nils-herrmann/clean_scirepeval huggingface dataset:
A spatiotemporal compression based approach for efficient big data processing on Cloud

a Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia b Joowing Australia Pty Ltd., 26 Entally Drive, Wheelers Hill, VIC 3150,  University Drive, Burnaby, BC V5A 1S6, Canada d Department of Computing and Information Systems, The University of Melbourne, VIC 3110,
Text Localization in Natural Scene Images Based on Conditional Random Field

This paper proposes a novel hybrid method to robustly and accurately localize texts in natural scene images. A text region detector is designed to generate a text confidence map, based on which text components can be segmented by local binarization approach. A Conditional Random Field (CRF) model, considering the unary component property as well as binary neighboring component relationship, is then presented to label components as "text" or "non-text". Last, text components are grouped into text lines with an energy minimization approach. Experimental results show that the proposed method gives promising performance comparing with the existing methods on ICDAR 2003 competition dataset.
Chapter 3 A VIRTUAL HUMAN DIALOGUE MODEL FOR NON-TEAM INTERACTION

We describe the dialogue model for the virtual humans developed at the Institute for Creative Technologies at the University of Southern California. The dialogue model contains a rich set of information state and dialogue moves to allow a wide range of behaviour in multimodal, multiparty interaction. We extend this model to enable non-team negotiation, using ideas from social science literature on negotiation and implemented strategies and dialogue moves for this area. We present a virtual human doctor who uses this model to engage in multimodal negotiation dialogue with people from other organisations. The doctor is part of the SASO-ST system, used for training for non-team interactions.
Selecting skyline services for QoS-based web service composition

Web service composition enables seamless and dynamic integration of business applications on the web. The performance of the composed application is determined by the performance of the involved web services. Therefore, non-functional, quality of service aspects are crucial for selecting the web services to take part in the composition. Identifying the best candidate web services from a set of functionally-equivalent services is a multi-criteria decision making problem. The selected services should optimize the overall QoS of the composed application, while satisfying all the constraints specified by the client on individual QoS parameters. In this paper, we propose an approach based on the notion of skyline to effectively and efficiently select services for composition, reducing the number of candidate services to be considered. We also discuss how a provider can improve its service to become more competitive and increase its potential of being included in composite applications. We evaluate our approach experimentally using both real and synthetically generated datasets.
TextWorld: A Learning Environment for Text-based Games

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive playthrough of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.
Quantitative Assessment of Operational Security : Models and Tools

This paper proposes a novel approach to help computing system administrators in monitoring the security of their systems. This approach is based on modeling the system as a privilege graph exhibiting operational security vulnerabilities and on transforming this privilege graph into a Markov chain corresponding to all possible successful attack scenarios. A set of tools has been developed to generate automatically the privilege graph of a Unix system, to transform it into the corresponding Markov chain and to compute characteristic measures of the operational system security.
ThreadSanitizer: data race detection in practice

Data races are a particularly unpleasant kind of threading bugs. They are hard to find and reproduce -- you may not observe a bug during the entire testing cycle and will only see it in production as rare unexplainable failures. This paper presents ThreadSanitizer -- a dynamic detector of data races. We describe the hybrid algorithm (based on happens-before and locksets) used in the detector. We introduce what we call dynamic annotations -- a sort of race detection API that allows a user to inform the detector about any tricky synchronization in the user program. Various practical aspects of using ThreadSanitizer for testing multithreaded C++ code at Google are also discussed.
Derandomizing Kernel Address Space Layout for Memory Introspection and Forensics

Modern OS kernels including Windows, Linux, and Mac OS all have adopted kernel Address Space Layout Randomization (ASLR), which shifts the base address of kernel code and data into different locations in different runs. Consequently, when performing introspection or forensic analysis of kernel memory, we cannot use any pre-determined addresses to interpret the kernel events. Instead, we must derandomize the address space layout and use the new addresses. However, few efforts have been made to derandomize the kernel address space and yet there are many questions left such as which approach is more efficient and robust. Therefore, we present the first systematic study of how to derandomize a kernel when given a memory snapshot of a running kernel instance. Unlike the derandomization approaches used in traditional memory exploits in which only remote access is available, with introspection and forensics applications, we can use all the information available in kernel memory to generate signatures and derandomize the ASLR. In other words, there exists a large volume of solutions for this problem. As such, in this paper we examine a number of typical approaches to generate strong signatures from both kernel code and data based on the insight of how kernel code and data is updated, and compare them from efficiency (in terms of simplicity, speed etc.) and robustness (e.g., whether the approach is hard to be evaded or forged) perspective. In particular, we have designed four approaches including brute-force code scanning, patched code signature generation, unpatched code signature generation, and read-only pointer based approach, according to the intrinsic behavior of kernel code and data with respect to kernel ASLR. We have gained encouraging results for each of these approaches and the corresponding experimental results are reported in this paper.
A Threat for Tablet PCs in Public Space: Remote Visualization of Screen Images Using EM Emanation

The use of tablet PCs is spreading rapidly, and accordingly users browsing and inputting personal information in public spaces can often be seen by third parties. Unlike conventional mobile phones and notebook PCs equipped with distinct input devices (e.g., keyboards), tablet PCs have touchscreen keyboards for data input. Such integration of display and input device increases the potential for harm when the display is captured by malicious attackers. This paper presents the description of reconstructing tablet PC displays via measurement of electromagnetic (EM) emanation. In conventional studies, such EM display capture has been achieved by using non-portable setups. Those studies also assumed that a large amount of time was available in advance of capture to obtain the electrical parameters of the target display. In contrast, this paper demonstrates that such EM display capture is feasible in real time by a setup that fits in an attaché case. The screen image reconstruction is achieved by performing a prior course profiling and a complemental signal processing instead of the conventional fine parameter tuning. Such complemental processing can eliminate the differences of leakage parameters among individuals and therefore correct the distortions of images. The attack distance, 2 m, makes this method a practical threat to general tablet PCs in public places. This paper discusses possible attack scenarios based on the setup described above. In addition, we describe a mechanism of EM emanation from tablet PCs and a countermeasure against such EM display capture.
Creating knowledge maps by exploiting dependent relationships

Knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. In more recent times, researchers have investigated knowledge in a more applied way with the chief aim of bringing knowledge to life in machines. Artificial Intelligence has provided some degree of rigour to the study of knowledge and Expert Systems are able to use knowledge to solve problems and answer questions. Current business, social, political and technological pressures have forced organisations to take greater control of the knowledge asset. Software suppliers and others, offering valuable solutions in this area have unfortunately clouded the issue of knowledge. Information and data control are seen as implicit knowledge management tools and many have abandoned the search for explicit knowledge management methods. Knowledge representation schemes help to identify knowledge. They allow for human understanding and machine application and they can support the automated use of knowledge in problem solving. Some of these representation methods also employ spatial techniques that add an extra dimension to human understanding. Knowledge mapping defined in this work uses learning dependency to organise the map and draws on the ideas of what knowledge is and on spatial representation structures. Knowledge maps can support metrics that provide information about the knowledge asset. Knowledge maps create a visible knowledge framework that supports the explicit management of knowledge by organisation managers and directors. Knowledge maps also offer other advantages to the organisation, the individual and to educational institutions.
Hardware/Software Design Considerations for Automotive Embedded Systems

An increasing number of safety-critical functions is taken over by embedded systems in today's automobiles. While standard microcontrollers are the dominant hardware platform in these systems, the decreasing costs of new devices as field programmable gate arrays (FPGAs) make it interesting to consider them for automotive applications. In this paper, a comparison of microcontrollers and FPGAs with respect to safety and reliability properties is presented. For this comparison, hardware fault handling was considered as well as software fault handling. Own empirical evaluations in the area of software fault handling identified advantages of FPGAs with respect to the encapsulation of real-time functions. On the other hand, several dependent failures were detected in versions developed independently on microcontrollers and FPGAs.
FACE RECOGNITION BASED ATTENDANCE MANAGEMENT SYSTEM WITH RASPBERRY PI 2 USING EIGEN FACES ALGORITHM

In recent trends industries, organizations and many companies are using personal identification strategies like finger print identification, RFID for tracking attendance and etc. Among of all these personal identification strategies face recognition is most natural, less time taken and high efficient one. It’s has several applications in attendance management systems and security systems. The main strategy involve in this paper is taking attendance in organizations, industries and etc. using face detection and recognition technology. A time period is settled for taking the attendance and after completion of time period attendance will directly stores into storage device mechanically without any human intervention. A message will send to absent student parent mobile using GSM technology. This attendance will be uploaded into web server using Ethernet. This raspberry pi 2 module is used in this system to achieve high speed of operation. Camera is interfaced to one USB port of raspberry pi 2. Eigen faces algorithm is used for face detection and recognition technology. Eigen faces algorithm is less time taken and high effective than other algorithms like viola-jones algorithm etc. the attendance will directly stores in storage device like pen drive that is connected to one of the USB port of raspberry pi 2. This system is most effective, easy and less time taken for tracking attendance in organizations with period wise without any human intervention.
Design of Variable-Speed Dish-Stirling Solar–Thermal Power Plant for Maximum Energy Harness

Analysis on a developed dynamic model of the dish-Stirling (DS) system shows that maximum solar energy harness can be realized through controlling the Stirling engine speed. Toward this end, a control scheme is proposed for the doubly fed induction generator coupled to the DS system, as a means to achieve maximum power point tracking as the solar insolation level varies. Furthermore, the adopted fuzzy supervisory control technique is shown to be effective in controlling the temperature of the receiver in the DS system as the speed changes. Simulation results and experimental measurements validate the maximum energy harness ability of the proposed variable-speed DS solar-thermal system.
Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories

Sketch is an important media for human to communicate ideas, which reflects the superiority of human intelligence. Studies on sketch can be roughly summarized into recognition and generation. Existing models on image recognition failed to obtain satisfying performance on sketch classification. But for sketch generation, a recent study proposed a sequence-to-sequence variational-auto-encoder (VAE) model called sketch-rnn which was able to generate sketches based on human inputs. The model achieved amazing results when asked to learn one category of object, such as an animal or a vehicle. However, the performance dropped when multiple categories were fed into the model. Here, we proposed a model called sketch-pix2seq which could learn and draw multiple categories of sketches. Two modifications were made to improve the sketch-rnn model: one is to replace the bidirectional recurrent neural network (BRNN) encoder with a convolutional neural network(CNN); the other is to remove the Kullback-Leibler divergence from the objective function of VAE. Experimental results showed that models with CNN encoders outperformed those with RNN encoders in generating human-style sketches. Visualization of the latent space illustrated that the removal of KL-divergence made the encoder learn a posterior of latent space that reflected the features of different categories. Moreover, the combination of CNN encoder and removal of KL-divergence, i.e., the sketchpix2seq model, had better performance in learning and generating sketches of multiple categories and showed promising results in creativity tasks.
Combining monocular and stereo-vision for real-time vehicle ranging and tracking on multilane highways

In this paper, we introduce a novel stereo-monocular fusion approach to on-road localization and tracking of vehicles. Utilizing a calibrated stereo-vision rig, the proposed approach combines monocular detection with stereo-vision for on-road vehicle localization and tracking for driver assistance. The system initially acquires synchronized monocular frames and calculates depth maps from the stereo rig. The system then detects vehicles in the image plane using an active learning-based monocular vision approach. Using the image coordinates of detected vehicles, the system then localizes the vehicles in real-world coordinates using the calculated depth map. The vehicles are tracked both in the image plane, and in real-world coordinates, fusing information from both the monocular and stereo modalities. Vehicles' states are estimated and tracked using Kalman filtering. Quantitative analysis of tracks is provided. The full system takes 46ms to process a single frame.
Semantic Scene Completion from a Single Depth Image

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation. Previous work has considered scene completion and semantic labeling of depth maps separately. However, we observe that these two problems are tightly intertwined. To leverage the coupled nature of these two tasks, we introduce the semantic scene completion network (SSCNet), an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum. Our network uses a dilation-based 3D context module to efficiently expand the receptive field and enable 3D context learning. To train our network, we construct SUNCG - a manually created largescale dataset of synthetic 3D scenes with dense volumetric annotations. Our experiments demonstrate that the joint model outperforms methods addressing each task in isolation and outperforms alternative approaches on the semantic scene completion task. The dataset and code is available at http://sscnet.cs.princeton.edu.
A one-year longitudinal study of English and Japanese vowel production by Japanese adults and children in an English-speaking setting

The effect of age of acquisition on first- and second-language vowel production was investigated. Eight English vowels were produced by Native Japanese (NJ) adults and children as well as by age-matched Native English (NE) adults and children. Productions were recorded shortly after the NJ participants' arrival in the USA and then one year later. In agreement with previous investigations [Aoyama, et al.,  233-250 (2004)], children were able to learn more, leading to higher accuracy than adults in a year's time. Based on the spectral quality and duration comparisons, NJ adults had more accurate production at Time 1, but showed no improvement over time. The NJ children's productions, however, showed significant differences from the NE children's for English "new" vowels /ɪ/, /ε/, /ɑ/, /ʌ/ and /ʊ/ at Time 1, but produced all eight vowels in a native-like manner at Time 2. An examination of NJ speakers' productions of Japanese /i/, /a/, /u/ over time revealed significant changes for the NJ Child Group only. Japanese /i/ and /a/ showed changes in production that can be related to second language (L2) learning. The results suggest that L2 vowel production is affected importantly by age of acquisition and that there is a dynamic interaction, whereby the first and second language vowels affect each other.
ROC Curve, Lift Chart and Calibration Plot

This paper presents ROC curve, lift chart and calibration plot, three well known graphical techniques that are useful for evaluating the quality of classification models used in data mining and machine learning. Each technique, normally used and studied separately, defines its own measure of classification quality and its visualization. Here, we give a brief survey of the methods and establish a common mathematical framework which adds some new aspects, explanations and interrelations between these techniques. We conclude with an empirical evaluation and a few examples on how to use the presented techniques to boost classification accuracy.
Computing the Nearest Correlation Matrix — A Problem from Finance ∗

Given a symmetric matrix what is the nearest correlation matrix, that is, the nearest symmetric positive semidefinite matrix with unit diagonal? This problem arises in the finance industry, where the correlations are between stocks. For distance measured in two weighted Frobenius norms we characterize the solution using convex analysis. We show how the modified alternating projections method can be used to compute the solution for the more commonly used of the weighted Frobenius norms. In the finance application the original matrix has many zero or negative eigenvalues; we show that for a certain class of weights the nearest correlation matrix has correspondingly many zero eigenvalues and that this fact can be exploited in the computation.
Shadow removal for aerial imagery by information theoretic intrinsic image analysis

We present a novel technique for shadow removal based on an information theoretic approach to intrinsic image analysis. Our key observation is that any illumination change in the scene tends to increase the entropy of observed texture intensities. Similarly, the presence of texture in the scene increases the entropy of the illumination function. Consequently, we formulate the separation of an image into texture and illumination components as minimization of entropies of each component. We employ a non-parametric kernel-based quadratic entropy formulation, and present an efficient multi-scale iterative optimization algorithm for minimization of the resulting energy functional. Our technique may be employed either fully automatically, using a proposed learning based method for automatic initialization, or alternatively with small amount of user interaction. As we demonstrate, our method is particularly suitable for aerial images, which consist of either distinctive texture patterns, e.g. building facades, or soft shadows with large diffuse regions, e.g. cloud shadows.
Are computers effective lie detectors? A meta-analysis of linguistic cues to deception.

This meta-analysis investigates linguistic cues to deception and whether these cues can be detected with computer programs. We integrated operational definitions for 79 cues from 44 studies where software had been used to identify linguistic deception cues. These cues were allocated to six research questions. As expected, the meta-analyses demonstrated that, relative to truth-tellers, liars experienced greater cognitive load, expressed more negative emotions, distanced themselves more from events, expressed fewer sensory-perceptual words, and referred less often to cognitive processes. However, liars were not more uncertain than truth-tellers. These effects were moderated by event type, involvement, emotional valence, intensity of interaction, motivation, and other moderators. Although the overall effect size was small, theory-driven predictions for certain cues received support. These findings not only further our knowledge about the usefulness of linguistic cues to detect deception with computers in applied settings but also elucidate the relationship between language and deception.