nils-herrmann/clean_scirepeval huggingface dataset:
Computational Learning Theory: Survey and Selected Bibliography

Permission to copy without fee all or part of this material ie granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notica is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 24th ANNUAL ACM STOC 5/92/VICTORiA, B. C., CANADA G 1992 ACM ()-89791.51 2-7/92/0004/03~j -..$1 .~() the only textbook in the field is Natarajan’s [101]. Surveys by Laird [83] and Valiant [129] are valuable. Somewhat more peripheral are the European meetings on Analogical and Inductive Inference, AH, and the AI machine learning communit y’s annual International Conference on Machine Learning. In addition, the general AI meetings, AAAI and IJCAI, currently have a large number of papers devoted to learning, as do the neural net meetings.
Potential mechanisms and implications for the formation of tau oligomeric strains.

The culmination of many years of increasing research into the toxicity of tau aggregation in neurodegenerative disease has led to the consensus that soluble, oligomeric forms of tau are likely the most toxic entities in disease. While tauopathies overlap in the presence of tau pathology, each disease has a unique combination of symptoms and pathological features; however, most study into tau has grouped tau oligomers and studied them as a homogenous population. Established evidence from the prion field combined with the most recent tau and amyloidogenic protein research suggests that tau is a prion-like protein, capable of seeding the spread of pathology throughout the brain. Thus, it is likely that tau may also form prion-like strains or diverse conformational structures that may differ by disease and underlie some of the differences in symptoms and pathology in neurodegenerative tauopathies. The development of techniques and new technology for the detection of tau oligomeric strains may, therefore, lead to more efficacious diagnostic and treatment strategies for neurodegenerative disease.
An introduction to question answering with ConceptRDF

With the development of information technologies, a great amount of semantic data is being generated on the web. Consequently, finding efficient ways of accessing this data becomes more and more important. Question answering is a good compromise between intuitiveness and expressivity, which has attracted the attention of researchers from different communities. In this paper, we propose an intelligent questing answering system for answering questions about concepts. It is based on ConceptRDF, which is an RDF presentation of the ConceptNet knowledge base. We use it as a knowledge base for answering questions. Our experimental results show that our approach is promising: it can answer questions about concepts at a satisfactory level of accuracy (reaches 94.5%).
Web-Based Text Mining of Hotel Customer Comments Using SAS ® Text Miner and Megaputer Polyanalyst ®

This paper presents text mining using SAS® Text Miner and Megaputer PolyAnalyst® specifically applied for hotel customer survey data, and its data management. The paper reviews current literature of text mining, and discusses features of these two text mining software packages in analyzing unstructured qualitative data in the following key steps: data preparation, data analysis, and result reporting. Some screen shots are presented for web-based hotel customer survey data as well as comparisons and conclusions.
Lumbar drainage for subarachnoid hemorrhage: technical considerations and safety analysis.

Recent and on going clinical research trials may indicate that the incidence of vasospasm is less in patients treated with lumbar drains following SAH than with patients treated with external ventricular drainage (EVD) alone. These studies have sparked interest in the more aggressive use of LDs in patients with aneurysmal SAH but some clinicians have been concerned about safety.



This paper is a review of 25 patients with aneurysmal SAH cared for over a 2.2 year period at the University of Mississippi Medical Center Neurosciences Critical Care Unit that were managed with a LD. In general the LD was used subsequent to an EVD, but in some cases alone, in order to improve evacuation of blood from the cisterns around the base of the brain.



This retrospective cohort review demonstrates that lumbar drainage is safe in the setting of aneurysmal SAH. Of the 25 patients treated with a lumbar drain only one suffered a reversible neurological event. No other complications occurred.



LDs are safe when used in the setting of aneurysmal SAH if appropriate patient selection and good technique are employed.
An ontology-based approach for exploring knowledge in fundamental particles of Physics

Particle physics is a branch of physics that studies the elementary constituents of matter and radiation, and the interactions between them. Particle physicists have described the fundamental structure of matter using the fundamental forces of physics in the form of an elegant series of equations. However, fundamental particle of physics often faces problems from a shortage of specialists. The knowledge base in the field of fundamental particles is also very large and widely dispersed, which makes it difficult for humans to achieve expertise. This is clearly proved from the smaller number of research studies in fundamental particle of physics. The specific knowledge of particle physics is not widespread, so techniques to manage and share knowledge are necessary. This work proposes an ontology-based approach for describing networking physics domains related to fundamental particles of physics. This networking ontology is employed to implement the recommender web service system to help users to recommend fundamental particles related to the networking physics domain. The experiments on the system are conducted with particle physics specialists. Moreover, ontologies are developed using the HOZO tool in OWL format which can be easily integrated with any other ontology in computer science and engineering.
The Effects and Interactions of Data Quality and Problem Complexity on Classification

Data quality remains a persistent problem in practice and a challenge for research. In this study we focus on the four dimensions of data quality noted as the most important to information consumers, namely accuracy, completeness, consistency, and timeliness. These dimensions are of particular concern for operational systems, and most importantly for data warehouses, which are often used as the primary data source for analyses such as classification, a general type of data mining. However, the definitions and conceptual models of these dimensions have not been collectively considered with respect to data mining in general or classification in particular. Nor have they been considered for problem complexity. Conversely, these four dimensions of data quality have only been indirectly addressed by data mining research. Using definitions and constructs of data quality dimensions, our research evaluates the effects of both data quality and problem complexity on generated data and tests the results in a real-world case. Six different classification outcomes selected from the spectrum of classification algorithms show that data quality and problem complexity have significant main and interaction effects. From the findings of significant effects, the economics of higher data quality are evaluated for a frequent application of classification and illustrated by the real-world case.
Bowenoid papulosis and invasive Bowen's disease: a multidisciplinary approach.

Elena Campione1, Chiara Centonze2, Laura Diluvio1, Augusto Orlandi3, Cesidio Cipriani4, Alessandro Di Stefani3, Emilio Piccione2, Sergio Chimenti1 and Luca Bianchi1 Departments of 1Dermatology, 2Gynaecology and 3Anatomic Pathology, University of Rome, Tor Vergata, Viale Oxford, 81, IT-00133 Rome, and 4Department of Nuclear Medicine, S. Eugenio Hospital, Rome, Italy. E-mail: campioneelena@hotmail.com Accepted Nov 7, 2011;  2012
Multi-sensor self-localization based on Maximally Stable Extremal Regions

This contribution presents a precise localization method for advanced driver assistance systems. A Maximally Stable Extremal Region (MSER) detector is used to extract bright areas, i.e. lane markings, from grayscale camera images. Furthermore, this algorithm is also used to extract features from a laser scanner grid map. These regions are automatically stored as landmarks in a geospatial data base during a map creation phase. A particle filter is then employed to perform the pose estimation. For the weight update of the filter the similarity between the set of online MSER detections and the set of mapped landmarks within the field of view is evaluated. Hereby, a two stage sensor fusion is carried out. First, in order to have a large field of view available, not only a forward facing camera but also a rearward facing camera is used and the detections from both sensors are fused. Secondly, the weight update also integrates the features detected from the laser grid map, which is created using measurements of three laser scanners. The performance of the proposed algorithm is evaluated on a 7 km long stretch of a rural road. The evaluation reveals that a relatively good position estimation and a very accurate orientation estimation (0.01 deg ± 0.22 deg) can be achieved using the presented localization method. In addition, an evaluation of the localization performance based only on each of the respective kinds of MSER features is provided in this contribution and compared to the combined approach.
A Novel Graph-based Mechanism for Identifying Traffic Vulnerabilities in Smart Home IoT

Smart home IoT devices have been more prevalent than ever before but the relevant security considerations fail to keep up with due to device and technology heterogeneity and resource constraints, making IoT systems susceptible to various attacks. In this paper, we propose a novel graph-based mechanism to identify the vulnerabilities in communication of IoT devices for smart home systems. Our approach takes one or more packet capture files as inputs to construct a traffic graph by passing the captured messages, identify the correlated subgraphs by examining the attribute-value pairs associated with each message, and then quantify their vulnerabilities based on the sensitivity levels of different keywords. To test the effectiveness of our approach, we setup a smart home system that can control a smart bulb LB100 via either the smartphone APP for LB100 or the Google Home speaker. We collected and analyzed 58,714 messages and exploited 6 vulnerable correlated sub graphs, based on which we implemented 6 attack cases that can be easily reproduced by attackers with little knowledge of IoT. This study is novel as our approach takes only the collected traffic files as inputs without requiring the knowledge of the device firmware while being able to identify new vulnerabilities. With this approach, we won the third prize out of 20 teams in a hacking competition.
Luca Cistriani UAV Design Engineer Galileo Avionica Simulators and UAV Business Unit Via

UAV operations are examined from a performance and logistic flexibility point of view in order to set up requirements to be input for the multiobjective optimization of a two component simple rotation flap slotted airfoil with high thickness to chord ratio. The airfoil selected among a wide range of geometries optimizing the two design points has been investigated using CFD for stability at low Reynolds numbers and sensitivity to parameters like free stream turbulence and in-flight icing. Wind tunnel tests have been performed for the two dimensional wing section and for a complete UAV aircraft configuration in order to confirm theoretical previsions. Finally, flight tests of the prototype aircraft have been executed with results in good agreement with the previous design and test work.  Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 01 NOV 2007 2. REPORT TYPE N/A 3. DATES COVERED 4. TITLE AND SUBTITLE Falco UAV Low Reynolds Airfoil Design and Testing at Galileo Avionica 5a.  NUMBER 5b. GRANT NUMBER 5c.   NUMBER 5e. TASK NUMBER 5f.   11.   Approved for public release, distribution unlimited 13.  The original document contains color images.
A subcutaneous Raman needle probe.

Raman spectroscopy is a powerful tool for studying the biochemical composition of tissues and cells in the human body. We describe the initial results of a feasibility study to design and build a miniature, fiber optic probe incorporated into a standard hypodermic needle. This probe is intended for use in optical biopsies of solid tissues to provide valuable information of disease type, such as in the lymphatic system, breast, or prostate, or of such tissue types as muscle, fat, or spinal, when identifying a critical injection site. The optical design and fabrication of this probe is described, and example spectra of various ex vivo samples are shown.
Active Learning to Rank using Pairwise Supervision

This paper investigates learning a ranking function using pairwise constraints in the context of human-machine interaction. As the performance of a learnt ranking model is predominantly determined by the quality and quantity of training data, in this work we explore an active learning to rank approach. Furthermore, since humans may not be able to confidently provide an order for a pair of similar instances we explore two types of pairwise supervision: (i) a set of “strongly” ordered pairs which contains confidently ranked instances, and (ii) a set of “weakly” ordered pairs which consists of similar or closely ranked instances. Our active knowledge injection is performed by querying domain experts on pairwise orderings, where informative pairs are located by considering both local and global uncertainties. Under this active scheme, querying of pairs which are uninformative or outliers instances would not occur. We evaluate the proposed approach on three real world datasets and compare with representative methods. The promising experimental results demonstrate the superior performance of our approach, and validate the effectiveness of actively using pairwise orderings to improve ranking performance.
Two-handed interaction on a tablet display

A touchscreen can be overlaid on a tablet computer to support asymmetric two-handed interaction in which the preferred hand uses a stylus and the non-preferred hand operates the touchscreen. The result is a portable device that allows both hands to interact directly with the display, easily constructed from commonly available hardware. The method for tracking the independent motions of both hands is described. A wide variety of existing two-handed interaction techniques can be used on this platform, as well as some new ones that exploit the reconfigurability of touchscreen interfaces. Informal tests show that, when the non-preferred hand performs simple actions, users find direct manipulation on the display with both hands to be comfortable, natural, and efficient.
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning

Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefit from large datasets and continuous use. In this paper, a dueling architecture based deep double-Q network (D3QN) is proposed for obstacle avoidance, using only monocular RGB vision. Based on the dueling and double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a simulator even with very noisy depth information predicted from RGB image. Extensive experiments show that D3QN enables twofold acceleration on learning compared with a normal deep Q network and the models trained solely in virtual environments can be directly transferred to real robots, generalizing well to various new environments with previously unseen dynamic objects.
Low-Rank Matrix Completion in the Presence of High Coherence

Prevalent matrix completion methods capture only the low-rank property which gives merely a constraint that the data points lie on some low-dimensional subspace, but generally ignore the extra structures (beyond low-rank) that specify in more detail how the data points lie on the subspace. Whenever the data points are not uniformly distributed on the low-dimensional subspace, the row-coherence of the target matrix to recover could be considerably high and, accordingly, prevalent methods might fail even if the target matrix is fairly low-rank. To relieve this challenge, we suggest to consider a model termed low-rank factor decomposition (LRFD), which imposes an additional restriction that the data points must be represented as linear, compressive combinations of the bases in a given dictionary. We show that LRFD can effectively mitigate the challenges of high row-coherence, provided that its dictionary is configured properly. Namely, it is mathematically proven that if the dictionary is well-conditioned and low-rank, then LRFD can weaken the dependence on the row-coherence. In particular, if the dictionary itself is low-rank, then the dependence on the row-coherence can be entirely removed. Subsequently, we devise two practical algorithms to obtain proper dictionaries in unsupervised environments: one uses the existing matrix completion methods to construct the dictionary in LRFD, and the other tries to learn a proper dictionary from the data given. Experiments on randomly generated matrices and motion datasets show superior performance of our proposed algorithms.
Determining Graduation Rates in Engineering for Community College Transfer Students Using Data Mining *

This study presents a unique synthesized set of data for community college students entering the university with the intention of earning a degree in engineering. Several cohorts of longitudinal data were combined with transcript-level data from both the community college and the university to measure graduation rates in engineering. The emphasis of the study is to determine academic variables that had significant correlations with graduation in engineering, and levels of these academic variables. The article also examines the utility of data mining methods for understanding the academic variables related to achievement in science, technology, engineering, and mathematics. The practical purpose of each model is to develop a useful strategy for policy, based on success variables, that relates to the preparation and achievement of this important group of students as they move through the community college pathway.
Vehicles on RFID: Error-Cognitive Vehicle Localization in GPS-Less Environments

GPS has been commonly used for vehicle localization. However, since GPS signals are very sensitive to terrain and interference, they may become unavailable in places such as lower layers of multilayer bridges, streets besides high buildings, tunnels, etc. To address this issue, various techniques have been proposed. Nevertheless, none of them are applicable to safety-critical and highly mobile vehicular network applications, which require high localization accuracy anywhere and anytime. Meanwhile, with the advent of radio frequency identification (RFID) systems on roads (RSR), passive RFID tags with road-related information are expected to be deployed on road surfaces for driving safety and efficiency. In this paper, we introduce a novel RFID-based approach for vehicle localization in GPS-less road environments, where passive RFID tags are deployed on the roads and readers are installed on vehicles. To achieve accurate vehicle localization, we propose an error-cognitive localization system, which leverages both reader-to-tag and vehicle-to-vehicle communications to adaptively cognize and correct localization errors. Extensive simulation and experiment have verified the effectiveness of our proposed system. Particularly, our field tests show that, with sparsely deployed tags, which cost just a few dollars per kilometer, a vehicle can localize itself with  submeter errors on average.
Does recent research on adolescent brain development inform the mature minor doctrine?

US Supreme Court rulings concerning sanctions for juvenile offenders have drawn on the science of brain development and concluded that adolescents are inherently less mature than adults in ways that render them less culpable. This conclusion departs from arguments made in cases involving the mature minor doctrine, in which teenagers have been portrayed as comparable to adults in their capacity to make medical decisions. I attempt to reconcile these apparently incompatible views of adolescents' decision-making competence. Adolescents are indeed less mature than adults when making decisions under conditions that are characterized by emotional arousal and peer pressure, but adolescents aged 15 and older are just as mature as adults when emotional arousal is minimized and when they are not under the influence of peers, conditions that typically characterize medical decision-making. The mature minor doctrine, as applied to individuals 15 and older, is thus consistent with recent research on adolescent development.
Early intervention and early experience.

For 4 decades, vigorous efforts have been based on the premise that early intervention for children of poverty and, more recently, for children with developmental disabilities can yield significant improvements in cognitive, academic, and social outcomes. The history of these efforts is briefly summarized and a conceptual framework presented to understand the design, research, and policy relevance of these early interventions. This framework, biosocial developmental contextualism, derives from social ecology, developmental systems theory, developmental epidemiology, and developmental neurobiology. This integrative perspective predicts that fragmented, weak efforts in early intervention are not likely to succeed, whereas intensive, high-quality, ecologically pervasive interventions can and do. Relevant evidence is summarized in 6 principles about efficacy of early intervention. The public policy challenge in early intervention is to contain costs by more precisely targeting early interventions to those who most need and benefit from these interventions. The empirical evidence on biobehavioral effects of early experience and early intervention has direct relevance to federal and state policy development and resource allocation.
Reinforcement learning can account for associative and perceptual learning on a visual decision task

We recently showed that improved perceptual performance on a visual motion direction–discrimination task corresponds to changes in how an unmodified sensory representation in the brain is interpreted to form a decision that guides behavior. Here we found that these changes can be accounted for using a reinforcement-learning rule to shape functional connectivity between the sensory and decision neurons. We modeled performance on the basis of the readout of simulated responses of direction-selective sensory neurons in the middle temporal area (MT) of monkey cortex. A reward prediction error guided changes in connections between these sensory neurons and the decision process, first establishing the association between motion direction and response direction, and then gradually improving perceptual sensitivity by selectively strengthening the connections from the most sensitive neurons in the sensory population. The results suggest a common, feedback-driven mechanism for some forms of associative and perceptual learning.