nils-herrmann/clean_scirepeval huggingface dataset:
CMUcam3: An Open Programmable Embedded Vision Sensor

In this paper we present CMUcam3, a low-cost, open source, em bedded computer vision platform. The CMUcam3 is the third generation o f the CMUcam system and is designed to provide a flexible and easy to use ope n source development environment along with a more powerful hardware platfo rm. The goal of the system is to provide simple vision capabilities to small emb dded systems in the form of an intelligent sensor that is supported by an open sou rce community. The hardware platform consists of a color CMOS camera, a frame bu ff r, a low cost 32bit ARM7TDMI microcontroller, and an MMC memory card slot. T he CMUcam3 also includes 4 servo ports, enabling one to create entire, w orking robots using the CMUcam3 board as the only requisite robot processor. Cus tom C code can be developed using an optimized GNU toolchain and executabl es can be flashed onto the board using a serial port without external download ing hardware. The development platform includes a virtual camera target allowi ng for rapid application development exclusively on a PC. The software environment c omes with numerous open source example applications and libraries includi ng JPEG compression, frame differencing, color tracking, convolutions, histog ramming, edge detection, servo control, connected component analysis, FAT file syste m upport, and a face detector.
An Evaluation Study on Log Parsing and Its Use in Log Mining

Logs, which record runtime information of modern systems, are widely utilized by developers (and operators) in system development and maintenance. Due to the ever-increasing size of logs, data mining models are often adopted to help developers extract system behavior information. However, before feeding logs into data mining models, logs need to be parsed by a log parser because of their unstructured format. Although log parsing has been widely studied in recent years, users are still unaware of the advantages of different log parsers nor the impact of them on subsequent log mining tasks. Thus they often re-implement or even re-design a new log parser, which would be time-consuming yet redundant. To address this issue, in this paper, we study four log parsers and package them into a toolkit to allow their reuse. In addition, we obtain six insightful findings by evaluating the performance of the log parsers on five datasets with over ten million raw log messages, while their effectiveness on a real-world log mining task has been thoroughly examined.
Towards a quantum programming language

We propose the design of a programming language for quantum computing. Traditionally, quantum algorithms are frequently expressed at the hardware level, for instance in terms of the quantum circuit model or quantum Turing machines. These approaches do not encourage structured programming or abstractions such as data types. In this paper, we describe the syntax and semantics of a simple quantum programming language with high-level features such as loops, recursive procedures, and structured data types. The language is functional in nature, statically typed, free of run-time errors, and it has an interesting denotational semantics in terms of complete partial orders of superoperators.
Stress Detection Using Wearable Physiological Sensors

As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into ”stressful” or ”non-stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection.
A New Arbiter PUF for Enhancing Unpredictability on FPGA

In general, conventional Arbiter-based Physically Unclonable Functions (PUFs) generate responses with low unpredictability. The N-XOR Arbiter PUF, proposed in 2007, is a well-known technique for improving this unpredictability. In this paper, we propose a novel design for Arbiter PUF, called Double Arbiter PUF, to enhance the unpredictability on field programmable gate arrays (FPGAs), and we compare our design to conventional N-XOR Arbiter PUFs. One metric for judging the unpredictability of responses is to measure their tolerance to machine-learning attacks. Although our previous work showed the superiority of Double Arbiter PUFs regarding unpredictability, its details were not clarified. We evaluate the dependency on the number of training samples for machine learning, and we discuss the reason why Double Arbiter PUFs are more tolerant than the N-XOR Arbiter PUFs by evaluating intrachip variation. Further, the conventional Arbiter PUFs and proposed Double Arbiter PUFs are evaluated according to other metrics, namely, their uniqueness, randomness, and steadiness. We demonstrate that 3-1 Double Arbiter PUF archives the best performance overall.
Mobile Device Security: A Survey on Mobile Device Threats, Vulnerabilities and their Defensive Mechanism

Mobile communication has become a serious business tool nowadays. Mobile devices are the major platform for the users to transfer and exchange diverse data for communication. These devices are variably used for applications like banking, personal digital assistance, remote working, m-commerce, internet access, entertainment and medical usage. However people are still hesitant to use mobile devices because of its security issue. It is necessary to provide a reliable and easy to use method for securing these mobile devices against unauthorized access and diverse attacks. It is preferred to apply biometrics for the security of mobile devices and improve reliability over wireless services. This paper deals with various threats and vulnerabilities that affect the mobile devices and also it discusses how biometrics can be a solution to the mobile devices ensuring security.
Improving player balancing in racing games

In competitive games where players' skill levels are mis-matched, the play experience can be unsatisfying for both stronger and weaker players. Player balancing provides assistance for less-skilled players in order to make games more competitive and engaging. Although player balancing can be seen in many real-world games, there is little work on the design and effectiveness of these techniques outside of shooting games. In this paper we provide new knowledge about player balancing in the popular and competitive rac-ing genre. We studied issues of noticeability and balancing effectiveness in a prototype racing game, and tested the effects of several balancing techniques on performance and play experience. The techniques significantly improved the balance of player performance, were preferred by both experts and novices, increased novices' feelings of competi-tiveness, and did not detract from experts' experience. Our results provide new understanding of the design and use of player balancing for racing games, and provide novel tech-niques that can also be applied to other genres.
Behavior Trees in Robotics and AI: An Introduction

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.
The Kinematics of Contact and Grasp

The kinematics of contact describe the motion of a point of contact over the surfaces of two contacting objects in response to a relative motion of these objects. Using concepts from differential geometry, I derive a set of equations, called the contact equations, that embody this relationship. I employ the contact equations to design the following applications to be executed by an end-effector with tactile sensing capability: (1) determining the curvature form of an unknown object at a point of contact; and (2) following the surface of an unknown object. The contact equations also serve as a basis for an investigation of the kinematics of grasp. I derive the relationship between the relative motion of two fingers grasping an object and the motion of the points of contact over the object surface. Based on this analysis, we explore the following applications: (1) rolling a sphere between two arbitrarily shaped fingers ; (2) fine grip adjustment (i.e., having two fingers that grasp an unknown object locally optimize their grip for maximum stability ).
Focused plenoptic camera and rendering

bstract. Plenoptic cameras, constructed with internal microlens rrays, capture both spatial and angular information, i.e., the full 4-D adiance, of a scene. The design of traditional plenoptic cameras ssumes that each microlens image is completely defocused with espect to the image created by the main camera lens. As a result, nly a single pixel in the final image is rendered from each microlens mage, resulting in disappointingly low resolution. A recently develped alternative approach based on the focused plenoptic camera ses the microlens array as an imaging system focused on the imge plane of the main camera lens. The flexible spatioangular tradeff that becomes available with this design enables rendering of final mages with significantly higher resolution than those from traditional lenoptic cameras. We analyze the focused plenoptic camera in ptical phase space and present basic, blended, and depth-based endering algorithms for producing high-quality, high-resolution imges. We also present our graphics-processing-unit-based impleentations of these algorithms, which are able to render full screen efocused images in real time.
What Every Programmer Should Know About Memory

As CPU cores become both faster and more numerous, the limiting factor for most programs is now, and will be for some time, memory access. Hardware designers have come up with ever more sophisticated memory handling and acceleration techniques–such as CPU caches–but these cannot work optimally without some help from the programmer. Unfortunately, neither the structure nor the cost of using the memory subsystem of a computer or the caches on CPUs is well understood by most programmers. This paper explains the structure of memory subsystems in use on modern commodity hardware, illustrating why CPU caches were developed, how they work, and what programs should do to achieve optimal performance by utilizing them.
Biometric template selection and update: a case study in fingerprints

A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a claimed identity. Most systems store multiple templates per user in order to account for variations observed in a person’s biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype #ngerprint templates for a #nger from a given set of #ngerprint impressions. The #rst method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 di4erent #ngers, with 200 impressions per #nger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques. ? 2003 Pattern Recognition Society.
Separation of Singing Voice from Music

Songs are representation of audio signal and musical instruments. An audio signal separation system should be able to identify different audio signals such as speech, background noise and music. In a song the singing voice provides useful information regarding pitch range, music content, music tempo and rhythm. An automatic singing voice separation system is used for attenuating or removing the music accompaniment. The paper presents survey of the various algorithm and method for separating singing voice from musical background. From the survey it is observed that most of researchers used Robust Principal Component Analysis method for separation of singing voice from music background, by taking into account the rank of music accompaniment and the sparsity of singing voices.
Toward Simple Strategy for Optimal Tracking and Localization of Robots With Adaptive Particle Filtering

The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and simple adaptive strategy to increase the efficiency and drastically reduce the computational effort in particle filters (PFs). The purpose of the adaptive approach (dispersion-based adaptive particle filter - DAPF) is to provide higher number of particles during the initial searching state (when the localization presents greater uncertainty) and fewer particles during the subsequent state (when the localization exhibits less uncertainty). With the aim of studying the dynamical PF behavior regarding others and putting the proposed algorithm into practice, we designed a methodology based on different target applications and a Kinect sensor. The various experiments conducted for both color tracking and mobile robot localization problems served to demonstrate that the DAPF algorithm can be further generalized. As a result, the DAPF approach significantly improved the computational performance over two well-known filtering strategies: 1) the classical PF with fixed particle set sizes, and 2) the adaptive technique named Kullback-Leiber distance.
Real-time synthesis of Chinese visual speech and facial expressions using MPEG-4 FAP features in a three-dimensional avatar

This paper describes our initial work in developing a real-time audio-visual Chinese speech synthesizer with a 3D expressive avatar. The avatar model is parameterized according to the MPEG-4 facial animation standard . This standard offers a compact set of facial animation parameters (FAPs) and feature points (FPs) to enable realization of 20 Chinese visemes and 7 facial expressions (i.e. 27 target facial configurations). The Xface  open source toolkit enables us to define the influence zone for each FP and the deformation function that relates them. Hence we can easily animate a large number of coordinates in the 3D model by specifying values for a small set of FAPs and their FPs. FAP values for 27 target facial configurations were estimated from available corpora. We extended the dominance blending approach to effect animations for coarticulated visemes superposed with expression changes. We selected six sentiment-carrying text messages and synthesized expressive visual speech (for all expressions, in randomized order) with neutral audio speech. A perceptual experiment involving 11 subjects shows that they can identify the facial expression that matches the text message’s sentiment 85% of the time.
Natural Language Generation enhances human decision-making with uncertain information

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentations for uncertain data and, for the first time, measure their effects on human decision-making. We show that the use of Natural Language Generation (NLG) improves decision-making under uncertainty, compared to state-of-theart graphical-based representation methods. In a task-based study with 442 adults, we found that presentations using NLG lead to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations).
BEAM: A framework for business ecosystem analysis and modeling

C. H. Tian B. K. Ray J. Lee R. Cao W. Ding This paper presents a framework for the modeling and analysis of business model designs involving a network of interconnected business entities. The framework includes an ecosystem-modeling component, a simulation component, and a serviceanalysis component, and integrates methods from value network modeling, game theory analysis, and multiagent systems. A role-based paradigm is introduced for characterizing ecosystem entities in order to easily allow for the evolution of the ecosystem and duplicated functionality for entities. We show how the framework can be used to provide insight into value distribution among the entities and evaluation of business model performance under different scenarios. The methods are illustrated using a case study of a retail business-to-business service ecosystem.
The Modified Checklist for Autism in Toddlers: an initial study investigating the early detection of autism and pervasive developmental disorders.

Autism, a severe disorder of development, is difficult to detect in very young children. However, children who receive early intervention have improved long-term prognoses. The Modified Checklist for Autism in Toddlers (M-CHAT), consisting of 23 yes/no items, was used to screen 1,293 children. Of the 58 children given a diagnostic/developmental evaluation, 39 were diagnosed with a disorder on the autism spectrum. Six items pertaining to social relatedness and communication were found to have the best discriminability between children diagnosed with and without autism/PDD. Cutoff scores were created for the best items and the total checklist. Results indicate that the M-CHAT is a promising instrument for the early detection of autism.
Interactive Visualization of Serial Periodic Data

Serial periodic data exhibit both serial and periodic properties. For example, time continues forward serially, but weeks, months, and years are periods that recur. While there are extensive visualization techniques for exploring serial data, and a few for exploring periodic data, no existing technique simultaneously displays serial and periodic attributes of a data set. We introduce a spiral visualization technique, which displays data along a spiral to highlight serial attributes along the spiral axis and periodic ones along the radii. We show several applications of the spiral visualization to data exploration tasks, present our implementation, discuss the capacity for data analysis, and present findings of our informal study with users in data-rich scientific domains.
SimNets: A Generalization of Convolutional Networks

We present a deep layered architecture that generalizes classical convolutional neural networks (ConvNets). The architecture, called SimNets, is driven by two operators, one being a similarity function whose family contains the convolution operator used in ConvNets, and the other is a new soft max-min-mean operator called MEX that realizes classical operators like ReLU and max pooling, but has additional capabilities that make SimNets a powerful generalization of ConvNets. Three interesting properties emerge from the architecture: (i) the basic input to hidden layer to output machinery contains as special cases kernel machines with the Exponential and Generalized Gaussian kernels, the output units being ”neurons in feature space” (ii) in its general form, the basic machinery has a higher abstraction level than kernel machines, and (iii) initializing networks using unsupervised learning is natural. Experiments demonstrate the capability of achieving state of the art accuracy with networks that are an order of magnitude smaller than comparable ConvNets.
Binding theory and grammatical specific language impairment in children

This study investigates the intrasentential assignment of reference to pronouns (him, her) and anaphors (himself, herself) as characterized by Binding Theory in a subgroup of "Grammatical specifically language-impaired" (SLI) children. The study aims to (1) provide further insight into the underlying nature of Grammatical SLI in children and (2) elucidate the relationship between different sources of knowledge, that is, syntactic knowledge versus knowledge of lexical properties and pragmatic inference in the assignment of intrasentential coreference. In two experiments, using a picture-sentence pair judgement task, the children's knowledge of the lexical properties versus syntactic knowledge (Binding Principles A and B) in the assignment of reflexives and pronouns was investigated. The responses of 12 Grammatical SLI children (aged 9:3 to 12:10) and three language ability (LA) control groups of 12 children (aged 5:9 to 9:1) were compared. The results indicated that the SLI children and the LA controls may use a combination of conceptual-lexical and pragmatic knowledge (e.g., semantic gender, reflexive marking of the predicate, and assignment of theta roles) to help assign reference to anaphors and pronouns. The LA controls also showed appropriate use of the syntactic knowledge. In contrast, the SLI children performed at chance when syntactic information was crucially required to rule out inappropriate coreference. The data are consistent with an impairment with the (innate) syntactic knowledge characterized by Binding Theory which underlies reference assignment to anaphors and pronouns. We conclude that the SLI children's syntactic representation is underspecified with respect to coindexation between constituents and the syntactic properties of pronouns. Support is provided for the proposal that Grammatical SLI children have a modular language deficit with syntactic dependent structural relationships between constituents, that is, a Representational Deficit with Dependent Relationships (RDDR). Further consideration of the linguistic characteristics of this deficit is made in relation to the hypothesized syntactic representations of young normally developing children.