nils-herrmann/clean_scirepeval huggingface dataset:
Towards An Information Security Maturity Model for Secure e-Government Services: A Stakeholders View

The paper proposes a comprehensive information security maturity model (ISMM) that addresses both technical and socio/non-technical security aspects. The model is intended for securing e-government services (implementation and service delivery) in an emerging and increasing security risk environment. The paper utilizes extensive literature review and survey study approaches. A total of eight existing ISMMs were selected and critically analyzed. Models were then categorized into security awareness, evaluation and management orientations. Based on the model’s strengths – three models were selected to undergo further analyses and then synthesized. Each of the three selected models was either from the security awareness, evaluation or management orientations category. To affirm the findings – a survey study was conducted into six government organizations located in Tanzania. The study was structured to a large extent by the security controls adopted from the Security By Consensus (SBC) model. Finally, an ISMM with five critical maturity levels was proposed. The maturity levels were: undefined, defined, managed, controlled and optimized. The papers main contribution is the proposed model that addresses both technical and non-technical security services within the critical maturity levels. Additionally, the paper enhances awareness and understanding on the needs for security in e-government services to stakeholders.
An introduction to the five-factor model and its applications.

The five-factor model of personality is a hierarchical organization of personality traits in terms of five basic dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Research using both natural language adjectives and theoretically based personality questionnaires supports the comprehensiveness of the model and its applicability across observers and cultures. This article summarizes the history of the model and its supporting evidence; discusses conceptions of the nature of the factors; and outlines an agenda for theorizing about the origins and operation of the factors. We argue that the model should prove useful both for individual assessment and for the elucidation of a number of topics of interest to personality psychologists.
Design of an IoT based autonomous vehicle with the aid of computer vision

A web controlled and partially autonomous vehicle system is presented in this paper. It highlights the idea to develop a remote controlled car which can be driven from anywhere using internet over a secured server. This car will also have limited automation features like traffic light detection, obstacle avoidance system and lane detection system so that it can drive itself safely in case of connectivity failure. The main goal here is to minimize the risk of human life and ensure highest safety during driving. At the same time the car will assure comfort and convenience to the controller. A miniature car including the above features has been developed which showed optimum performance in a simulated environment. The system mainly consists of a Raspberry Pi, an Arduino, a Picamera, a sonar module, a web interface and internet modem. The Raspberry Pi was mainly used for the Computer Vision algorithms and for streaming video through internet. The proposed system is very cheap and very efficient in terms of automation.
Culture, personality, and subjective well-being: integrating process models of life satisfaction.

The authors examined the interplay of personality and cultural factors in the prediction of the affective (hedonic balance) and the cognitive (life satisfaction) components of subjective well-being (SWB). They predicted that the influence of personality on life satisfaction is mediated by hedonic balance and that the relation between hedonic balance and life satisfaction is moderated by culture. As a consequence, they predicted that the influence of personality on life satisfaction is also moderated by culture. Participants from 2 individualistic cultures (United States, Germany) and 3 collectivistic cultures (Japan, Mexico, Ghana) completed measures of Extraversion, Neuroticism, hedonic balance, and life satisfaction. As predicted, Extraversion and Neuroticism influenced hedonic balance to the same degree in all cultures, and hedonic balance was a stronger predictor of life satisfaction in individualistic than in collectivistic cultures. The influence of Extraversion and Neuroticism on life satisfaction was largely mediated by hedonic balance. The results suggest that the influence of personality on the emotional component of SWB is pancultural, whereas the influence of personality on the cognitive component of SWB is moderated by culture.
Geography of Twitter networks

We use a sample of publicly available data on Twitter to study networks of mostly weak asymmetric ties. We show that a substantial share of ties lie within the same metropolitan region. As we examine ties between regional clusters, we find that distance, national borders and the difference in languages all affect the pattern of ties. However, Twitter connections show the more substantial correlation with the network of airline flights, highlighting the importance of looking not just at distance but at pre-existing ties between places.
The Effects of a Ketogenic Diet on Exercise Metabolism and Physical Performance in Off-Road Cyclists

The main objective of this research was to determine the effects of a long-term ketogenic diet, rich in polyunsaturated fatty acids, on aerobic performance and exercise metabolism in off-road cyclists. Additionally, the effects of this diet on body mass and body composition were evaluated, as well as those that occurred in the lipid and lipoprotein profiles due to the dietary intervention. The research material included eight male subjects, aged 28.3 ± 3.9 years, with at least five years of training experience that competed in off-road cycling. Each cyclist performed a continuous exercise protocol on a cycloergometer with varied intensity, after a mixed and ketogenic diet in a crossover design. The ketogenic diet stimulated favorable changes in body mass and body composition, as well as in the lipid and lipoprotein profiles. Important findings of the present study include a significant increase in the relative values of maximal oxygen uptake (VO2max) and oxygen uptake at lactate threshold (VO2 LT) after the ketogenic diet, which can be explained by reductions in body mass and fat mass and/or the greater oxygen uptake necessary to obtain the same energy yield as on a mixed diet, due to increased fat oxidation or by enhanced sympathetic activation. The max work load and the work load at lactate threshold were significantly higher after the mixed diet. The values of the respiratory exchange ratio (RER) were significantly lower at rest and during particular stages of the exercise protocol following the ketogenic diet. The heart rate (HR) and oxygen uptake were significantly higher at rest and during the first three stages of exercise after the ketogenic diet, while the reverse was true during the last stage of the exercise protocol conducted with maximal intensity. Creatine kinase (CK) and lactate dehydrogenase (LDH) activity were significantly lower at rest and during particular stages of the 105-min exercise protocol following the low carbohydrate ketogenic diet. The alterations in insulin and cortisol concentrations due to the dietary intervention confirm the concept that the glucostatic mechanism controls the hormonal and metabolic responses to exercise.
SIFT Hardware Implementation for Real-Time Image Feature Extraction

This paper introduces a high-speed all-hardware scale-invariant feature transform (SIFT) architecture with parallel and pipeline technology for real-time extraction of image features. The task-level parallel and pipeline structure are exploited between the hardware blocks, and the data-level parallel and pipeline architecture are exploited inside each block. Two identical random access memories are adopted with ping-pong operation to execute the key point detection module and the descriptor generation module in task-level parallelism. With speeding up the key point detection module of SIFT, the descriptor generation module has become the bottleneck of the system's performance; therefore, this paper proposes an optimized descriptor generation algorithm. A novel window-dividing method is proposed with square subregions arranged in 16 directions, and the descriptors are generated by reordering the histogram instead of window rotation. Therefore, the main orientation detection block and descriptor generation block run in parallel instead of interactively. With the optimized algorithm cooperating with pipeline structure inside each block, we not only improve the parallelism of the algorithm, but also avoid floating data calculation to save hardware consumption. Thus, the descriptor generation module leads the speed almost 15 times faster than a recent solution. The proposed system was implemented on field programmable gate array and the overall time to extract SIFT features for an image having 512×512 pixels is only 6.55 ms (sufficient for real-time applications), and the number of feature points can reach up to 2900.
Interactive Organ Segmentation Using Graph Cuts

An N-dimensional image is divided into “object” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds providing necessary clues about the image content. Our objective is to find the cheapest way to cut the edges in the graph so that the object seeds are completely separated from the background seeds. If the edge cost is a decreasing function of the local intensity gradient then the minimum cost cut should produce an object/background segmentation with compact boundaries along the high intensity gradient values in the image. An efficient, globally optimal solution is possible via standard min-cut/max-flow algorithms for graphs with two terminals. We applied this technique to interactively segment organs in various 2D and 3D medical images.
Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach

The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note.



We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets.



The convolutional recurrent neural network with neural word embeddings trained-medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier using hybrid bag-of-words and clinically relevant UMLS concepts as the feature representation, with term frequency-inverse document frequency (tf-idf)-weighting, outperformed other shallow learning classifiers on iDASH and MGH datasets with AUC of 0.957 and 0.964, and F1 scores of 0.932 and 0.934 respectively. We trained classifiers on one dataset, applied to the other dataset and yielded the threshold of F1 score of 0.7 in classifiers for half of the medical subdomains we studied.



Our study shows that a supervised learning-based NLP approach is useful to develop medical subdomain classifiers. The deep learning algorithm with distributed word representation yields better performance yet shallow learning algorithms with the word and concept representation achieves comparable performance with better clinical interpretability. Portable classifiers may also be used across datasets from different institutions.
CARLsim 3: A user-friendly and highly optimized library for the creation of neurobiologically detailed spiking neural networks

Spiking neural network (SNN) models describe key aspects of neural function in a computationally efficient manner and have been used to construct large-scale brain models. Large-scale SNNs are challenging to implement, as they demand high-bandwidth communication, a large amount of memory, and are computationally intensive. Additionally, tuning parameters of these models becomes more difficult and time-consuming with the addition of biologically accurate descriptions. To meet these challenges, we have developed CARLsim 3, a user-friendly, GPU-accelerated SNN library written in C/C++ that is capable of simulating biologically detailed neural models. The present release of CARLsim provides a number of improvements over our prior SNN library to allow the user to easily analyze simulation data, explore synaptic plasticity rules, and automate parameter tuning. In the present paper, we provide examples and performance benchmarks highlighting the library's features.
Volume electron microscopy for neuronal circuit reconstruction

The last decade has seen a rapid increase in the number of tools to acquire volume electron microscopy (EM) data. Several new scanning EM (SEM) imaging methods have emerged, and classical transmission EM (TEM) methods are being scaled up and automated. Here we summarize the new methods for acquiring large EM volumes, and discuss the tradeoffs in terms of resolution, acquisition speed, and reliability. We then assess each method's applicability to the problem of reconstructing anatomical connectivity between neurons, considering both the current capabilities and future prospects of the method. Finally, we argue that neuronal 'wiring diagrams' are likely necessary, but not sufficient, to understand the operation of most neuronal circuits: volume EM imaging will likely find its best application in combination with other methods in neuroscience, such as molecular biology, optogenetics, and physiology.
A user's guide to support vector machines.

The Support Vector Machine (SVM) is a widely used classifier in bioinformatics. Obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. We provide the user with a basic understanding of the theory behind SVMs and focus on their use in practice. We describe the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.
Weighted Convolutional Neural Network Ensemble

We introduce a new method to combine the output probabilities of convolutional neural networks which we call Weighted Convolutional Neural Network Ensemble. Each network has an associated weight that makes networks with better performance have a greater influence at the time to classify in relation to networks that performed worse. This new approach produces better results than the common method that combines the networks doing just the average of the output probabilities to make the predictions. We show the validity of our proposal by improving the classification rate on a common image classification benchmark.
T-Fuzz: Fuzzing by Program Transformation

Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep execution paths of the program because the randomly generated inputs fail complex sanity checks, e.g., checks on magic values, checksums, or hashes. To improve coverage, existing approaches rely on imprecise heuristics or complex input mutation techniques (e.g., symbolic execution or taint analysis) to bypass sanity checks. Our novel method tackles coverage from a different angle: by removing sanity checks in the target program. T-Fuzz leverages a coverage-guided fuzzer to generate inputs. Whenever the fuzzer can no longer trigger new code paths, a light-weight, dynamic tracing based technique detects the input checks that the fuzzer-generated inputs fail. These checks are then removed from the target program. Fuzzing then continues on the transformed program, allowing the code protected by the removed checks to be triggered and potential bugs discovered. Fuzzing transformed programs to find bugs poses two challenges: (1) removal of checks leads to over-approximation and false positives, and (2) even for true bugs, the crashing input on the transformed program may not trigger the bug in the original program. As an auxiliary post-processing step, T-Fuzz leverages a symbolic execution-based approach to filter out false positives and reproduce true bugs in the original program. By transforming the program as well as mutating the input, T-Fuzz covers more code and finds more true bugs than any existing technique. We have evaluated T-Fuzz on the DARPA Cyber Grand Challenge dataset, LAVA-M dataset and 4 real-world programs (pngfix, tiffinfo, magick and pdftohtml). For the CGC dataset, T-Fuzz finds bugs in 166 binaries, Driller in 121, and AFL in 105. In addition, found 3 new bugs in previously-fuzzed programs and libraries.
Evaluating the accuracy of wearable heart rate monitors

Heart rate monitors have become a popular tool, which have been built in many different wearable devices, such as Smart Watches and Chest Straps. These wearable monitoring of heart rate (HR) during exercises have given the user some understanding of the heart rate variations. Even though this is not 100% accurate as electrocardiogram (ECG), its being very popular tool for consumers. The technology uses in smart watches is photoplethysmography (PPG) signal and Chest Straps use ECG signal. The main aim of this paper is to compare two wearable devices to monitor heart rate. 50 healthy volunteers participated in this study by completing three different exercises sitting, walking and jogging. The experiment results illustrates that it's harder to get a unique percentage for the variation between the readings of these two devices, however in normal condition both the devices read almost the similar value, but during the exercises it varies and can be with a maximum of 10% variation in the readings. Overall it appears that chest strap utilizing ECG signal appears to be a more accurate than the Apple Watch which utilizes PPG for monitoring heart rates while exercising.
2 How to Make Cognitive Illusions Disappear : Beyond “ Heuristics and Biases ”

Most so-called “errors” in probabilistic reasoning are in fact not violations of probability theory. Examples of such “errors” include overconfi dence bias, conjunction fallacy, and base-rate neglect. Researchers have relied on a very narrow normative view, and have ignored conceptual distinctions—for example, single case versus relative frequency—fundamental to probability theory. By recognizing and using these distinctions, however, we can make apparently stable “errors” disappear, reappear, or even invert. I suggest what a reformed understanding of judgments under uncertainty might look like.
Ant colonies for the traveling salesman problem

We describe an artificial ant colony capable of solving the traveling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks, and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.
Climate of scepticism : US newspaper coverage of the science of climate change

This two-part study integrates a quantitative review of one year of US newspaper coverage of climate science with a qualitative, comparative analysis of media-created themes and frames using a social constructivist approach. In addition to an examination of newspaper articles, this paper includes a reflexive comparison with attendant wire stories and scientific texts. Special attention is given to articles constructed with and framed by rhetoric emphasising uncertainty, controversy, and climate scepticism. r 2005
Multi-kernel Correlation Filter for Visual Tracking

Correlation filter based trackers are ranked top in terms of performances. Nevertheless, they only employ a single kernel at a time. In this paper, we will derive a multi-kernel correlation filter (MKCF) based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features to further improve the performance. Moreover, it may easily introduce location and representation errors to search several discrete scales for the proper one of the object bounding box, because normally the discrete candidate scales are determined and the corresponding feature pyramid are generated ahead of searching. In this paper, we will propose a novel and efficient scale estimation method based on optimal bisection search and fast evaluation of features. Our scale estimation method is the first one that uses the truly minimal number of layers of feature pyramid and avoids constructing the pyramid before searching for proper scales.
Dynamically Adaptive Multipath Routing based on AODV

Mobile ad hoc networks are typically characterized by high mobility and frequent link failures that result in low throughput and high end-to-end delay. To reduce the number of route discoveries due to such broken paths, multipath routing can be utilized so that alternate paths are available. Current approaches to multipath routing make use of pre-computed routes determined during route discovery. These solutions, however, suffer during high mobility because the alternate paths are not actively maintained. Hence, precisely when needed, the routes are often broken. To overcome this problem, we present an adaptive multipath solution. In this approach, multiple paths are formed during the route discovery process. All the paths are maintained by means of periodic update packets unicast along each path. These update packets measure the signal strength of each hop along the alternate paths. At any point of time, only the path with the strongest signal strength is used for data transmission. In this paper, we present two variations of our protocol and evaluate both with respect to two previously published multipath routing protocols. Simulation results show that the proposed solutions result in significant performance improvement.