nils-herrmann/clean_scirepeval huggingface dataset:
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG

This paper describes an application of one-class support vector machine (SVM) novelty detection for detecting seizures in humans. Our technique maps intracranial electroencephalogram (EEG) time series into corresponding novelty sequences by classifying short-time, energy-based statistics computed from one-second windows of data. We train a classifier on epochs of interictal (normal) EEG. During ictal (seizure) epochs of EEG, seizure activity induces distributional changes in feature space that increase the empirical outlier fraction. A hypothesis test determines when the parameter change differs significantly from its nominal value, signaling a seizure detection event. Outputs are gated in a “one-shot” manner using persistence to reduce the false alarm rate of the system. The detector was validated using leave-one-out cross-validation (LOO-CV) on a sample of 41 interictal and 29 ictal epochs, and achieved 97.1% sensitivity, a mean detection latency of GARDNER, KRIEGER, VACHTSEVANOS AND LITT 1026 -7.58 seconds, and an asymptotic false positive rate (FPR) of 1.56 false positives per hour (Fp/hr). These results are better than those obtained from a novelty detection technique based on Mahalanobis distance outlier detection, and comparable to the performance of a supervised learning technique used in experimental implantable devices (Echauz et al., 2001). The novelty detection paradigm overcomes three significant limitations of competing methods: the need to collect seizure data, precisely mark seizure onset and offset times, and perform patient-specific parameter tuning for detector training.
A high voltage battery charger with smooth charge mode transition in BCD process

A 60 V battery charger implemented using TSMC 0.25 µm Bipolar-CMOS-DMOS (BCD) 60 V process is presented in this work. A novel transition method is proposed to ensure smooth transitions between constant current (CC) and constant voltage (CV) modes. Since the proposed approach would require only additional diodes, power consumption and chip area are conserved in comparison with other sophisticated methods proposed in prior works. The charger sources a current of 50 mA in CC mode and has an efficiency of 75% ∼ 80% throughout the charging sequence. The supply voltage is kept at 3 volts higher than the battery voltage throughout the charging sequence to maintain charging efficiency. A thermal protection circuit is included in this design to prevent the charger from operating at a temperature over the maximum allowed temperature.
Dissociating explicit and procedural-learning based systems of perceptual category learning

A fundamental question is whether people have available one category learning system, or many. Most multiple systems advocates postulate one explicit and one implicit system. Although there is much agreement about the nature of the explicit system, there is less agreement about the nature of the implicit system. In this article, we review a dual systems theory of category learning called competition between verbal and implicit systems (COVIS) developed by Ashby et al. The explicit system dominates the learning of verbalizable, rule-based category structures and is mediated by frontal brain areas such as the anterior cingulate, prefrontal cortex (PFC), and head of the caudate nucleus. The implicit system, which uses procedural learning, dominates the learning of non-verbalizable, information-integration category structures, and is mediated by the tail of the caudate nucleus and a dopamine-mediated reward signal. We review nine studies that test six a priori predictions from COVIS, each of which is supported by the data.
An LED Driver With Pulse Current Driving Technique

A novel driving technique, called pulse current driving technique for LED driver, is proposed. A complete introduction and analysis of the proposed driving technique and the conventional driving technique will be presented in this paper. The LED driver supply maximum peak current is 220 mA and operating frequency is between 500 kHz and1 MHz. Simulation and experimental results are presented to verify the analytical results. The LED driver is fabricated with CMOS 0.35-μm technology and chip area with pads is 0.89 mm 2. The design of the proposed circuit structure is simple, without additional output capacitance compensation for stability and with small power consumption, and is implemented in integrated circuit. As a result of the above benefits, the proposed approach is believed to be suitable in portable application.
Assessing Personality Profiles of Software Developers in Agile Development Teams

Agile methodologies are changing the way we develop software. Their emphasis on teamoriented development, joint code ownership, and reliance on people rather than predefined processes to guide activities, is transforming software development into a socio-technical process. As methodologies become increasingly more people and team-oriented, there is an urgent need to investigate the personality profiles of software developers and their likely impact on the productivity of the development team. A review of the IS research literature on personality studies found Jungian typology operationalized as Myers-Briggs Type Indicator (MBTI) to be the most popular approach for assessing personality profiles. We compared the Five Factor Model (FFM) of personality, which is currently gaining popularity among personality psychologists, with MBTI. Our analysis, based on extant research literature in personality psychology and group behavior, suggests that FFM not only provides better measures for all factors that are measured by MBTI, but it also allows us to assess Neuroticism, an important personality trait that is of interest to researchers studying work groups, such as the agile development team. Our finding has important implications for researchers studying the agile development process. It is also highly relevant to studies investigating the personality profiles of IS professionals. Thus, our study attempts to bring in fresh insights from Personality Psychology, our reference discipline, to enrich IS research.
Multitarget miss distance via optimal assignment

The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.
Adapting microsoft SQL server for cloud computing

Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.
Speed or accuracy? a study in evaluation of simultaneous speech translation

Simultaneous speech translation is a technology that attempts to reduce the delay inherent in speech translation by beginning translation before the end of explicit sentence boundaries. Despite best efforts, there is still often a trade-off between speed and accuracy in these systems, with systems with less delay also achieving lower accuracy. However, somewhat surprisingly, there is no previous work examining the relative importance of speed and accuracy, and thus given two systems with various speeds and accuracies, it is difficult to say with certainty which is better. In this paper, we make the first steps towards evaluation of simultaneous speech translation systems in consideration of both speed and accuracy. We collect user evaluations of speech translation results with different levels of accuracy and delay, and using this data to learn the parameters of an evaluation measure that can judge the trade-off between these two factors. Based on these results, we find that considering both accuracy and delay in the evaluation of speech translation results helps improve correlations with human judgements, and that users placed higher relative importance on reducing delay when results were presented through text, rather than speech.
Impact of wireless communications technologies on elder people healthcare: Smart home in Australia

Over the last three decades, there has been a dramatic rise in ageing populations in most countries. Older people are remaining in nursing home care due to the fact that general services and medical support are provided. However, these costly environments often negatively affect older people due to high cost, limited staff and the social impacts they have. A way to overcome these challenges is to place the elderly instead in a smart home environment. The aim of this study is to describe the impact of wireless communications technologies on elderly people in a smart home. This has been compared with conditions in nursing homes. Using smart wireless sensors, wireless communications and ambient intelligent system, it is possible to create systems capable of measuring vital signs of the person in their own home. The findings indicate that wireless technology is the most suitable basis for a communications framework in a smart home to assist older people.
Automatic Arabic Text Classification

Automated document classification is an important text mining task especially with the rapid growth of the number of online documents present in Arabic language. Text classification aims to automatically assign the text to a predefined category based on linguistic features. Such a process has different useful applications including, but not restricted to, e-mail spam detection, web page content filtering, and automatic message routing. This paper presents the results of experiments on document classification achieved on seven different Arabic corpora using statistical methodology. The performance of two popular classification algorithms in classifying the aforementioned corpora has been evaluated.
Sentiment Analysis using Deep Convolutional Neural Networks with Distant Supervision Master Thesis

This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.
Differential Impairment of Remembering the Past and Imagining Novel Events after Thalamic Lesions

Vividly remembering the past and imagining the future (mental time travel) seem to rely on common neural substrates and mental time travel impairments in patients with brain lesions seem to encompass both temporal domains. However, because future thinking—or more generally imagining novel events—involves the recombination of stored elements into a new event, it requires additional resources that are not shared by episodic memory. We aimed to demonstrate this asymmetry in an event generation task administered to two patients with lesions in the medial dorsal thalamus. Because of the dense connection with pFC, this nucleus of the thalamus is implicated in executive aspects of memory (strategic retrieval), which are presumably more important for future thinking than for episodic memory. Compared with groups of healthy matched control participants, both patients could only produce novel events with extensive help of the experimenter (prompting) in the absence of episodic memory problems. Impairments were most pronounced for imagining personal fictitious and impersonal events. More precisely, the patients' descriptions of novel events lacked content and spatio-temporal relations. The observed impairment is unlikely to trace back to disturbances in self-projection, scene construction, or time concept and could be explained by a recombination deficit. Thus, although memory and the imagination of novel events are tightly linked, they also partly rely on different processes.
Weighted Task Regularization for Multitask Learning

Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of tasks is not large, the learning outcome may suffer if there exists outlier tasks that inappropriately bias majority. Rather than identifying or discarding such outlier tasks, we present a weighted regularized multitask learning framework based on regularized multitask learning, which uses statistical metrics, such as Kullback-Leibler divergence, to assign weights prior to regularization process that robustly reduces the impact of outlier tasks and results in better learned models for all tasks. We then show that this formulation can be solved using dual form like optimizing a standard support vector machine with varied kernels. We perform experiments using both synthetic dataset and real-world dataset from petroleum industry which shows that our methodology outperforms existing methods.
Multi-User Millimeter Wave MIMO With Full-Dimensional Lens Antenna Array

Millimeter wave (mm-wave) communication using lens antenna arrays is a promising technique for realizing cost-effective large multiple-input multiple-output (MIMO) systems with only limited radio frequency chains. This paper studies a multi-user mm-wave single-sided lens MIMO system, where the base station (BS) is equipped with a full-dimensional lens antenna array and each mobile station employs the conventional antenna arrays. By exploiting the angle-dependent energy focusing property of lens antenna array and the multi-path sparsity of mm-wave channels, we propose a low-complexity single-carrier (SC)-based path-division multiple access (PDMA) scheme for the general wide-band frequency-selective channels. To this end, a new technique called path delay compensation is proposed at the BS to transform the multi-user frequency-selective MIMO channels to parallel frequency-flat small-size MIMO channels. In addition, we propose an efficient channel estimation scheme tailored for the SC-based PDMA, which requires negligible training overhead in practical mm-wave systems and yet leads to comparable performance as that with perfect channel state information. Numerical results show that the proposed design achieves comparable performance as the state-of-the-art benchmark systems in terms of spectrum efficiency, but with significantly reduced hardware/power consumption cost and signal processing complexity.
The development of deep learning in synthetic aperture radar imagery

The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents the next stage in the evolution of machine intelligence which places the onus of identifying salient features on the network rather than researcher. This paper will outline machine learning techniques as it has been used previously on SAR; what is deep learning and where it fits in compared to traditional machine learning; what benefits can be derived by applying it to Synthetic Aperture Radar imagery; and finally describe some obstacles that still need to be overcome in order to provide constient and long term results from deep learning in SAR.
Design and Implementation of a Wearable Gas Sensor Network for Oil and Gas Industry Workers

Industrial environment usually involves some types of hazardous substances including toxic and/or flammable gases. Accidental gas leakage can cause potential dangers to a plant, its employees and surrounding neighborhoods. Around 64% of accidents that happen in the oil fields are due to combustibles and/or toxic gases. The safety plan of most industries includes measures to reduce risk to humans and plants by incorporating early-warning devices, such as gas detectors. Most existing tools for monitoring gases are stationary and incapable of accurately measuring individual exposures that depend on personal lifestyles and environment. This paper provides a design and implementation of a wearable gas sensor network by building sensor nodes with wireless communication modules which communicate their data along the network. The system is designed to be flexible, low cost, low maintenance and with accurate performance to detect toxic gases in a timely fashion to warn employees before an existence of a disaster.
Clustering with Missing Values : No Imputation Required

Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as just as reliable as the truly observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.
Advanced Features for Enterprise-Wide Role-Based Access Control

The administration of users and access rights in large enterprises is a complex and challenging task. Roles are a powerful concept for simplifying access control, but their implementation is normally restricted to single systems and applications. In this article we define Enterprise Roles capable of spanning all IT systems in an organisation. We show how the Enterprise Role-Based Access Control (ERBAC) model exploits the RBAC model outlined in the NIST standard draft[5] and describe its extensions. We have implemented ERBAC as a basic concept of SAM Jupiter, a commercial security administration tool. Based on practical experience with the deployment of Enterprise Roles during SAM implementation projects in large organisations, we have enhanced the ERBAC model by including different ways of parametrising the roles. We show that using parameters can significantly reduce the number of roles needed in an enterprise and simplify the role structure, thereby reducing the administration effort considerably. The enhanced ERBAC features are illustrated by reallife examples.
A Rule-Based Framework of Metadata Extraction from Scientific Papers

Most scientific documents on the web are unstructured or semi-structured, and the automatic document metadata extraction process becomes an important task. This paper describes a framework for automatic metadata extraction from scientific papers. Based on a spatial and visual knowledge principle, our system can extract title, authors and abstract from scientific papers. We utilize format information such as font size and position to guide the metadata extraction process. The experiment results show that our system achieves a high accuracy in header metadata extraction which can effectively assist the automatic index creation for digital libraries.
A study of function of foot's medial longitudinal arch using biped humanoid robot

The humanoid robot, WABIAN-2R, has achieved human-like walking with knee-stretched, heel contact and toe off motions by using a foot mechanism with a passive toe joint. However, the foot structure is different from a human's. In this paper, we describe a new foot mechanism capable of mimicking the human's foot arch structure to figure out the function of the arch structure. Especially, the developed foot mimics the elastic properties of the arch of the human's foot and the change of the arch height during walking. The foot mechanism consists of a passive joint in the internal toe, a passive joint in the external toe, and a joint in the foot arch. We conducted several walking experiments by using WABIAN-2R, and the function of the arch structure is clarified quantitatively. As a result, we confirmed that the arch elasticity could absorb a foot-landing force at the plantar contact phase and the change of the arch height contributed to a strong thrust at the push-off phase.