nils-herrmann/clean_scirepeval huggingface dataset:
Calibrating Uncertainties in Object Localization Task

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confidence interval for its bounding box. While recent Bayesian deep learning methods provide a principled way to estimate this uncertainty, the estimates for the bounding boxes obtained using these methods are uncalibrated. In this paper, we address this problem for the single-object localization task by adapting an existing technique for calibrating regression models. We show, experimentally, that the resulting calibrated model obtains more reliable uncertainty estimates.
Learning to Cluster for Proposal-Free Instance Segmentation

This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering. We applied the approach to instance segmentation, which is at the intersection of image semantic segmentation and object detection. We utilize the most fundamental property of instance labeling- the pairwise relationship between pixels-as the supervision to formulate the learning objective, then apply it to train a fully convolutional network (FCN) for learning to perform pixel-wise clustering. The resulting clusters can be used as the instance labeling directly. To support labeling of an unlimited number of instance, we further formulate ideas from graph coloring theory into the proposed learning objective. The evaluation on the Cityscapes dataset demonstrates strong performance and therefore proof of the concept. Moreover, our approach won the second place in the lane detection competition of 2017 CVPR Autonomous Driving Challenge, and was the top performer without using external data.
Adversarial Training for Unsupervised Bilingual Lexicon Induction

Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.
High numerical aperture Fourier ptychography: principle, implementation and characterization.

Fourier ptychography (FP) utilizes illumination control and computational post-processing to increase the resolution of bright-field microscopes. In effect, FP extends the fixed numerical aperture (NA) of an objective lens to form a larger synthetic system NA. Here, we build an FP microscope (FPM) using a 40X 0.75NA objective lens to synthesize a system NA of 1.45. This system achieved a two-slit resolution of 335 nm at a wavelength of 632 nm. This resolution closely adheres to theoretical prediction and is comparable to the measured resolution (315 nm) associated with a standard, commercially available 1.25 NA oil immersion microscope. Our work indicates that Fourier ptychography is an attractive method to improve the resolution-versus-NA performance, increase the working distance, and enlarge the field-of-view of high-resolution bright-field microscopes by employing lower NA objectives.
Microorganisms with Claimed Probiotic Properties: An Overview of Recent Literature

Probiotics are defined as live microorganisms, which when administered in adequate amounts, confer a health benefit on the host. Health benefits have mainly been demonstrated for specific probiotic strains of the following genera: Lactobacillus, Bifidobacterium, Saccharomyces, Enterococcus, Streptococcus, Pediococcus, Leuconostoc, Bacillus, Escherichia coli. The human microbiota is getting a lot of attention today and research has already demonstrated that alteration of this microbiota may have far-reaching consequences. One of the possible routes for correcting dysbiosis is by consuming probiotics. The credibility of specific health claims of probiotics and their safety must be established through science-based clinical studies. This overview summarizes the most commonly used probiotic microorganisms and their demonstrated health claims. As probiotic properties have been shown to be strain specific, accurate identification of particular strains is also very important. On the other hand, it is also demonstrated that the use of various probiotics for immunocompromised patients or patients with a leaky gut has also yielded infections, sepsis, fungemia, bacteraemia. Although the vast majority of probiotics that are used today are generally regarded as safe and beneficial for healthy individuals, caution in selecting and monitoring of probiotics for patients is needed and complete consideration of risk-benefit ratio before prescribing is recommended.
An Introduction to Feature Extraction

This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems.
Learning robust failure response for autonomous vision based flight

The ability of autonomous mobile robots to react to and recover from potential failures of on-board systems is an important area of ongoing robotics research. With increasing emphasis on robust systems and long-term autonomy, mobile robots must be able to respond safely and intelligently to dangerous situations. Recent developments in computer vision have made autonomous vision based navigation possible. However, vision systems are known to be imperfect and prone to failure due to variable lighting, terrain changes, and other environmental variables. We describe a system for learning simple failure recovery maneuvers based on experience. This involves both recognizing when the vision system is prone to failure, and associating failures with appropriate responses that will most likely help the robot recover. We implement this system on an autonomous quadrotor and demonstrate that behaviors learned with our system are effective in recovering from situational perception failure, thereby improving reliability in cluttered and uncertain environments.
PREVENTION OF HEART PROBLEM USING ARTIFICIAL INTELLIGENCE

Heart is the most important organ of a human body as it not only circulates oxygen and other vital nutrients through blood to different parts of the body and helping in the metabolic activities but also removes metabolic wastes. Thus, even a minor problem can affect the whole organism. Hence, researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In this paper we have analysed the different prescribed data of patients from different parts of India. Using this data, we have built a model which gets trained using this data and tries to predict whether a new out-of-sample data has a probability of having any heart attack or not. This model can help in the decision making along with the doctor to treat the patient well and creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along with the AUC-ROC helps in building/fixing the algorithm inside the model.
Thermal conductivity of individual silicon nanowires

The thermal conductivities of individual single crystalline intrinsic Si nanowires with diameters of 22, 37, 56, and 115 nm were measured using a microfabricated suspended device over a temperature range of 20–320 K. Although the nanowires had well-defined crystalline order, the thermal conductivity observed was more than two orders of magnitude lower than the bulk value. The strong diameter dependence of thermal conductivity in nanowires was ascribed to the increased phonon-boundary scattering and possible phonon spectrum modification.
A Cube Model for Competency Development : Implications for Psychology Educators and Regulators

This article provides a conceptual framework for training in professional psychology focused on the construct of competency. The authors present a 3-dimensional competency model delineating the domains of knowledge, skills, attitudes, and values that serve as the foundation required of all psychologists, the domains of functional competencies that broadly define what psychologists do, and the stages of professional development from doctoral education to lifelong learning through continuing education. The goal in presenting this model is to provide a conceptual frame of reference for those responsible for psychology education, credentialing, and regulation.
Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs

In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed. In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an underlaid device-to-device (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, the optimal values for the UAV altitude, which lead to the maximum system sum-rate and coverage probability, exist. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, can be minimized. Finally, in order to provide full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.
System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM)

With the advent of the Internet, more and more public universities in Malaysia are putting in effort to introduce e-learning in their respective universities. Using a structured questionnaire derived from the literature, data was collected from 250 undergraduate students from a public university in Penang, Malaysia. Data was analyzed using AMOS version 16. The results of the structural equation model indicated that service quality (β = 0.20, p < 0.01), information quality (β = 0.37, p < 0.01) and system quality (β = 0.20, p < 0.01) were positively related to user satisfaction explaining a total of 45% variance. The second regression analysis was to examine the impact of user satisfaction on continuance intention. The results showed that satisfaction (β = 0.31, p < 0.01), system quality (β = 0.18, p < 0.01) and service quality (β = 0.30, p < 0.01) were positively related to continuance intention explaining 44% of the variance. Implications from these findings to e-learning system developers and implementers were further elaborated.
KIBO robot demo: engaging young children in programming and engineering

Robotics offers a playful and tangible way for young children to engage with technology and engineering concepts during their foundational early childhood years. This paper describes the development of KIBO, a newly created robotics kit for children ages 4-7 developed at Tufts University through funding from the National Science Foundation. Designed explicitly for very young children, the KIBO kit has gone through several design iterations based on feedback from teachers and children over the past three years. The newest version of KIBO allows young children to become engineers by constructing robots using motors, sensors, and craft materials. Children also become programmers by exploring sequences, loops, and variables. In line with screen-time recommendations for young children, KIBO is programmed to move using tangible programming blocks- no computer, tablet, or screen required.
Good Practice in CNN Feature Transfer

The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification. Systematically, we study three facts in CNN transfer. 1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one. 2) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps. Our observation suggests that the Conv5 feature yields very competitive accuracy under such pooling step. 3) We find that the simple combination of pooled features extracted across variou s CNN layers is effective in collecting evidences from both low and high level descriptors. Following these good practices, we are capable of improving the state of the art on a number of benchmarks to a large margin.
Improving Awareness of Social Engineering Attacks

Social engineering is a method of attack involving the exploitation of human weakness, gullibility and ignorance. Although related techniques have existed for some time, current awareness of social engineering and its many guises is relatively low and efforts are therefore required to improve the protection of the user community. This paper begins by examining the problems posed by social engineering, and outlining some of the previous efforts that have been made to address the threat. This leads toward the discussion of a new awareness-raising website that has been specifically designed to aid users in understanding and avoiding the risks. Findings from an experimental trial involving 46 participants are used to illustrate that the system served to increase users’ understanding of threat concepts, as well as providing an engaging environment in which they would be likely to persevere with their learning.
Ground Truth and Performance Evaluation of Lane Border Detection

Lane-border detection is one of the best-developed modules in vision-based driver assistance systems today. However, there is still a need for further improvement for challenging road and traffic situations, and a need to design tools for quantitative performance evaluation. This paper discusses and refines a previously published method to generate ground truth for lane markings from recorded video, applies two lanedetection methods to such video data, and then illustrates the proposed performance evaluation by comparing calculated ground truth with detected lane positions. This paper also proposes appropriate performance measures that are required to evaluate the proposed method.
LSTM-based Deep Learning Models for non-factoid answer selection

In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.
Incremental frequency count - a post BWT-stage for the Burrows-Wheeler compression algorithm

The stage after the Burrows-Wheeler Transform (BWT) has a key function inside the Burrows-Wheeler compression algorithm as it transforms the BWT output from a local context into a global context. This paper presents the Incremental Frequency Count stage, a post-BWT stage. The new stage is paired with a run length encoding stage between the BWT and entropy coding stage of the algorithm. It offers high throughput similar to a Move To Front stage, and at the same time good compression rates like the strong but slow Weighted Frequency Count stage. The properties of the Incremental Frequency Count stage are compared to the Move To Front and Weighted Frequency Count stages by their compression rates and speeds on the Calgary and large Canterbury corpora.
Magneto-Dielectric Nanocomposite for Antenna Miniaturization and SAR Reduction

A 900-MHz meander planar inverted-F antenna (PIFA) on a magneto-dielectric nanocomposite (MDNC) substrate for mobile communication is presented. Cobalt nanoparticles were synthesized with polymer matrix, and its properties were measured up to 4 GHz. Bandwidth, gain, and radiation efficiency of antenna on different substrates (MDNC, High K material, and FR4) were compared, and it is demonstrated that MDNC is beneficial for antenna miniaturization with acceptable antenna performance. Head effects due to the antenna were studied, and specific absorption rate (SAR) was calculated. The simulation results demonstrate that the MDNC reduces the head effects and the magnetic loss of MDNC helps to decrease SAR due to the antenna.
A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification

Deep convolutional neural networks (CNNs) have shown superior performance on the task of single-label image classification. However, the applicability of CNNs to multi-label images still remains an open problem, mainly because of two reasons. First, each image is usually treated as an inseparable entity and represented as one instance, which mixes the visual information corresponding to different labels. Second, the correlations amongst labels are often overlooked. To address these limitations, we propose a deep multi-modal CNN for multi-instance multi-label image classification, called MMCNN-MIML. By combining CNNs with multi-instance multi-label (MIML) learning, our model represents each image as a bag of instances for image classification and inherits the merits of both CNNs and MIML. In particular, MMCNN-MIML has three main appealing properties: 1) it can automatically generate instance representations for MIML by exploiting the architecture of CNNs; 2) it takes advantage of the label correlations by grouping labels in its later layers; and 3) it incorporates the textual context of label groups to generate multi-modal instances, which are effective in discriminating visually similar objects belonging to different groups. Empirical studies on several benchmark multi-label image data sets show that MMCNN-MIML significantly outperforms the state-of-the-art baselines on multi-label image classification tasks.
Heart rate variability interventions for concussion and rehabilitation

The study of heart rate variability (HRV) has emerged as an essential component of cardiovascular health, as well as a physiological mechanism by which one can increase the interactive communication between the cardiac and the neurocognitive systems (i.e., the body and the brain). It is well-established that lack of HRV implies cardiopathology, morbidity, reduced quality-of-life, and precipitous mortality. On the positive, optimal HRV has been associated with good cardiovascular health, autonomic nervous system (ANS) control, emotional regulation, and enhanced neurocognitive processing. In addition to health benefits, optimal HRV has been shown to improve neurocognitive performance by enhancing focus, visual acuity and readiness, and by promoting emotional regulation needed for peak performance. In concussed athletes and soldiers, concussions not only alter brain connectivity, but also alter cardiac functioning and impair cardiovascular performance upon exertion. Altered sympathetic and parasympathetic balance in the ANS has been postulated as a critical factor in refractory post concussive syndrome (PCS). This article will review both the pathological aspects of reduced HRV on athletic performance, as well as the cardiovascular and cerebrovascular components of concussion and PCS. Additionally, this article will review interventions with HRV biofeedback (HRV BFB) training as a promising and underutilized treatment for sports and military-related concussion. Finally, this article will review research and promising case studies pertaining to use of HRV BFB for enhancement of cognition and performance, with applicability to concussion rehabilitation.
Integration of Cloud computing and Internet of Things: A survey

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use is expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios. In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms – both proprietary and open source – and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet.  2015 *Manuscript Click here to view linked References