nils-herrmann/clean_scirepeval huggingface dataset:
People Tracking with Mobile Robots Using Sample-based Joint Probabilistic Data Association Filters

One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple people. The experiments furthermore show that the approach outperforms other techniques developed so far. KEY WORDS—multi-target tracking, data association, particle filters, people tracking, mobile robot perception
Network Intrusion Detection Systems based Neural Network: A Comparative Study

Neural networks are artificial learning systems. For more than two decades, they have help for detecting hostile behaviors in a computer system. This review describes those systems and theirs limits. It defines and gives neural networks characteristics. It also itemizes neural networks which are used in intrusion detection systems. The state of the art on IDS made from neural networks is reviewed. In this paper, we also make a taxonomy and a comparison of neural networks intrusion detection systems. We end this review with a set of remarks and future works that can be done in order to improve the systems that have been presented. This work is the result of a meticulous scan of the literature.
Industrial IoT security threats and concerns by considering Cisco and Microsoft IoT reference models

This paper investigates security concerns and issues for Industrial Internet of Things (IIoT). The IIoT is an emerging transformation, bringing great values to every industry. Although this rapid alter in industries create values, but there are concerns about security issues, most of which would be still unknown due to the novelty of this platform. In order to provide a guideline for those who want to investigate IoT security and contribute to its improvement, this paper attempts to provide a list of security threats and issues on the cloud-side layer of IoT, which consists of data accumulation and abstraction levels. For this reason, we choose Cisco and Microsoft Azure IoT Architecture as reference models. Then, two layers of Cisco reference architecture model have been chosen to be investigated for their security issues. Finally, consideration of security issues has been briefly explained.
Methods to determine aerobic endurance.

Physiological testing of elite athletes requires the correct identification and assessment of sports-specific underlying factors. It is now recognised that performance in long-distance events is determined by maximal oxygen uptake (V(2 max)), energy cost of exercise and the maximal fractional utilisation of V(2 max) in any realised performance or as a corollary a set percentage of V(2 max) that could be endured as long as possible. This later ability is defined as endurance, and more precisely aerobic endurance, since V(2 max) sets the upper limit of aerobic pathway. It should be distinguished from endurance ability or endurance performance, which are synonymous with performance in long-distance events. The present review examines methods available in the literature to assess aerobic endurance. They are numerous and can be classified into two categories, namely direct and indirect methods. Direct methods bring together all indices that allow either a complete or a partial representation of the power-duration relationship, while indirect methods revolve around the determination of the so-called anaerobic threshold (AT). With regard to direct methods, performance in a series of tests provides a more complete and presumably more valid description of the power-duration relationship than performance in a single test, even if both approaches are well correlated with each other. However, the question remains open to determine which systems model should be employed among the several available in the literature, and how to use them in the prescription of training intensities. As for indirect methods, there is quantitative accumulation of data supporting the utilisation of the AT to assess aerobic endurance and to prescribe training intensities. However, it appears that: there is no unique intensity corresponding to the AT, since criteria available in the literature provide inconsistent results; and the non-invasive determination of the AT using ventilatory and heart rate data instead of blood lactate concentration ([La(-)](b)) is not valid. Added to the fact that the AT may not represent the optimal training intensity for elite athletes, it raises doubt on the usefulness of this theory without questioning, however, the usefulness of the whole [La(-)](b)-power curve to assess aerobic endurance and predict performance in long-distance events.
Biologically inspired smart fabric design : applications , prerequisites and limitations

This paper focuses on biomimetic design in the field of technical textiles / smart fabrics. Biologically inspired design is a very promising approach that has provided many elegant solutions. Firstly, a few bio-inspired innovations are presented, followed the introduction of trans-disciplinary research as a useful tool for defining the design problem and giving solutions. Furthermore, the required methods for identifying and applying biological analogies are analysed. Finally, the bio-mimetic approach is questioned and the difficulties, limitations and errors that a designer might face when adopting it are discussed. Researchers and product developers that use this approach should also problematize on the role of biomimetic design: is it a practice that redirects us towards a new model of sustainable development or is it just another tool for generating product ideas in order to increase a company’s competitiveness in the global market?
Birds of a feather don't always fly farthest: similarity in Big Five personality predicts more negative marital satisfaction trajectories in long-term marriages.

Decades of research suggest that similarity in demographics, values, activities, and attitudes predicts higher marital satisfaction. The present study examined the relationship between similarity in Big Five personality factors and initial levels and 12-year trajectories of marital satisfaction in long-term couples, who were in their 40s and 60s at the beginning of the study. Across the entire sample, greater overall personality similarity predicted more negative slopes in marital satisfaction trajectories. In addition, spousal similarity on Conscientiousness and Extraversion more strongly predicted negative marital satisfaction outcomes among the midlife sample than among the older sample. Results are discussed in terms of the different life tasks faced by young, midlife, and older adults, and the implications of these tasks for the "ingredients" of marital satisfaction.
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3 . State and parameter estimation

Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented.
CEO Overcon fi dence and Corporate Investment ∗

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A Hitchhiker's Guide On Distributed Training of Deep Neural Networks

Deep learning has led to tremendous advancements in the field of Artificial Intelligence. One caveat however is the substantial amount of compute needed to train these deep learning models. Training a benchmark dataset like ImageNet on a single machine with a modern GPU can take upto a week, distributing training on multiple machines has been observed to drastically bring this time down. Recent work has brought down ImageNet training time to a time as low as 4 minutes by using a cluster of 2048 GPUs. This paper surveys the various algorithms and techniques used to distribute training and presents the current state of the art for a modern distributed training framework. More specifically, we explore the synchronous and asynchronous variants of distributed Stochastic Gradient Descent, various All Reduce gradient aggregation strategies and best practices for obtaining higher throughout and lower latency over a cluster such as mixed precision training, large batch training and gradient compression.
A New Digital Signature Scheme from Layered Cellular Automata

Cellular Automata (CA) is one of the important tools to design cryptographic algorithms, and in the past years many researchers have explored several cryptographic algorithms based on CA. However, most of reported CAbased cryptographic algorithms focus on the symmetric key encryption schemes and few CA-based asymmetric encryption scheme has been proposed, let alone the CAbased digital signature scheme. In this paper, to fill this gap, we present a new digital signature scheme based on the layered CA technique. Specifically, in the proposed layered CA-based digital signature scheme, we combine the transition rules of some one-dimensional (1D) reversible CAs to generate the rules of a two-dimensional (2D) CA, where the reverse of the 1D transition rules are kept as the private key and the 2D transition rules are set as the corresponding public key. Based on the hardness assumption of the layered CA reversibility (LCAR) problem, we formally prove the proposed scheme is semantically secure against chosen-message attack in the random oracle model.
Regret Minimization for Partially Observable Deep Reinforcement Learning

Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial observations by using finite length observation histories or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to an advantage-like function and is robust to partially observed state. We demonstrate that this new algorithm can substantially outperform strong baseline methods on several partially observed reinforcement learning tasks: learning first-person 3D navigation in Doom and Minecraft, and acting in the presence of partially observed objects in Doom and Pong.
Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Recent work has shown how to train Convolutional Neural Networks (CNNs) rapidly on large image datasets , then transfer the knowledge gained from these models to a variety of tasks . Following , in this work, we demonstrate similar scalability and transfer for Recurrent Neural Networks (RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to train a character-level 4096-dimension multiplicative LSTM (mLSTM)  for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset  in four hours. This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset . Converging large batch RNN models can be challenging. Recent work has suggested scaling the learning rate as a function of batch size, but we find that simply scaling the learning rate as a function of batch size leads either to significantly worse convergence or immediate divergence for this problem. We provide a learning rate schedule that allows our model to converge with a 32k batch size. Since our model converges over the Amazon Reviews dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while substantial, is commercially available, this work opens up large scale unsupervised NLP training to most commercial applications and deep learning researchers 11Our code is publicly available: https://github.com/NVIDIA/sentiment-discovery, A model can be trained over most public or private text datasets overnight.
Measuring Catastrophic Forgetting in Neural Networks

Deep multi-layer perceptron neural networks are used in many state-of-the-art systems for machine perception (e.g., speech-to-text, image classification, and object detection). Once a network is trained to do a specific task, e.g., finegrained bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as finegrained flower recognition. When new tasks are added, deep neural networks are prone to catastrophically forgetting previously learned information. Catastrophic forgetting has hindered the use of neural networks in deployed applications that require lifelong learning. There have been multiple attempts to develop schemes that mitigate catastrophic forgetting, but these methods have yet to be compared and the kinds of tests used to evaluate individual methods vary greatly. In this paper, we compare multiple mechanisms designed to mitigate catastrophic forgetting in neural networks. Experiments showed that the mechanism(s) that are critical for optimal performance vary based on the incremental training paradigm and type of data being used.
Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD.

In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.
Real Time Pattern Recognition and Identification of Co-ordinates with LabVIEW

Digital Image processing( DIP ) is a theme of awesome significance basically for any task, either for essential varieties of photograph indicators or complex mechanical frameworks utilizing assumed vision. In this paperbasics of the image processing in LabVIEW have been described in brief. It involves capturing the image of an object that is to be analysed and compares it with the reference image template of the object by pattern matching algorithm. The co-ordinates of the image is also be identified by tracking of object on the screen. A basic pattern matching algorithm is modified to snap and track the image on real-time basis.  LabVIEW, IMAQ, Pattern matching, Realtime tracking, .
Boosting LiDAR-Based Semantic Labeling by Cross-modal Training Data Generation

Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-theart perception systems. In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation. In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network utilizes virtual image projections of the 3D point clouds for efficient inference. Further, we propose an automated process for large-scale cross-modal training data generation called Autolabeling, in order to boost semantic labeling performance while keeping the manual annotation effort low. The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset. LiLaNet is shown to significantly outperform current state-ofthe-art CNN architectures for LiDAR data. Applying our automatically generated large-scale training data yields a boost of up to 14 percentage points compared to networks trained on manually annotated data only.
The Sources and Consequences of Mobile Technostress in the Workplace

In this study, we explore the phenomenon of mobile technostress: stress experienced by users of mobile information and communication technologies. We examine the impacts of mobile technostress on individuals’ job satisfaction. Based on the Transaction Based Model of stress and the existing literature on technostress, a conceptual model was proposed to understand this phenomenon. Two sources of mobile technostress have been identified: techno-overload and techno-insecurity. We hypothesize that techno-overload and techno-insecurity exert a negative impact on job satisfaction. The individual level mobile technostress inhibitors (i.e., self-efficacy) are identified as helping individuals reduce stress. We also hypothesize that self-efficacy has a positive impact on job satisfaction. Furthermore, the moderator effects of habit are also explored. We hypothesize that habit will negatively moderate the relationship between mobile technostress creators and job satisfaction, and positively moderate the relationship between mobile technostress inhibitors and job satisfaction. The methodological design as well as potential theoretical and practical implications has also been discussed.
Repeatability of central and peripheral corneal thickness measurements with the iVue 100 optical coherence tomographer

The cornea is the main refracting surface of the eye1,2,3, comprised of five layers with a sixth corneal layer positioned between the stroma and Descemet’s membrane being  The cornea serves many functions which include acting as a convex refracting surface, serving as a barrier against foreign bodies and aiding in stabilisation of the tear film.2 The average central corneal thickness, in a non-diseased eye, is expected to be 0.56 mm in young persons (under 25 years of age) and increases to around 0.57 mm by the age of 65  Corneal thickness, however, is not uniform as it is generally thinner in the centre, thickens towards the periphery reaching almost 0.7 mm and exhibits diurnal  The stromal layer makes up almost 90% of the total corneal
The Neural Bases of Emotion Regulation: Reappraisal and Suppression of Negative Emotion

Emotion regulation strategies are thought to differ in when and how they influence the emotion-generative process. However, no study to date has directly probed the neural bases of two contrasting (e.g., cognitive versus behavioral) emotion regulation strategies. This study used functional magnetic resonance imaging (fMRI) to examine cognitive reappraisal (a cognitive strategy thought to have its impact early in the emotion-generative process) and expressive suppression (a behavioral strategy thought to have its impact later in the emotion-generative process).



Seventeen women viewed 15 sec neutral and negative emotion-eliciting films under four conditions--watch-neutral, watch-negative, reappraise-negative, and suppress-negative--while providing emotion experience ratings and having their facial expressions videotaped.



Reappraisal resulted in early (0-4.5 sec) prefrontal cortex (PFC) responses, decreased negative emotion experience, and decreased amygdala and insular responses. Suppression produced late (10.5-15 sec) PFC responses, decreased negative emotion behavior and experience, but increased amygdala and insular responses.



These findings demonstrate the differential efficacy of reappraisal and suppression on emotional experience, facial behavior, and neural response and highlight intriguing differences in the temporal dynamics of these two emotion regulation strategies.