nils-herrmann/clean_scirepeval huggingface dataset:
5G-Enabled Tactile Internet

The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts, which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud, and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.
Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster

This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses. The claim spotting model is built using a human-labeled dataset of check-worthy factual claims from the U.S. general election debate transcripts. The paper explains the architecture and the components of the system and the evaluation of the model. It presents a case study of how ClaimBuster live covers the 2016 U.S. presidential election debates and monitors social media and Australian Hansard for factual claims. It also describes the current status and the long-term goals of ClaimBuster as we keep developing and expanding it.
Kernel sparse representation with local patterns for face recognition

In this paper we propose a novel kernel sparse representation classification (SRC) framework and utilize the local binary pattern (LBP) descriptor in this framework for robust face recognition. First we develop a kernel coordinate descent (KCD) algorithm for 11 minimization in the kernel space, which is based on the covariance update technique. Then we extract LBP descriptors from each image and apply two types of kernels (χ2 distance based and Hamming distance based) with the proposed KCD algorithm under the SRC framework for face recognition. Experiments on both the Extended Yale B and the PIE face databases show that the proposed method is more robust against noise, occlusion, and illumination variations, even with small number of training samples.
The CIPS-SIGHAN CLP2010 Chinese Word Segmentation Backoff

The CIPS-SIGHAN CLP 2010 Chinese Word Segmentation Bakeoff was held in the summer of 2010 to evaluate the current state of the art in word segmentation. It focused on the crossdomain performance of Chinese word segmentation algorithms. Eighteen groups submitted 128 results over two tracks (open training and closed training), four domains (literature, computer science, medicine and finance) and two subtasks (simplified Chinese and traditional Chinese). We found that compared with the previous Chinese word segmentation bakeoffs, the performance of cross-domain Chinese word segmentation is not much lower, and the out-of-vocabulary recall is improved.
Otocephaly: report of five new cases and a literature review.

Otocephaly, characterized by mandibular hypoplasia or agnathia, ventromedial auricular malposition (melotia) and/or auricular fusion (synotia), and microstomia with oroglossal hypoplasia or aglossia, is an extremely rare anomalad, identified in less than 1 in 70,000 births. The malformation spectrum is essentially lethal, because of ventilatory problems, and represents a developmental field defect of blastogenesis primarily affecting thefirst branchial arch derivatives. Holoprosencephaly is the most commonly identified association, but skeletal, genitourinary, and cardiovascular anomalies, and situs inversus have been reported. Polyhydramnios may be the presenting feature, but prenatal diagnosis has been uncommon. We present five new cases of otocephaly, the largest published series to date, with comprehensive review of the literature and an update of research in the etiopathogenesis of this malformation complex. One of our cases had situs inversus, and two presented with unexplained polyhydramnios. Otocephaly, while quite rare, should be considered in the differential diagnosis of this gestational complication.
Providing Safe Water : Evidence from Randomized Evaluations

This paper uses a public economics framework to review evidence from randomized trials on domestic water access and quality in developing countries and to assess the case for subsidies. Water treatment can cost-effectively reduce reported diarrhea. However, many consumers have low willingness to pay for cleaner water; few households purchase household water treatment under retail models. Free point-of-collection water treatment systems designed to make water treatment convenient and salient can generate take-up of approximately 60% at a projected cost as low as $20 per year of life saved, comparable to vaccine costs. In contrast, the limited existing evidence suggests that many consumers value better access to water, but it does not yet demonstrate that better access improves health. The randomized impact evaluations reviewed have also generated methodological insights on a range of topics, including (a) the role of survey effects in health data collection, (b) methods to test for sunk-cost effects, (c) divergence in revealed preference and stated preference valuation measures, and (d) parameter estimation for structural policy simulations. 237 A nn u. R ev . R es ou r. E co n. 2 01 0. 2: 23 725 6. D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g by H ar va rd U ni ve rs ity o n 08 /2 4/ 11 . F or p er so na l u se o nl y.
A taxonomy of architectural patterns for self-adaptive systems

Autonomic systems are able to adapt themselves to unpredicted and unexpected situations. Such adaptation capabilities can reside in individual components as well as in ensembles of components. In particular, a variety of different architectural patterns can be conceived to support self-adaptation at the level both of components and of ensembles. In this paper, we propose a classification of such self-adaptation patterns -- for both the component level and the system level -- by means of a taxonomy organized around the locus in which the feedback loops promoting adaptation reside. We show that the proposed classification covers most self-adaptation patterns, and enables deriving further ones by applying a simple set of composition mechanisms. Three examples of existing patterns of the taxonomy are detailed in the paper to show the applicability of the approach. As discussed in the paper, the advantage of the proposed classification is twofold: it enables identifying the (possibly common) properties of the existing self-adaptation patterns; and, consequently, it can help developers in choosing the most appropriate self-adaptation patterns for the development of autonomic systems.
Mouth gesture and voice command based robot command interface

In this paper we present a voice command and mouth gesture based robot command interface which is capable of controlling three degrees of freedom. The gesture set was designed in order to avoid head rotation and translation, and thus relying solely in mouth movements. Mouth segmentation is performed by using the normalized a* component, as in . The gesture detection process is carried out by a Gaussian Mixture Model (GMM) based classifier. After that, a state machine stabilizes the system response by restricting the number of possible movements depending on the initial state. Voice commands are modeled using a Hidden Markov Model (HMM) isolated word recognition scheme. The interface was designed taking into account the specific pose restrictions found in the DaVinci Assisted Surgery command console.
Artificial intelligence and personal identity

Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them.
A New Principle for Climbing Wheeled Robots: Serpentine Climbing with the Open WHEEL Platform

This paper describes an innovative principle for climbing obstacles with a two-axle and four-wheel robot with articulated frame. It is based on axle reconfiguration while ensuring permanent static stability. A simple example is demonstrated based on the OpenWHEEL platform with a serial mechanism connecting front and rear axles of the robot. A generic tridimensional multibody simulation is provided with Adams software. It permits to validate the concept and to get an approach of control laws for every type of inter-axle mechanism. This climbing principle permits to climb obstacles as high as the wheel while keeping energetic efficiency of wheel propulsion and using only one supplemental actuator. Applications to electric wheelchairs, quads and all terrain vehicles (ATV) are envisioned
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images

Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection. Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-ofthe-art real-time computation time for high-resolution images (6-20ms per 360×360 image) on Cornell dataset. Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot arm with small parallel gripper and RGB-D camera for grasping challenging small, novel objects. With accurate vision-robot coordinate calibration through our proposed learning-based, fully automatic approach, our proposed method yielded 90% success rate.
Scaling betweenness centrality using communication-efficient sparse matrix multiplication

Betweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths leading through it. We propose Maximal Frontier Betweenness Centrality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor of p1/3 less communication on p processors than the best known alternatives, for graphs with n vertices and average degree k = n/p2/3. We formulate, implement, and prove the correctness of MFBC for weighted graphs by leveraging monoids instead of semirings, which enables a surprisingly succinct formulation. MFBC scales well for both extremely sparse and relatively dense graphs. It automatically searches a space of distributed data decompositions and sparse matrix multiplication algorithms for the most advantageous configuration. The MFBC implementation outperforms the well-known CombBLAS library by up to 8x and shows more robust performance. Our design methodology is readily extensible to other graph problems.
Big Five personality factors , hardiness , and social judgment as predictors of leader performance *

The purpose of this paper is to evaluate the influence of psychological hardiness, social judgment, and “Big Five” personality dimensions on leader performance in U.S. military academy cadets at West Point.  Army Cadets were studied in two different organizational contexts: (a)summer field training, and (b)during academic semesters. Leader performance was measured with leadership grades (supervisor ratings) aggregated over four years at West Point.  After controlling for general intellectual abilities, hierarchical regression results showed leader performance in the summer field training environment is predicted by Big Five Extraversion, and Hardiness, and a trend for Social Judgment. During the academic period context, leader performance is predicted by mental abilities, Big Five Conscientiousness, and Hardiness, with a trend for Social Judgment.  Results confirm the importance of psychological hardiness, extraversion, and conscientiousness as factors influencing leader effectiveness, and suggest that social judgment aspects of emotional intelligence can also be important. These results also show that different Big Five personality factors may influence leadership in different organizational
Gestures for industry Intuitive human-robot communication from human observation

Human-robot collaborative work has the potential to advance quality, efficiency and safety in manufacturing. In this paper we present a gestural communication lexicon for human-robot collaboration in industrial assembly tasks and establish methodology for producing such a lexicon. Our user experiments are grounded in a study of industry needs, providing potential real-world applicability to our results. Actions required for industrial assembly tasks are abstracted into three classes: part acquisition, part manipulation, and part operations. We analyzed the communication between human pairs performing these subtasks and derived a set of communication terms and gestures. We found that participant-provided gestures are intuitive and well suited to robotic implementation, but that interpretation is highly dependent on task context. We then implemented these gestures on a robot arm in a human-robot interaction context, and found the gestures to be easily interpreted by observers. We found that observation of human-human interaction can be effective in determining what should be communicated in a given human-robot task, how communication gestures should be executed, and priorities for robotic system implementation based on frequency of use.
Design of HERIC configuration based grid connected single phase transformer less photovoltaic inverter

In a grid connected photovoltaic system, the main aim is to design an efficient solar inverter with higher efficiency and which also controls the power that the inverter injects into the grid. The effectiveness of the general PV system anticipate on the productivity by which the direct current of the solar module is changed over into alternating current. The fundamental requirement to interface the solar module to the grid with increased productivity includes: Low THD of current injected to the grid, maximum power point, and high power factor. In this paper, a two stage topology without galvanic isolation is been carried out for a single phase grid connected photovoltaic inverter. The output from the PV panel is given to the DC/DC boost converter, maximum power point tracking (MPPT) control technique is being used to control the gate pulse of the IGBT of boost converter. The boosted output is fed to the highly efficient and reliable inverter concept (HERIC) inverter in order to convert DC into AC with higher efficiency.
Revisiting password rules: facilitating human management of passwords

Password rules were established in the context of past security concerns. Recent work in computer security challenges the conventional wisdom of expert password advice, such as change your passwords often, do not reuse your passwords, or do not write your passwords down. The effectiveness of these rules for protecting user accounts against real world attacks is questioned. We review the latest research examining password rules for general-purpose user authentication on the web, and discuss the arguments behind the continued acceptance or the rejection of the rules based on empirical evidence and solid justifications. Following the review, we recommend an updated set of password rules.
On Techniques for Angle Compensation in Nonideal Iris Recognition

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word ldquononidealrdquo is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.
A new era in brown adipose tissue biology: molecular control of brown fat development and energy homeostasis.

Brown adipose tissue (BAT) is specialized to dissipate chemical energy in the form of heat as a defense against cold and excessive feeding. Interest in the field of BAT biology has exploded in the past few years because of the therapeutic potential of BAT to counteract obesity and obesity-related diseases, including insulin resistance. Much progress has been made, particularly in the areas of BAT physiology in adult humans, developmental lineages of brown adipose cell fate, and hormonal control of BAT thermogenesis. As we enter into a new era of brown fat biology, the next challenge will be to develop strategies for activating BAT thermogenesis in adult humans to increase whole-body energy expenditure. This article reviews the recent major advances in this field and discusses emerging questions.
Computationally designed libraries for rapid enzyme stabilization

The ability to engineer enzymes and other proteins to any desired stability would have wide-ranging applications. Here, we demonstrate that computational design of a library with chemically diverse stabilizing mutations allows the engineering of drastically stabilized and fully functional variants of the mesostable enzyme limonene epoxide hydrolase. First, point mutations were selected if they significantly improved the predicted free energy of protein folding. Disulfide bonds were designed using sampling of backbone conformational space, which tripled the number of experimentally stabilizing disulfide bridges. Next, orthogonal in silico screening steps were used to remove chemically unreasonable mutations and mutations that are predicted to increase protein flexibility. The resulting library of 64 variants was experimentally screened, which revealed 21 (pairs of) stabilizing mutations located both in relatively rigid and in flexible areas of the enzyme. Finally, combining 10-12 of these confirmed mutations resulted in multi-site mutants with an increase in apparent melting temperature from 50 to 85°C, enhanced catalytic activity, preserved regioselectivity and a >250-fold longer half-life. The developed Framework for Rapid Enzyme Stabilization by Computational libraries (FRESCO) requires far less screening than conventional directed evolution.
Deep Community Detection

A deep community in a graph is a connected component that can only be seen after removal of nodes or edges from the rest of the graph. This paper formulates the problem of detecting deep communities as multi-stage node removal that maximizes a new centrality measure, called the local Fiedler vector centrality (LFVC), at each stage. The LFVC is associated with the sensitivity of algebraic connectivity to node or edge removals. We prove that a greedy node/edge removal strategy, based on successive maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. Under a stochastic block model framework, we show that the greedy LFVC strategy can extract deep communities with probability one as the number of observations becomes large. We apply the greedy LFVC strategy to real-world social network datasets. Compared with conventional community detection methods we demonstrate improved ability to identify important communities and key members in the network.
HEMnet: Integration of Electronic Medical Records with Molecular Interaction Networks and Domain Knowledge for Survival Analysis

The continual growth of electronic medical record (EMR) databases has paved the way for many data mining applications, including the discovery of novel disease-drug associations and the prediction of patient survival rates. However, these tasks are hindered because EMRs are usually segmented or incomplete. EMR analysis is further limited by the overabundance of medical term synonyms and morphologies, which causes existing techniques to mismatch records containing semantically similar but lexically distinct terms. Current solutions fill in missing values with techniques that tend to introduce noise rather than reduce it. In this paper, we propose to simultaneously infer missing data and solve semantic mismatching in EMRs by first integrating EMR data with molecular interaction networks and domain knowledge to build the HEMnet, a heterogeneous medical information network. We then project this network onto a low-dimensional space, and group entities in the network according to their relative distances. Lastly, we use this entity distance information to enrich the original EMRs. We evaluate the effectiveness of this method according to its ability to separate patients with dissimilar survival functions. We show that our method can obtain significant (p-value < 0.01) results for each cancer subtype in a lung cancer dataset, while the baselines cannot.
Probenecid, a gout remedy, inhibits pannexin 1 channels.

Probenecid is a well-established drug for the treatment of gout and is thought to act on an organic anion transporter, thereby affecting uric acid excretion in the kidney by blocking urate reuptake. Probenecid also has been shown to affect ATP release, leading to the suggestion that ATP release involves an organic anion transporter. Other pharmacological evidence and the observation of dye uptake, however, suggest that the nonvesicular release of ATP is mediated by large membrane channels, with pannexin 1 being a prominent candidate. In the present study we show that probenecid inhibited currents mediated by pannexin 1 channels in the same concentration range as observed for inhibition of transport processes. Probenecid did not affect channels formed by connexins. Thus probenecid allows for discrimination between channels formed by connexins and pannexins.