nils-herrmann/clean_scirepeval huggingface dataset:
The attention schema theory: a mechanistic account of subjective awareness

We recently proposed the attention schema theory, a novel way to explain the brain basis of subjective awareness in a mechanistic and scientifically testable manner. The theory begins with attention, the process by which signals compete for the brain's limited computing resources. This internal signal competition is partly under a bottom-up influence and partly under top-down control. We propose that the top-down control of attention is improved when the brain has access to a simplified model of attention itself. The brain therefore constructs a schematic model of the process of attention, the 'attention schema,' in much the same way that it constructs a schematic model of the body, the 'body schema.' The content of this internal model leads a brain to conclude that it has a subjective experience. One advantage of this theory is that it explains how awareness and attention can sometimes become dissociated; the brain's internal models are never perfect, and sometimes a model becomes dissociated from the object being modeled. A second advantage of this theory is that it explains how we can be aware of both internal and external events. The brain can apply attention to many types of information including external sensory information and internal information about emotions and cognitive states. If awareness is a model of attention, then this model should pertain to the same domains of information to which attention pertains. A third advantage of this theory is that it provides testable predictions. If awareness is the internal model of attention, used to help control attention, then without awareness, attention should still be possible but should suffer deficits in control. In this article, we review the existing literature on the relationship between attention and awareness, and suggest that at least some of the predictions of the theory are borne out by the evidence.
Seamless Image Stitching of Scenes with Large Motions and Exposure Differences

This paper presents a technique to automatically stitch multiple images at varying orientations and exposures to create a composite panorama that preserves the angular extent and dynamic range of the inputs. The main contribution of our method is that it allows for large exposure differences, large scene motion or other misregistrations between frames and requires no extra camera hardware. To do this, we introduce a two-step graph cut approach. The purpose of the first step is to fix the positions of moving objects in the scene. In the second step, we fill in the entire available dynamic range. We introduce data costs that encourage consistency and higher signal-to-noise ratios, and seam costs that encourage smooth transitions. Our method is simple to implement and effective. We demonstrate the effectiveness of our approach on several input sets with varying exposures and camera orientations.
Augmenting feature model through customer preference mining by hybrid sentiment analysis

A feature model is an essential tool to identify variability and commonality within a product line of an enterprise, assisting stakeholders to configure product lines and to discover opportunities for reuse. However, the number of product variants needed to satisfy individual customer needs is still an open question, as feature models do not incorporate any direct customer preference information. In this paper, we propose to incorporate customer preference information into feature models using sentiment analysis of user-generated online product reviews. The proposed sentiment analysis method is a hybrid combination of affective lexicons and a rough-set technique. It is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers. Such opinionated customer preference information is regarded as one attribute of the features, which helps to decide the number of variants needed within a product line. Finally, we demonstrate the feasibility and potential of the proposed method via an application case of Kindle Fire HD tablets.
Hue-Preserving Color Blending

We propose a new perception-guided compositing operator for color blending. The operator maintains the same rules for achromatic compositing as standard operators (such as the over operator), but it modifies the computation of the chromatic channels. Chromatic compositing aims at preserving the hue of the input colors; color continuity is achieved by reducing the saturation of colors that are to change their hue value. The main benefit of hue preservation is that color can be used for proper visual labeling, even under the constraint of transparency rendering or image overlays. Therefore, the visualization of nominal data is improved. Hue-preserving blending can be used in any existing compositing algorithm, and it is particularly useful for volume rendering. The usefulness of hue-preserving blending and its visual characteristics are shown for several examples of volume visualization.
Phantom-limb pain: characteristics, causes, and treatment

Phantom-limb pain is a common sequela of amputation, occurring in up to 80% of people who undergo the procedure. It must be differentiated from non-painful phantom phenomena, residual-limb pain, and non-painful residual-limb phenomena. Central changes seem to be a major determinant of phantom-limb pain; however, peripheral and psychological factors may contribute to it. A comprehensive model of phantom-limb pain is presented that assigns major roles to pain occurring before the amputation and to central as well as peripheral changes related to it. So far, few mechanism-based treatments for phantom-limb pain have been proposed. Most published reports are based on anecdotal evidence. Interventions targeting central changes seem promising. The prevention of phantom-limb pain by peripheral analgesia has not yielded consistent results. Additional measures that reverse or prevent the formation of central memory processes might be more effective.
Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.
Convergence of Stochastic Gradient Descent for PCA

We consider the problem of principal component analysis (PC A) in a streaming stochastic setting, where our goal is to find a direction of approximate maximal va riance, based on a stream of i.i.d. data points inR. A simple and computationally cheap algorithm for this is st ochastic gradient descent (SGD), which incrementally updates its estimate based on ea ch new data point. However, due to the non-convex nature of the problem, analyzing its performanc e has been a challenge. In particular, existing guarantees rely on a non-trivial eigengap assumption on the covariance matrix, which is intuitively unnecessary. In this paper, we provide (to the best of our kno wledge) the first eigengap-free convergence guarantees for SGD in the context of PCA. This also part ially resolves an open problem posed in . Moreover, under an eigengap assumption, we show tha t the same techniques lead to new SGD convergence guarantees with better dependence on the eigen gap.
Decentralized Edge Clouds

Cloud computing services are traditionally deployed on centralized computing infrastructures confined to a few data centers, while cloud applications run in a single data center. However, the cloud's centralized nature can be limiting in terms of performance and cost for applications where users, data, and computation are distributed. The authors present an overview of distributed clouds that might be better suited for such applications. They briefly describe the distributed cloud landscape and introduce Nebula, a highly decentralized cloud that uses volunteer edge resources. The authors provide insights into some of its key properties and design issues, and describe a distributed MapReduce application scenario to illustrate the benefits and trade-offs of using distributed and decentralized clouds for distributed data-intensive computing applications.
Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference

Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover, we explore the impact of different levels of privacy information on the performances of our method, which gives us insights on what resources are more likely to be treated as private by users and influence users' behaviors at selecting Apps.
A 45nm 4Gb 3-Dimensional Double-Stacked Multi-Level NAND Flash Memory with Shared Bitline Structure

Recently, 3-dimensional (3D) memories have regained attention as a potential future memory solution featuring low cost, high density and high performance. We present a 3D double stacked 4Gb MLC NAND flash memory device with shared bitline structure, with a cell size of 0.0021mum2/bit per unit feature area. The device is designed to support 3D stacking and fabricated by S3 and 45nm floating-gate CMOS technologies.
Short Tamil sentence similarity calculation using knowledge-based and corpus-based similarity measures

Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever system to measure semantic similarity for Tamil short phrases using a hybrid approach that makes use of knowledge-based and corpus-based techniques. We tested this system with 2000 general sentence pairs and 100 mathematical sentence pairs. For the dataset of 2000 sentence pairs, this approach achieved a Mean Squared Error of 0.195 and a Pearson Correlation factor of 0.815. For the 100 mathematical sentence pairs, this approach achieved an 85% of accuracy.
Neural Network-Based Spectrum Estimation for Online WPE Dereverberation

In this paper, we propose a novel speech dereverberation framework that utilizes deep neural network (DNN)-based spectrum estimation to construct linear inverse filters. The proposed dereverberation framework is based on the state-of-the-art inverse filter estimation algorithm called weighted prediction error (WPE) algorithm, which is known to effectively reduce reverberation and greatly boost the ASR performance in various conditions. In WPE, the accuracy of the inverse filter estimation, and thus the deverberation performance, is largely dependent on the estimation of the power spectral density (PSD) of the target signal. Therefore, the conventional WPE iteratively performs the inverse filter estimation, actual dereverberation and the PSD estimation to gradually improve the PSD estimate. However, while such iterative procedure works well when sufficiently long acoustically-stationary observed signals are available, WPE’s performance degrades when the duration of observed/accessible data is short, which typically is the case for real-time applications using online block-batch processing with small batches. To solve this problem, we incorporate the DNN-based spectrum estimator into the framework ofWPE, because a DNN can estimate the PSD robustly even from very short observed data. We experimentally show that the proposed framework outperforms the conventional WPE, and improves the ASR performance in real noisy reverberant environments in both single-channel and multichannel cases.
Advances in data stream mining

Mining data streams has been a focal point of research interest over the past decade. Hardware and software advances have contributed to the significance of this area of research by introducing faster than ever data generation. This rapidly generated data has been termed as data streams. Credit card transactions, Google searches, phone calls in a city, and many others\are typical data streams. In many important applications, it is inevitable to analyze this streaming data in real time. Traditional data mining techniques have fallen short in addressing the needs of data stream mining. Randomization, approximation, and adaptation have been used extensively in developing new techniques or adopting exiting ones to enable them to operate in a streaming environment. This paper reviews key milestones and state of the art in the data stream mining area. Future insights are also be presented.
Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0

Digital twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the internet of things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings digital twins to life and makes them experimentable; digital twins become experimentable digital twins (EDTs). Initially, these EDTs communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in virtual testbeds, providing new foundations for comprehensive simulation-based systems engineering. Its focus is on EDTs, which become more detailed with every single application. Thus, complete digital representations of the respective real assets and their behaviors are created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which EDTs are used in combination with real hardware, thus realizing complex control algorithms, innovative user interfaces, or mental models for intelligent systems.
Performance Study of an In-Car Switched Ethernet Network without Prioritization

This paper presents the current state of our research in realtime communication of an IP-based in-car network. The Internet Protocol (IP) will serve as convergence layer of different specific in-car network protocols and IEEE 802.3 Ethernet will be the basic technology to transport IP. In this work, we evaluate a legacy switched Ethernet network without any Quality of Service (QoS) mechanisms. While there are arguments for not using QoS mechanisms, we give evidence that communication requirements and service constraints of a more and more streaming intensive in-car network cannot be met without. We argue for a setup with different traffic types: CAN and FlexRay like control messages, camera streaming, video and audio streaming, and bulk traffic. We will also argue for a simple double star topology as a valid assumption where the target architecture of the IP-based in-car network is not yet clear. Setup and simulation will serve as framework and motivation for future work: Analyzing IP-based real-time communication using QoS mechanisms characterizing traffic classes after IEEE 802.1Q and IEEE 802.1 Audio Video Bridging (AVB).
The COST 2100 MIMO channel model

The COST 2100 channel model is a geometry- based stochastic channel model (GSCM) that can reproduce the stochastic properties of MIMO channels over time, frequency, and space. In contrast to other popular GSCMs, the COST 2100 approach is generic and flexible, making it suitable to model multi-user or distributed MIMO scenarios. In this article a concise overview of the COST 2100 channel model is presented. Main concepts are described, together with useful implementation guidelines. Recent developments, including dense multipath components, polarization, and multi-link aspects, are also discussed.
Age, executive function, and social decision making: a dorsolateral prefrontal theory of cognitive aging.

Current neuropsychological models propose that some age-related cognitive changes are due to frontal-lobe deterioration. However, these models have not considered the possible subdivision of the frontal lobes into the dorsolateral and ventromedial regions. This study assessed the age effects on 3 tasks of executive function and working memory, tasks dependent on dorsolateral prefrontal dysfunction; and 3 tasks of emotion and social decision making, tasks dependent on ventromedial prefrontal dysfunction. Age-related differences in performance were found on all tasks dependent on dorsolateral prefrontal dysfunction. In contrast, age-related differences were not found on the majority of the tasks dependent on ventromedial prefrontal dysfunction. The results support a specific dorsolateral prefrontal theory of cognitive changes with age, rather than a global decline in frontal-lobe function.
A new approach for Face Recognition Based on PCA & Double LDA Treatment combined with SVM

In this paper we propose a faces recognition system. This system does not directly reproduce human vision on machine, but it seeks to find algorithms to achieve similar results by identifying a person using 2D image of his face. The descriptors used for features extraction, combine two algorithms: Principal Component Analysis (PCA) and a double Linear Discriminate Analysis (LDA) treatment. We chose the Support Vector Machine as an output classifier. Our approach has ensured a satisfactory recognition rate and a gain in terms of memory.
Chronovolumes: A Direct Rendering Technique for Visualizing Time-Varying Data

We present a new method for displaying time varying volumetric data. The core of the algorithm is an integration through time producing a single view volume that captures the essence of multiple time steps in a sequence. The resulting view volume then can be viewed with traditional raycasting techniques. With different time integration functions, we can generate several kinds of resulting chronovolumes, which illustrate differing types of time varying features to the user. By utilizing graphics hardware and texture memory, the integration through time can be sped up, allowing the user interactive control over the temporal transfer function and exploration of the data.
SystemML: Declarative Machine Learning on Spark

The rising need for custom machine learning (ML) algorithms and the growing data sizes that require the exploitation of distributed, data-parallel frameworks such as MapReduce or Spark, pose significant productivity challenges to data scientists. Apache SystemML addresses these challenges through declarative ML by (1) increasing the productivity of data scientists as they are able to express custom algorithms in a familiar domain-specific language covering linear algebra primitives and statistical functions, and (2) transparently running these ML algorithms on distributed, data-parallel frameworks by applying cost-based compilation techniques to generate efficient, low-level execution plans with in-memory single-node and large-scale distributed operations. This paper describes SystemML on Apache Spark, end to end, including insights into various optimizer and runtime techniques as well as performance characteristics. We also share lessons learned from porting SystemML to Spark and declarative ML in general. Finally, SystemML is open-source, which allows the database community to leverage it as a testbed for further research.
Mining DNS for malicious domain registrations

Millions of new domains are registered every day and the many of them are malicious. It is challenging to keep track of malicious domains by only Web content analysis due to the large number of domains. One interesting pattern in legitimate domain names is that many of them consist of English words or look like meaningful English while many malicious domain names are randomly generated and do not include meaningful words. We show that it is possible to transform this intuitive observation into statistically informative features using second order Markov models. Four transition matrices are built from known legitimate domain names, known malicious domain names, English words in a dictionary, and based on a uniform distribution. The probabilities from these Markov models, as well as other features extracted from DNS data, are used to build a Random Forest classifier. The experimental results demonstrate that our system can quickly catch malicious domains with a low false positive rate.