nils-herrmann/clean_scirepeval huggingface dataset:
Border detection in dermoscopy images using statistical region merging.

As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it.



In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm.



The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method).



The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.
Estimating Crowd Density in an RF-Based Dynamic Environment

Crowd density estimating is a crucial service in many applications (e.g., smart guide, crowd control, etc.), which is often conducted using pattern recognition technologies based on video surveillance. However, these kinds of methods are high cost, and cannot work well in low-light environments. Radio frequency based technologies are adopted more and more in indoor application, since radio signal strength (RSS) can be easily obtained by various wireless devices without additional cost. In this paper, we introduce a low cost crowd density estimating method using wireless sensor networks. The proposed approach is a device-free crowd counting approach without objects carrying any assistive device. It is hard to count objects based on RSS measurement, since different number of mobile people at different positions often generates different RSS due to the multipath phenomenon. This paper utilizes the space-time relativity of crowd distribution to reduce the estimation errors. The proposed approach is an iterative process, which contains three phases: the training phase, the monitoring phase, and the calibrating phase. Our experiments are implemented based on TelosB sensor platform. We also do some large-scale simulations to verify the feasibility and the effectiveness of our crowd density estimating approach.
A Generalized Representer Theorem

Wahba’s classical representer theorem states that the solutions of certain risk minimization problems involving an empirical risk term and a quadratic regularizer can be written as expansions in terms of the training examples. We generalize the theorem to a larger class of regularizers and empirical risk terms, and give a self-contained proof utilizing the feature space associated with a kernel. The result shows that a wide range of problems have optimal solutions that live in the finite dimensional span of the training examples mapped into feature space, thus enabling us to carry out kernel algorithms independent of the (potentially infinite) dimensionality of the feature space.
Pellet: A practical OWL-DL reasoner

In this report, we present Pellet: a complete and capable OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is written in Java and is open source under a very liberal license. It is used in a number of projects, from pure research to industrial settings. Pellet is the first sound and complete OWL-DL reasoner with extensive support for rea-soning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. It has proven to be a reliable tool for working with OWL-DL ontologies and experimenting with OWL extensions. In this paper we describe Pellet’s features, architecture and special capabilities, along with an empirical comparison of its performance against other leading OWL-DL reasoners.
Cultural determinants of organizational social media adoption

In this research-in-progress paper, we present a two-step approach to measure the impact of cultural values on organizational social media adoption. We build on the GLOBE framework to measure societal culture and the Competing Values Framework to measure organizational culture. We define organizational social media adoption as the use of social networking, blogs, and media sharing tools in order to communicate and collaborate with customers, partners, and organization members. Cultural values appear to be salient factors for the ongoing adoption of social media in organizations.
Ray Tracing Non-Polygonal Objects: Implementation and Performance Analysis using Embree

Free-form surfaces and implicit surfaces must be tessellated before being rendered with rasterization techniques. However ray tracing provides the means to directly render such objects without the need to first convert into polygonal meshes. Since ray tracing can handle triangle meshes as well, the question of which method is most suitable in terms of performance, quality and memory usage is addressed in this thesis. Bézier surfaces and NURBS surfaces along with basic algebraic implicit surfaces are implemented in order to test the performance relative to polygonal meshes approximating the same objects. The parametric surfaces are implemented using an iterative Newtonian method that converges on the point of intersection using a bounding volume hierarchy that stores the initial guesses. Research into intersecting rays with parametric surfaces is surveyed in order to find additional methods that speed up the computation. The implicit surfaces are implemented using common direct algebraic methods. All of the intersection tests are implemented using the Embree ray tracing API as well as a SIMD library in order to achieve interactive framerates on a CPU. The results show that both Bézier surfaces and NURBS surfaces can achieve interactive framerates on a CPU using SIMD computation, with Bézier surfaces coming close to the performance of polygonal counterparts. The implicit surfaces implemented outperform even the simplest polygonal approximations.
Compiling Database Application Programs

There is a trend towards increased specialization of data management software for performance reasons. In this paper, we study the automatic specialization and optimization of database application programs – sequences of queries and updates, augmented with control flow constructs as they appear in database scripts, UDFs, transactional workloads and triggers in languages such as PL/SQL. We show how to build an optimizing compiler for database application programs using generative programming and state-of-the-art compiler technology. We evaluate a hand-optimized low-level implementation of TPCC, and identify the key optimization techniques that account for its good performance. Our compiler fully automates these optimizations and, applied to this benchmark, outperforms the manually optimized baseline by a factor of two. By selectively disabling some of the optimizations in the compiler, we derive a clinical and precise way of obtaining insight into their individual performance contributions.
CMOS SPADs: Design Issues and Research Challenges for Detectors, Circuits, and Arrays

Solid-state single photon detectors are playing a significant role in the development of high-performance single-photon imaging systems for fluorescence lifetime imaging, time-of-flight positron emission tomography and Raman Spectroscopy applications. The main driving factors are the unparalleled levels of miniaturization and portability, low fabrication costs, and high overall performance resulting from the integration of single-photon avalanche diodes (SPADs) with mixed-signal circuits in deep-submicron (DSM) complementary metal-oxide-semiconductor (CMOS) technology. At the heart of such imaging systems is the SPAD, capable of single-photon sensitivity and sub-nanosecond time resolution, and its associated circuitry, which in DSM CMOS, is capable of high-speed, low-power mixed-mode signal processing. In this paper, we review and discuss the most recent developments in DSM CMOS SPAD detectors, circuits and arrays and investigate issues of scalability, miniaturization and performance trade-offs involved in designing SPAD imaging systems. Design considerations, research challenges, and future directions for CMOS SPAD image sensors will be highlighted and addressed.
Algorithmic Aspects of Vertex Elimination on Graphs

We consider a graph-theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of linear equations. We give a new linear-time algorithm to calculate the fill-in produced by any elimination ordering, and we give two new related algorithms for finding orderings with special properties. One algorithm, based on breadth-first search, finds a perfect elimination ordering, if any exists, in O(n + e) time, if the problem graph has n vertices and e edges. An extension of this algorithm finds a minimal (but not necessarily minimum) ordering in O(ne) time. We conjecture that the problem of finding a minimum ordering is
Why does deep and cheap learning work so well?

We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through “cheap learning” with exponentially fewer parameters than generic ones. We explore how properties frequently encountered in physics such as symmetry, locality, compositionality, and polynomial log-probability translate into exceptionally simple neural networks. We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one. We formalize these claims using information theory and discuss the relation to the renormalization group. We prove various “no-flattening theorems” showing when efficient linear deep networks cannot be accurately approximated by shallow ones without efficiency loss; for example, we show that n variables cannot be multiplied using fewer than 2 neurons in a single hidden layer.
A core ontology for requirements

In their seminal paper (ACM T. Softw.  (1997), 1–30), Zave and Jackson established a core ontology for Requirements Engineering (RE) and used it to formulate the “requirements problem”, thereby defining what it means to successfully complete RE. Starting from the premise that the stakeholders of the system-to-be communicate to the software engineer the information needed to perform RE, Zave and Jackson’s ontology is shown to be incomplete, in that it does not cover all classes of basic concerns – namely, the beliefs, desires, intentions, and evaluations – that the stakeholders communicate. In response, we provide a new core ontology for requirements that covers these classes of basic stakeholder concerns. The proposed new core ontology leads to a new formulation of the requirements problem. We thereby establish a new framework for the information that needs to be elicited over the course of RE and new criteria for determining whether an RE problem has been successfully addressed.
BGPmon: A Real-Time, Scalable, Extensible Monitoring System

This paper presents a new system, called BGPmon, for monitoring the Border Gateway Protocol (BGP). BGP is the routing protocol for the global Internet. Monitoring BGP is important for both operations and research; a number of public and private BGP monitors are deployed and widely used. These existing monitors typically collect data using a full implementation of a BGP router. In contrast, BGPmon eliminates the unnecessary functions of route selection and data forwarding to focus solely on the monitoring function. BGPmon uses a publish/subscribe overlay network to provide real-time access to vast numbers of peers and clients. All routing events are consolidated into a single XML stream. XML allows us to add additional features such as labeling updates to allow easy identification of useful data by clients. Clients subscribe to BGPmon and receive the XML stream, performing tasks such as archiving, filtering, or real-time data analysis. BGPmon enables scalable real-time monitoring data distribution by allowing monitors to peer with each other and form an overlay network to provide new services and features without modifying the monitors. We illustrate the effectiveness of the BGPmon data using the Cyclops route monitoring system.
A New Approach for Visualizing UML Class Diagrams

UML diagrams have become increasingly important in the engineering and reengineering processes for software systems. Of particular interest are UML class diagrams whose purpose is to display class hierarchies (generalizations), associations, aggregations, and compositions in one picture. The combination of hierarchical and non-hierarchical relations poses a special challenge to a graph layout tool. Existing layout tools treat hierarchical and non-hierarchical relations either alike or as separate tasks in a two-phase process as in, e.g., [Seemann 1997]. We suggest a new approach for visualizing UML class diagrams leading to a balanced mixture of the following aesthetic criteria: Crossing minimization, bend minimization, uniform direction within each class hierarchy, no nesting of one class hierarchy within another, orthogonal layout, merging of multiple inheritance edges, and good edge labelling. We have realized our approach within the graph drawing library GoVisual. Experiments show the superiority to state-of-the-art and industrial standard layouts.
The Random Neural Network: A Survey

The Random Neural Network (RNN) is a recurrent neural network model inspired by the spiking behaviour of biological neuronal networks. Contrary to most Artificial Neural Networks (ANN) models, neurons in RNN interact by probabilistically exchanging excitatory and inhibitory spiking signals. The model is described by analytical equations, has a low complexity supervised learning algorithm and is a universal approximator for bounded continuous functions. RNN has been applied in a variety of areas including pattern recognition, classification, image processing, combinatorial optimisation and communication systems. It has also inspired research activity in modelling interacting entities in various systems such as queueing and gene-regulatory networks. This paper presents a review of the theory, extension models, learning algorithms and applications of the RNN.
Smart digital door lock for the home automation

In this paper, we propose a smart digital door lock system for home automation. A digital door lock system is equipment that uses the digital information such as a secret code, semi-conductors, smart card, and finger prints as the method for authentication instead of the legacy key system. In our proposed system, a ZigBee module is embedded in digital door lock and the door lock acts as a central main controller of the overall home automation system. Technically, our proposed system is the network of sensor nodes and actuators with digital door lock as base station. A door lock system proposed here consists of RFID reader for user authentication, touch LCD, motor module for opening and closing of the door, sensor modules for detecting the condition inside the house, communication module, and control module for controlling other modules. Sensor nodes for environment sensing are deployed at appropriate places at home. Status of individual ZigBee module can be monitored and controlled by the centralized controller, digital door lock. As the door lock is the first and last thing people come across in entering and leaving the home respectively, the home automation function in digital door lock system enables user to conveniently control and monitor home environment and condition all at once before entering or leaving the house. Furthermore, it also allows users to remotely monitor the condition inside the house through Internet or any other public network. The biggest advantage of our proposed system over existing ones is that it can be easily installed when and where necessary without requirement of any infrastructures and proper planning.
An empirical model to predict security vulnerabilities using code complexity metrics

Complexity is often hypothesized to be the enemy of software security. If this hypothesis is true, complexity metrics may be used to predict the locale of security problems and can be used to prioritize inspection and testing efforts. We performed statistical analysis on nine complexity metrics from the JavaScript Engine in the Mozilla application framework to find differences in code metrics between vulnerable and nonvulnerable code and to predict vulnerabilities. Our initial results show that complexity metrics can predict vulnerabilities at a low false positive rate, but at a high false negative rate.
Cognitive computing for coalition situational understanding

The term cognitive computing (CC) refers to computer systems that harness multiple techniques from artificial intelligence (AI) and signal processing (SP). Situational understanding (SU) involves creating and reasoning about models of an environment and events. Coalition operations are defined by multiple partners seeking to achieve a common purpose. This paper characterises the SU problem in a coalition operations context — coalition situational understanding (CSU) — in terms of a set of problem attributes. The paper argues that CSU problems require CC system solutions involving a hybrid of AI and SP approaches. The paper outlines some of the architectural choices for CC CSU systems.
Activity Monitoring: Noticing Interesting Changes in Behavior

We introduce a problem class which we term activity monitoring. Such problems involve monitoring the behavior of a large population of entities for interesting events requiring action. We present a framework within which each of the individual problems has a natural expression, as well as a methodology for evaluating performance of activity monitoring techniques. We show that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.
Texture Synthesis by Non-parametric Sampling

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.
Random synaptic feedback weights support error backpropagation for deep learning

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.
Effects of level of meditation experience on attentional focus: is the efficiency of executive or orientation networks improved?

The present investigation examined the contributions of specific attentional networks to long-term trait effects of meditation. It was hypothesized that meditation could improve the efficiency of executive processing (inhibits prepotent/incorrect responses) or orientational processing (orients to specific objects in the attentional field). Participants (50 meditators and 10 controls) were given the Stroop (measures executive attention) and Global-Local Letters (measures orientational attention) tasks. Results showed that meditation experience was associated with reduced interference on the Stroop task (p < 0.03), in contrast with a lack of effect on interference in the Global-Local Letters task. This suggests that meditation produces long-term increases in the efficiency of the executive attentional network (anterior cingulate/prefrontal cortex) but no effect on the orientation network (parietal systems). The amount of time participants spent meditating each day, rather than the total number of hours of meditative practice over their lifetime, was negatively correlated with interference on the Stroop task (r = -0.31, p < 0.005).
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009; Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.
Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today’s automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.