nils-herrmann/clean_scirepeval huggingface dataset:
Ultra-wideband channel model for communication around the human body

Using ultra-wideband (UWB) wireless sensors placed on a person to continuously monitor health information is a promising new application. However, there are currently no detailed models describing the UWB radio channel around the human body making it difficult to design a suitable communication system. To address this problem, we have measured radio propagation around the body in a typical indoor environment and incorporated these results into a simple model. We then implemented this model on a computer and compared experimental data with the simulation results. This paper proposes a simple statistical channel model and a practical implementation useful for evaluating UWB body area communication systems.
Enterprise resource planning: Developments and directions for operations management research

Enterprise Resource Planning (ERP) has come to mean many things over the last several decades. Divergent applications by practitioners and academics, as well as by researchers in alternative fields of study, has allowed for both considerable proliferation of information on the topic but also for a considerable amount of confusion regarding the meaning of the term. In reviewing ERP research two distinct research streams emerge. The first focuses on the fundamental corporate capabilities driving ERP as a strategic concept. A second stream focuses on the details associated with implementing information systems and their relative success and cost. This paper briefly discusses these research streams and suggests some ideas for related future research.
Neuroscience-Inspired Artificial Intelligence

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.
GRIFFIN: Guarding Control Flows Using Intel Processor Trace

Researchers are actively exploring techniques to enforce control-flow integrity (CFI), which restricts program execution to a predefined set of targets for each indirect control transfer to prevent code-reuse attacks. While hardware-assisted CFI enforcement may have the potential for advantages in performance and flexibility over software instrumentation, current hardware-assisted defenses are either incomplete (i.e., do not enforce all control transfers) or less efficient in comparison. We find that the recent introduction of hardware features to log complete control-flow traces, such as Intel Processor Trace (PT), provides an opportunity to explore how efficient and flexible a hardware-assisted CFI enforcement system may become. While Intel PT was designed to aid in offline debugging and failure diagnosis, we explore its effectiveness for online CFI enforcement over unmodified binaries by designing a parallelized method for enforcing various types of CFI policies. We have implemented a prototype called GRIFFIN in the Linux 4.2 kernel that enables complete CFI enforcement over a variety of software, including the Firefox browser and its jitted code. Our experiments show that GRIFFIN can enforce fine-grained CFI policies with shadow stack as recommended by researchers at a performance that is comparable to software-only instrumentation techniques. In addition, we find that alternative logging approaches yield significant performance improvements for trace processing, identifying opportunities for further hardware assistance.
Genetic CNN

The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness.,,In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which motivates us to adopt the genetic algorithm to efficiently explore this large search space. The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string. The genetic algorithm is initialized by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via a standalone training process on a reference dataset. We run the genetic process on CIFAR10, a small-scale dataset, demonstrating its ability to find high-quality structures which are little studied before. The learned powerful structures are also transferrable to the ILSVRC2012 dataset for large-scale visual recognition.
Blogs are Echo Chambers: Blogs are Echo Chambers

In the last decade, blogs have exploded in number, popularity and scope. However, many commentators and researchers speculate that blogs isolate readers in echo chambers, cutting them off from dissenting opinions. Our empirical paper tests this hypothesis. Using a hand-coded sample of over 1,000 comments from 33 of the world's top blogs, we find that agreement outnumbers disagreement in blog comments by more than 3 to 1. However, this ratio depends heavily on a blog's genre, varying between 2 to 1 and 9 to 1. Using these hand-coded blog comments as input, we also show that natural language processing techniques can identify the linguistic markers of agreement. We conclude by applying our empirical and algorithmic findings to practical implications for blogs, and discuss the many questions raised by our work.
Effective 3D action recognition using EigenJoints

In this paper, we propose an effective method to recognize human actions using 3D skeleton joints recovered from 3D depth data of RGBD cameras. We design a new action feature descriptor for action recognition based on differences of skeleton joints, i.e., EigenJoints which combine action information including static posture, motion property, and overall dynamics. Accumulated Motion Energy (AME) is then proposed to perform informative frame selection, which is able to remove noisy frames and reduce computational cost. We employ non-parametric Naïve-Bayes-Nearest-Neighbor (NBNN) to classify multiple actions. The experimental results on several challenging datasets demonstrate that our approach outperforms the state-of-the-art methods. In addition, we investigate how many frames are necessary for our method to perform classification in the scenario of online action recognition. We observe that the first 30% to 40% frames are sufficient to achieve comparable results to that using the entire video sequences on the MSR Action3D dataset.
Complexity of Information Systems Development Projects: Conceptualization and Measurement Development

This paper conceptualizes and develops valid measurements of the key dimensions of information systems development project (ISDP) complexity. A conceptual framework is proposed to define four components of ISDP complexity: structural organizational complexity, structural IT complexity, dynamic organizational complexity, and dynamic IT complexity. Measures of ISDP complexity are generated based on literature review, field interviews, focus group discussions and two pilot tests with 76 IS managers. The measures are then tested using both exploratory and confirmatory data analyses with survey responses from managers of 541 ISDPs. Results from both the exploratory and confirmatory analyses support the fourcomponent conceptualization of ISDP complexity. The final 20-item measurements of ISDP complexity are shown to adequately satisfy the criteria for unidimensionality, convergent validity, discriminant validity, reliability, factorial invariance across different types of ISDPs, and nomological validity. Implications of the study results to theory development and practice as well as future research directions are discussed.
Construction and animation of anatomically based human hand models

The human hand is a masterpiece of mechanical complexity, able to perform fine motor manipulations and powerful work alike. Designing an animatable human hand model that features the abilities of the archetype created by Nature requires a great deal of anatomical detail to be modeled. In this paper, we present a human hand model with underlying anatomical structure. Animation of the hand model is controlled by muscle contraction values. We employ a physically based hybrid muscle model to convert these contraction values into movement of skin and bones. Pseudo muscles directly control the rotation of bones based on anatomical data and mechanical laws, while geometric muscles deform the skin tissue using a mass-spring system. Thus, resulting animations automatically exhibit anatomically and physically correct finger movements and skin deformations. In addition, we present a deformation technique to create individual hand models from photographs. A radial basis warping function is set up from the correspondence of feature points and applied to the complete structure of the reference hand model, making the deformed hand model instantly animatable.
Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms

This paper proposes a novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFSs) and Bayesian algorithms. The NFS, after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. An online model update scheme is developed on the basis of the probability density function (PDF) of the NFS residuals between the actual and predicted condition data. Bayesian estimation algorithms adopt the model's predicted data as prior information in combination with online measurements to update the degree of belief in the forecasting estimations. In order to simplify the implementation of the proposed approach, a recursive Bayesian algorithm called particle filtering is utilized to calculate in real time a posterior PDF by a set of random samples (or particles) with associated weights. When new data become available, the weights of all particles are updated, and then, predictions are carried out, which form the PDF of the predicted estimations. The developed method is evaluated via two experimental cases-a cracked carrier plate and a faulty bearing. The prediction performance is compared with three prevalent machine condition predictors-recurrent neural networks, NFSs, and recurrent NFSs. The results demonstrate that the proposed approach can predict machine conditions more accurately.
Jump: virtual reality video

We present Jump, a practical system for capturing high resolution, omnidirectional stereo (ODS) video suitable for wide scale consumption in currently available virtual reality (VR) headsets. Our system consists of a video camera built using off-the-shelf components and a fully automatic stitching pipeline capable of capturing video content in the ODS format. We have discovered and analyzed the distortions inherent to ODS when used for VR display as well as those introduced by our capture method and show that they are small enough to make this approach suitable for capturing a wide variety of scenes. Our stitching algorithm produces robust results by reducing the problem to one of pairwise image interpolation followed by compositing. We introduce novel optical flow and compositing methods designed specifically for this task. Our algorithm is temporally coherent and efficient, is currently running at scale on a distributed computing platform, and is capable of processing hours of footage each day.
Would I lie to you? Self-serving lies and other-oriented lies told across different media

This study set out to investigate in which type of media individuals are more likely to tell self-serving and other-oriented lies, and whether this varied according to the recipient of the lie. One hundred and fifty participants rated on a likert-point scale how likely they would tell a lie. Participants were more likely to tell self-serving lies to people not well-known to them. They were more likely to tell self-serving lies in email, followed by phone, and finally face-to-face. Participants were more likely to tell other-oriented lies to individuals they felt close to and this did not vary according to the type media. Participants were also more likely to tell harsh truths to people not well-known to them via email.
The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects

The Berlin brain-computer interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. Muller, and G. Curio. (2007) The non-invasive Berlin brain-computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage.  with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naive subjects that 8 out of 14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further four subjects at >70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.
Two-legged stair-climbing wheelchair and its stair dimension measurement using distance sensors

This paper presents a stair-climbing wheelchair mechanism and its stair dimension measurement method using distance sensors. The wheelchair climbs stairs using two legs having 7 degrees of freedom. The size of the proposed wheelchair is equal to or smaller than that of other commercially available wheelchairs. The proposed wheelchair can climb steep stairs (more than 30° slope), while the seat trajectory is smoothly controlled. Distance sensors are used for measuring the heights and widths of the stairs and the wheelchair direction error. Experiments were performed to test the stair-climbing mechanism and the sensing algorithm for the stair feature estimation.
The effect of inter-implant distance on the height of inter-implant bone crest.

The biologic width around implants has been well documented in the literature. Once an implant is uncovered, vertical bone loss of 1.5 to 2 mm is evidenced apical to the newly established implant-abutment interface. The purpose of this study was to evaluate the lateral dimension of the bone loss at the implant-abutment interface and to determine if this lateral dimension has an effect on the height of the crest of bone between adjacent implants separated by different distances.



Radiographic measurements were taken in 36 patients who had 2 adjacent implants present. Lateral bone loss was measured from the crest of bone to the implant surface. In addition, the crestal bone loss was also measured from a line drawn between the tops of the adjacent implants. The data were divided into 2 groups, based on the inter-implant distance at the implant shoulder.



The results demonstrated that the lateral bone loss was 1.34 mm from the mesial implant shoulder and 1.40 mm from the distal implant shoulder between the adjacent implants. In addition, the crestal bone loss for implants with a greater than 3 mm distance between them was 0.45 mm, while the implants that had a distance of 3 mm or less between them had a crestal bone loss of 1.04 mm.



This study demonstrates that there is a lateral component to the bone loss around implants in addition to the more commonly discussed vertical component. The clinical significance of this phenomenon is that the increased crestal bone loss would result in an increase in the distance between the base of the contact point of the adjacent crowns and the crest of bone. This could determine whether the papilla was present or absent between 2 implants as has previously been reported between 2 teeth. Selective utilization of implants with a smaller diameter at the implant-abutment interface may be beneficial when multiple implants are to be placed in the esthetic zone so that a minimum of 3 mm of bone can be retained between them at the implant-abutment level.
Vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. The implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.
Building a large-scale corpus for evaluating event detection on twitter

Despite the popularity of Twitter for research, there are very few publicly available corpora, and those which are available are either too small or unsuitable for tasks such as event detection. This is partially due to a number of issues associated with the creation of Twitter corpora, including restrictions on the distribution of the tweets and the difficultly of creating relevance judgements at such a large scale. The difficulty of creating relevance judgements for the task of event detection is further hampered by ambiguity in the definition of event. In this paper, we propose a methodology for the creation of an event detection corpus. Specifically, we first create a new corpus that covers a period of 4 weeks and contains over 120 million tweets, which we make available for research. We then propose a definition of event which fits the characteristics of Twitter, and using this definition, we generate a set of relevance judgements aimed specifically at the task of event detection. To do so, we make use of existing state-of-the-art event detection approaches and Wikipedia to generate a set of candidate events with associated tweets. We then use crowdsourcing to gather relevance judgements, and discuss the quality of results, including how we ensured integrity and prevented spam. As a result of this process, along with our Twitter corpus, we release relevance judgements containing over 150,000 tweets, covering more than 500 events, which can be used for the evaluation of event detection approaches.
An Active Patch Model for Real World Texture and Appearance Classification

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets.
Dynamic purity analysis for java programs

The pure methods in a program are those that exhibit functional or side effect free behaviour, a useful property in many contexts. However, existing purity investigations present primarily staticresults. We perform a detailed examination of dynamic method purityin Java programs using a JVM-based analysis. We evaluate multiple purity definitions that range from strong to weak, consider purity forms specific to dynamic execution, and accomodate constraintsimposed by an example consumer application, memoization. We show that while dynamic method purity is actually fairly consistent between programs, examining pure invocation counts and the percentage of the byte code instruction stream contained within some pure method reveals great variation. We also show that while weakening purity definitions exposes considerable dynamic purity, consumer requirements can limitthe actual utility of this information.
RIOT : One OS to Rule Them All in the IoT

The Internet of Things (IoT) embodies a wide spectrum of machines ranging from sensors powered by 8-bits microcontrollers, to devices powered by processors roughly equivalent to those found in entry-level smartphones. Neither traditional operating systems (OS) currently running on internet hosts, nor typical OS for sensor networks are capable to fulfill all at once the diverse requirements of such a wide range of devices. Hence, in order to avoid redundant developments and maintenance costs of IoT products, a novel, unifying type of OS is needed. The following analyzes requirements such an OS should fulfill, and introduces RIOT, a new OS satisfying these demands.  Network, internet, things, objects, IoT, routing, OS, energy, efficient, operating system, protocol, IPv6, wireless, radio, constrained, embedded RIOT: One OS to Rule Them All in the IoT 3