nils-herrmann/clean_scirepeval huggingface dataset:
A Bayesian Approach to Learning Bayesian Networks with Local Structure

Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has concentrated on using decision-tree representations for the CPDs. In addition, researchers typically apply non-Bayesian (or asymptotically Bayesian) scoring functions such as MDL to evaluate the goodness-oft of networks to the data. In this paper we investigate a Bayesian approach to learning Bayesian networks that contain the more general decision-graph representations of the CPDs. First, we describe how to evaluate the posterior probability|that is, the Bayesian score|of such a network, given a database of observed cases. Second, we describe various search spaces that can be used, in conjunction with a scoring function and a search procedure, to identify one or more high-scoring networks. Finally, we present an experimental evaluation of the search spaces, using a greedy algorithm and a Bayesian scoring function.
Diamond: Nesting the Data Center Network With Wireless Rings in 3-D Space

The introduction of wireless transmissions into the data center has shown to be promising in improving cost effectiveness of data center networks DCNs. For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack switches, we propose a novel DCN architecture, Diamond, which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3-D ring reflection spaces RRSs which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60-GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.
Real-time staircase detection from a wearable stereo system

We address the problem of staircase detection, in the context of a navigation aid for the visually impaired. The requirements for such a system are robustness to viewpoint, distance, scale, real-time operation, high detection rate and low false alarm rate. Our approach uses classifiers trained using Haar features and Ad-aboost learning. This first stage does detect staircases, but produces many false alarms. The false alarm rate is drastically reduced by using spatial context in the form of the estimated ground plane, and by enforcing temporal consistency. We have validated our approach on many real sequences under various weather conditions, and are presenting some of the quantitative results here.
Archaeological Evidence for the Emergence of Language , Symbolism , and Music — An Alternative Multidisciplinary Perspective

In recent years, there has been a tendency to correlate the origin of modern culture and language with that of anatomically modern humans. Here we discuss this correlation in the light of results provided by our first hand analysis of ancient and recently discovered relevant archaeological and paleontological material from Africa and Europe. We focus in particular on the evolutionary significance of lithic and bone technology, the emergence of symbolism, Neandertal behavioral patterns, the identification of early mortuary practices, the anatomical evidence for the acquisition of language, the
Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment

Despite two decades of intensive research, it remains a challenge to design a practical anonymous two-factor authentication scheme, for the designers are confronted with an impressive list of security requirements (e.g., resistance to smart card loss attack) and desirable attributes (e.g., local password update). Numerous solutions have been proposed, yet most of them are shortly found either unable to satisfy some critical security requirements or short of a few important features. To overcome this unsatisfactory situation, researchers often work around it in hopes of a new proposal (but no one has succeeded so far), while paying little attention to the fundamental question: whether or not there are inherent limitations that prevent us from designing an “ideal” scheme that satisfies all the desirable goals? In this work, we aim to provide a definite answer to this question. We first revisit two foremost proposals, i.e. Tsai et al.'s scheme and Li's scheme, revealing some subtleties and challenges in designing such schemes. Then, we systematically explore the inherent conflicts and unavoidable trade-offs among the design criteria. Our results indicate that, under the current widely accepted adversarial model, certain goals are beyond attainment. This also suggests a negative answer to the open problem left by Huang et al. in 2014. To the best of knowledge, the present study makes the first step towards understanding the underlying evaluation metric for anonymous two-factor authentication, which we believe will facilitate better design of anonymous two-factor protocols that offer acceptable trade-offs among usability, security and privacy.
An overview of anomaly detection techniques: Existing solutions and latest technological trends

As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems—the cyberspace’s equivalent to the burglar alarm—join ranks with firewalls as one of the fundamental technologies for network security. However, today’s commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system/network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or ‘‘zero day’’ attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area.
DETECTORS AND DESCRIPTORS FOR PHOTOGRAMMETRIC APPLICATIONS

This paper reports about interest operators, region detectors and region descriptors for photogrammetric applications. Features are the primary input for many applications like registration, 3D reconstruction, motion tracking, robot navigation, etc. Nowadays many detectors and descriptors algorithms are available, providing corners, edges and regions of interest together with n-dimensional vectors useful in matching procedures. The main algorithms are here described and analyzed, together with their proprieties. Experiments concerning the repeatability, localization accuracy and quantitative analysis are performed and reported. Details on how improve to location accuracy of region detectors are also reported.
Social Networking Websites , Personality Ratings , and the Organizational Context : More Than Meets the Eye ?

We examined the psychometric properties of the Big Five personality traits assessed through social networking profiles in 2 studies consisting of 274 and 244 social networking website (SNW) users. First, SNW ratings demonstrated sufficient interrater reliability and internal consistency. Second, ratings via SNWs demonstrated convergent validity with self-ratings of the Big Five traits. Third, SNW ratings correlated with job performance, hirability, and academic performance criteria; and the magnitude of these correlations was generally larger than for self-ratings. Finally, SNW ratings accounted for significant variance in the criterion measures beyond self-ratings of personality and cognitive ability. We suggest that SNWs may provide useful information for potential use in organizational research and practice, taking into consideration various legal and ethical issues.jasp_881 1..30
Metamaterial-Inspired Engineering of Antennas

A variety of antennas have been engineered with metamaterials (MTMs) and metamaterial-inspired constructs to improve their performance characteristics. Examples include electrically small, near-field resonant parasitic (NFRP) antennas that require no matching network and have high radiation efficiencies. Experimental verification of their predicted behaviors has been obtained. Recent developments with this NFRP electrically small paradigm will be reviewed. They include considerations of increased bandwidths, as well as multiband and multifunctional extensions.
Single Image Super-resolution Using Deformable Patches

We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.
A Remote Patient Monitoring System for Congestive Heart Failure

Congestive heart failure (CHF) is a leading cause of death in the United States affecting approximately 670,000 individuals. Due to the prevalence of CHF related issues, it is prudent to seek out methodologies that would facilitate the prevention, monitoring, and treatment of heart disease on a daily basis. This paper describes WANDA (Weight and Activity with Blood Pressure Monitoring System); a study that leverages sensor technologies and wireless communications to monitor the health related measurements of patients with CHF. The WANDA system is a three-tier architecture consisting of sensors, web servers, and back-end databases. The system was developed in conjunction with the UCLA School of Nursing and the UCLA Wireless Health Institute to enable early detection of key clinical symptoms indicative of CHF-related decompensation. This study shows that CHF patients monitored by WANDA are less likely to have readings fall outside a healthy range. In addition, WANDA provides a useful feedback system for regulating readings of CHF patients.
Analyzing behavior of DDoS attacks to identify DDoS detection features in SDN

Software Defined Network (SDN) facilitates network programmers with easier network monitoring, identification of anomalies, instant implementation of changes, central control to the whole network in a cost effective and efficient manner. These features could be beneficial for securing and maintaining entire network. Being a promising network paradigm, it draws a lot of attention from researchers in security domain. But it's logically centralized control tends to single point of failure, increasing the risk of attacks such as Distributed Denial of Service (DDoS) attack. In this paper, we have tried to identify various possibilities of DDoS attacks in SDN environment with the help of attack tree and an attack model. Further, an attempt to analyze the impact of various traditional DDoS attacks on SDN components is done. Such analysis helps in identifying the type of DDoS attacks that impose bigger threat on SDN architecture and also the features that could play important role in identification of these attacks are deduced.
Doping engineering for improved immunity against BV softness and BV shift in trench power MOSFET

In this paper, we report typical soft breakdown and BVDSS walk-in/walk-out phenomena observed in the development of ON semiconductor's T8 60V trench power MOSFET. These breakdown behaviors show strong correlation with doping profile. We propose two 1D location-dependent variables, Qint(y) and Cave(y), to assist the study, and demonstrate the effectiveness of them in revealing hidden information behind regular SIMS data. Our study details the methodology of engineering doping profiles for improved breakdown stability.
On stigma and its consequences: evidence from a longitudinal study of men with dual diagnoses of mental illness and substance abuse.

Numerous studies have demonstrated a strong connection between the experience of stigma and the well-being of the stigmatized. But in the area of mental illness there has been controversy surrounding the magnitude and duration of the effects of labeling and stigma. One of the arguments that has been used to downplay the importance of these factors is the substantial body of evidence suggesting that labeling leads to positive effects through mental health treatment. However, as Rosenfield (1997) points out, labeling can simultaneously induce both positive consequences through treatment and negative consequences through stigma. In this study we test whether stigma has enduring effects on well-being by interviewing 84 men with dual diagnoses of mental disorder and substance abuse at two points in time--at entry into treatment, when they were addicted to drugs and had many psychiatric symptoms and then again after a year of treatment, when they were far less symptomatic and largely drug- and alcohol-free. We found a relatively strong and enduring effect of stigma on well-being. This finding indicates that stigma continues to complicate the lives of the stigmatized even as treatment improves their symptoms and functioning. It follows that if health professionals want to maximize the well-being of the people they treat, they must address stigma as a separate and important factor in its own right.
A new metaheuristic algorithm for continuous engineering optimization : harmony search theory and practice

Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search (HS) meta-heuristic algorithm-based approach for engineering optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Various engineering optimization problems, including mathematical function minimization and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.
Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing

The accuracy of Automated Speech Recognition (ASR) technology has improved, but it is still imperfect in many settings. Researchers who evaluate ASR performance often focus on improving the Word Error Rate (WER) metric, but WER has been found to have little correlation with human-subject performance on many applications. We propose a new captioning-focused evaluation metric that better predicts the impact of ASR recognition errors on the usability of automatically generated captions for people who are Deaf or Hard of Hearing (DHH). Through a user study with 30 DHH users, we compared our new metric with the traditional WER metric on a caption usability evaluation task. In a side-by-side comparison of pairs of ASR text output (with identical WER), the texts preferred by our new metric were preferred by DHH participants. Further, our metric had significantly higher correlation with DHH participants' subjective scores on the usability of a caption, as compared to the correlation between WER metric and participant subjective scores. This new metric could be used to select ASR systems for captioning applications, and it may be a better metric for ASR researchers to consider when optimizing ASR systems.
Working memory and the vividness of imagery.

The working memory framework was used to investigate the factors determining the phenomenological vividness of images. Participants rated the vividness of visual or auditory images under control conditions or while performing tasks that differentially disrupted the visuospatial sketchpad and phonological loop subsystems of working memory. In Experiments 1, 2, and 6, participants imaged recently presented novel visual patterns and sequences of tones; ratings of vividness showed the predicted interaction between stimulus modality and concurrent task. The images in experiments 3, 4, 5, and 6 were based on long-term memory (LTM). They also showed an image modality by task interaction, with a clear effect of LTM variables (meaningfulness, activity, bizarreness, and stimulus familiarity), implicating both working memory and LTM in the experience of vividness.
Robust Cyber-Physical Systems: Concept, models, and implementation

In this paper we comprehensively survey the concept and strategies for building a resilient and integrated cyber-physical system (CPS). Here resilience refers to a 3S-oriented design, that is, stability, security, and systematicness: Stability means the CPS can achieve a stable sensing-actuation close-loop control even though the inputs (sensing data) have noise or attacks; Security means that the system can overcome the cyber-physical interaction attacks; and Systematicness means that the system has a seamless integration of sensors and actuators. We will also explain the CPS modeling issues since they serve as the basics of 3S design. We will use two detailed examples from our achieved projects to explain how to achieve a robust, systematic CPS design: Case study 1 is on the design of a rehabilitation system with cyber (sensors) and physical (robots) integration. Case Study 2 is on the implantable medical device design. It illustrates the nature of CPS security principle. The dominant feature of this survey is that it has both principle discussions and practical cyber-physical coupling design. 1 Index Terms – Cyber-Physical Systems, Stability, Security, Sensors and Actuators, Survey
Robot assisted facial expressions with segmented shape memory alloy actuators

This paper introduces a robotic technology based supporting device, the Robot Mask, to enhance facial expressiveness and support physiotherapy for facial paralyzed persons. The wearable device, which consists of Shape Memory Alloy (SMA) based linear actuators, functions by pulling the facial skin towards anatomically selected directions. Since facial expressions are silent, SMA were selected over electrical motors. This paper introduces a compact and fully controllable actuation unit with position feedback and a novel controlling scenario that uses the selected hybrid actuation of bidirectional multi segment SMA wires in series to pull the wires. When designing the actuators, a biomechanical analysis was conducted to find anatomical parameters of natural smiles, and the Robot Mask was evaluated for its suitability as a facial expression supporter.
Telco Churn Prediction with Big Data

We show that telco big data can make churn prediction much more easier from the $3$V's perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having $0.96$ precision for the top $50000$ predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value.
Assessment , Enhancement , and Verification Determinants of the Self-Evaluation Process

The 3 major self-evaluation motives were compared: self-assessment (people pursue accurate selfknowledge), self-enhancement (people pursue favorable self-knowledge), and self-verification (people pursue highly certain self-knowledge). Ss considered the possession of personality traits that were either positive or negative and either central or peripheral by asking themselves questions that varied in diagnosticity (the extent to which the questions could discriminate between a trait and its alternative) and in confirmation value (the extent to which the questions confirmed possession of a trait). Ss selected higher diagnosticity questions when evaluating themselves on central positive rather than central negative traits and confirmed possession of their central positive rather than central negative traits. The self-enhancement motive emerged as the most powerful determinant of the self-evaluation process, followed by the self-verification motive.
Prediction as a candidate for learning deep hierarchical models of data

Recent findings [HOT06] have made possible the learning of deep layered hierarchical representations of data mimicking the brains working. It is hoped that this paradigm will unlock some of the power of the brain and lead to advances towards true AI. In this thesis I implement and evaluate state-of-the-art deep learning models and using these as building blocks I investigate the hypothesis that predicting the time-to-time sensory input is a good learning objective. I introduce the Predictive Encoder (PE) and show that a simple non-regularized learning rule, minimizing prediction error on natural video patches leads to receptive fields similar to those found in Macaque monkey visual area V1. I scale this model to video of natural scenes by introducing the Convolutional Predictive Encoder (CPE) and show similar results. Both models can be used in deep architectures as a deep learning module.