nils-herrmann/clean_scirepeval huggingface dataset:
DENIAL OF SERVICE ATTACKS : ANALOGY BETWEEN INTERNET AND MANET

Distributed Denial of Service (DDoS) attacks have become a large problem for users of computer systems connected to the Internet. DDoS attackers hijack secondary victim systems using them to wage a coordinated large-scale attack against primary victim systems. With little or no advance warning, a DDoS attack can easily exhaust the computing and communication resources of its victim within a short period of time. This survey attempts a comprehensive scoping of the DDoS problem for both Internet and Non-Internet. It highlights the existing and new taxonomies to categorize DDoS attack networks, to classify the different techniques used in a DDoS attack. These taxonomies help us to understand the similarities and differences in DDoS attacks and the scope of the DDoS problem. In context to the survey, we present a striking analogy between different Ddos attacks in both internet and Non-Internet. Finally, this work is intended to stimulate research into creative, effective and efficient detection mechanisms for DDoS attacks, and to assist in creating comprehensive solutions that will provide a more generalized and effective approach in countering both known and derivative DdoS attacks on different types of network.
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a “model reconciliation problem” (MRP), where the AI system in effect suggests changes to the human’s model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes.
Overhead line fault detection using GSM technology

In this paper, a novel technique for the fault detection, classification & protection of transmission lines is proposed. The proposed system uses different protective equipment's, voltage sense section, microcontroller section, LED display section & GSM (global system for mobile communication) module. The faults like all series (1LO, 2LO, 3LO etc.) & shunt faults (L-G, L-L, L-L-G, L-L-L, L-L-L-G) get detected & classified according to characteristics condition of current & voltage at the occurrence of fault in the three phase overhead lines. The sensed signals are given to microcontroller for detection & classification of faults. Also wireless mobile communication technique i.e. GSM is used simultaneously to send message to responsible person on mobile. Type of fault gets displayed on fault display section. Simultaneously fault gets interrupted using protective devices. (fuse, circuit breakers etc.
2-Bit Magnitude Comparator Design Using Different Logic Styles

2-bit magnitude comparator design using different logic styles is proposed in this brief. Comparison is most basic arithmetic operation that determines if one number is greater than, equal to, or less than the other number. Comparator is most fundamental component that performs comparison operation. This brief presents comparison between different logic styles used to design 2-Bit magnitude comparator. Comparison between different designs is calculated by simulation that is performed at 90nm technology in Tanner EDA Tool.
Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R

Item response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch modeling) for computing Rasch models and several extensions. A main characteristic of some IRT models, the Rasch model being the most prominent, concerns the separation of two kinds of parameters, one that describes qualities of the subject under investigation, and the other relates to qualities of the situation under which the response of a subject is observed. Using conditional maximum likelihood (CML) estimation both types of parameters may be estimated independently from each other. IRT models are well suited to cope with dichotomous and polytomous responses, where the response categories may be unordered as well as ordered. The incorporation of linear structures allows for modeling the effects of covariates and enables the analysis of repeated categorical measurements. The eRm package fits the following models: the Rasch model, the rating scale model (RSM), and the partial credit model (PCM) as well as linear reparameterizations through covariate structures like the linear logistic test model (LLTM), the linear rating scale model (LRSM), and the linear partial credit model (LPCM). We use an unitary, efficient CML approach to estimate the item parameters and their standard errors. Graphical and numeric tools for assessing goodness-of-fit are provided.
Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique

Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood vessel. First, optic disc and blood vessel pixels are detected from blue plane of the image. Then, using OD location the vessel pixels are connected. The detection scheme utilizes basic operations like edge detection, binary thresholding and morphological operation. This method was evaluated on standard retinal image databases, such as STARE and DRIVE. Experimental results demonstrate that the high accuracy achieved by the proposed method is comparable to that reported by the most accurate methods in literature, alongside a substantial reduction of execution time. Thus the method may provide a reliable solution in automatic mass screening and diagnosis of the retinal diseases.
Opening black box Data Mining models using Sensitivity Analysis

There are several supervised learning Data Mining (DM) methods, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, that often attain high quality predictions, although the obtained models are difficult to interpret by humans. In this paper, we open these black box DM models by using a novel visualization approach that is based on a Sensitivity Analysis (SA) method. In particular, we propose a Global SA (GSA), which extends the applicability of previous SA methods (e.g. to classification tasks), and several visualization techniques (e.g. variable effect characteristic curve), for assessing input relevance and effects on the model's responses. We show the GSA capabilities by conducting several experiments, using a NN ensemble and SVM model, in both synthetic and real-world datasets.
The Zachman Framework and the OMG''s Model Driven Architecture

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TAILOR: A Record Linkage Tool Box

Data cleaning is a vital process that ensures the quality of data stored in real-world databases. Data cleaning problems are frequently encountered in many research areas, such as knowledge discovery in databases, data warehousing, system integration and e-services. The process of identifying the record pairs that represent the same entity (duplicate records), commonly known as record linkage, is one of the essential elements of data cleaning. In this paper, we address the record linkage problem by adopting a machine learning approach. Three models are proposed and are analyzed empirically. Since no existing model, including those proposed in this paper, has been proved to be superior, we have developed an interactive Record Linkage Toolbox named TAILOR. Users of TAILOR can build their own record linkage models by tuning system parameters and by plugging in in-house developed and public domain tools. The proposed toolbox serves as a framework for the record linkage process, and is designed in an extensible way to interface with existing and future record linkage models. We have conducted an extensive experimental study to evaluate our proposed models using not only synthetic but also real data. Results show that the proposed machine learning record linkage models outperform the existing ones both in accuracy and in performance.
A Haptic Tool for Group Work on Geometrical Concepts Engaging Blind and Sighted Pupils

In the study presented here, two haptic and visual applications for learning geometrical concepts in group work in primary school have been designed and evaluated. The aim was to support collaborative learning among sighted and visually impaired pupils. The first application is a static flattened 3D environment that supports learning to distinguish between angles by means of a 3D haptic device providing touch feedback. The second application is a dynamic 3D environment that supports learning of spatial geometry. The scene is a room with a box containing geometrical objects, which pupils can pick up and move around. The applications were evaluated in four schools with groups of two sighted and one visually impaired pupil. The results showed the support for the visually impaired pupil and for the collaboration to be satisfying. A shared understanding of the workspace could be achieved, as long as the virtual environment did not contain movable objects. Verbal communication was crucial for the work process but haptic guiding to some extent substituted communication about direction. When it comes to joint action between visually impaired and sighted pupils a number of interesting problems were identified when the dynamic and static virtual environments were compared. These problems require further investigation. The study extends prior work in the areas of assistive technology and multimodal communication by evaluating functions for joint haptic manipulation in the unique setting of group work in primary school.
A Literature Survey of Benchmark Functions For Global Optimization Problems

Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.
Adiposity and cancer risk: new mechanistic insights from epidemiology

Excess body adiposity, commonly expressed as body mass index (BMI), is a risk factor for many common adult cancers. Over the past decade, epidemiological data have shown that adiposity–cancer risk associations are specific for gender, site, geographical population, histological subtype and molecular phenotype. The biological mechanisms underpinning these associations are incompletely understood but need to take account of the specificities observed in epidemiology to better inform future prevention strategies.
Automatic Layer Separation using Light Field Imaging

We propose a novel approach that jointly removes reflection or translucent layer from a scene and estimates scene depth. The input data are captured via light field imaging. The problem is couched as minimizing the rank of the transmitted scene layer via Robust Principle Component Analysis (RPCA). We also impose regularization based on piecewise smoothness, gradient sparsity, and layer independence to simultaneously recover 3D geometry of the transmitted layer. Experimental results on synthetic and real data show that our technique is robust and reliable, and can handle a broad range of challenging layer separation problems.
Evidence of Online Shopping : A Consumer Perspective

Although electronic commerce (e-retailing) has received considerable research attentions these years, little research has examined the effect of e-commerce's Web presentation on retail customer shopping experiences for Jordanian online retail shoppers. Five factors were employed to define their effect on the purchase decision through internet. Questionnaire used to collect data, and distributed to a stratified sample of 215 respondents. Data for 181 questionnaires were valid and analyzed by using descriptive and analytical statistics. We found that promotion (r = 0.344) and security (r = 0.316) are the most influential factors on consumer purchase decision through internet while eprice(r=0.028) was not related significantly with the consumer purchasing decision. The Infrastructure for internet (r = 0.175) and product and service characteristics (r = 0.264) were moderately related to the consumer’ decision process.
End-to-End Learning of Task-Oriented Dialogs

In this thesis proposal, we address the limitations of conventional pipeline design of taskoriented dialog systems and propose end-toend learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learningfrom-simulation approaches to improve the online interactive learning efficiency.
Canine responses to hypoglycemia in patients with type 1 diabetes.

Anecdotal evidence suggests that domestic dogs may be able to detect hypoglycemia in their human caregivers; scientific investigation of this phenomenon, however, is sorely lacking. This study thus aimed to investigate how pet dogs respond to the hypoglycemic episodes of their owners with type 1 diabetes.



Two hundred and twelve dog owners (64.2% female) with medically diagnosed type 1 diabetes participated in the study. All participants owned at least 1 dog. Each person completed a purpose-designed questionnaire developed to collect information on their dogs' responses (if any) to their hypoglycemic episodes.



One hundred and thirty-eight (65.1%) respondents indicated that their dog had shown a behavioral reaction to at least one of their hypoglycemic episodes, with 31.9% of animals reacting to 11 or more events. Canine sex, age, breed status, and length of pet ownership were unrelated to hypoglycemia-response likelihood. Thirty-six percent of the sample believed that their dogs reacted most of the times they went "low"; 33.6% indicated that their pets reacted before they themselves were aware they were hypoglycemic. Dogs' behavioral responses to their owners' hypoglycemic episodes varied. Most animals behaved in a manner suggestive of attracting their owners' attention, for example, vocalizing (61.5%), licking them (49.2%), nuzzling them (40.6%), jumping on top of them (30.4%), and/or staring intently at their faces (41.3%). A smaller proportion showed behavioral responses suggestive of fear, including trembling (7.2%), running away from the owner (5.1%), and/or hyperventilating (2.2%).



The findings suggest that behavioral reactions to hypoglycemic episodes in pet owners with type 1 diabetes commonly occur in untrained dogs. Further research is now needed to elucidate the mechanism(s) that dogs use to perform this feat.
Using machine learning for software aging detection in Android system

Software aging is a common experience in Android operating system, as the gradual performance degradation is usually complained by the users. However, the mathematical modelling and detection of such experience is still an emerging issue due to the complexity and relatively young age of Android. This paper applies and compares three machine learning algorithms, namely decision tree, Support Vector Machine (SVM), and Deep Belief Network (DBn), for the detection of software aging in Android. In addition to the traditional aging indicator of launch time (LT), this paper also investigates the effectiveness of page fault (PF) and multiple labels (i.e., the combination of LT and PF). Experimental results show that the accuracy of DBN is comparable to decision tree and SVM when the data volume increases to 5000, which means DBN and other similar algorithms suitable for high dimensional and large data may also play a role in software aging. The results also reveal that PF is a little more stable than LT in terms of variance of accuracy, and it can also be used a good indicator for aging of Android. However, the performance of multiple labels is not improved in our experiments. The observations of this paper are supposed to assist further analysis of software aging in Android and the design of corresponding rejuvenation methods.
A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement

Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy.
Design and fabrication of a lightweight additive-manufactured Ka-band horn antenna array

In this paper a 2×2 Ka-band lightweight horn antenna array demonstrator is designed and fabricated by means of an additive manufacturing process optimized for complex RF, microwave and mm-wave antenna components. The proposed waveguide feeding network aims to exploit the flexibility offered by this novel fabrication technique and can be easily translated to antenna arrays with more elements. The electromagnetic performance of the featured structure is validated by means of full-wave numerical simulations. The horn antenna array is fabricated and under experimental RF characterization.