nils-herrmann/clean_scirepeval huggingface dataset:
Models of Incremental Concept Formation

Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling the type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASS1T, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions.
Deep Gaussian Process Regression ( DGPR )

A Gaussian Process Regression model is equivalent to an infinitely wide neural network with single hidden layer and similarly a DGP is a multi-layer neural network with multiple infinitely wide hidden layers [Neal, 1995]. DGPs employ a hierarchical structural of GP mappings and therefore are arguably more flexible, have a greater capacity to generalize, and are able to provide better predictive performance [Damianou, 2015]. Then it comes into my mind that why we would like to proceed to be deep and what are the benefits about being deep. It has been argued that the addition of non-linear hidden layers can also potentially overcome practical limitations of shallow GPs [Bui et al., 2016]. So what are the limitations exactly? Actually a GPR model is fully specified by a mean function E [·] and the covariance function cov [·, ·]. Conventionally we manually set the mean function to be 0. Then we can say that a GPR model is fully specified by its covariance function which also can be denoted as the kernel. Let us briefly examine the priors on functions encoded by some commonly used kernels 2 Expressing Structure with Kernels
An empirical analysis and comparison of apriori and FP- growth algorithm for frequent pattern mining

In this paper, we determine the empirical comparison of Apriori and FP-growth algorithm for frequent item set sequences for Web Usage data. We define the data structure, its implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. Web usage mining itself can be defined further depending on the type of usage data is considered like web server data, application server data and application level data. User logs that are collected at web server are also known as web server data. Some of the characteristic data collected at a web server include IP addresses of users, page references, and access time of the users and these are the main input to the present research. The comparison of algorithm concentrates on web usage mining and particularly focuses on determining the web usage patterns of websites from the server log files. In our analysis we take into empirical comparison for properties like memory size, input data, pre-fetching, scalability and processing efficiency etc, in order to better understand the results of the evaluation.
A Riemannian approach to graph embedding

In this paper, we make use of the relationship between the Laplace–Beltrami operator and the graph Laplacian, for the purposes of embedding a graph onto a Riemannian manifold. To embark on this study, we review some of the basics of Riemannian geometry and explain the relationship between the Laplace–Beltrami operator and the graph Laplacian. Using the properties of Jacobi fields, we show how to compute an edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths on the manifold between nodes. For the particular case of a constant sectional curvature surface, we use the Kruskal coordinates to compute edge weights that are proportional to the geodesic distance between points. We use the resulting edge-weight matrix to embed the nodes of the graph onto a Riemannian manifold. To do this, we develop a method that can be used to perform double centring on the Laplacian matrix computed from the edge-weights. The embedding coordinates are given by the eigenvectors of the centred Laplacian. With the set of embedding coordinates at hand, a number of graph manipulation tasks can be performed. In this paper, we are primarily interested in graph-matching. We recast the graph-matching problem as that of aligning pairs of manifolds subject to a geometric transformation. We show that this transformation is Pro-crustean in nature. We illustrate the utility of the method on image matching using the COIL database. 2006 Pattern Recognition Society.
Basic Concepts of Expert System Shells and an Efficient Model for Knowledge Acquisition

An Expert System is an intelligent computer program that employs knowledge and inference procedures to solve problems that are considered difficult enough to require significant human expertise for their solutions . Since the implementation of the first expert systems (the DENDRAL, MYCIN, Dipmeter Advisor, XCON, to mention but a few.) in the early 1970s, and the successes that followed, coupled with the advantages that humankind benefited from these early expert systems and their successors; one would expect that four decades later, there would have been a boom in the use of expert systems to perform more specialized tasks in different specific domains and application areas. The reasons for this could stem, asides other non-intrinsically-technical factors (such as financial implication, cultural and religious beliefs and restrictions surrounding the implementation of Expert Systems especially in third-world African Countries), either from a lack of adequate knowledge of the existence of Expert System Shells and/or the facilities, utilities and tools they provide that make the development of new expert systems sort of a “pretty easy endeavour”. This paper lays emphasis on the basic concept of Expert System Shells with the aim of highlighting their basic usage and creating some form of in-depth generic exposé into some of the component facilities and utilities they provide; proposing a more efficient model for Knowledge Acquisition – which would make the Knowledge Acquisition Bottleneck of developing Expert Systems a thing of no more concern – as well as highlight the functions performed by Expert System Shells, in a bid to emphasize their reusability across different other specific application areas either within or outside the same domain.
Autonomous dirt detection for cleaning in office environments

The advances of technologies for mobile robotics enable the application of robots to increasingly complex tasks. Cleaning office buildings on a daily basis is a problem that could be partially automatized with a cleaning robot that assists the cleaning professional yielding a higher cleaning capacity. A typical task in this domain is the selective cleaning, that is a focused cleaning effort to dirty spots, which speeds up the overall cleaning procedure significantly. To enable a robotic cleaner to accomplish this task, it is first necessary to distinguish dirty areas from the clean remainder. This paper discusses a vision-based dirt detection system for mobile cleaning robots that can be applied to any surface and dirt without previous training, that is fast enough to be executed on a mobile robot and which achieves high dirt recognition rates of 90% at an acceptable false positive rate of 45%. The paper also introduces a large database of real scenes which was used for the evaluation and is publicly available.
Comparison of nucleus and inflammatory cell detection methods on Pap smear images

Detection and identification of cells in a Pap smear test is very important for determining cell abnormalities. Detection of cells becomes an important stage in early detection of cervical cancer. The presence of inflammatory cells in Pap smear images often results in the identification of nuclei in the early detection of cervical cancer in Pap smear image becomes difficult to do. The success of inflaming the inflammatory cells and detecting the nucleus will facilitate the process of identifying the nucleus. This will greatly help the development of information acquisition system technology and the classification of single Pap smear cell image for early detection of cervical cancer. The paper aims to compare three methods of nuclear detection and inflammatory cell in a single Pap smear image. The results of the comparison of nucleus and inflammatory cell in the test of data consisting of 84 images with inflammatory cells, showed that the K-Means and Bayesian classification method has not been able to accurately detect the nuclei and inflammatory cell, rather than the nuclei and inflammatory cells detection base on the combination of gray level thresholding method.
An RFID warehouse robot

RFID is one of the latest trend in the industry. Its potential application can range from warehouse to library management. This project is aimed to build an autonomous robot with RFID application. The project integrates RFID reader and PIC microcontroller as the main components. The movement control comprises servo-motor with infrared sensors for the line follower. The whole programming operation was carried out by assembly language using MPLab 7.3. The robot has the ability to identify the items by reading the tag on the items. The robot will pick up the item and navigate to prescribed destination using line follower module to store the item at the appropriate place and location. A small white platform with black line is built for demonstration and testing.
Quality models: Role and value in software engineering

Software quality is the totality of features and characteristics of a product or a service that bears on its ability to satisfy the given needs. Poor quality of the software product in sensitive systems may lead to loss of human life, permanent injury, mission failure, or financial loss. So the quality of the project should be maintained at appropriate label. To maintain the quality, there are different quality models. ″A high quality product is one which has associated with it a number of quality factors. These could be described in the requirements specification; they could be cultured, in that they are normally associated with the artifact through familiarity of use and through the shared experience of users. In this paper, we will discuss all the quality models: McCall's quality model, Boehm's quality model, Dromey's quality model, and FURPS quality model and focus on a comparison between these models, and find the key differences between them.
Autonomous intelligent cruise control

Abstruct-Vehicle following and its effects on traffic flow has been an active area of research. Human driving involves reaction times, delays, and human errors that affect traffic flow adversely. One way to eliminate human errors and delays in vehicle following is to replace the human driver with a computer control system and sensors. The purpose of this paper is to develop an autonomous intelligent cruise control (AICC) system for automatic vehicle following, examine its effect on traffic flow, and compare its performance with that of the human driver models. The AICC system developed is not cooperative; Le., it does not exchange information with other vehicles and yet is not susceptible to oscillations and " slinky " effects. The elimination of the " slinky " effect is achieved by using a safety distance separation rule that is proportional to the vehicle velocity (constant time headway) and by designing the control system appropriately. The performance of the AICC system is found to be superior to that of the human driver models considered. It has a faster and better transient response that leads to a much smoother and faster traffic flow. Computer simulations are used to study the performance of the proposed AICC system and analyze vehicle following in a single lane, without passing, under manual and automatic control. In addition, several emergency situations that include emergency stopping and cut-in cases were simulated. The simulation results demonstrate the effectiveness of the AICC system and its potentially beneficial effects on traffic flow.
A Deep CNN-Based Framework For Enhanced Aerial Imagery Registration with Applications to UAV Geolocalization

In this paper we present a novel framework for geolocalizing Unmanned Aerial Vehicles (UAVs) using only their onboard camera. The framework exploits the abundance of satellite imagery, along with established computer vision and deep learning methods, to locate the UAV in a satellite imagery map. It utilizes the contextual information extracted from the scene to attain increased geolocalization accuracy and enable navigation without the use of a Global Positioning System (GPS), which is advantageous in GPS-denied environments and provides additional enhancement to existing GPS-based systems. The framework inputs two images at a time, one captured using a UAV-mounted downlooking camera, and the other synthetically generated from the satellite map based on the UAV location within the map. Local features are extracted and used to register both images, a process that is performed recurrently to relate UAV motion to its actual map position, hence performing preliminary localization. A semantic shape matching algorithm is subsequently applied to extract and match meaningful shape information from both images, and use this information to improve localization accuracy. The framework is evaluated on two different datasets representing different geographical regions. Obtained results demonstrate the viability of proposed method and that the utilization of visual information can offer a promising approach for unconstrained UAV navigation and enable the aerial platform to be self-aware of its surroundings thus opening up new application domains or enhancing existing ones.
ISM 433-MHz Miniaturized Antenna Using the Shielding Box of Mobile Terminals

A miniaturized monopole antenna for ISM 433-MHz frequency band is designed, fabricated, and measured. The whole structure proposed in this letter consists of a metal shielding box for transceiver circuit, a rectangular ground for battery, and a meander-line monopole. The size of the structure is 51 × 82 mm2, and the area left of the antenna itself is just about 49 × 13 mm2. To make the antenna work in such a small area, and maintain good performance, the metal shielding box of the transceiver circuit in this work is connected to the meander-line monopole. Actually, the shielding box does not serve as a part of the ground, but a part of the antenna itself, which plays an important role in matching and increasing the antenna's electrical length. Meanwhile, no additional area is needed compared to the conventional design. The measured - 10-dB bandwidth is 5 MHz. The comparison between the traditional design and the proposed design as well as the critical parameter study is presented in this letter.
Crypto @ 1408 Bit – A New Dimension of PKI Hardware Accelerator

A new architecture for a very fast and secure public key crypto-coprocessor Crypto@1408Bit usable in Smart Card ICs is presented. The key elements of Crypto@1408Bit architecture are a very fast Look Ahead Algorithm for modular multiplication, a very fast and secure serial-parallel adder, a fast and chip area efficient carry handling and a sufficient number of working registers enabling easy programming. With this architecture a new dimension of crypto performance and security against side channel attacks is achieved. Compared to crypto-coprocessors currently available on the Smart Card IC market Crypto@1408Bit offers a performance more than an order of magnitude faster. The security of the crypto-coprocessor relies on hardware and software security features like dual-rail-security logic against differential power attacks, high secure registers for critical operands and an register length with up to 128 Bit buffer for randomization of operands.
Multiclass Spectral Clustering

We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigendecomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms. We then solve an optimal discretization problem, which seeks a discrete solution closest to the continuous optima. The discretization is efficiently computed in an iterative fashion using singular value decomposition and nonmaximum suppression. The resulting discrete solutions are nearly global-optimal. Our method is robust to random initialization and converges faster than other clustering methods. Experiments on real image segmentation are reported. Spectral graph partitioning methods have been successfully applied to circuit layout [3, 1], load balancing  and image segmentation [10, 6]. As a discriminative approach, they do not make assumptions about the global structure of data. Instead, local evidence on how likely two data points belong to the same class is first collected and a global decision is then made to divide all data points into disjunct sets according to some criterion. Often, such a criterion can be interpreted in an embedding framework, where the grouping relationships among data points are preserved as much as possible in a lower-dimensional representation. What makes spectral methods appealing is that their global-optima in the relaxed continuous domain are obtained by eigendecomposition. However, to get a discrete solution from eigenvectors often requires solving another clustering problem, albeit in a lower-dimensional space. That is, eigenvectors are treated as geometrical coordinates of a point set. Various clustering heuristics such as Kmeans [10, 9], transportation , dynamic programming , greedy pruning or exhaustive search [3, 10] are subsequently employed on the new point set to retrieve partitions. We show that there is a principled way to recover a discrete optimum. This is based on a fact that the continuous optima consist not only of the eigenvectors, but of a whole family spanned by the eigenvectors through orthonormal transforms. The goal is to find the right orthonormal transform that leads to a discretization.
The pathophysiology of retinopathy of prematurity: an update of previous and recent knowledge.

Retinopathy of prematurity (ROP) is a disease that can cause blindness in very low birthweight infants. The incidence of ROP is closely correlated with the weight and the gestational age at birth. Despite current therapies, ROP continues to be a highly debilitating disease. Our advancing knowledge of the pathogenesis of ROP has encouraged investigations into new antivasculogenic therapies. The purpose of this article is to review the findings on the pathophysiological mechanisms that contribute to the transition between the first and second phases of ROP and to investigate new potential therapies. Oxygen has been well characterized for the key role that it plays in retinal neoangiogenesis. Low or high levels of pO2 regulate the normal or abnormal production of hypoxia-inducible factor 1 and vascular endothelial growth factors (VEGF), which are the predominant regulators of retinal angiogenesis. Although low oxygen saturation appears to reduce the risk of severe ROP when carefully controlled within the first few weeks of life, the optimal level of saturation still remains uncertain. IGF-1 and Epo are fundamentally required during both phases of ROP, as alterations in their protein levels can modulate disease progression. Therefore, rhIGF-1 and rhEpo were tested for their abilities to prevent the loss of vasculature during the first phase of ROP, whereas anti-VEGF drugs were tested during the second phase. At present, previous hypotheses concerning ROP should be amended with new pathogenetic theories. Studies on the role of genetic components, nitric oxide, adenosine, apelin and β-adrenergic receptor have revealed new possibilities for the treatment of ROP. The genetic hypothesis that single-nucleotide polymorphisms within the β-ARs play an active role in the pathogenesis of ROP suggests the concept of disease prevention using β-blockers. In conclusion, all factors that can mediate the progression from the avascular to the proliferative phase might have significant implications for the further understanding and treatment of ROP.
pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.
Gallbladder ascariasis with uneventful worm migration back to the duodenum: A case report.

Ascariasis, a worldwide parasitic disease, is regarded by some authorities as the most common parasitic infection in humans. The causative organism is Ascaris lumbricoides, which normally lives in the lumen of the small intestine. From the intestine, the worm can invade the bile duct or pancreatic duct, but invasion into the gallbladder is quite rare because of the anatomical features of the cystic duct, which is narrow and tortuous. Once it enters the gallbladder, it is exceedingly rare for the worm to migrate back to the intestine. We report a case of gallbladder ascariasis with worm migration back into the intestine, in view of its rare presentation.