nils-herrmann/clean_scirepeval huggingface dataset:
Effect of episodic and working memory impairments on semantic and cognitive procedural learning at alcohol treatment entry.

Chronic alcoholism is known to impair the functioning of episodic and working memory, which may consequently reduce the ability to learn complex novel information. Nevertheless, semantic and cognitive procedural learning have not been properly explored at alcohol treatment entry, despite its potential clinical relevance. The goal of the present study was therefore to determine whether alcoholic patients, immediately after the weaning phase, are cognitively able to acquire complex new knowledge, given their episodic and working memory deficits.



Twenty alcoholic inpatients with episodic memory and working memory deficits at alcohol treatment entry and a control group of 20 healthy subjects underwent a protocol of semantic acquisition and cognitive procedural learning. The semantic learning task consisted of the acquisition of 10 novel concepts, while subjects were administered the Tower of Toronto task to measure cognitive procedural learning.



Analyses showed that although alcoholic subjects were able to acquire the category and features of the semantic concepts, albeit slowly, they presented impaired label learning. In the control group, executive functions and episodic memory predicted semantic learning in the first and second halves of the protocol, respectively. In addition to the cognitive processes involved in the learning strategies invoked by controls, alcoholic subjects seem to attempt to compensate for their impaired cognitive functions, invoking capacities of short-term passive storage. Regarding cognitive procedural learning, although the patients eventually achieved the same results as the controls, they failed to automate the procedure. Contrary to the control group, the alcoholic groups' learning performance was predicted by controlled cognitive functions throughout the protocol.



At alcohol treatment entry, alcoholic patients with neuropsychological deficits have difficulty acquiring novel semantic and cognitive procedural knowledge. Compared with controls, they seem to use more costly learning strategies, which are nonetheless less efficient. These learning disabilities need to be considered when treatment requiring the acquisition of complex novel information is envisaged.
Visual–Inertial Navigation Systems for Aerial Robotics: Sensor Fusion and Technology

In this paper, we comprehensively discuss the current progress of visual–inertial (VI) navigation systems and sensor fusion research with a particular focus on small unmanned aerial vehicles, known as microaerial vehicles (MAVs). Such fusion has become very topical due to the complementary characteristics of the two sensing modalities. We discuss the pros and cons of the most widely implemented VI systems against the navigational and maneuvering capabilities of MAVs. Considering the issue of optimum data fusion from multiple heterogeneous sensors, we examine the potential of the most widely used advanced state estimation techniques (both linear and nonlinear as well as Bayesian and non-Bayesian) against various MAV design considerations. Finally, we highlight several research opportunities and potential challenges associated with each technique.
A comparison of GPU execution time prediction using machine learning and analytical modeling

Today, most high-performance computing (HPC) platforms have heterogeneous hardware resources (CPUs, GPUs, storage, etc.) A Graphics Processing Unit (GPU) is a parallel computing coprocessor specialized in accelerating vector operations. The prediction of application execution times over these devices is a great challenge and is essential for efficient job scheduling. There are different approaches to do this, such as analytical modeling and machine learning techniques. Analytic predictive models are useful, but require manual inclusion of interactions between architecture and software, and may not capture the complex interactions in GPU architectures. Machine learning techniques can learn to capture these interactions without manual intervention, but may require large training sets. In this paper, we compare three different machine learning approaches: linear regression, support vector machines and random forests with a BSP-based analytical model, to predict the execution time of GPU applications. As input to the machine learning algorithms, we use profiling information from 9 different applications executed over 9 different GPUs. We show that machine learning approaches provide reasonable predictions for different cases. Although the predictions were inferior to the analytical model, they required no detailed knowledge of application code, hardware characteristics or explicit modeling. Consequently, whenever a database with profile information is available or can be generated, machine learning techniques can be useful for deploying automated on-line performance prediction for scheduling applications on heterogeneous architectures containing GPUs.
The application of soil temperature measurement by LM35 temperature sensors

Consider revising medium temperature used LM35 temperature sensor, what is an economic and feasible method. This study mainly researches the applicability of LM35 temperature sensor in soil temperature testing field. Selected the sensor, and based on the theoretical equation between the sensor output voltage and Celsius temperature; introduced correction coefficient, carried through the calibration experiment of the sensor; further more, it is applied to the potted rice's soil temperature detection. The calibration results show that, each sensor correction coefficient is different from others, but these numerical are close to 1, the linear relationship was very significant between tested medium temperature and sensor output voltage. In the key trial period of rice potted, used LM35DZ type temperature sensor to measure the soil temperature. The analysis result show that, the changing trends are basically equal both soil temperature and air temperature, and the characteristics of soil temperatures are lag. The variance analysis shows that, the difference was not significant paper film covered and without covered on soil temperature.
Multimodal sentiment analysis: Sentiment analysis using audiovisual format

Multimodal sentiment analysis is the analysis of emotions, attitude, and opinion from audiovisual format. A company can improve the quality of its product and services by analyzing the reviews about the product [5]. Sentiment analysis is widely used in managing customer relations. There are many textual reviews from which we cannot extract emotions by traditional sentiment analysis techniques. Some sentences in the textual reviews may derive deep emotions but do not contain any keyword to detect those emotions, so we used audiovisual reviews in order to detect emotions from the facial expressions of the customer. In this paper we take audiovisual input and extract emotions from video and audio in parallel from audiovisual input, finally classify the overall review as positive, negative or neutral based on the emotions detected.
Five-Year Experience with Modified Fogli (Loré’s Fascia Fixation) Platysmaplasty

Over a 5-year period, my technique of approaching and modifying the aging neck has changed from direct suturing of the medial platysma bands and strong posterior traction of the platysma to superior elevation of the posterior platysma and strong fixation to the tympanoparotid fascia. When indicated, redundant anterior platysmal bands are resected instead of approximated. Evaluation of these anterior bands preoperatively for thickness and degree of descent allows more precise decision-making regarding the need to resect redundant and excessively thick or long anterior platysmal bands. The added advantage of the strong fixation of the platysma in a vertical fashion to Loré’s fascia (the tympanoparotid fascia) leads not only to an improvement in the contour of the jawline and submental area but also to the undervalued aesthetic feature of a defined contour and visually increased length of the sternomastoid muscle.
Sparse FIR Filter Design Based on Interpolation Technique

In this paper, a design method based on interpolation technique is proposed to design narrow-band sparse FIR filters and centrosymmetric bandpass filters. The design method is realized by cascading a model filter with a masking filter. The model filter is first designed and then interpolated to generate the desired impulse response replica. A sparse masking filter is used to mask the extra unwanted passbands. Unlike the interpolated FIR filter technique, both subfilters are designed as sparse filters by multiplierless algorithms to improve the overall sparsity of the filter. Simulation results show that the proposed cascaded realization of FIR filters results a significant improvement of sparsity than the conventional direct form or transposed direct form. It is also shown that the proposed method generates sparser solutions than the interpolated FIR filter technique due to the sparse design of subfilters.
Deep learning fusion conceptual frameworks for complex human activity recognition using mobile and wearable sensors

Human Activity recognition (HAR) is an important area of research in ubiquitous computing and Human Computer Interaction. To recognize activities using mobile or wearable sensor, data are collected using appropriate sensors, segmented, needed features extracted and activities categories using discriminative models (SVM, HMM, MLP etc.). Feature extraction is an important stage as it helps to reduce computation time and ensure enhanced recognition accuracy. Earlier researches have used statistical features which require domain expert and handcrafted features. However, the advent of deep learning that extracts salient features from raw sensor data and has provided high performance in computer vision, speech and image recognition. Based on the recent advances recorded in deep learning for human activity recognition, we briefly reviewed the different deep learning methods for human activities implemented recently and then propose a conceptual deep learning frameworks that can be used to extract global features that model the temporal dependencies using Gated Recurrent Units. The framework when implemented would comprise of seven convolutional layer, two Gated recurrent unit and Support Vector Machine (SVM) layer for classification of activity details. The proposed technique is still under development and will be evaluated with benchmarked datasets and compared with other baseline deep learning algorithms.
Factors associated with sudden death of individuals requiring restraint for excited delirium.

The purpose of this article is to identify and rank factors associated with sudden death of individuals requiring restraint for excited delirium. Eighteen cases of such deaths witnessed by emergency medical service (EMS) personnel are reported. The 18 cases reported were restrained with the wrists and ankles bound and attached behind the back. This restraint technique was also used for all 196 surviving excited delirium victims encountered during the study period. Unique to these data is a description of the initial cardiopulmonary arrest rhythm in 72% of the sudden death cases. Associated with all sudden death cases was struggle by the victim with forced restraint and cessation of struggling with labored or agonal breathing immediately before cardiopulmonary arrest. Also associated was stimulant drug use (78%), chronic disease (56%), and obesity (56%). The primary cardiac arrest rhythm of ventricular tachycardia was found in 1 of 13 victims with confirmed initial cardiac rhythms, with none found in ventricular fibrillation. Our findings indicate that unexpected sudden death when excited delirium victims are restrained in the out-of-hospital setting is not infrequent and can be associated with multiple predictable but usually uncontrollable factors.
Discrete Surface Ricci Flow: Theory and Applications

Conformal geometry is at the core of pure mathematics. Conformal structure is more flexible than Riemaniann metric but more rigid than topology. Conformal geometric methods have played important roles in engineering fields. This work introduces a theoretically rigorous and practically efficient method for computing Riemannian metrics with prescribed Gaussian curvatures on discrete surfaces—discrete surface Ricci flow, whose continuous counter part has been used in the proof of Poincaré conjecture. Continuous Ricci flow conformally deforms a Riemannian metric on a smooth surface such that the Gaussian curvature evolves like a heat diffusion process. Eventually, the Gaussian curvature becomes constant and the limiting Riemannian metric is conformal to the original one. In the discrete case, surfaces are represented as piecewise linear triangle meshes. Since the Riemannian metric and the Gaussian curvature are discretized as the edge lengths and the angle deficits, the discrete Ricci flow can be defined as the deformation of edge lengths driven by the discrete curvature. The existence and uniqueness of the solution and the convergence of the flow process are theoretically proven, and numerical algorithms to compute Riemannian metrics with prescribed Gaussian curvatures using discrete Ricci flow are also designed. Discrete Ricci flow has broad applications in graphics, geometric modeling, and medical imaging, such as surface parameterization, surface matching, manifold splines, and construction of geometric structures on general surfaces.
Linking Design-Time and Run-Time: A Graph-Based Uniform Workflow Provenance Model

Workflow is an important way to mashup reusable software services to create value-added data analytics services. Workflow provenance is core to understand how services and workflows behaved in the past, which knowledge can be used to provide a better recommendation. Existing workflow provenance management systems handle various types of provenance separately. A typical data science exploration scenario, however, calls for an integrated view of provenance and seamless transition among different types of provenance. In this paper, a graph-based, uniform provenance model is proposed to link together design-time and run-time provenance, by combining retrospective provenance, prospective provenance, and evolution provenance. Such a unified provenance model will not only facilitate workflow mining and exploration, but also facilitate workflow interoperability. The model is formalized into colored Petri nets for verification and monitoring management. A SQL-like query language is developed, which supports basic queries, recursive queries, and cross-provenance queries. To verify the effectiveness of our model, A web-based, collaborative workflow prototyping system is developed as a proof-of-concept. Experiments have been conducted to evaluate the effectiveness of the proposed SQL-like graph query against SQL query.
Additivity models for suprathreshold distortion in quantized wavelet-coded images

The additivity of wavelet subband quantization distortions was investigated in an unmasked detection task and in masked detection and discrimination tasks. Contrast thresholds were measured for both simple targets (artifacts induced by uniform quantization of individual discrete wavelet transform subbands) and compound targets (artifacts induced by uniform quantization of pairs of discrete wavelet transform subbands) in the presence of no mask and eight different natural image maskers. The results were used to assess summation between wavelet subband quantization distortions on orientation and spatial-frequency dimensions. In the unmasked detection experiment, subthreshold quantization distortions pooled in a non-linear fashion and the amount of summation agreed with those of previous summation-atthreshold experiments (ß=2.43; relative sensitivity=1.33). In the masked detection and discrimination experiments, suprathreshold quantization distortions pooled in a linear fashion. Summation increased as the distortions became increasingly suprathreshold but quickly settled to near-linear values. Summation on the spatial-frequency dimension was greater than summation on the orientation dimension for all suprathreshold contrasts. A high degree of uncertainty imposed by the natural image maskers precludes quantifying an absolute measure of summation.
Mobile ad hoc cloud: A survey

The unabated flurry of research activities to augment various mobile devices in terms of compute-intensive task execution by leveraging heterogeneous resources of available devices in the local vicinity has created a new research domain called mobile ad hoc cloud (MAC) or mobile cloud. It is a new type of mobile cloud computing (MCC). MAC is deemed to be a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and rich impressive experience to mobile users. However, MAC is yet in its infancy, and a comprehensive survey of the domain is still lacking. In this paper, we survey the state-of-the-art research efforts carried out in the MAC domain. We analyze several problems inhibiting the adoption of MAC and review corresponding solutions by devising a taxonomy. Moreover, MAC roots are analyzed and taxonomized as architectural components, applications, objectives, characteristics, execution model, scheduling type, formation technologies, and node types. The similarities and differences among existing proposed solutions by highlighting the advantages and disadvantages are also investigated. We also compare the literature based on objectives. Furthermore, our study advocates that the problems stem from the intrinsic characteristics of MAC by identifying several new principles. Lastly, several open research challenges such as incentives, heterogeneity-ware task allocation, mobility, minimal data exchange, and security and privacy are presented as future research directions.
Flexible resource allocation for reliable virtual cluster computing systems

Virtualization and cloud computing technologies now make it possible to create scalable and reliable virtual high performance computing clusters. Integrating these technologies, however, is complicated by fundamental and inherent differences in the way in which these systems allocate resources to computational tasks. Cloud computing systems immediately allocate available resources or deny requests. In contrast, parallel computing systems route all requests through a queue for future resource allocation. This divergence of allocation policies hinders efforts to implement efficient, responsive, and reliable virtual clusters.
 In this paper, we present a continuum of four scheduling polices along with an analytical resource prediction model for each policy to estimate the level of resources needed to operate an efficient, responsive, and reliable virtual cluster system. We show that it is possible to estimate the size of the virtual cluster system needed to provide a predictable grade of service for a realistic high performance computing workload and estimate the queue wait time for a partial or full resource allocation. Moreover, we show that it is possible to provide a reliable virtual cluster system using a limited pool of spare resources. The models and results we present are useful for cloud computing providers seeking to operate efficient and cost-effective virtual cluster systems.
Learning Analytics in Massive Open Online Courses

Educational technology has obtained great importance over the last fifteen years. At present, the umbrella of educational technology incorporates multitudes of engaging online environments and fields. Learning analytics and Massive Open Online Courses (MOOCs) are two of the most relevant emerging topics in this domain. Since they are open to everyone at no cost, MOOCs excel in attracting numerous participants that can reach hundreds and hundreds of thousands. Experts from different disciplines have shown significant interest in MOOCs as the phenomenon has rapidly grown. In fact, MOOCs have been proven to scale education in disparate areas. Their benefits are crystallized in the improvement of educational outcomes, reduction of costs and accessibility expansion. Due to their unusual massiveness, the large datasets of MOOC platforms require advanced tools and methodologies for further examination. The key importance of learning analytics is reflected here. MOOCs offer diverse challenges and practices for learning analytics to tackle. In view of that, this thesis combines both fields in order to investigate further steps in the learning analytics capabilities in MOOCs. The primary research of this dissertation focuses on the integration of learning analytics in MOOCs, and thereafter looks into examining students' behavior on one side and bridging MOOC issues on the other side. The research was done on the Austrian iMooX xMOOC platform. We followed the prototyping and case studies research methodology to carry out the research questions of this dissertation. The main contributions incorporate designing a general learning analytics framework, learning analytics prototype, records of students' behavior in nearly every MOOC's variables (discussion forums, interactions in videos, self-assessment quizzes, login frequency), a cluster of student engagement...