nils-herrmann/clean_scirepeval huggingface dataset:
Temporal plannability by variance of the episode length

Optimization of decision problems in stochastic environments is usually concerned with maximizing the probability of achieving the goal and minimizing the expected episode length. For interacting agents in time-critical applications, learning of the possibility of scheduling of subtasks (events) or the full task is an additional relevant issue. Besides, there exist highly stochastic problems where the actual trajectories show great variety from episode to episode, but completing the task takes almost the same amount of time. The identification of sub-problems of this nature may promote e.g., planning, scheduling and segmenting Markov decision processes. In this work, formulae for the average duration as well as the standard deviation of the duration of events are derived. We show, that the emerging Bellman-type equation is a simple extension of Sobel’s work (1982) and that methods of dynamic programming as well as methods of reinforcement learning can be applied. Computer demonstration on a toy problem serve to highlight the principle.
Embedding security in Software Development Life Cycle (SDLC)

Users that expects greater reliability, security and ease of use demands a better software. With the advancement of new technologies brings along higher expectations from the software. The users want to access a software, which is bug free with ease of use. To achieve this, a structured framework or well-defined life cycle should be considered. Life cycle should not only tell what functions it need to perform, but it should provide high-level security. Objective of this paper is to merge traditional SDLC (Software Development Life Cycle) with risk and security properties into one life cycle. As in traditional SDLC, security testing is done at the end but it should consider security and risk factors at each stage to prevent major bugs at the later stages.
Dimensions of video game behavior and their relationships with personality

As video games become increasingly popular pastimes, it becomes more important to understand how different individuals behave when they play these games. Previous research has focused mainly on behavior in massively multiplayer online role-playing games; therefore, in the current study we sought to extend on this research by examining the connections between personality traits and behaviors in video games more generally. Two hundred and nineteen university students completed measures of personality traits, psychopathic traits, and a questionnaire regarding frequency of different behaviors during video game play. A principal components analysis of the video game behavior questionnaire revealed four factors: Aggressing, Winning, Creating, and Helping. Each behavior subscale was significantly correlated with at least one personality trait. Men reported significantly more Aggressing, Winning, and Helping behavior than women. Controlling for participant sex, Aggressing was negatively correlated with Honesty–Humility, Helping was positively correlated with Agreeableness, and Creating was negatively correlated with Conscientiousness. Aggressing was also positively correlated with all psychopathic traits, while Winning and Creating were correlated with one psychopathic trait each. Frequency of playing video games online was positively correlated with the Aggressing, Winning, and Helping scales, but not with the Creating scale. The results of the current study provide support for previous research on personality and behavior in massively multiplayer online role-playing games.
A Declarative Rendering Model for Multiclass Density Maps

Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model—a simple yet expressive JSON grammar associated with visual semantics—that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.
Iterative Design of an Upper Limb Rehabilitation Game with Tangible Robots

Rehabilitation aims to ameliorate deficits in motor control via intensive practice with the affected limb. Current strategies, such as one-on-one therapy done in rehabilitation centers, have limitations such as treatment frequency and intensity, cost and requirement of mobility. Thus, a promising strategy is home-based therapy that includes task specific exercises. However, traditional rehabilitation tasks may frustrate the patient due to their repetitive nature and may result in lack of motivation and poor rehabilitation. In this article, we propose the design and verification of an effective upper extremity rehabilitation game with a tangible robotic platform named Cellulo as a novel solution to these issues. We first describe the process of determining the design rationales to tune speed, accuracy and challenge. Then we detail our iterative participatory design process and test sessions conducted with the help of stroke, brachial plexus and cerebral palsy patients (18 in total) and 7 therapists in 4 different therapy centers. We present the initial quantitative results, which support several aspects of our design rationales and conclude with our future study plans.
Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications

The conventional digital hardware computational blocks with different structures are designed to compute the precise results of the assigned calculations. The main contribution of our proposed Bio-inspired Imprecise Computational blocks (BICs) is that they are designed to provide an applicable estimation of the result instead of its precise value at a lower cost. These novel structures are more efficient in terms of area, speed, and power consumption with respect to their precise rivals. Complete descriptions of sample BIC adder and multiplier structures as well as their error behaviors and synthesis results are introduced in this paper. It is then shown that these BIC structures can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.
Staging and response assessment in lymphomas: the new Lugano classification.

Staging and response criteria were initially developed for Hodgkin lymphoma (HL) over 60 years ago, but not until 1999 were response criteria published for non-HL (NHL). Revisions to these criteria for both NHL and HL were published in 2007 by an international working group, incorporating PET for response assessment, and were widely adopted. After years of experience with these criteria, a workshop including representatives of most major international lymphoma cooperative groups and cancer centers was held at the 11(th) International Conference on Malignant Lymphoma (ICML) in June, 2011 to determine what changes were needed. An Imaging Task Force was created to update the relevance of existing imaging for staging, reassess the role of interim PET-CT, standardize PET-CT reporting, and to evaluate the potential prognostic value of quantitative analyses using PET and CT. A clinical task force was charged with assessing the potential of PET-CT to modify initial staging. A subsequent workshop was help at ICML-12, June 2013.   Arbor classification will still be used for disease localization, patients should be treated as limited disease [I (E), II (E)], or extensive disease [III-IV (E)], directed by prognostic and risk factors. Since symptom designation A and B are frequently neither recorded nor accurate, and are not prognostic in most widely used prognostic indices for HL or the various types of NHL, these designations need only be applied to the limited clinical situations where they impact treatment decisions (e.g., stage II HL). PET-CT can replace the bone marrow biopsy (BMBx) for HL. A positive PET of bone or bone marrow is adequate to designate advanced stage in DLBCL. However, BMBx can be considered in DLBCL with no PET evidence of BM involvement, if identification of discordant histology is relevant for patient management, or if the results would alter treatment. BMBx remains recommended for staging of other histologies, primarily if it will impact therapy. PET-CT will be used to assess response in FDG-avid histologies using the 5-point scale, and included in new PET-based response criteria, but CT should be used in non-avid histologies. The definition of PD can be based on a single node, but must consider the potential for flare reactions seen early in treatment with newer targeted agents which can mimic disease progression. Routine surveillance scans are strongly discouraged, and the number of scans should be minimized in practice and in clinical trials, when not a direct study question. Hopefully, these recommendations will improve the conduct of clinical trials and patient management.
Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses

State-of-the-art hearing prostheses are equipped with acoustic noise reduction algorithms to improve speech intelligibility. Currently, one of the major challenges is to perform acoustic noise reduction in so-called cocktail party scenarios with multiple speakers, in particular because it is difficult-if not impossible-for the algorithm to determine which are the target speaker(s) that should be enhanced, and which speaker(s) should be treated as interfering sources. Recently, it has been shown that electroencephalography (EEG) can be used to perform auditory attention detection, i.e., to detect to which speaker a subject is attending based on recordings of neural activity. In this paper, we combine such an EEG-based auditory attention detection (AAD) paradigm with an acoustic noise reduction algorithm based on the multi-channel Wiener filter (MWF), leading to a neuro-steered MWF. In particular, we analyze how the AAD accuracy affects the noise suppression performance of an adaptive MWF in a sliding-window implementation, where the user switches his attention between two speakers.
An efficient and simple under-sampling technique for imbalanced time series classification

Imbalanced time series classification (TSC) involving many real-world applications has increasingly captured attention of researchers. Previous work has proposed an intelligent-structure preserving over-sampling method (SPO), which the authors claimed achieved better performance than other existing over-sampling and state-of-the-art methods in TSC. The main disadvantage of over-sampling methods is that they significantly increase the computational cost of training a classification model due to the addition of new minority class instances to balance data-sets with high dimensional features. These challenging issues have motivated us to find a simple and efficient solution for imbalanced TSC. Statistical tests are applied to validate our conclusions. The experimental results demonstrate that this proposed simple random under-sampling technique with SVM is efficient and can achieve results that compare favorably with the existing complicated SPO method for imbalanced TSC.
Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework

Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political instability and lack of mechanism for enforcement of contracts that can affect both direct foreign investment and also portfolio investment, have been incorporated. The explanatory variables chosen are the 3 month Rupee Dollar futures exchange rate (FX4), NIFTY returns (NIFTYR), Dow Jones Industrial Average returns (DJIAR), Hang Seng returns (HSR), DAX returns (DR), crude oil price (COP), CBOE VIX (CV) and India VIX (IV). To forecast the exchange rate, we have used two different classes of frameworks namely, Artificial Neural Network (ANN) based models and Time Series Econometric models. Multilayer Feed Forward Neural Network (MLFFNN) and Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network are the approaches that we have used as ANN models. Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) techniques are the ones that we have used as Time Series Econometric methods. Within our framework, our results indicate that, although the two different approaches are quite efficient in forecasting the exchange rate, MLFNN and NARX are the most efficient. Journal of Insurance and Financial Management ARTICLE INFO
The Neural Architecture of Music-Evoked Autobiographical Memories

The medial prefrontal cortex (MPFC) is regarded as a region of the brain that supports self-referential processes, including the integration of sensory information with self-knowledge and the retrieval of autobiographical information. I used functional magnetic resonance imaging and a novel procedure for eliciting autobiographical memories with excerpts of popular music dating to one's extended childhood to test the hypothesis that music and autobiographical memories are integrated in the MPFC. Dorsal regions of the MPFC (Brodmann area 8/9) were shown to respond parametrically to the degree of autobiographical salience experienced over the course of individual 30 s excerpts. Moreover, the dorsal MPFC also responded on a second, faster timescale corresponding to the signature movements of the musical excerpts through tonal space. These results suggest that the dorsal MPFC associates music and memories when we experience emotionally salient episodic memories that are triggered by familiar songs from our personal past. MPFC acted in concert with lateral prefrontal and posterior cortices both in terms of tonality tracking and overall responsiveness to familiar and autobiographically salient songs. These findings extend the results of previous autobiographical memory research by demonstrating the spontaneous activation of an autobiographical memory network in a naturalistic task with low retrieval demands.
Reliability analysis and architecture of a hybrid-redundant digital system: generalized triple modular redundancy with self-repair

The objective to attain fault-tolerant computing has been gaining an increasing amount of attention in the past several years. A digital computer is said to be fault-tolerant when it can carry out its programs correctly in the presence of logic faults, which are defined as any deviations of the logic variables in a computer from the design values. Faults can be either of transient or permanent duration. Their principal causes are: (1) component failures (either permanent or intermitent) in the circuits of the computer, and (2) external interference with the functioning of the computer, such as electric noise or transient variations in power supplies, electromagnetic interference, etc.
Computational design of twisty joints and puzzles

We present the first computational method that allows ordinary users to create complex twisty joints and puzzles inspired by the Rubik's Cube mechanism. Given a user-supplied 3D model and a small subset of rotation axes, our method automatically adjusts those rotation axes and adds others to construct a "non-blocking" twisty joint in the shape of the 3D model. Our method outputs the shapes of pieces which can be directly 3D printed and assembled into an interlocking puzzle. We develop a group-theoretic approach to representing a wide class of twisty puzzles by establishing a connection between non-blocking twisty joints and the finite subgroups of the rotation group SO(3). The theoretical foundation enables us to build an efficient system for automatically completing the set of rotation axes and fast collision detection between pieces. We also generalize the Rubik's Cube mechanism to a large family of twisty puzzles.
Highly Efficient Secondary-Resonant Active Clamp Flyback Converter

Both high switching frequency and high efficiency are critical in reducing power adapter size. The active clamp flyback (ACF) topology allows zero voltage soft switching (ZVS) under all line and load conditions, eliminates leakage inductance and snubber losses, and enables high frequency and high power density power conversion. Traditional ACF ZVS operation relies on the resonance between leakage inductance and a small primary-side clamping capacitor, which leads to increased rms current and high conduction loss. This also causes oscillatory output rectifier current and impedes the implementation of synchronous rectification. This paper proposes a secondary-side resonance scheme to shape the primary current waveform in a way that significantly improves synchronous rectifier operation and reduces primary rms current. The concept is verified with a  ${\mathbf{25}}\hbox{--}{\text{W/in}}^{3}$ high-density 45-W adapter prototype using a monolithic gallium nitride power IC. Over 93% full-load efficiency was demonstrated at the worst case 90-V ac input and maximum full-load efficiency was 94.5%.
Sequential Hybrid Procedure for Persistent Atrial Fibrillation

Catheter ablation of persistent atrial fibrillation yields an unsatisfactorily high number of failures. The hybrid approach has recently emerged as a technique that overcomes the limitations of both surgical and catheter procedures alone.



We investigated the sequential (staged) hybrid method, which consists of a surgical thoracoscopic radiofrequency ablation procedure followed by radiofrequency catheter ablation 6 to 8 weeks later using the CARTO 3 mapping system. Fifty consecutive patients (mean age 62±7 years, 32 males) with long-standing persistent atrial fibrillation (41±34 months) and a dilated left atrium (>45 mm) were included and prospectively followed in an unblinded registry. During the electrophysiological part of the study, all 4 pulmonary veins were found to be isolated in 36 (72%) patients and a complete box-lesion was confirmed in 14 (28%) patients. All gaps were successfully re-ablated. Twelve months after the completed hybrid ablation, 47 patients (94%) were in normal sinus rhythm (4 patients with paroxysmal atrial fibrillation required propafenone and 1 patient underwent a redo catheter procedure). The majority of arrhythmias recurred during the first 3 months. Beyond 12 months, there were no arrhythmia recurrences detected. The surgical part of the procedure was complicated by 7 (13.7%) major complications, while no serious adverse events were recorded during the radiofrequency catheter part of the procedure.



The staged hybrid epicardial-endocardial treatment of long-standing persistent atrial fibrillation seems to be extremely effective in maintenance of normal sinus rhythm compared to radiofrequency catheter or surgical ablation alone. Epicardial ablation alone cannot guarantee durable transmural lesions.


 www.ablace.cz
Labelling Topics using Unsupervised Graph-based Methods

‘Description’: ‘Microsoft will accelerate your journey to cloud computing with an! agile and responsive datacenter built from your existing technology investments.’,! ‘DisplayUrl’: ‘www.microsoft.com/en-us/server-cloud/ datacenter/virtualization.aspx’,! ‘ID’: ‘a42b0908-174e-4f25-b59c-70bdf394a9da’,! ‘Title’: ‘Microsoft | Server & Cloud | Datacenter | Virtualization ...’,! ‘Url’: ‘http://www.microsoft.com/en-us/server-cloud/datacenter/ virtualization.aspx’,! ...! Data!  228! #Candidate Labels: ~6,000! Domains: BLOGS, BOOKS, NEWS, PUBMED! Candidate labels rated by humans (0-3) ! Published by Lau et al. (2011). 4. Scoring Candidate Labels! Candidate Label: L = {w1, w2, ..., wm}! Scoring Function: Task: The aim of the task is to associate labels with automatically generated topics.
Land-mark placement for reliable localization of automatic guided vehicle in warehouse environment

The reliable and effective localization system is the basis of Automatic Guided Vehicle (AGV) to complete given tasks automatically in warehouse environment. However, there are no obvious features that can be used for localization of AGV to be extracted in warehouse environment and it dose make it difficult to realize the localization of AGV. So in this paper, we concentrate on the problem of optimal landmarks placement in warehouse so as to improve the reliability of localization. Firstly, we take the practical warehouse environment into consideration and transform the problem of landmarks placement into an optimization problem which aims at maximizing the difference degree between each basic unit of localization. Then Genetic Algorithm (GA) is used to solve the optimization problem. Then we match the observed landmarks with the already known ones stored in the map and the Triangulation method is used to estimate the position of AGV after the matching has been done. Finally, experiments in a real warehouse environment validate the effectiveness and reliability of our method.
Objective Classes for Micro-Facial Expression Recognition

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset (Chinese Academy of Sciences Micro-expression II) are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP (Local Binary Patterns from Three Orthogonal Planes), HOOF (Histograms of Oriented Optical Flow) and HOG 3D (3D Histogram of Oriented Gradient) feature descriptors. The experiments are evaluated on two benchmark FACS (Facial Action Coding System) coded datasets: CASME II and SAMM (A Spontaneous Micro-Facial Movement). The best result achieves 86.35% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.