS-cog.
Fall Detection using Recurrent Neural Networks

Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury. This can be done by using either ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work is to study the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning (DL), to implement an effective algorithm for on-line fall detection. The proposed classifier is based on a Recurrent Neural Network (RNN) model with underlying Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained by the SisFall authors.
A Framework for Out of Memory SVD Algorithms

Many important applications – from big data analytics to information retrieval, gene expression analysis, and numerical weather prediction – require the solution of large dense singular value decompositions (SVD). In many cases the problems are too large to fit into the computer’s main memory, and thus require specialized out-of-core algorithms that use disk storage. In this paper, we analyze the SVD communications, as related to hierarchical memories, and design a class of algorithms that minimizes them. This class includes out-of-core SVDs but can also be applied between other consecutive levels of the memory hierarchy, e.g., GPU SVD using the CPU memory for large problems. We call these out-of-memory (OOM) algorithms. To design OOM SVDs, we first study the communications for both classical one-stage blocked SVD and two-stage tiled SVD. We present the theoretical analysis and strategies to design, as well as implement, these communication avoiding OOM SVD algorithms. We show performance results for multicore architecture that illustrate our theoretical findings and match our performance models.