nils-herrmann/clean_scirepeval huggingface dataset:
An evaluation of lightweight classification methods for identifying malicious URLs

Recent research has shown that it is possible to identify malicious URLs through lexical analysis of their URL structures alone. This paper intends to explore the effectiveness of these lightweight classification algorithms when working with large real world datasets including lists of malicious URLs obtained from Phishtank as well as largely filtered benign URLs obtained from proxy traffic logs. Lightweight algorithms are defined as methods by which URLs are analysed that do not use external sources of information such as WHOIS lookups, blacklist lookups and content analysis. These parameters include URL length, number of delimiters as well as the number of traversals through the directory structure and are used throughout much of the research in the paradigm of lightweight classification. Methods which include external sources of information are often called fully featured classifications and have been shown to be only slightly more effective than a purely lexical analysis when considering both false-positives and false-negatives. This distinction allows these algorithms to be run client side without the introduction of additional latency, but still providing a high level of accuracy through the use of modern techniques in training classifiers. Analysis of this type will also be useful in an incident response analysis where large numbers of URLs need to be filtered for potentially malicious URLs as an initial step in information gathering as well as end user implementations such as browser extensions which could help protect the user from following potentially malicious links. Both AROW and CW classifier update methods will be used as prototype implementations and their effectiveness will be compared to fully featured analysis results. These methods are interesting because they are able to train on any labelled data, including instances in which their prediction is correct, allowing them to build a confidence in specific lexical features. This makes it possible for them to be trained using noisy input data, making them ideal for real world applications such as link filtering and information gathering.
Tweets and Votes: A Study of the 2011 Singapore General Election

This study focuses on the uses of Twitter during the elections, examining whether the messages posted online are reflective of the climate of public opinion. Using Twitter data obtained during the official campaign period of the 2011 Singapore General Election, we test the predictive power of tweets in forecasting the election results. In line with some previous studies, we find that during the elections the Twitter sphere represents a rich source of data for gauging public opinion and that the frequency of tweets mentioning names of political parties, political candidates and contested constituencies could be used to make predictions about the share of votes at the national level, although the accuracy of the predictions was significantly lower that in the studies done in Germany and the UK. At the level of constituency the predictive power of tweets was much weaker, although still better than chance. The findings suggest that the context in which the elections take place also matters, and that issues like media freedoms, competitiveness of the elections and specifics of the electoral system may lead to certain over- and under-estimations of voting sentiment. The implications for future research are discussed.
Multi-label Classification Using Ensembles of Pruned Sets

This paper presents a pruned sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods.
Stateful Intrusion Detection for High-Speed Networks

As networks become faster there is an emerging need for security analysis techniques that can keep up with the increased network throughput. Existing network-based intrusion detection sensors can barely keep up with bandwidths of a few hundred Mbps. Analysis tools that can deal with higher throughput are unable to maintain state between different steps of an attack or they are limited to the analysis of packet headers. We propose a partitioning approach to network security analysis that supports in-depth, stateful intrusion detection on high-speed links. The approach is centered around a slicing mechanism that divides the overall network traffic into subsets of manageable size. The traffic partitioning is done so that a single slice contains all the evidence necessary to detect a specific attack, making sensorto-sensor interactions unnecessary. This paper describes the approach and presents a first experimental evaluation of its effectiveness.
WatsonPaths: Scenario-Based Question Answering and Inference over Unstructured Information

ural language questions as input and produces precise answers along with accurate confidences as output (Ferrucci et al. 2010). In 2011, in a modified version of the quiz show Jeopardy!, Watson defeated two of the best human players. Jeopardy! questions are usually factoid questions — the answer and supporting evidence are usually stated explicitly in some document in the corpus. While in practice we may retrieve multiple redundant documents, in principle the answer could be expressed succinctly in one. The main challenges for a factoid question-answering system are retrieving the correct document, and then extracting the correct answer from the document. At the core of Watson’s question answering is a suite of algorithms that match passages containing candidate answers to the original question. These algorithms have been described in a series of articles (Chu-Carroll et al. 2012; Ferrucci 2012; Gondek et al. 2012; Lally et al. 2012; McCord, Murdock, and Boguraev 2012; Murdock et al. 2012a; 2012b). In some important applications, however, questions do not have this “factoid” character. Consider the Articles
Outlier Detection for High Dimensional Data

The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimensional domains in which the data can contain hundreds of dimensions. Many recent algorithms use concepts of proximity in order to find outliers based on their relationship to the rest of the data. However, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective of proximity-based definitions. Consequently, for high dimensional data, the notion of finding meaningful outliers becomes substantially more complex and non-obvious. In this paper, we discuss new techniques for outlier detection which find the outliers by studying the behavior of projections from the data set.
Wound repair and regeneration

The repair of wounds is one of the most complex biological processes that occur during human life. After an injury, multiple biological pathways immediately become activated and are synchronized to respond. In human adults, the wound repair process commonly leads to a non-functioning mass of fibrotic tissue known as a scar. By contrast, early in gestation, injured fetal tissues can be completely recreated, without fibrosis, in a process resembling regeneration. Some organisms, however, retain the ability to regenerate tissue throughout adult life. Knowledge gained from studying such organisms might help to unlock latent regenerative pathways in humans, which would change medical practice as much as the introduction of antibiotics did in the twentieth century.
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

We present a new tool for training neural network language mo dels (NNLMs), scoring sentences, and generating text. The to ol has been written using Python library Theano, which allows r esearcher to easily extend it and tune any aspect of the traini ng process. Regardless of the flexibility, Theano is able to gen erate extremely fast native code that can utilize a GPU or multi ple CPU cores in order to parallelize the heavy numerical com putations. The tool has been evaluated in difficult Finnish a nd English conversational speech recognition tasks, and sign ifica t improvement was obtained over our best back-off n-gram models. The results that we obtained in the Finnish task were com pared to those from existing RNNLM and RWTHLM toolkits, and found to be as good or better, while training times were an order of magnitude shorter.
Randomized instruction set emulation

Injecting binary code into a running program is a common form of attack. Most defenses employ a “guard the doors” approach, blocking known mechanisms of code injection. Randomized instruction set emulation (RISE) is a complementary method of defense, one that performs a hidden randomization of an application's machine code. If foreign binary code is injected into a program running under RISE, it will not be executable because it will not know the proper randomization. The paper describes and analyzes RISE, describing a proof-of-concept implementation built on the open-source Valgrind IA32-to-IA32 translator. The prototype effectively disrupts binary code injection attacks, without requiring recompilation, linking, or access to application source code. Under RISE, injected code (attacks) essentially executes random code sequences. Empirical studies and a theoretical model are reported which treat the effects of executing random code on two different architectures (IA32 and PowerPC). The paper discusses possible extensions and applications of the RISE technique in other contexts.
Master Motor Map (MMM) — Framework and toolkit for capturing, representing, and reproducing human motion on humanoid robots

We present an extended version of our work on the design and implementation of a reference model of the human body, the Master Motor Map (MMM) which should serve as a unifying framework for capturing human motions, their representation in standard data structures and formats as well as their reproduction on humanoid robots. The MMM combines the definition of a comprehensive kinematics and dynamics model of the human body with 104 DoF including hands and feet with procedures and tools for unified capturing of human motions. We present online motion converters for the mapping of human and object motions to the MMM model while taking into account subject specific anthropométrie data as well as for the mapping of MMM motion to a target robot kinematics. Experimental evaluation of the approach performed on VICON motion recordings demonstrate the benefits of the MMM as an important step towards standardized human motion representation and mapping to humanoid robots.
Digital Image Forensics vs. Image Composition: An Indirect Arms Race

The field of image composition is constantly trying to improve the ways in which an image can be altered and enhanced. While this is usually done in the name of aesthetics and practicality, it also provides tools that can be used to maliciously alter images. In this sense, the field of digital image forensics has to be prepared to deal with the influx of new technology, in a constant arms-race. In this paper, the current state of this armsrace is analyzed, surveying the state-of-the-art and providing means to compare both sides. A novel scale to classify image forensics assessments is proposed, and experiments are performed to test composition techniques in regards to different forensics traces. We show that even though research in forensics seems unaware of the advanced forms of image composition, it possesses the basic tools to detect it.
Simple SMS spam filtering on independent mobile phone

The amount of Short Message Service (SMS) spam is increasing. Various solutions to filter SMS spam on mobile phones have been proposed. Most of these use Text Classification techniques that consist of training, filtering, and updating processes. However, they require a computer or a large amount of SMS data in advance to filter SMS spam, especially for the training. This increases hardware maintenance and communication costs. Thus, we propose to filter SMS spamon independentmobile phones using Text Classification techniques. The training, filtering, and updating processes are performed on an independent mobile phone. The mobile phone has storage, memory and CPU limitations compared with a computer. As such, we apply a probabilistic Naïve Bayes classifier using word occurrences for screening because of its simplicity and fast performance. Our experiment on an Android mobile phone shows that it can filter SMS spamwith reasonable accuracy, minimum storage consumption, and acceptable processing time without support from a computer or using a large amount of SMS data for training. Thus, we conclude that filtering SMS spam can be performed on independent mobile phones. We can reduce the number of word attributes by almost 50% without reducing accuracy significantly, using our usability-based approach.
Survey and taxonomy of IP address lookup algorithms

Algorithms, IEEE.it can be combined with many IP address lookup algorithms for fast update. Surveys on address lookup algorithms were given in 5 11 9. Ruiz-Sanchez, E.W. Dabbous, Survey and Taxonomy of. IP.AbstractIP address lookup is a key bottleneck for. Lookup algorithm based on a new memory organization. Survey and taxonomy of IP address lookup.IP routing requires that a router perform a longest-prefix-match address lookup for each incoming datagram in order to determine the datagrams next. A very quick survey at the time of writing indicates that.
Fast and efficient implementation of Convolutional Neural Networks on FPGA

State-of-the-art CNN models for Image recognition use deep networks with small filters instead of shallow networks with large filters, because the former requires fewer weights. In the light of above trend, we present a fast and efficient FPGA based convolution engine to accelerate CNN models over small filters. The convolution engine implements Winograd minimal filtering algorithm to reduce the number of multiplications by 38% to 55% for state-of-the-art CNNs. We exploit the parallelism of the Winograd convolution engine to scale the overall performance. We show that our overall design sustains the peak throughput of the convolution engines. We propose a novel data layout to reduce the required memory bandwidth of our design by half. One noteworthy feature of our Winograd convolution engine is that it hides the computation latency of the pooling layer. As a case study we implement VGG16 CNN model and compare it with previous approaches. Compared with the state-of-the-art reduced precision VGG16 implementation, our implementation achieves 1.2× improvement in throughput by using 3× less multipliers and 2× less on-chip memory without impacting the classification accuracy. The improvements in throughput per multiplier and throughput per unit on-chip memory are 3.7× and 2.47× respectively, compared with the state-of-the-art design.
The Meaning in Life Questionnaire : Assessing the Presence of and Search for Meaning in Life

Counseling psychologists often work with clients to increase their well-being as well as to decrease their distress. One important aspect of well-being, highlighted particularly in humanistic theories of the counseling process, is perceived meaning in life. However, poor measurement has hampered research on meaning in life. In 3 studies, evidence is provided for the internal consistency, temporal stability, factor structure, and validity of the Meaning in Life Questionnaire (MLQ), a new 10-item measure of the presence of, and the search for, meaning in life. A multitrait–multimethod matrix demonstrates the convergent and discriminant validity of the MLQ subscales across time and informants, in comparison with 2 other meaning scales. The MLQ offers several improvements over current meaning in life measures, including no item overlap with distress measures, a stable factor structure, better discriminant validity, a briefer format, and the ability to measure the search for meaning.
Estimating crowd densities and pedestrian flows using wi-fi and bluetooth

The rapid deployment of smartphones as all-purpose mobile computing systems has led to a wide adoption of wireless communication systems such as Wi-Fi and Bluetooth in mobile scenarios. Both communication systems leak information to the surroundings during operation. This information has been used for tracking and crowd density estimations in literature. However, an estimation of pedestrian flows has not yet been evaluated with respect to a known ground truth and, thus, a reliable adoption in real world scenarios is rather difficult. With this paper, we fill in this gap. Using ground truth provided by the security check process at a major German airport, we discuss the quality and feasibility of pedestrian flow estimations for both WiFi and Bluetooth captures. We present and evaluate three approaches in order to improve the accuracy in comparison to a naive count of captured MAC addresses. Such counts only showed an impractical Pearson correlation of 0.53 for Bluetooth and 0.61 for Wi-Fi compared to ground truth. The presented extended approaches yield a superior correlation of 0.75 in best case. This indicates a strong correlation and an improvement of accuracy. Given these results, the presented approaches allow for a practical estimation of pedestrian flows.
Action recognition using global spatio-temporal features derived from sparse representations

Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. One way to alleviate this difficulty is to focus the computations to informative (salient) regions of the video. In this paper, we propose a novel global spatio-temporal selfsimilarity measure to score saliency using the ideas of dictionary learning and sparse coding. In contrast to existing methods that use local spatio-temporal feature detectors along with descriptors (such as HOG, HOG3D, HOF, etc.), dictionary learning helps consider the saliency in a global setting (on the entire video) in a computationally efficient way. We consider only a small percentage of the most salient (least self-similar) regions found using our algorithm, over which spatio-temporal descriptors such as HOG and region covariance descriptors are computed. The ensemble of such block descriptors in a bag-of-features framework provides a holistic description of the motion sequence which can be used in a classification setting. Experiments on several benchmark datasets in video based action classification demonstrate that our approach performs competitively to the state of the art.
A diversity-penalizing ensemble training method for deep learning

A common way to improve the performance of deep learning is to train an ensemble of neural networks and combine them during decoding. However, this is computationally expensive in test time. In this paper, we propose an diversity-penalizing ensemble training (DPET) procedure, which trains an ensemble of DNNs, whose parameters were differently initialized, and penalizes differences between each individual DNN’s output and their average output. This way each model learns to emulate the average of the whole ensemble of models, and in test time we can use one arbitrarily chosen member of the ensemble. Experimental results on a variety of speech recognition tasks show that this technique is effective, and gives us most of the WER improvement of the ensemble method while being no more expensive in test time than using a single model.
Designing a socially assistive robot for personalized number concepts learning in preschool children

Designing technological systems for personalized education is an iterative and interdisciplinary process that demands a deep understanding of the application domain, the limitations of current methods and technologies, and the computational methods and complexities behind user modeling and adaptation. We present our design process and the Socially Assistive Robot (SAR) tutoring system to support the efforts of educators in teaching number concepts to preschool children. We focus on the computational considerations of designing a SAR system for young children that may later be personalized along multiple dimensions. We conducted an initial data collection to validate that the system is at the proper challenge level for our target population, and discovered promising patterns in participants' learning styles, nonverbal behavior, and performance. We discuss our plans to leverage the data collected to learn and validate a computational, multidimensional model of number concepts learning.
Adaptation of questionnaire measuring working conditions and health problems among Iranian nursing personnel.

To adapt a questionnaire in the Persian language measuring working conditions and health problems among nursing personnel. A further aim was to test the validity and reliability of the questionnaire.



The adapted questionnaire was based on three well-established questionnaires. Physical working conditions items were from Nurse Early Exit Study. Psychosocial working conditions scales were included from Copenhagen Psychosocial Questionnaire which contains two scales on general and mental health as well. The Nordic Musculoskeletal Questionnaire was the origin of the musculoskeletal disorders questions. During the culture adaptation process, an expert panel method was used. To achieve equivalence between the sources and target version, some changes were made by the expert panel. Then the questionnaire was examined in the field for face validity and construct validity (n = 92) among Iranian nursing personnel from two hospitals. Construct validity was assessed using a priori hypothesized correlations of the outcomes with exposures. Finally the adaptation process was completed by reliability assessment using Cronbach's alpha and Intra-class Correlation Coefficient (ICC).



The construct validity result was the correlation of the health outcome with the work-related exposure (physical rs = .71 and psychosocial rs = .66). In the reliability assessment, Cronbach's alpha and ICC were .60 and .70 respectively.



The findings show that the adapted questionnaire has an acceptable conceptual structure and provides reliable information from the nursing profession. Consequently, the questionnaire is applicable to work situation studies among nurses and other health care workers.