nils-herrmann/clean_scirepeval huggingface dataset:
Online Keyword Spotting with a Character-Level Recurrent Neural Network

In this paper, we propose a context-aware keyword spotting m odel employing a character-level recurrent neural network (RNN) for spoken term detection in co tinuous speech. The RNN is end-toend trained with connectionist temporal classification (CT C) to generate the probabilities of character and word-boundary labels. There is no need for the phonetic t ranscription, senone modeling, or system dictionary in training and testing. Also, keywords can easi ly be added and modified by editing the text based keyword list without retraining the RNN. Moreover, th e unidirectional RNN processes an infinitely long input audio streams without pre-segmentation and keyw ords are detected with low-latency before the utterance is finished. Experimental results show that th e proposed keyword spotter significantly outperforms the deep neural network (DNN) and hidden Markov m del (HMM) based keyword-filler model even with less computations.
From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.
Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.
Differentially Private Empirical Risk Minimization

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.
A Comparative Study of Methods for Transductive Transfer Learning

The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While previous work has studied the supervised version of this problem, we study the more challenging case of unsupervised transductive transfer learning, where no labeled data from the target domain are available at training. We describe some current state-of-the-art inductive and transductive approaches and then adapt these models to the problem of transfer learning for protein name extraction. In the process, we introduce a novel maximum entropy based technique, iterative feature transformation (IFT), and show that it achieves comparable performance with state-of-the-art transductive SVMs. We also show how simple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the transductive transfer learners.
Auto-directed video stabilization with robust L1 optimal camera paths

We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based on a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering of camera paths that only suppresses high frequency jitter. We incorporate additional constraints on the path of the camera directly in our algorithm, allowing for stabilized and retargeted videos. Our approach accomplishes this without the need of user interaction or costly 3D reconstruction of the scene, and works as a post-process for videos from any camera or from an online source.
Lung nodule detection in CT images using deep convolutional neural networks

Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.
Semantic Structural Alignment of Neural Representational Spaces Enables Translation between English and Chinese Words

Two sets of items can share the same underlying conceptual structure, while appearing unrelated at a surface level. Humans excel at recognizing and using alignments between such underlying structures in many domains of cognition, most notably in analogical reasoning. Here we show that structural alignment reveals how different people's neural representations of word meaning are preserved across different languages, such that patterns of brain activation can be used to translate words from one language to another. Groups of Chinese and English speakers underwent fMRI scanning while reading words in their respective native languages. Simply by aligning structures representing the two groups' neural semantic spaces, we successfully infer all seven Chinese–English word translations. Beyond language translation, conceptual structural alignment underlies many aspects of high-level cognition, and this work opens the door to deriving many such alignments directly from neural representational content.
Deformable Model Fitting by Regularized Landmark Mean-Shift

Deformable model fitting has been actively pursued in the computer vision community for over a decade. As a result, numerous approaches have been proposed with varying degrees of success. A class of approaches that has shown substantial promise is one that makes independent predictions regarding locations of the model’s landmarks, which are combined by enforcing a prior over their joint motion. A common theme in innovations to this approach is the replacement of the distribution of probable landmark locations, obtained from each local detector, with simpler parametric forms. In this work, a principled optimization strategy is proposed where nonparametric representations of these likelihoods are maximized within a hierarchy of smoothed estimates. The resulting update equations are reminiscent of mean-shift over the landmarks but with regularization imposed through a global prior over their joint motion. Extensions to handle partial occlusions and reduce computational complexity are also presented. Through numerical experiments, this approach is shown to outperform some common existing methods on the task of generic face fitting.
Improving Diversity in Ranking using Absorbing Random Walks

We introduce a novel ranking algorithm called GRASSHOPPER, which ranks items with an emphasis on diversity. That is, the top items should be different from each other in order to have a broad coverage of the whole item set. Many natural language processing tasks can benefit from such diversity ranking. Our algorithm is based on random walks in an absorbing Markov chain. We turn ranked items into absorbing states, which effectively prevents redundant items from receiving a high rank. We demonstrate GRASSHOPPER’s effectiveness on extractive text summarization: our algorithm ranks between the 1st and 2nd systems on DUC 2004 Task 2; and on a social network analysis task that identifies movie stars of the world.
A Stable Network-Aware VM Placement for Cloud Systems

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
G-spot anatomy: a new discovery.

The anatomic existence of the G-spot has not been documented yet.



To identify the anatomic structure of the G-spot.


A stratum-by-stratum vaginal wall dissection on a fresh cadaver.



Primary outcome is the identification of the G-spot and the secondary outcome is its measurements and anatomic description of the G-spot.



The G-spot has a distinguishable anatomic structure that is located on the dorsal perineal membrane, 16.5 mm from the upper part of the urethral meatus, and creates a 35° angle with the lateral border of the urethra. The lower pole (tail) and the upper pole (head) were located 3 and 15 mm next to the lateral border of the urethra, respectively. Grossly, the G-spot appeared as a well-delineated sac with walls that resembled fibroconnective tissues and resembled erectile tissues. The superior surface of the sac had bluish irregularities visible through the coat. Upon opening the sac's upper coat, blue grape-like anatomic compositions of the G-spot emerged with dimensions of length (L) of 8.1 mm × width (W) of 3.6-1.5 mm × height (H) of 0.4 mm. The G-spot structure had three distinct areas: the proximal part (the head) L 3.4 mm × W 3.6 mm, the middle part L 3.1 mm × W 3.3 mm, and the distal part (tail) L 3.3 mm × W 3.0 mm. From the distal tail, a rope-like structure emerged, which was seen for approximately 1.6 mm and then disappeared into the surrounding tissue.



The anatomic existence of the G-spot was documented with potential impact on the practice and clinical research in the field of female sexual function.
An Efficient Algorithm for Disease Prediction with Multi Dimensional Data

The main objective of this study is to create a fast, easy and an efficient algorithm for disease prediction , with less error rate and can apply with even large data sets and show reasonable patterns with dependent variables. For disease identification and prediction in data mining a new hybrid algorithm was constructed. The Disease Identification and Prediction(DIP) algorithm, which is the combination of decision tree and association rule is used to predict the chances of some of the disease hits in some particular areas. It also shows the relationship between different parameters for the prediction. To implement this algorithm using vb.net software was also developed.
Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.
Robotics in the early childhood classroom : learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade

In recent years there has been an increasing focus on the missing ‘‘T’’ of technology and ‘‘E’’ of engineering in early childhood STEM (science, technology, engineering, mathematics) curricula. Robotics offers a playful and tangible way for children to engage with both T and E concepts during their foundational early childhood years. This study looks at N = 60 children in pre-kindergarten through second grade who completed an 8-week robotics curriculum in their classrooms using the KIWI robotics kit combined with a tangible programming language. Children were assessed on their knowledge of foundational robotics and programming concepts upon completion of the curriculum. Results show that beginning in pre-kindergarten, children were able to master basic robotics and programming skills, while the older children were able to master increasingly complex concepts using the same robotics kit in the same amount of time. Implications for developmentally appropriate design of technology, as well as structure and pace of robotics curricula for young children are addressed.
Views MED 23 : a new Mediator of H 2 B monoubiquitylation

The Mediator multiprotein complex physically links transcription factors to RNA polymerase II and the basal transcription machinery. While the Mediator complex has been shown to be required for transcriptional initiation and elongation, the understanding of its interplay with histone modifying enzymes and posttranslational modifications remains elusive. In this issue of The EMBO Journal, Yao et al (2015) report that the MED23 subunit of the Mediator complex physically associates with the heterodimeric RNF20/40 E3-ligase complex to facilitate the monoubiquitylation of histone H2B on gene bodies of actively transcribed genes.
Hadoop Hbase-0.20.2 performance evaluation

Hbase is the open source version of BigTable - distributed storage system developed by Google for the management of large volume of structured data. Hbase emulates most of the functionalities provided by BigTable. Like most non SQL database systems, Hbase is written in Java. The current work's purpose is to evaluate the performances of the Hbase-0.20.2 implementation in comparison with those of the Hbase-0.20.0 implementation, and, of course, with the performances offered by BigTable. The tests aim at evaluating the performances regarding the random writing and random reading of rows, how they are affected by increasing the number of servers, by the number of column families and by the system configuration parameters.
Big data-driven optimization for mobile networks toward 5G

Big data offers a plethora of opportunities to mobile network operators for improving quality of service. This article explores various means of integrating big data analytics with network optimization toward the objective of improving the user quality of experience. We first propose a framework of Big Data-Driven (BDD) mobile network optimization. We then present the characteristics of big data that are collected not only from user equipment but also from mobile networks. Moreover, several techniques in data collection and analytics are discussed from the viewpoint of network optimization. Certain user cases on the application of the proposed framework for improving network performance are also given in order to demonstrate the feasibility of the framework. With the integration of the emerging fifth generation (5G) mobile networks with big data analytics, the quality of our daily mobile life is expected to be tremendously enhanced.
A smart traffic solution for High Priority Vehicles

Traffic lights are designed to ensure smooth traffic in the cities. The current traffic system is running same over the past few years. As number of vehicles on the roads is increasing constantly, this system is failing to serve traffic congestion problems especially on the intersections. Due to traffic congestion High Priority Vehicles (HPV) also get stuck in traffic which results delay in their services. HPV like ambulances, fire brigade etc. have to serve various causalities. It is very important for HPV to reach on time. There is a need of system that aims to provide path to HPV to reach as quickly as possible. The proposed work provides priority based approach. It aims at building a user interactive system for HPV in which an HPV driver can send request to the system to which the system responds smartly. Priority of Road Segments (RS) at an intersection is calculated and traffic light turns green for the RS with highest priority. The algorithm is tested on SUMO (Simulation of Urban Mobility) and it has shown positive results by saving more than 50% time in various traffic intensities (low, moderate, high).
A Resilient , Untethered Soft Robot

A pneumatically powered, fully untethered mobile soft robot is described. Composites consisting of silicone elastomer, polyaramid fabric, and hollow glass microspheres were used to fabricate a sufficiently large soft robot to carry the miniature air compressors, battery, valves, and controller needed for autonomous operation. Fabrication techniques were developed to mold a 0.65 meter long soft body with modified Pneu-Net actuators capable of operating at the elevated pressures (up to 138 kPa) required to actuate the legs of the robot and hold payloads of up to 8 kg. The soft robot is safe to handle, and its silicone body is innately resilient to a variety of adverse environmental conditions including snow, puddles of water, direct (albeit limited) exposure to flames, and the crushing force of being run over by an automobile.