nils-herrmann/clean_scirepeval huggingface dataset:
Computer Architecture Techniques for Power-Efficiency

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Holistic processing predicts face recognition.

The concept of holistic processing is a cornerstone of face-recognition research. In the study reported here, we demonstrated that holistic processing predicts face-recognition abilities on the Cambridge Face Memory Test and on a perceptual face-identification task. Our findings validate a large body of work that relies on the assumption that holistic processing is related to face recognition. These findings also reconcile the study of face recognition with the perceptual-expertise work it inspired; such work links holistic processing of objects with people's ability to individuate them. Our results differ from those of a recent study showing no link between holistic processing and face recognition. This discrepancy can be attributed to the use in prior research of a popular but flawed measure of holistic processing. Our findings salvage the central role of holistic processing in face recognition and cast doubt on a subset of the face-perception literature that relies on a problematic measure of holistic processing.
A simple model of ocean waves

We present a simple model for the surface of the ocean, suitable for the modeling and rendering of most common waves where the disturbing force is from the wind and the restoring force from gravity.It is based on the Gerstner, or Rankine, model where particles of water describe circular or elliptical stationary orbits. The model can easily produce realistic waves shapes which are varied according to the parameters of the orbits. The surface of the ocean floor affects the refraction and the breaking of waves on the shore. The model can also determine the position, direction, and speed of breakers.The ocean surface is modeled as a parametric surface, permitting the use of traditional rendering methods, including ray-tracing and adaptive subdivision. Animation is easy, since time is built into the model. The foam generated by the breakers is modeled by particle systems whose direction, speed and life expectancy is given by the surface model.To give designers control over the shape of the ocean, the model of the overall surface includes multiple trains of waves, each with its own set of parameters and optional stochastic elements. The overall "randomness" and "short-crestedness" of the ocean is achieved by a combination of small variations within a train and large variations between trains.Rendered examples of oceans waves generated by the model are given and a 10 second animation is described.
Patterns of Brain Activity Supporting Autobiographical Memory, Prospection, and Theory of Mind, and Their Relationship to the Default Mode Network

The ability to rise above the present environment and reflect upon the past, the future, and the minds of others is a fundamentally defining human feature. It has been proposed that these three self-referential processes involve a highly interconnected core set of brain structures known as the default mode network (DMN). The DMN appears to be active when individuals are engaged in stimulus-independent thought. This network is a likely candidate for supporting multiple processes, but this idea has not been tested directly. We used fMRI to examine brain activity during autobiographical remembering, prospection, and theory-of-mind reasoning. Using multivariate analyses, we found a common pattern of neural activation underlying all three processes in the DMN. In addition, autobiographical remembering and prospection engaged midline DMN structures to a greater degree and theory-of-mind reasoning engaged lateral DMN areas. A functional connectivity analysis revealed that activity of a critical node in the DMN, medial prefrontal cortex, was correlated with activity in other regions in the DMN during all three tasks. We conclude that the DMN supports common aspects of these cognitive behaviors involved in simulating an internalized experience.
Spatially and Scale Adaptive Total Variation Based Regularization and Anisotropic Diiusion in Image Processing

In image processing, it is often desirable to remove noise, smooth or sharpen image features, or to otherwise enhance the image. Total Variation (TV) based regularization is a model case of geometry-driven diiusion for image processing. In our papers 14] and 15], we analyze the precise eeects of TV based regularization by analytically nding exact solutions to the TV regularization problem. In this paper, we used these results to develop adaptive TV regularization schemes, as well as a scheme for automatic scale recognition. We develop our results by considering the unconstrained (Tikhonov) formulation of the TV regularization problem, in which a regularization parameter must be chosen to determine the balance between the goodness of t to the original (e.g. measured) data and the amount of regularization to be done to the image. We rst discuss how to choose the regularization parameter based on the local features of objects in the image by using the theory presented in 15]. We then discuss how to choose the regularization parameter based on the noise level in the image, and we nally discuss how to develop adaptive regularization schemes which are driven by both features and noise level in the image. We discuss several heuristics and give two schemes for doing adaptive regularization based on our results, which use a priori information to manually deene a spatially varying regularization parameter. Using the same theory, we also give a simple scheme for local scale recognition. Our results are equally applicable to the constrained formulation of the TV regularization problem, as well as to other geometry-driven diiusion schemes, as these other schemes can be thought of as variations or modiications of the model TV regularization scheme.
A three-way merge for XML documents

Three-way merging is a technique that may be employed for reintegrating changes to a document in cases where multiple independently modified copies have been made. While tools for three-way merge of ASCII text files exist in the form of the ubiquitous diff and patch tools these are of limited applicability to XML documents.
 We present a method for three-way merging of XML which is targeted at merging XML formats that model human-authored documents as ordered trees (e.g. rich text formats structured text drawings etc.). To this end we investigate a number of use cases on XML merging (collaborative editing propagating changes across document variants) from which we derive a set of high-level merge rules. Our merge is based on these rules.
 We propose that our merge is easy to both understand and implement yet sufficiently expressive to handle several important cases of merging on document structure that are beyond the capabilities of traditional text-based tools. In order to justify these claims we applied our merging method to the merging tasks contained in the use cases. The overall performance of the merge was found to be satisfactory.
 The key contributions of this work are: a set of merge rules derived from use cases on XML merging a compact and versatile XML merge in accordance with these rules and a classification of conflicts in the context of that merge.
Named entity recognition and disambiguation using linked data and graph-based centrality scoring

Named Entity Recognition (NER) is a subtask of information extraction and aims to identify atomic entities in text that fall into predefined categories such as person, location, organization, etc. Recent efforts in NER try to extract entities and link them to linked data entities. Linked data is a term used for data resources that are created using semantic web standards such as DBpedia. There are a number of online tools that try to identify named entities in text and link them to linked data resources. Although one can use these tools via their APIs and web interfaces, they use different data resources and different techniques to identify named entities and not all of them reveal this information. One of the major tasks in NER is disambiguation that is identifying the right entity among a number of entities with the same names; for example "apple" standing for both "Apple, Inc." the company and the fruit. We developed a similar tool called NERSO, short for Named Entity Recognition Using Semantic Open Data, to automatically extract named entities, disambiguating and linking them to DBpedia entities. Our disambiguation method is based on constructing a graph of linked data entities and scoring them using a graph-based centrality algorithm. We evaluate our system by comparing its performance with two publicly available NER tools. The results show that NERSO performs better.
News from OPUS : A Collection of Multilingual Parallel Corpora with Tools and Interfaces

The opus corpus is a growing resource providing various multilingual parallel corpora from different domains. In this article we introduce resources that have recently been added to opus. We also look at some corpus-specific problems and the solutions used in preparing the parallel data for the inclusion in our collection. In particular, we discuss the alignment of movie subtitles and the conversion of biomedical documents and localization data to a sentence aligned xml format. opus also includes various tools and interfaces besides the actual data. We will briefly describe our corpus processing and query tools and a newly added lexical database of word alignments.
Optimization of Robotic Arm Trajectory Using Genetic Algorithm

The paper presents a genetic algorithm based design approach of the robotic arm trajectory control with the optimization of various criterions. The described methodology is based on the inverse kinematics problem and it additionally considers the minimization of the operating-time, and/or the minimization of energy consumption as well as the minimization of the sum of all rotation changes during the operation cycle. Each criterion evaluation includes the computationally demanding simulation of the arm movement. The proposed approach was verified and all the proposed criterions have been compared on the trajectory optimization of the industrial robot ABB IRB 6400FHD, which has six degrees of freedom.
An SRAM Design in 65nm and 45nm Technology Nodes Featuring Read and Write-Assist Circuits to Expand Operating Voltage

This paper describes a 32Mb SRAM that has been designed and fabricated in a 65nm low-power CMOS technology. The design has also been migrated to 45nm bulk and SOI technologies. The 68mm die features read and write-assist circuit techniques that expand the operating voltage range and improve manufacturability across technology platforms
A high-voltage output driver in a 2.5-V 0.25-/spl mu/m CMOS technology

The design of a high-voltage output driver in a digital 0.25-/spl mu/m 2.5-V technology is presented. The use of stacked devices with a self-biased cascode topology allows the driver to operate at three times the nominal supply voltage. Oxide stress and hot carrier degradation is minimized since the driver operates within the voltage limits imposed by the design rules of a mainstream CMOS technology. The proposed high-voltage architecture uses a switching output stage. The realized prototype delivers an output swing of 6.46 V to a 50-/spl Omega/ load with a 7.5-V supply and an input square wave of 10 MHz. A PWM signal with a dual-tone sinusoid at 70 kHz and 250 kHz results in an IM3 of -65 dB and an IM2 of -67 dB. The on-resistance is 5.9 /spl Omega/.
On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

In this paper, we utilize results from convex analysis and monotone operator theory to derive additional properties of the softmax function that have not yet been covered in the existing literature. In particular, we show that the softmax function is the monotone gradient map of the log-sum-exp function. By exploiting this connection, we show that the inverse temperature parameter determines the Lipschitz and co-coercivity properties of the softmax function. We then demonstrate the usefulness of these properties through an application in game-theoretic reinforcement learning.
Do physiological data relate to traditional usability indexes?

Task performance data and subjective assessment data are widely used as usability measures in the human-computer interaction (HCI) field. Recently, physiology has also been explored as a metric for evaluating usability. However, it is not clear how physiological measures relate to traditional usability evaluation measures. In this paper, we investigated the relationships among three kinds of data: task performance, subjective assessment and physiological measures. We found evidence that physiological data correlate with task performance data in a video game: with a decrease of the task performance level, the normalized galvanic skin response (GSR) increases. In addition, physiological data are mirrored in subjective reports assessing stress level. The research provides an initial step toward establishing a new usability method using physiology as a complementary measure for traditional HCI evaluation.
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to solve several tasks simultaneously. Learning to solve many tasks simultaneously has been a long-standing, core goal of artificial intelligence, inspired by infant development and motivated by the desire to build flexible robot manipulators capable of many diverse behaviours. We show that the IU agent not only learns to solve many tasks simultaneously but it also learns faster than agents that target a single task at-a-time. In some cases, where the single task DDPG method completely fails, the IU agent successfully solves the task. To demonstrate this, we build a playroom environment using the MuJoCo physics engine, and introduce a grounded formal language to automatically generate tasks.
A File Fragment Classification Method Based on Grayscale Image

File fragment classification is an important and difficult problem in digital forensics. Previous works in this area mainly relied on specific byte sequences in file headers and footers, or statistical analysis and machine learning algorithms on data from the middle of the file. This paper introduces a new approach to classify file fragment based on grayscale image. The proposed method treats a file fragment as a grayscale image, and uses image classification method to classify file fragment. Furthermore, two models based on file-unbiased and type-unbiased are proposed to verify the validity of the proposed method. Compared with previous works, the experimental results are promising. An average classification accuracy of 39.7% in file-unbiased model and 54.7% in type-unbiased model are achieved on 29 file types.
Embedded control of a four-rotor UAV

This paper presents the design of an embedded-control architecture for a four-rotor unmanned air vehicle (UAV) to perform autonomous hover flight. A non-linear control law based on nested saturations technique is presented that stabilizes the state of the aircraft around the origin. The control law was implemented in a microcontroller to stabilize the aircraft in real time. In order to obtain experimental results we have built a low-cost on-board system which drives the aircraft in position and orientation. The nonlinear controller has been successfully tested experimentally
An $X$ -Band Lumped-Element Wilkinson Combiner With Embedded Impedance Transformation

An integrated equal-split Wilkinson power combiner/divider tailored for operation in the X-band is reported in this letter. The combiner features differential input/output ports with different characteristic impedances, thus embedding an impedance transformation feature. Over the frequency range from 8 to 14 GHz it shows insertion loss of 1.4dB, return loss greater than 12 dB and isolation greater than 10 dB. It is implemented in a SiGe bipolar technology, and it occupies an area of 0.12 mm2.
Does Neural Machine Translation Benefit from Larger Context?

We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence. These models lead to better performance, both in terms of general translation quality and pronoun prediction, when trained on small corpora, although this improvement largely disappears when trained with a larger corpus. We also discover that attention-based neural machine translation is well suited for pronoun prediction and compares favorably with other approaches that were specifically designed for this task.
Urban planning and building smart cities based on the Internet of Things using Big Data analytics

The rapid growth in the population density in urban cities demands tolerable provision of services and infrastructure. To meet the needs of city inhabitants. Thus, increase in the request for embedded devices, such as sensors, actuators, and smartphones, etc., which is providing a great business potential towards the new era of Internet of Things (IoT); in which all the devices are capable of interconnecting and communicating with each other over the Internet. Therefore, the Internet technologies provide a way towards integrating and sharing a common communication medium. Having such knowledge, in this paper, we propose a combined IoT-based system for smart city development and urban planning using Big Data analytics. We proposed a complete system, which consists of various types of sensors deployment including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects, etc. A four-tier architecture is proposed which include 1) Bottom Tier-1: which is responsible for IoT sources, data generations, and collections 2) Intermediate Tier-1: That is responsible for all type of communication between sensors, relays, base stations, the internet, etc. 3) Intermediate Tier 2: it is responsible for data management and processing using Hadoop framework, and 4) Top tier: is responsible for application and usage of the data analysis and results generated. The system implementation consists of various steps that start from data generation and collecting, aggregating, filtration, classification, preprocessing, computing and decision making. The proposed system is implemented using Hadoop with Spark, voltDB, Storm or S4 for real time processing of the IoT data to generate results in order to establish the smart city. For urban planning or city future development, the offline historical data is analyzed on Hadoop using MapReduce programming. IoT datasets generated by smart homes, smart parking weather, pollution, and vehicle data sets are used for analysis and evaluation. Such type of system with full functionalities does not exist. Similarly, the results show that the proposed system is more scalable and efficient than the existing systems. Moreover, the system efficiency is measured in term of throughput and processing time.
Human Symbol Manipulation Within an Integrated Cognitive Architecture

This article describes the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6-day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT-R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements.
Advances in Knowledge Discovery and Data Mining

Evaluating a trained system is an important component of machine learning. Labeling test data for large scale evaluation of a trained model can be extremely time consuming and expensive. In this paper we propose strategies for estimating performance of a classifier using as little labeling resource as possible. Specifically, we assume a labeling budget is given and the goal is to get a good estimate of the classifier performance using the provided labeling budget. We propose strategies to get a precise estimate of classifier accuracy under this restricted labeling budget scenario. We show that these strategies can reduce the variance in estimation of classifier accuracy by a significant amount compared to simple random sampling (over 65% in several cases). In terms of labeling resource, the reduction in number of samples required (compared to random sampling) to estimate the classifier accuracy with only 1% error is high as 60% in some cases.
AI Methods in Transportation ROAD AND TRAFFIC SIGN DETECTION AND RECOGNITION

This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs, the requirements and difficulties behind road signs detection and recognition, how to deal with outdoor images, and the different techniques used in the image segmentation based on the colour analysis, shape analysis. It shows also the techniques used for the recognition and classification of the road signs. Although image processing plays a central role in the road signs recognition, especially in colour analysis, but the paper points to many problems regarding the stability of the received information of colours, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field, and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer, and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness, and to get faster systems for real-time applications.
From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view

In this article, we present the current status of the Internet of Things, and discuss how the current situation of many "Intranets" of Things should evolve into a much more integrated and heterogeneous system. We also summarize what in our opinion are the main wirelessand mobility-related technical challenges that lie ahead, and outline some initial ideas on how such challenges can be addressed in order to facilitate the IoT's development and acceptance in the next few years. We also describe a case study on the IoT protocol architecture.