nils-herrmann/clean_scirepeval huggingface dataset:
Twenty Security Considerations for Cloud-Supported Internet of Things

To realize the broad vision of pervasive computing, underpinned by the “Internet of Things” (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends toward the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy, and personal safety risks that arise beyond these subsystems; i.e., from the wide-scale, cross-platform openness that cloud services bring to IoT. In this paper, we focus on security considerations for IoT from the perspectives of cloud tenants, end-users, and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyze the current state of cloud-supported IoT to make explicit the security considerations that require further work.
Milk or Wine: Does Software Security Improve with Age?

We examine the code base of the OpenBSD operating system to determine whether its security is increasing over time. We measure the rate at which new code has been introduced and the rate at which vulnerabilities have been reported over the last 7.5 years and fifteen versions. We learn that 61% of the lines of code in today’s OpenBSD are foundational: they were introduced prior to the release of the initial version we studied and have not been altered since. We also learn that 62% of reported vulnerabilities were present when the study began and can also be considered to be foundational. We find strong statistical evidence of a decrease in the rate at which foundational vulnerabilities are being reported. However, this decrease is anything but brisk: foundational vulnerabilities have a median lifetime of at least 2.6 years. Finally, we examined the density of vulnerabilities in the code that was altered/introduced in each version. The densities ranged from 0 to 0.033 vulnerabilities reported per thousand lines of code. These densities will increase as more vulnerabilities are reported. ∗This work is sponsored by the I3P under Air Force Contract FA8721-05-0002. Opinions, interpretations, conclusions and recommendations are those of the author(s) and are not necessarily endorsed by the United States Government. †This work was produced under the auspices of the Institute for Information Infrastructure Protection (I3P) research program. The I3P is managed by Dartmouth College, and supported under Award number 2003-TK-TX-0003 from the U.S. Department of Homeland Security, Science and Technology Directorate. Points of view in this document are those of the authors and do not necessarily represent the official position of the U.S. Department of Homeland Security, the Science and Technology Directorate, the I3P, or Dartmouth College. ‡Currently at the University of Cambridge
Face Recognition Using Active Appearance Models

We present a new framework for interpreting face images and i mage sequences using an Active Appearance Model (AAM). The AA M contains a statistical, photo-realistic model of the shape and grey-l evel appearance of faces. This paper demonstrates the use of the AAM’s efficient iterat ive matching scheme for image interpretation. We use the AAM as a basis for face re cognition, obtain good results for difficult images. We show how the AAM fra mework allows identity information to be decoupled from other variation, allowing evidence of identity to be integrated over a sequence. The AAM approach m akes optimal use of the evidence from either a single image or image sequence. Since we derive a complete description of a given image our method can be used a s the basis for a range of face image interpretation tasks.
Fulfilling the Needs of eSports Consumers: A Uses and Gratifications Perspective

eSports services are situated between cooperation distinctive for many hedonic activities and competition relevant to the creation of hedonic behavior. This raises the challenge for eSports providers to offer services that fulfill consumers' needs. Against this background, we apply Uses and Gratifications theory (Rayburn and Palmgreen, 1984) and investigate which competitive and hedonic need gratifications drive continuous use of eSports. We conduct ten in-depth expert interviews and a multiple regression analysis based on survey data collected from 360 eSports players. With competition, challenge, and escapism both competitive and hedonic need gratifications drive continuous eSports use.
INFLUENCES OF DIFFERENT MATERIALS ON THE MEASUREMENTS OF A TERRESTRIAL LASER SCANNER ( TLS )

During the last years terrestrial laser scanning became a standard method of data acquisition for various applications in close range domain, like industrial production, forest inventories, plant engineering and construction, car navigation and – one of the most important fields – the recording and modelling of buildings. To use laser scanning data in an adequate way, a quality assessment of the laser scanner is inevitable. In the literature some publications can be found concerning the data quality of terrestrial laser scanners. Most of these papers concentrate on the geometrical accuracy of the scanner (errors of instrument axis, range accuracy using target etc.). In this paper a special aspect of quality assessment will be discussed: the influence of different materials and object colours on the recorded measurements of a TLS. The effects on the geometric accuracy as well as on the simultaneously acquired intensity values are the topics of our investigations. A TRIMBLE GX scanner was used for several test series. The study of different effects refer to materials commonly used at building façades, i.e. grey scaled and coloured sheets, various species of wood, a metal plate, plasters of different particle size, light-transmissive slides and surfaces of different conditions of wetness. The tests concerning a grey wedge show a dependence on the brightness where the mean square error (MSE) decrease from black to white, and therefore, confirm previous results of other research groups. Similar results had been obtained with coloured sheets. In this context an important result is that the accuracy of measurements at night-time has proved to be much better than at day time. While different species of wood and different conditions of wetness have no significant effect on the range accuracy the study of a metal plate delivers MSE values considerably higher than the accuracy of the scanner, if the angle of incidence is approximately orthogonal. Also light-transmissive slides cause enormous MSE values. It can be concluded that high precision measurements should be carried out at night-time and preferable on bright surfaces without specular characteristics.
MarrNet : 3 D Shape Reconstruction via 2 . 5 D Sketches

3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-based approaches, as 3D object annotations are scarce in real images. Previous work chose to train on synthetic data with ground truth 3D information, but suffered from domain adaptation when tested on real data. In this work, we propose MarrNet, an end-to-end trainable model that sequentially estimates 2.5D sketches and 3D object shape. Our disentangled, two-step formulation has three advantages. First, compared to full 3D shape, 2.5D sketches are much easier to be recovered from a 2D image; models that recover 2.5D sketches are also more likely to transfer from synthetic to real data. Second, for 3D reconstruction from 2.5D sketches, systems can learn purely from synthetic data. This is because we can easily render realistic 2.5D sketches without modeling object appearance variations in real images, including lighting, texture, etc. This further relieves the domain adaptation problem. Third, we derive differentiable projective functions from 3D shape to 2.5D sketches; the framework is therefore end-to-end trainable on real images, requiring no human annotations. Our model achieves state-of-the-art performance on 3D shape reconstruction.
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction

We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that a character-based encoder-decoder model is particularly effective, outperforming other results on the AESW Shared Task on its own, and showing gains over a word-based counterpart. Our final model—a combination of three character-based encoder-decoder models, one word-based encoder-decoder model, and a sentence-level CNN—is the highest performing system on the AESW 2016 binary prediction Shared Task.
Multicriteria decision analysis (MCDA): Central Porto high-speed railway station

Over recent years, Portugal has been working towards their integration in the trans-European high-speed railway network. While in Spain several new high-speed lines are already under construction or beginning operation, in Portugal the necessary studies are still being carried out. One of these studies concerned the location of one of the high-speed railway stations to be built in the city of Porto, meant to connect the second most populated metropolitan area in Portugal to the high-speed railway network. The goal for the study was to compare alternative siting strategies, defined by the Portuguese high-speed railway authority. These strategies were materialized in a set of location alternatives, which were evaluated from a range of technical, economical, social and environmental criteria. This paper describes the multicriteria decision analysis (MCDA) approach by which the best alternative was identified from the given set of possible alternatives.
Wodel: a domain-specific language for model mutation

Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as main assets in all development phases. While many languages for model manipulation exist (e.g., for model transformation or code generation), there is a lack of frameworks to define and apply model mutations.
 A model mutant is a variation of an original model, created by specific model mutation operations. Model mutation has many applications, for instance, in the areas of model transformation testing, model-based testing or education.
 In this paper, we present a domain-specific language, called Wodel, for the specification and generation of model mutants. Wodel is domain-independent, as it can be used to generate mutants of models conforming to arbitrary metamodels. Its development environment is extensible, permitting the incorporation of post-processors for different applications. As an example, we show an application consisting on the automated generation of exercises for particular domains (automata, class diagrams, electronic circuits, etc.).
Iterative Entity Alignment via Joint Knowledge Embeddings

Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature construction to complete alignment. In this paper, we present a novel approach for entity alignment via joint knowledge embeddings. Our method jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities. During this process, we can align entities according to their semantic distance in this joint semantic space. More specifically, we present an iterative and parameter sharing method to improve alignment performance. Experiment results on realworld datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment, and can further improve knowledge graph completion performance on various KGs with the favor of joint knowledge embeddings.
Reverberation robust acoustic modeling using i-vectors with time delay neural networks

In reverberant environments there are long term interactions between speech and corrupting sources. In this paper a time delay neural network (TDNN) architecture, capable of learning long term temporal relationships and translation invariant representations, is used for reverberation robust acoustic modeling. Further, iVectors are used as an input to the neural network to perform instantaneous speaker and environment adaptation, providing 10% relative improvement in word error rate. By subsampling the outputs at TDNN layers across time steps, training time is reduced. Using a parallel training algorithm we show that the TDNN can be trained on ∼ 5500 hours of speech data in 3 days using up to 32 GPUs. The TDNN is shown to provide results competitive with state of the art systems in the IARPA ASpIRE challenge, with 27.7% WER on the dev test set.
Measuring large-scale distributed systems: case of BitTorrent Mainline DHT

Peer-to-peer networks have been quite thoroughly measured over the past years, however it is interesting to note that the BitTorrent Mainline DHT has received very little attention even though it is by far the largest of currently active overlay systems, as our results show. As Mainline DHT differs from other systems, existing measurement methodologies are not appropriate for studying it. In this paper we present an efficient methodology for estimating the number of active users in the network. We have identified an omission in previous methodologies used to measure the size of the network and our methodology corrects this. Our method is based on modeling crawling inaccuracies as a Bernoulli process. It guarantees a very accurate estimation and is able to provide the estimate in about 5 seconds. Through experiments in controlled situations, we demonstrate the accuracy of our method and show the causes of the inaccuracies in previous work, by reproducing the incorrect results. Besides accurate network size estimates, our methodology can be used to detect network anomalies, in particular Sybil attacks in the network. We also report on the results from our measurements which have been going on for almost 2.5 years and are the first long-term study of Mainline DHT.
Interactive affordance map building for a robotic task

We describe a technique to build an affordance map interactively for robotic tasks. Affordances are predicted by a trained classifier using geometric features extracted from objects. Based on 2D occupancy grid, a Markov Random Field (MRF) model builds an affordance map with relational affordance with neighboring cells. The quality of the affordance map is refined by sequences of interactive manipulations selected from the model to yield the highest reduction in uncertainty.
Simple passive lossless snubber for high-power multilevel inverters

A passive lossless snubber circuit for multilevel inverters is proposed in this paper. The topology is simple and requires no extra control circuit. In order to reduce the high-voltage stress on power switches with this snubber circuit, an improved snubber circuit is presented by adding separate low-power direct current voltage sources into the original one. The operating principles and design considerations are described in detail in this paper. A prototype of a three-phase three-level diode-clamped inverter with the improved passive lossless snubber is built and tested. The simulation and experimental results indicate that not only can it realize the soft switching operation of the three-level inverter with low-voltage stress but also the topology and the control are simple.
Extended Lexical-Semantic Classification Of English Verbs

Lexical-semantic verb classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest and the most widely deployed classification in English is Levin’s (1993) taxonomy of verbs and their classes. While this resource is attractive in being extensive enough for some NLP use, it is not comprehensive. In this paper, we present a substantial extension to Levin’s taxonomy which incorporates 57 novel classes for verbs not covered (comprehensively) by Levin. We also introduce 106 novel diathesis alternations, created as a side product of constructing the new classes. We demonstrate the utility of our novel classes by using them to support automatic subcategorization acquisition and show that the resulting extended classification has extensive coverage over the English verb lexicon.
Cocktail Party Processing via Structured Prediction

While human listeners excel at selectively attending to a conversation in a cocktail party, machine performance is still far inferior by comparison. We show that the cocktail party problem, or the speech separation problem, can be effectively approached via structured prediction. To account for temporal dynamics in speech, we employ conditional random fields (CRFs) to classify speech dominance within each time-frequency unit for a sound mixture. To capture complex, nonlinear relationship between input and output, both state and transition feature functions in CRFs are learned by deep neural networks. The formulation of the problem as classification allows us to directly optimize a measure that is well correlated with human speech intelligibility. The proposed system substantially outperforms existing ones in a variety of noises.
Structure from Motion without Correspondence

A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information. The problem is framed as finding the maximum likelihood structure and motion given only the 2D measurements, integrating over all possible assignments of 3D features to 2D measurements. This goal is achieved by means of an algorithm which iteratively refines a probability distribution over the set of all correspondence assignments. At each iteration a new structure from motion problem is solved, using as input a set of ’virtual measurements’ derived from this probability distribution. The distribution needed can be efficiently obtained by Markov Chain Monte Carlo sampling. The approach is cast within the framework of Expectation-Maximization,which guarantees convergence to a local maximizer of the likelihood. The algorithm works well in practice, as will be demonstrated using results on several real image sequences.
How robotic products become social products: An ethnographic study of cleaning in the home

Robots that work with people foster social relationships between people and systems. The home is an interesting place to study the adoption and use of these systems. The home provides challenges from both technical and interaction perspectives. In addition, the home is a seat for many specialized human behaviors and needs, and has a long history of what is collected and used to functionally, aesthetically, and symbolically fit the home. To understand the social impact of robotic technologies, this paper presents an ethnographic study of consumer robots in the home. Six families' experience of floor cleaning after receiving a new vacuum (a Roomba robotic vacuum or the Flair, a handheld upright) was studied. While the Flair had little impact, the Roomba changed people, cleaning activities, and other product use. In addition, people described the Roomba in aesthetic and social terms. The results of this study, while initial, generate implications for how robots should be designed for the home.
Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e., mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users’ interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.
Parallelization of Bresenham's line and circle algorithms

Parallel algorithm for line and circle drawing that are based on J.E. Bresenham's line and circle algorithms (see Commun. ACM, vol.20, no.2, p.100-6 (1977)) are presented. The new algorithms are applicable on raster scan CRTs, incremental pen plotters, and certain types of printers. The line algorithm approaches a perfect speedup of P as the line length approaches infinity, and the circle algorithm approaches a speedup greater than 0.9P as the circle radius approaches infinity. It is assumed that the algorithm are run in a multiple-instruction-multiple-data (MIMD) environment, that the raster memory is shared, and that the processors are dedicated and assigned to the task (of line or circle drawing).<<ETX>>
An Analysis of the Cloud Computing Security Problem

Cloud computing is a new computational paradigm that offers an innovative business model for organizations to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing, the model security is still questionable which impacts the cloud model adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper we introduce a detailed analysis of the cloud security problem. We investigated the problem from the cloud architecture perspective, the cloud offered characteristics perspective, the cloud stakeholders’ perspective, and the cloud service delivery models perspective. Based on this analysis we derive a detailed specification of the cloud security problem and key features that should be covered by any proposed security solution.
An actuator with physically variable stiffness for highly dynamic legged locomotion

Running is a complex dynamical task which places strict design requirements on both the physical components and software control systems of a robot. This paper explores some of those requirements and illustrates how a variable compliance actuation system can satisfy them. We present the design, analysis, simulation, and benchtop experimental validation of such an actuator system. We demonstrate, through simulation, the application of our prototype actuator to the problem of biped running.