nils-herrmann/clean_scirepeval huggingface dataset:
Routing and scheduling in Multi-AGV systems based on dynamic banker algorithm

In this paper we propose modified Banker's algorithm for routing and scheduling in multi-AGV systems. A mission path, determined by string algebra is executed in a way that some unsafe states are allowed in order to achieve better utilization of vehicles. Moreover, a method for splitting a mission path is proposed so that number of resources (arcs) needed for mission execution is gradually reduced which provides less restrictions on availability vector, thus allowing more traveling space for other vehicles. Results shown at the end of the paper demonstrate strength of the proposed method compared with standard Banker's algorithm.
Reversible Alopecia with Localized Scalp Necrosis After Accidental Embolization of the Parietal Artery with Hyaluronic Acid

Hyaluronic acid (HA) filler injection is widely used for soft-tissue augmentation. Complications associated with HA filling are not uncommon; however, HA-induced alopecia is a rarely reported complication that could result in severe secondary psychological trauma. The etiology, clinical traits, treatment strategies, outcomes, and possible reversibility of HA-induced alopecia have not been characterized. Here, we report a case in which bilateral temple injections of 6.5 mL of HA led to persistent pain over the left scalp for several days. Although the pain was relieved at day 9 after 600 U of hyaluronidase were injected in the left temple, the patient developed localized alopecia at the left temporoparietal region with central skin necrosis at day 15. After topical applications of recombinant bovine basic fibroblast growth factor gel and 2% minoxidil spay, the necrotic skin wound was healed at day 42. Hair regrowth and normal hair density were restored at day 74. Analyses of Doppler ultrasound examinations and histopathology of the skin biopsy suggested that mild ischemia of the left temporoparietal region led to reversible alopecia, while the permanent hair loss in the left parietal area was associated with severe skin ischemia. Therefore, the key to treatment would be to focus on the effective correction of severe ischemia-induced skin necrosis to prevent permanent hair loss.
Three-dimensional plotting and printing of an implant drilling guide: simplifying guided implant surgery.

To present an efficient workflow for the production of implant drilling guides using virtual planning tools. For this purpose, laser surface scanning, cone beam computed tomography, computer-aided design and manufacturing, and 3-dimensional (3D) printing were combined.



Intraoral optical impressions (iTero, Align Technologies, Santa Clara, CA) and digital 3D radiographs (cone beam computed tomography) were performed at the first consultation of 1 exemplary patient. With image processing techniques, the intraoral surface data, acquired using an intraoral scanner, and radiologic 3D data were fused. The virtual implant planning process (using virtual library teeth) and the in-office production of the implant drilling guide was performed after only 1 clinical consultation of the patient. Implant surgery with a computer-aided design and manufacturing produced implant drilling guide was performed during the second consultation. The production of a scan prosthesis and multiple preoperative consultations of the patient were unnecessary.



The presented procedure offers another step in facilitating the production of drilling guides in dental implantology. Four main advantages are realized with this procedure. First, no additional scan prosthesis is needed. Second, data acquisition can be performed during the first consultation. Third, the virtual planning is directly transferred to the drilling guide without a loss of accuracy. Finally, the treatment cost and time required are reduced with this facilitated production process.
Learning Privately from Multiparty Data

Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party’s private data? We propose to transfer the ‘knowledge’ of the local classifier ensemble by first creating labeled data from auxiliary unlabeled data, and then train a global -differentially private classifier. We show that majority voting is too sensitive and therefore propose a new risk weighted by class probabilities estimated from the ensemble. Relative to a non-private solution, our private solution has a generalization error bounded by O( −2M−2) where M is the number of parties. This allows strong privacy without performance loss whenM is large, such as in crowdsensing applications. We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection.
An inventory for measuring clinical anxiety: psychometric properties.

The development ofa 2 l-item self-report inventory for measuring the severity of anxiety in psychiatric populations is described. The initial item pool of 86 items was drawn from three preexisting scales: the Anxiety Checklist, the Physician's Desk Reference Checklist, and the Situational Anxiety Checklist. A series of analyses was used to reduce the item pool. The resulting Beck Anxiety Inventory (BAI) is a 21-item scale that showed high internal consistency (a = .92) and test-retest reliability over 1 week, r(81) = .75. The BAI discriminated anxious diagnostic groups (panic disorder, generalized anxiety disorder, etc.) from nonanxious diagnostic groups (major depression, dysthymic disorder, etc). In addition, the BAI was moderately correlated with the revised Hamilton Anxiety Rating Scale, r(150) = .51, and was only mildly correlated with the revised Hamilton Depression Rating Scale, r(l 53) = .25.
MeshLab: an Open-Source Mesh Processing Tool

The paper presents MeshLab, an open source, extensible, mesh processing system that has been developed at the Visual Computing Lab of the ISTI-CNR with the helps of tens of students. We will describe the MeshLab architecture, its main features and design objectives discussing what strategies have been used to support its development. Various examples of the practical uses of MeshLab in research and professional frameworks are reported to show the various capabilities of the presented system.
Bias and causal associations in observational research

Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader's patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both. The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results. By contrast, non-differential misclassification tends to obscure real differences. Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.
Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke.

To compare the effects of robot-assisted movement training with conventional techniques for the rehabilitation of upper-limb motor function after stroke.



Randomized controlled trial, 6-month follow-up.



A Department of Veterans Affairs rehabilitation research and development center.



Consecutive sample of 27 subjects with chronic hemiparesis (>6mo after cerebrovascular accident) randomly allocated to group.



All subjects received twenty-four 1-hour sessions over 2 months. Subjects in the robot group practiced shoulder and elbow movements while assisted by a robot manipulator. Subjects in the control group received neurodevelopmental therapy (targeting proximal upper limb function) and 5 minutes of exposure to the robot in each session.



Fugl-Meyer assessment of motor impairment, FIMtrade mark instrument, and biomechanic measures of strength and reaching kinematics. Clinical evaluations were performed by a therapist blinded to group assignments.



Compared with the control group, the robot group had larger improvements in the proximal movement portion of the Fugl-Meyer test after 1 month of treatment (P<.05) and also after 2 months of treatment (P<.05). The robot group had larger gains in strength (P<.02) and larger increases in reach extent (P<.01) after 2 months of treatment. At the 6-month follow-up, the groups no longer differed in terms of the Fugl-Meyer test (P>.30); however, the robot group had larger improvements in the FIM (P<.04).



Compared with conventional treatment, robot-assisted movements had advantages in terms of clinical and biomechanical measures. Further research into the use of robotic manipulation for motor rehabilitation is justified.
Automatic Labelling of Topic Models

We propose a method for automatically labelling topics learned via LDA topic models. We generate our label candidate set from the top-ranking topic terms, titles of Wikipedia articles containing the top-ranking topic terms, and sub-phrases extracted from the Wikipedia article titles. We rank the label candidates using a combination of association measures and lexical features, optionally fed into a supervised ranking model. Our method is shown to perform strongly over four independent sets of topics, significantly better than a benchmark method.
Using Spoken Language Benchmarks to Characterize the Expressive Language Skills of Young Children With Autism Spectrum Disorders.

Spoken language benchmarks proposed by Tager-Flusberg et al. (2009) were used to characterize communication profiles of toddlers with autism spectrum disorders and to investigate if there were differences in variables hypothesized to influence language development at different benchmark levels.



The communication abilities of a large sample of toddlers with autism spectrum disorders (N = 105) were characterized in terms of spoken language benchmarks. The toddlers were grouped according to these benchmarks to investigate whether there were differences in selected variables across benchmark groups at a mean age of 2.5 years.



The majority of children in the sample presented with uneven communication profiles with relative strengths in phonology and significant weaknesses in pragmatics. When children were grouped according to one expressive language domain, across-group differences were observed in response to joint attention and gestures but not cognition or restricted and repetitive behaviors.



The spoken language benchmarks are useful for characterizing early communication profiles and investigating features that influence expressive language growth.
Privee: An Architecture for Automatically Analyzing Web Privacy Policies

To ensure the reliability of annotations we calculated the inter-annotator agreement by Krippendorff’s alpha, which indicated for all categories fair or good agreement (except for Ad Disclosure). It is striking that performance (F-1 score) correlates to agreement (Krippendorff’s alpha).  An Architecture for Automatically Analyzing Web Privacy Policies Sebastian Zimmeck and Steven M. Bellovin
Automated Music Emotion Recognition: A Systematic Evaluation

Automated music emotion recognition (MER) is a challenging task in Music Information Retrieval (MIR) with wide-ranging applications. Some recent studies pose MER as a continuous regression problem in the ArousalValence (AV) plane. These consist of variations on a common architecture having a universal model of emotional response, a common repertoire of low-level audio features, a bag-of-frames approach to audio analysis, and relatively small data sets. These approaches achieve some success at MER and suggest that further improvements are possible with current technology. Our contribution to the state of the art is to examine just how far one can go within this framework, and to investigate what the limitations of this framework are. We present the results of a systematic study conducted in an attempt to maximize the prediction performance of an automated MER system using the architecture described. We begin with a carefully constructed data set, emphasizing quality over quantity. We address affect induction rather than affect attribution. We consider a variety of algorithms at each stage of the training process, from preprocessing to feature selection and model selection, and we report the results of extensive testing. We found that: (1) none of the variations we considered leads to a substantial improvement in performance, which we present as evidence of a limit on what is achievable under this architecture, and (2) the size of the small data sets that are commonly used in the MER literature limits the possibility of improving the set of features used in MER due to the phenomenon of Subset Selection Bias. We conclude with some proposals for advancing the state of the art.
Cryptographic Hash-Function Basics: Definitions, Implications and Separations for Preimage Resistance, Second-Preimage Resistance, and Collision Resistance

We consider basic notions of security for cryptographic hash functions: collision resistance, preimage resistance, and second-preimage resistance. We give seven different definitions that correspond to these three underlying ideas, and then we work out all of the implications and separations among these seven definitions within the concrete-security, provable-security framework. Because our results are concrete, we can show two types of implications, conventional and provisional , where the strength of the latter depends on the amount of compression achieved by the hash function. We also distinguish two types of separations, conditional and unconditional . When constructing counterexamples for our separations, we are careful to preserve specified hash-function domains and ranges; this rules out some pathological counterexamples and makes the separations more meaningful in practice. Four of our definitions are standard while three appear to be new; some of our relations and separations have appeared, others have not. Here we give a modern treatment that acts to catalog, in one place and with carefully-considered nomenclature, the most basic security notions for cryptographic hash functions.
The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events.
Learning to trade via direct reinforcement

We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.
Deep Learning for Video Classification and Captioning

Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data. In this paper, we focus on reviewing two lines of research aiming to stimulate the comprehension of videos with deep learning: video classification and video captioning. While video classification concentrates on automatically labeling video clips based on their semantic contents like human actions or complex events, video captioning attempts to generate a complete and natural sentence, enriching the single label as in video classification, to capture the most informative dynamics in videos. In addition, we also provide a review of popular benchmarks and competitions, which are critical for evaluating the technical progress of this vibrant field.
The Narcissistic Personality Inventory: alternative form reliability and further evidence of construct validity.

The aim of this study was twofold: (a)to measure the alternate form reliability of the Narcissistic Personality Inventory, and (b)to determine its construct validity by correlating it with the four scales of the Eysenck Personality Questionnaire (EPQ). The alternate form reliability was .72. The Extraversion and Psychoticism scales of the EPQ were positively and significantly correlated with the narcissism measure, and the Lie scale showed a significant negative correlation. The Neuroticism scale showed a nonsignificant relationship with narcissism. In addition, the combined Extraversion and Psychoticism scales produced[ a Multiple R with the narcissism measure that accounted for significantly more of the variance in narcissism than did either measure alone.
COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency

More often than not, people are active in more than one social network. Identifying users from multiple heterogeneous social networks and integrating the different networks is a fundamental issue in many applications. The existing methods tackle this problem by estimating pairwise similarity between users in two networks. However, those methods suffer from potential inconsistency of matchings between multiple networks.
 In this paper, we propose COSNET (COnnecting heterogeneous Social NETworks with local and global consistency), a novel energy-based model, to address this problem by considering both local and global consistency among multiple networks. An efficient subgradient algorithm is developed to train the model by converting the original energy-based objective function into its dual form.
 We evaluate the proposed model on two different genres of data collections: SNS and Academia, each consisting of multiple heterogeneous social networks. Our experimental results validate the effectiveness and efficiency of the proposed model. On both data collections, the proposed COSNET method significantly outperforms several alternative methods by up to 10-30% (p << 0:001, t-test) in terms of F1-score. We also demonstrate that applying the integration results produced by our method can improve the accuracy of expert finding, an important task in social networks.
A comprehensive framework for testing graphical user interfaces

The widespread recognition of the usefulness of graphical user interfaces GUIs has established their importance as critical components of today's software. Although the use of GUIs continues to grow, GUI testing has remained a neglected research area. Since GUIs have c haracteristics that are diierent from those of conventional software, such as user events for input and graphical output, techniques developed to test conventional software cannot bedirectly applied to test GUIs. This thesis develops a uniied solution to the GUI testing problem with the particular goals of automation and integration of tools and techniques used in various phases of GUI testing. These goals are accomplished by developing a GUI testing framework with a GUI model as its central component. For eeciency and scalability, a GUI is represented as a hierarchy of components, each used as a basic unit of testing. The framework also includes a test coverage evaluator, test case generator, test oracle, test executor, and regression tester. The test coverage evaluator employs hierarchical, event-based coverage criteria to automatically specify what to test in a GUI and to determine whether the test suite has adequately tested the GUI. The test case generator employs plan generation techniques from artiicial intelligence to automatically generate a test suite. A test executor automatically executes all the test cases on the GUI. As test cases are being executed, a test oracle automatically determines the correctness of the GUI. The test oracle employs a model of the expected state of the GUI in terms of its constituent objects and their properties. After changes are made to a GUI, a regression tester partitions the original GUI test suite into valid test cases that represent correct inputtoutput for the modiied GUI and invalid test cases that no longer represent correct inputtoutput. The regression tester employs a new technique to reuse some of the invalid test cases by repairing them. A cursory exploration of extending the framework to handle the new testing requirements of web-user interfaces WUIs is also done. The framework iv has been implemented and experiments have demonstrated that the developed techniques are both practical and useful. v Acknowledgements I w ould like to thank my parents whose constant eeorts, encouragement and hard work made achieving the goal of obtaining a Ph.D. possible. I thank all my teachers in schools, colleges, and universities whose dedication and hard work helped lay the foundation for this work. Special thanks …