nils-herrmann/clean_scirepeval huggingface dataset:
Data-driven contextual modeling for 3D scene understanding

The recent development of fast depth map fusion technique enables the realtime, detailed scene reconstruction using commodity depth camera, making the indoor scene understanding more possible than ever. To address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts. Our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing the objects in the scene a priori. The key idea is that while contextual information could benefit the detection of either individual objects or object groups, both can contribute to object extraction when objects are unknown. Our method starts with a robust segmentation and partitions a subscene into segments, each of which represents either an independent object or a part of some object. A set of classifiers are trained for both individual objects and object groups, using a database of 3D scene models. We employ the multiple kernel learning (MKL) to learn per-category optimized classifiers for objects and object groups. Finally, we perform a graph matching to extract objects using the classifiers, thus grouping the segments into either an object or an object group. The output is an object-level labeled segmentation of the input subscene. Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes.
Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors

Human activity recognition involves classifying times series data, measured at inertial sensors such as accelerometers or gyroscopes, into one of pre-defined actions. Recently, convolutional neural network (CNN) has established itself as a powerful technique for human activity recognition, where convolution and pooling operations are applied along the temporal dimension of sensor signals. In most of existing work, 1D convolution operation is applied to individual univariate time series, while multi-sensors or multi-modality yield multivariate time series. 2D convolution and pooling operations are applied to multivariate time series, in order to capture local dependency along both temporal and spatial domains for uni-modal data, so that it achieves high performance with less number of parameters compared to 1D operation. However for multi-modal data existing CNNs with 2D operation handle different modalities in the same way, which cause interferences between characteristics from different modalities. In this paper, we present CNNs (CNN-pf and CNN-pff), especially CNN-pff, for multi-modal data. We employ both partial weight sharing and full weight sharing for our CNN models in such a way that modality-specific characteristics as well as common characteristics across modalities are learned from multi-modal (or multi-sensor) data and are eventually aggregated in upper layers. Experiments on benchmark datasets demonstrate the high performance of our CNN models, compared to state of the arts methods.
3D Printed Waveguide Slot Array Antennas

Waveguide slot array antennas can have high gain, since the power distribution network (waveguide) has low loss, enabling a large aperture size. Rapid response to requirements for frequency, gain, and skew angle inspired a search for an alternative to conventional, commercial slot array acquisition. The solution to this requirement is high-resolution 3-D printing of modified slot arrays combined with metal plating. Modification of the structure included removal of wall material in regions where these openings would not cause radiation, opening the structure to enable metal plating while not affecting gain. A 3-D-printed slot array requires no further assembly, unlike conventional waveguide-based arrays that require soldering or brazing, block machining, or plate assembled, brazed structures. Stereolithography 3-D printing of plastic slot array antennas, modified to enable metal plating, has been used to produce a 30 dBi realized gain slot array at 21 GHz for under $1000 USD in a few weeks. Metal plated, 3-D printed plastic slot arrays are also very lightweight in comparison with conventional metal structures. Performance of 3-D printed, metal plated slot arrays has been shown to be identical to conventional metal structures at frequencies up to 22 GHz.
Understanding determinants of cloud computing adoption using an integrated TAM-TOE model

The purpose of this paper is to integrate TAM model and TOE framework for cloud computing adoption at organizational level.  A conceptual framework was developed using technological and organizational variables of TOE framework as external variables of TAM model while environmental variables were proposed to have direct impact on cloud computing adoption. A questionnaire was used to collect the data from 280 companies in IT, manufacturing and finance sectors in India. The data were analyzed using exploratory and confirmatory factor analyses. Further, structural equation modeling was used to test the proposed model.  The study identified relative advantage, compatibility, complexity, organizational readiness, top management commitment, and training and education as important variables for affecting cloud computing adoption using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Also, competitive pressure and trading partner support were found directly affecting cloud computing adoption intentions. The model explained 62 percent of cloud computing adoption.  The model can be used as a guideline to ensure a positive outcome of the cloud computing adoption in organizations. It also provides relevant recommendations to achieve conducive implementation environment for cloud computing adoption.  This study integrates two of the information technology adoption models to improve predictive power of resulting model.
Indoor Positioning using Sensor-fusion in Android Devices September 2011

This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device’s different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.
Consumerization of IT - where is the Theory?

Consumerization of IT (CoIT) is a key trend affecting society at large, including organizations of all kinds. A consensus about the defining aspects of CoIT has not yet been reached. Some refer to CoIT as employees bringing their own devices and technologies to work, while others highlight different aspects. While the debate about the nature and consequences of CoIT is still ongoing, many definitions have already been proposed. In this paper, we review these definitions and what is known about CoIT thus far. To guide future empirical research in this emerging area, we also review several established theories that have not yet been applied to CoIT but in our opinion have the potential to shed a deeper understanding on CoIT and its consequences. We discuss which elements of the reviewed theories are particularly relevant for understanding CoIT and thereby provide targeted guidance for future empirical research employing these theories. Overall, our paper may provide a useful starting point for addressing the lack of theorization in the emerging CoIT literature stream and stimulate discussion about theorizing CoIT.
Spam Corpus Creation for TREC

TREC’s Spam Filtering Track (Cormack & Lynam, 2005) introduces a standard testing framework that is designed to model a spam filter’s usage as closely as possible, to measure quantities that reflect the filter’s effectiveness for its intended purpose, and to yield repeatable (i.e. controlled and statistically valid) results.  The corpus consists of a chronological sequence of email messages, and a gold standard judgement for each message. We are concerned here with the creation of appropriate corpora for use with the toolkit.
Feasibility of Fog Computing Deployment based on Docker Containerization over RaspberryPi

Fog computing is strongly emerging as a relevant and interest-attracting paradigm+technology for both the academic and industrial communities. However, architecture and methodological approaches are still prevalent in the literature, while few research activities have specifically targeted so far the issues of practical feasibility, cost-effectiveness, and efficiency of fog solutions over easily-deployable environments. In this perspective, this paper originally presents i) our fog-oriented framework for Internet-of-Things applications based on innovative scalability extensions of the open-source Kura gateway and ii) its Docker-based containerization over challenging and resource-limited fog nodes, i.e., RaspberryPi devices. Our practical experience and experimental work show the feasibility of using even extremely constrained nodes as fog gateways; the reported results demonstrate that good scalability and limited overhead can be coupled, via proper configuration tuning and implementation optimizations, with the significant advantages of containerization in terms of flexibility and easy deployment, also when working on top of existing, off-the-shelf, and limited-cost gateway nodes.
Agent-based modeling and simulation: Desktop ABMS

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS "is a third way of doing science," in addition to traditional deductive and inductive reasoning (Axelrod 1997b). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model of shopper behavior using spreadsheets.
Low-cost commodity depth sensor comparison and accuracy analysis

Low cost depth sensors have been a huge success in the field of computer vision and robotics, providing depth images even in untextured environments. The same characteristic applies to the Kinect V2, a time-of-flight camera with high lateral resolution. In order to assess advantages of the new sensor over its predecessor for standard applications, we provide an analysis of measurement noise, accuracy and other error sources with the Kinect V2. We examined the raw sensor data by using an open source driver. Further insights on the sensor design and examples of processing techniques are given to completely exploit the unrestricted access to the device.
A novel hybrid genetic algorithm for solving Sudoku puzzles

In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator and mutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more abundant genes is more likely to participate in crossover; such a designed crossover operator has possessed dual effects of self-experience and population experience based on the concept of tactfully combining PSO, thereby making the whole iterative process highly directional; crossover probability is a random number and mutation probability changes along with the fitness value of the optimal solution in the current population such that more possibilities of crossover and mutation could then be considered during the algorithm iteration. The simulation results show that the convergence rate and stability of the novel algorithm has significantly been improved.
LIME: Live Intrinsic Material Estimation

We present the first end-to-end approach for real-time material estimation for general object shapes with uniform material that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill-posed inverse rendering problem using recent advances in image-to-image translation techniques based on deep convolutional encoder-decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real-world image formation and obtains intermediate results even during run time. The estimation of material parameters at real-time frame rates enables exciting mixed-reality applications, such as seamless illumination-consistent integration of virtual objects into real-world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and demonstrate its effectiveness through quantitative and qualitative evaluation.
SearchTogether: an interface for collaborative web search

Studies of search habits reveal that people engage in many search tasks involving collaboration with others, such as travel planning, organizing social events, or working on a homework assignment. However, current Web search tools are designed for a single user, working alone. We introduce SearchTogether, a prototype that enables groups of remote users to synchronously or asynchronously collaborate when searching the Web. We describe an example usage scenario, and discuss the ways SearchTogether facilitates collaboration by supporting awareness, division of labor, and persistence. We then discuss the findings of our evaluation of SearchTogether, analyzing which aspects of its design enabled successful collaboration among study participants.
Truck Hacking: An Experimental Analysis of the SAE J1939 Standard

Consumer vehicles have been proven to be insecure; the addition of electronics to monitor and control vehicle functions have added complexity resulting in safety critical vulnerabilities. Heavy commercial vehicles have also begun adding electronic control systems similar to consumer vehicles. We show how the openness of the SAE J1939 standard used across all US heavy vehicle industries gives easy access for safetycritical attacks and that these attacks aren’t limited to one specific make, model, or industry. We test our attacks on a 2006 Class-8 semi tractor and 2001 school bus. With these two vehicles, we demonstrate how simple it is to replicate the kinds of attacks used on consumer vehicles and that it is possible to use the same attack on other vehicles that use the SAE J1939 standard. We show safety critical attacks that include the ability to accelerate a truck in motion, disable the driver’s ability to accelerate, and disable the vehicle’s engine brake. We conclude with a discussion for possibilities of additional attacks and potential remote attack vectors.
Monocular Template-based Reconstruction of Inextensible Surfaces

We present a monocular 3D reconstruction algorithm for inextensible deformable surfaces. It uses point correspondences between a single image of the deformed surface taken by a camera with known intrinsic parameters and a template. The main assumption we make is that the surface shape as seen in the template is known. Since the surface is inextensible, its deformations are isometric to the template. We exploit the distance preservation constraints to recover the 3D surface shape as seen in the image. Though the distance preservation constraints have already been investigated in the literature, we propose a new way to handle them. Spatial smoothness priors are easily incorporated, as well as temporal smoothness priors in the case of reconstruction from a video. The reconstruction can be used for 3D augmented reality purposes thanks to a fast implementation. We report results on synthetic and real data. Some of them are compared to stereo-based 3D reconstructions to demonstrate the efficiency of our method.
Online Test Tool to Determine the CEFR Reading Comprehension Level of Text

On the Common European Framework of Reference for Languages (CEFR) scale, the average reading comprehension level of the Dutch population is B1 and the average level of text provided by Dutch government organisations requires a considerably higher reading skills level (C1). This means that part of the population may have difficulty reading texts delivered to them by their own government. We built a simple and freely available online tool to give content editors an indication of the CEFR reading comprehension level of their texts. This paper describes the parameters of the tool and proposes a list of possible extensions to improve the quality and usability of the output.  Selection and peer-review under responsibility of the Scientific Programme Committee of the 5th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI 2013).
A Novel 28 GHz Beam Steering Array for 5G Mobile Device With Metallic Casing Application

The design of a novel practical 28 GHz beam steering phased array antenna for future fifth generation mobile device applications is presented in this communication. The proposed array antenna has 16 cavity-backed slot antenna elements that are implemented via the metallic back casing of the mobile device, in which two eight-element phased arrays are built on the left- and right-side edges of the mobile device. Each eight-element phased array can yield beam steering at broadside and gain of >15 dBi can be achieved at boresight. The measured 10 dB return loss bandwidth of the proposed cavity-backed slot antenna element was approximately 27.5–30 GHz. In addition, the impacts of user’s hand effects are also investigated.
SIF: Enforcing Confidentiality and Integrity in Web Applications

SIF (Servlet Information Flow) is a novel software framework for building high-assurance web applications, using language-based information-flow control to enforce security. Explicit, end-to-end confidentiality and integrity policies can be given either as compile-time program annotations, or as run-time user requirements. Compile-time and run-time checking efficiently enforce these policies. Information flow analysis is known to be useful against SQL injection and cross-site scripting, but SIF prevents inappropriate use of information more generally: the flow of confidential information to clients is controlled, as is the flow of low-integrity information from clients. Expressive policies allow users and application providers to protect information from one another. SIF moves trust out of the web application, and into the framework and compiler. This provides application deployers with stronger security assurance. Language-based information flow promises cheap, strong information security. But until now, it could not effectively enforce information security in highly dynamic applications. To build SIF, we developed new language features that make it possible to write realistic web applications. Increased assurance is obtained with modest enforcement overhead.
Temporal collaborative filtering with adaptive neighbourhoods

Collaborative Filtering aims to predict user tastes, by minimising the mean error produced when predicting hidden user ratings. The aim of a deployed recommender system is to iteratively predict users' preferences over a dynamic, growing dataset, and system administrators are confronted with the problem of having to continuously tune the parameters calibrating their CF algorithm. In this work, we formalise CF as a time-dependent, iterative prediction problem. We then perform a temporal analysis of the Netflix dataset, and evaluate the temporal performance of two CF algorithms. We show that, due to the dynamic nature of the data, certain prediction methods that improve prediction accuracy on the Netflix probe set do not show similar improvements over a set of iterative train-test experiments with growing data. We then address the problem of parameter selection and update, and propose a method to automatically assign and update per-user neighbourhood sizes that (on the temporal scale) outperforms setting global parameters.
Performance of a hierarchical temporal memory network in noisy sequence learning

As neurobiological evidence points to the neocortex as the brain region mainly involved in high-level cognitive functions, an innovative model of neocortical information processing has been recently proposed. Based on a simplified model of a neocortical neuron, and inspired by experimental evidence of neocortical organisation, the Hierarchical Temporal Memory (HTM) model attempts at understanding intelligence, but also at building learning machines. This paper focuses on analysing HTM's ability for online, adaptive learning of sequences. In particular, we seek to determine whether the approach is robust to noise in its inputs, and to compare and contrast its performance and attributes to an alternative Hidden Markov Model (HMM) approach. We reproduce a version of a HTM network and apply it to a visual pattern recognition task under various learning conditions. Our first set of experiments explore the HTM network's capability to learn repetitive patterns and sequences of patterns within random data streams. Further experimentation involves assessing the network's learning performance in terms of inference and prediction under different noise conditions. HTM results are compared with those of a HMM trained at the same tasks. Online learning performance results demonstrate the HTM's capacity to make use of context in order to generate stronger predictions, whereas results on robustness to noise reveal an ability to deal with noisy environments. Our comparisons also, however, emphasise a manner in which HTM differs significantly from HMM, which is that HTM generates predicted observations rather than hidden states, and each observation is a sparse distributed representation.
Epoch synchronous non-overlap-add (ESNOLA) method-based concatenative speech synthesis system for Bangla

In the last decade there has been a shift towards development of speech synthesizer using concatenative synthesis technique instead of parametric synthesis. There are a number of different methodologies for concatenative synthesis like TDPSOLA, PSOLA, and MBROLA. This paper, describes a concatenative speech synthesis system based on Epoch Synchronous Non Over Lapp Add (ESNOLA) technique, for standard colloquial Bengali, which uses the partnemes as the smallest signal units for concatenation. The system provided full control for prosody and intonation.