nils-herrmann/clean_scirepeval huggingface dataset:
Educational strategies in curriculum development: the SPICES model.

Six education strategies have been identified relating to the curriculum in a medical school. Each issue can be represented as a spectrum or continuum: student-centred/teacher-centred, problem-based/information-gathering, integrated/discipline-based, community-based/hospital-based, elective/uniform and systematic/apprenticeship-based. The factors supporting a more towards each end of the continuum are presented for each strategy. Newer schools tend to be more to the left on the continuum, established schools more to the right. Each school, however, has to decide where it stands on each issue and to establish its own profile. This SPICES model of curriculum strategy analysis can be used in curriculum planning or review, in tackling problems relating to the curriculum and in providing guidance relating to teaching methods and assessment.
Rapid Object Detection using a Boosted Cascade of Simple Features

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
A data mining framework for detecting subscription fraud in telecommunication

Service providing companies including telecommunication companies often receive substantial damage from customers’ fraudulent behaviors. One of the common types of fraud is subscription fraud in which usage type is in contradiction with subscription type. This study aimed at identifying customers’ subscription fraud by employing data mining techniques and adopting knowledge discovery process. To this end, a hybrid approach consisting of preprocessing, clustering, and classification phases was applied, and appropriate tools were employed commensurate to each phase. Specifically, in the clustering phase SOM and K-means were combined, and in the classification phase decision tree (C4.5), neural networks, and support vector machines as single classifiers and bagging, boosting, stacking, majority and consensus voting as ensembles were examined. In addition to using clustering to identify outlier cases, it was also possible – by defining new features – to maintain the results of clustering phase for the classification phase. This, in turn, contributed to better classification results. A real dataset provided by Telecommunication Company of Tehran was applied to demonstrate the effectiveness of the proposed method. The efficient use of synergy among these techniques significantly increased prediction accuracy. The performance of all single and ensemble classifiers is evaluated based on various metrics and compared by statistical tests. The results showed that support vector machines among single classifiers and boosted trees among all classifiers have the best performance in terms of various metrics. The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically. & 2010 Elsevier Ltd. All rights reserved.
Basic concepts and taxonomy of dependable and secure computing

This paper gives the main definitions relating to dependability, a generic concept including a special case of such attributes as reliability, availability, safety, integrity, maintainability, etc. Security brings in concerns for confidentiality, in addition to availability and integrity. Basic definitions are given first. They are then commented upon, and supplemented by additional definitions, which address the threats to dependability and security (faults, errors, failures), their attributes, and the means for their achievement (fault prevention, fault tolerance, fault removal, fault forecasting). The aim is to explicate a set of general concepts, of relevance across a wide range of situations and, therefore, helping communication and cooperation among a number of scientific and technical communities, including ones that are concentrating on particular types of system, of system failures, or of causes of system failures.
Curlybot: Designing a New Class of Computational Toys

We introduce an educational toy, called curlybot, as the basis for a new class of toys aimed at children in their early stages of development — ages four and up. curlybot is an autonomous two-wheeled vehicle with embedded electronics that can record how it has been moved on any flat surface and then play back that motion accurately and repeatedly. Children can use curlybot to develop intuitions for advanced mathematical and computational concepts, like differential geometry, through play away from a traditional computer.
In our preliminary studies, we found that children learn to use curlybot quickly. They readily establish an affective and body syntonic connection with curlybot, because of its ability to remember all of the intricacies of their original gesture; every pause, acceleration, and even the shaking in their hand is recorded. Programming by example in this context makes the educational ideas implicit in the design of curlybot accessible to young children.
Feature-enriched word embeddings for named entity recognition in open-domain conversations

Named entity recognition (NER) from open-domain conversation is challenging due to the informality of spoken language. Instead of increasing the size of labeled data, which is expensive and time-consuming, word embeddings learned from unlabeled data have been used by NER models to handle data sparsity. We propose a novel method for training the word embeddings specifically for the NER task. We show that our task-specific word embeddings outperform task-independent word embeddings when used as features of NER method.
On the Design and Quantification of Privacy Preserving Data Mining Algorithms

The increasing ability to track and collect large amounts of data with the use of current hardware technology has lead to an interest in the development of data mining algorithms which preserve user privacy. A recently proposed technique addresses the issue of privacy preservation by perturbing the data and reconstructing distributions at an aggregate level in order to perform the mining. This method is able to retain privacy while accessing the information implicit in the original attributes. The distribution reconstruction process naturally leads to some loss of information which is acceptable in many practical situations. This paper discusses an Expectation Maximization (EM) algorithm for distribution reconstruction which is more effective than the currently available method in terms of the level of information loss. Specifically, we prove that the EM algorithm converges to the maximum likelihood estimate of the original distribution based on the perturbed data. We show that when a large amount of data is available, the EM algorithm provides robust estimates of the original distribution. We propose metrics for quantification and measurement of privacy-preserving data mining algorithms. Thus, this paper provides the foundations for measurement of the effectiveness of privacy preserving data mining algorithms. Our privacy metrics illustrate some interesting results on the relative effectiveness of different perturbing distributions.
Accurate Linear-Time Chinese Word Segmentation via Embedding Matching

This paper proposes an embedding matching approach to Chinese word segmentation, which generalizes the traditional sequence labeling framework and takes advantage of distributed representations. The training and prediction algorithms have linear-time complexity. Based on the proposed model, a greedy segmenter is developed and evaluated on benchmark corpora. Experiments show that our greedy segmenter achieves improved results over previous neural network-based word segmenters, and its performance is competitive with state-of-the-art methods, despite its simple feature set and the absence of external resources for training.
Minimum mean square error detector for multimessage spread spectrum embedding

In this paper, a new minimum mean square error (MMSE) based detector is presented for multi-message detection in both conventional spread spectrum (SS) watermarking and improved spread spectrum (ISS) watermarking. The proposed detector yields an improved detection performance in terms of bit error rate (BER) for both SS and ISS multiple message embedding. Simulation results demonstrate that the proposed MMSE detector outperforms the conventional detector, the matched filter detector, by orders of magnitude.
Personality and patterns of Facebook usage

We show how users' activity on Facebook relates to their personality, as measured by the standard Five Factor Model. Our dataset consists of the personality profiles and Facebook profile data of 180,000 users. We examine correlations between users' personality and the properties of their Facebook profiles such as the size and density of their friendship network, number uploaded photos, number of events attended, number of group memberships, and number of times user has been tagged in photos. Our results show significant relationships between personality traits and various features of Facebook profiles. We then show how multivariate regression allows prediction of the personality traits of an individual user given their Facebook profile. The best accuracy of such predictions is achieved for Extraversion and Neuroticism, the lowest accuracy is obtained for Agreeableness, with Openness and Conscientiousness lying in the middle.
TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.
Perspectives and Visions of Computer Science Education in Primary and Secondary (K-12) Schools

In view of the recent developments in many countries, for example, in the USA and in the UK, it appears that computer science education (CSE) in primary or secondary schools (K-12) has reached a significant turning point, shifting its focus from ICT-oriented to rigorous computer science concepts. The goal of this special issue is to offer a publication platform for soundly based in-depth experiences that have been made around the world with concepts, approaches, or initiatives that aim at supporting this shift. For this purpose, the article format was kept as large as possible, enabling the authors to explain many facets of their concepts and experiences in detail. Regarding the structure of the articles, we had encouraged the authors to lean on the Darmstadt Model, a category system that was developed to support the development, improvement, and investigation of K-12 CSE across regional or national boundaries. This model could serve as a unifying framework that might provide a proper structure for a well-founded critical discussion about the future of K-12 CSE. Curriculum designers or policy stakeholders, who have to decide, which approach an upcoming national initiative should follow, could benefit from this discussion as well as researchers who are investigating K12 CSE in any regard. With this goal in mind, we have selected six extensive and two short case studies from the UK, New Zealand, USA/Israel, France, Sweden, Georgia (USA), Russia, and Italy that provide an in-depth analysis of K-12 CSE in their respective country or state.
Hybrid Metaheuristics for the Clustered Vehicle Routing Problem

The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This article presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is based on an Iterated Local Search algorithm, in which only feasible solutions are explored and problem-specific local search moves are utilized. The second algorithm is a Hybrid Genetic Search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed. Using this information, a sequence of clusters can be used as a solution representation and large neighborhoods can be efficiently explored by means of bi-directional dynamic programming, sequence concatenations, by using appropriate data structures. Extensive computational experiments are performed on benchmark instances from the literature, as well as new large scale ones. Recommendations on promising algorithm choices are provided relatively to average cluster size.
Crowds: Anonymity for Web Transactions

In this paper we introduce a system called Crowds for protecting users' anonymity on the world-wide-web. Crowds, named for the notion of “blending into a crowd,” operates by grouping users into a large and geographically diverse group (crowd) that collectively issues requests on behalf of its members. Web servers are unable to learn the true source of a request because it is equally likely to have originated from any member of the crowd, and even collaborating crowd members cannot distinguish the originator of a request from a member who is merely forwarding the request on behalf of another. We describe the design, implementation, security, performance, and scalability of our system. Our security analysis introduces degrees of anonymity as an important tool for describing and proving anonymity properties.
Flexible dual TCP/UDP streaming for H.264 HD video over WLANs

High Definition video streaming over WLANs faces many challenges because video data requires not only data integrity but also frames have strict playout deadline. Traditional streaming methods that rely solely on either UDP or TCP have difficulties meeting both requirements because UDP incurs packet loss while TCP incurs delay. This paper proposed a new streaming method called Flexible Dual-TCP/UDP Streaming Protocol (FDSP) that utilizes the benefit of both UDP and TCP. The FDSP takes advantage of the hierarchical structure of the H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit non-important elements. The proposed FDSP is implemented and validated under different wireless network conditions. Both visual quality and delay results are compared against pure-UDP and pure-TCP streaming methods. Our results show that FDSP effectively achieves a balance between delay and visual quality, thus it has advantage over traditional pure-UDP and pure-TCP methods.
PUBIC BONE FRACTURE AND DISPLACEMENT DETECTION USING X-RAY IMAGES

Diagnosing minute fractures in the cases of pubic bone injuries is crucial for speedy and successful patient treatment. Due to low resolution and dissimilar visual characteristics of fractures by their position, minute fracture detection using x-ray images can be challenging and time consuming to examine. The pubic bones are most dense, due to which visual detection of the fracture is very difficult. This paper presents a fracture detection technique for the pubic ring using Distance Regularized Level Set Evolution (DRLSE) technique for segmentation and detection of fracture using Canny Edge detector. Also examination of displacement of the pubic bone is done using Gray Level Co-occurrence Matrix (GLCM). Results so far have been positive and will be helpful in guiding the physicians and for better treatment of the patients.
Discriminating Gender on Twitter

Accurate prediction of demographic attributes from social media and other informal online content is valuable for marketing, personalization, and legal investigation. This paper describes the construction of a large, multilingual dataset labeled with gender, and investigates statistical models for determining the gender of uncharacterized Twitter users. We explore several different classifier types on this dataset. We show the degree to which classifier accuracy varies based on tweet volumes as well as when various kinds of profile metadata are included in the models. We also perform a large-scale human assessment using Amazon Mechanical Turk. Our methods significantly out-perform both baseline models and almost all humans on the same task.
Review and Comparative Study of Clustering Techniques

Clustering is an automatic learning technique which aims at grouping a set of objects into clusters so that objects in the same clusters should be similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in other clusters. Document clustering aims to group in an unsupervised way, a given document set into clusters such that documents within each clusters are more similar between each other than those in different clusters. Cluster analysis aims to organize a collection of patterns into clusters based on similarity. This paper focuses on survey of various clustering techniques. These techniques can be divided into several categories: Partitional algorithms, Hierarchical algorithms, Density based, and comparison of various algorithm is surveyed and shows how Hierarchical Clustering can be better than other techniques.
Cyber attack modeling and simulation for network security analysis

Cyber security methods are continually being developed. To test these methods many organizations utilize both virtual and physical networks which can be costly and time consuming. As an alternative, in this paper, we present a simulation modeling approach to represent computer networks and intrusion detection systems (IDS) to efficiently simulate cyber attack scenarios. The outcome of the simulation model is a set of IDS alerts that can be used to test and evaluate cyber security systems. In particular, the simulation methodology is designed to test information fusion systems for cyber security that are under development.
Output constraint control on path following of four-wheel independently actuated autonomous vehicles

This paper investigates the path following problem for four-wheel independently actuated (FWIA) autonomous vehicles (AVs). A novel output constraint controller is proposed to deal with the lateral offset control in path following and maintain the vehicle lateral stability in the presence of tire sliding effects. The innovation of this work includes the following two aspects: (1) A novel output constraint control strategy, hyperbolic projection method, is proposed to strictly bound the lateral offset to prevent vehicle transgressing the safety bound in the path following; (2) A robust LQR controller, is adopted to obtain the optimal active front steering (AFS) and direct yaw moment (DYC) control inputs, with the vehicle stability consideration, and to eliminate the parameter uncertainties and external disturbances. CarSim-Simulink joint simulation results indicate that the proposed controller can compactly bound the lateral offset to avoid transgressing the safe boundary during path following process, especially in extreme driving condition, in presence of tire sliding effects, system uncertainties and disturbances.
Semantic integration of UML class diagram with semantic validation on segments of mappings

Recently, attention has focused on the software development, specially by different teams that are geographically distant to support collaborative work. Management, description and modeling in such collaborative approach are through several tools and techniques based on UML models. It is now supported by a large number of tools. Most of these systems have the ability to compare different UML models, assist developers, designers and also provide operations for the merging and integration, to produce a coherent model. The contribution in this article is both to integrate a set of UML class diagrams using mappings that are result of alignment and assist designers and developers in the integration. In addition, we will present a detail integration of UML models with the validation of mappings between them. Such validation helps to achieve correct, consistent and coherent integrated model.
JMASM Algorithms and Code Pseudo-Random Number Generators for Vector Processors and Multicore Processors

There is a lack of good pseudo-random number generators capable of utilizing the vector processing capabilities and multiprocessing capabilities of modern computers. A suitable generator must have a feedback path long enough to fit the vector length or permit multiple instances with different parameter sets. The risk that multiple random streams from identical generators have overlapping subsequences can be eliminated by combining two different generators. Combining two generators can also increase randomness by remedying weaknesses caused by the need for mathematical tractability. Larger applications need higher precision. The article discusses hitherto ignored imprecisions caused by quantization errors in the application of generators with prime modulus and when generating uniformly distributed integers with an arbitrary interval length. A C++ software package that overcomes all these problems is offered. The RANVEC1 code combines a Mersenne Twister variant and a multiply-with-carry generator to produce vector output. It is suitable for processors with vector sizes up to 512 bits. Some theorists have argued that there is no theoretical proof that the streams from different generators are statistically independent. The article contends that the request for such a proof misunderstands the nature of the problem, and that the mathematical tractability that would allow such a proof would also defeat it. This calls for a more fundamental philosophical discussion of the status of proofs in relation to deterministic pseudo-random sequences.