nils-herrmann/clean_scirepeval huggingface dataset:
Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results

Face detection has witnessed immense progress in the last few years, with new milestones being surpassed every year. While many challenges such as large variations in scale, pose, appearance are successfully addressed, there still exist several issues which are not specifically captured by existing methods or datasets. In this work, we identify the next set of challenges that requires attention from the research community and collect a new dataset of face images that involve these issues such as weather-based degradations, motion blur, focus blur and several others. We demonstrate that there is a considerable gap in the performance of state-of-the-art detectors and real-world requirements. Hence, in an attempt to fuel further research in unconstrained face detection, we present a new annotated Unconstrained Face Detection Dataset (UFDD) with several challenges and benchmark recent methods. Additionally, we provide an in-depth analysis of the results and failure cases of these methods. The UFDD dataset as well as baseline results, evaluation code and image source are available at: www.ufdd.info/
An Efficient Shared Multi-Class Detection Cascade

We propose a novel multi-class object detector, that optimizes the detection costs while retaining a desired detection rate. The detector uses a cascade that unites the handling of similar object classes while separating off classes at appropriate levels of the cascade. No prior knowledge about the relationship between classes is needed as the classifier structure is automatically determined during the training phase. The detection nodes in the cascade use Haar wavelet features and Gentle AdaBoost, however the approach is not dependent on the specific features used and can easily be extended to other cases. Experiments are presented for several numbers of object classes and the approach is compared to other classifying schemes. The results demonstrate a large efficiency gain that is particularly prominent for a greater number of classes. Also the complexity of the training scales well with the number of classes.
Experiments in Open Domain Deception Detection

The widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts using a machine learning approach. First, we collect a new open domain deception dataset also containing demographic data such as gender and age. Second, we extract feature sets including n-grams, shallow and deep syntactic features, semantic features, and syntactic complexity and readability metrics. Third, we build classifiers that aim to predict deception, gender, and age. Our findings show that while deception detection can be performed in short texts even in the absence of a predetermined domain, gender and age prediction in deceptive texts is a challenging task. We further explore the linguistic differences in deceptive content that relate to deceivers gender and age and find evidence that both age and gender play an important role in people’s word choices when fabricating lies.
A comparison of machine learning algorithms applied to hand gesture recognition

Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using low-resolution images like the ones obtained with the camera in the current work.
Dynamic Factor Models

Macroeconometricians face a peculiar data structure. On the one hand, the number of years for which there is reliable and relevant data is limited and cannot readily be increased other than by the passage of time. On the other hand, for much of the postwar period statistical agencies have collected monthly or quarterly data on a great many related macroeconomic, financial, and sectoral variables. Thus macroeconometricians face data sets that have hundreds or even thousands of series, but the number of observations on each series is relatively short, for example 20 to 40 years of quarterly data. This chapter surveys work on a class of models, dynamic factor models (DFMs), which has received considerable attention in the past decade because of their ability to model simultaneously and consistently data sets in which the number of series exceeds the number of time series observations. Dynamic factor models were originally proposed by Geweke (1977) as a time-series extension of factor models previously developed for cross-sectional data. In early influential work, Sargent and Sims (1977) showed that two dynamic factors could explain a large fraction of the variance of important U.S. quarterly macroeconomic variables, including output, employment, and prices. This central empirical finding that a few factors can explain a large fraction of the variance of many macroeconomic series has been confirmed by many studies; see for example Giannone, The aim of this survey is to describe, at a level that is specific enough to be useful to researchers new to the area, the key theoretical results, applications, and empirical findings in the recent literature on DFMs. Bai and Ng (2008) and Stock and Watson 2 (2006) provide complementary surveys of this literature. Bai and Ng's (2008) survey is more technical than this one and focuses on the econometric theory and conditions; Stock and Watson (2006) focus on DFM-based forecasts in the context of other methods for forecasting with many predictors. The premise of a dynamic factor model is that a few latent dynamic factors, f t , drive the comovements of a high-dimensional vector of time-series variables, X t , which is also affected by a vector of mean-zero idiosyncratic disturbances, e t. These idiosyncratic disturbances arise from measurement error and from special features that are specific to an individual series (the effect of a Salmonella scare on restaurant employment, for example). The latent factors follow a time series process, which is …
An Experience Developing an IDS Stimulator for the Black-Box Testing of Network Intrusion Detection Systems

Signature-based intrusion detection systems use a set of attack descriptions to analyze event streams, looking for evidence of malicious behavior. If the signatures are expressed in a well-defined language, it is possible to analyze the attack signatures and automatically generate events or series of events that conform to the attack descriptions. This approach has been used in tools whose goal is to force intrusion detection systems to generate a large number of detection alerts. The resulting “alert storm” is used to desensitize intrusion detection system administrators and hide attacks in the event stream. We apply a similar technique to perform testing of intrusion detection systems. Signatures from one intrusion detection system are used as input to an event stream generator that produces randomized synthetic events that match the input signatures. The resulting event stream is then fed to a number of different intrusion detection systems and the results are analyzed. This paper presents the general testing approach and describes the first prototype of a tool, called Mucus, that automatically generates network traffic using the signatures of the Snort network-based intrusion detection system. The paper describes preliminary cross-testing experiments with both an open-source and a commercial tool and reports the results. An evasion attack that was discovered as a result of analyzing the test results is also presented.
A Unified Taxonomic Framework for Information Visualization

We present a taxonomy for Information Visualization (IV) that characterizes it in terms of data, task, skill and context, as well as a number of dimensions that relate to the input and output hardware, the software tools, as well as user interactions and human perceptual abil ities. We il lustrate the utilit y of the taxonomy by focusing particularly on the information retrieval task and the importance of taking into account human perceptual capabiliti es and limitations. Although the relevance of Psychology to IV is often recognised, we have seen relatively littl e translation of psychological results and theory to practical IV applications. This paper targets the better development of information visualizations through the introduction of a framework delineating the major factors in interface development. We believe that higher quality visualizations will result from structured developments that take into account these considerations and that the framework will also serve to assist the development of effective evaluation and assessment processes.
Multibody Structure and Motion: 3-D Reconstruction of Independently Moving Objects

This paper extends the recovery of structure and motion to im age sequences with several independently moving objects. The mot ion, structure, and camera calibration are all a-priori unknown. The fundamental constraint that we introduce is that multiple motions must share the same camer parameters. Existing work on independent motions has not employed this constr ai t, and therefore has not gained over independent static-scene reconstructi ons. We show how this constraint leads to several new results in st ructure and motion recovery, where Euclidean reconstruction becomes pos ible in the multibody case, when it was underconstrained for a static scene. We sho w how to combine motions of high-relief, low-relief and planar objects. Add itionally we show that structure and motion can be recovered from just 4 points in th e uncalibrated, fixed camera, case. Experiments on real and synthetic imagery demonstrate the v alidity of the theory and the improvement in accuracy obtained using multibody an alysis.
TOTAL QUALITY MANAGEMENT AS COMPETITIVE ADVANTAGE : A REVIEW AND EMPIRICAL STUDY

Total Quality Management (TQM) has become, according to one source, 'as pervasive a part of business thinking as quarterly financial results,' and yet TQM's role as a strategic resource remains virtually unexamined in strategic management research. Drawing on the resource approach and other theoretical perspectives, this article examines TQM as a potential source of sustainable competitive advantage, reviews existing empirical evidence, and reports findings from a new empirical study of TQM's performance consequences. The findings suggest that most features generally associated with TQM—such as quality training, process improvement, and benchmarking—do not generally produce advantage, but that certain tacit, behavioral, imperfectly imitable features—such as open culture, employee empowerment, and executive commitment—can produce advantage. The author concludes that these tacit resources, and not TQM tools and techniques, drive TQM success, and that organizations that acquire them can outperform competitors with or without the accompanying TQM ideology.
Modelling metaphor with attribute-based semantics

One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms. In this paper we present the first metaphor identification method that uses representations constructed from property norms. Such norms have been previously shown to provide a cognitively plausible representation of concepts in terms of semantic properties. Our results demonstrate that such property-based semantic representations provide a suitable model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.
DASSO : Connections Between the Dantzig Selector and Lasso

We propose a new algorithm, DASSO, for fitting the entire coef fici nt path of the Dantzig selector with a similar computational cost to the LA RS algorithm that is used to compute the Lasso. DASSO efficiently constructs a piecewi s linear path through a sequential simplex-like algorithm, which is remarkably si milar to LARS. Comparison of the two algorithms sheds new light on the question of how th e Lasso and Dantzig selector are related. In addition, we provide theoretical c onditions on the design matrix, X, under which the Lasso and Dantzig selector coefficient esti mates will be identical for certain tuning parameters. As a consequence, in many instances, we are able to extend the powerful non-asymptotic bounds that have been de veloped for the Dantzig selector to the Lasso. Finally, through empirical studies o f imulated and real world data sets we show that in practice, when the bounds hold for th e Dantzig selector, they almost always also hold for the Lasso. Some key words : Dantzig selector; LARS; Lasso;
Advanced Signal Processing for Vital Sign Extraction With Applications in UWB Radar Detection of Trapped Victims in Complex Environments

Ultra-wideband (UWB) radar plays an important role in search and rescue at disaster relief sites. Identifying vital signs and locating buried survivors are two important research contents in this field. In general, it is hard to identify a human's vital signs (breathing and heartbeat) in complex environments due to the low signal-to-noise ratio of the vital sign in radar signals. In this paper, advanced signal-processing approaches are used to identify and to extract human vital signs in complex environments. First, we apply Curvelet transform to remove the source-receiver direct coupling wave and background clutters. Next, singular value decomposition is used to de-noise in the life signals. Finally, the results are presented based on FFT and Hilbert-Huang transform to separate and to extract human vital sign frequencies, as well as the micro-Doppler shift characteristics. The proposed processing approach is first tested by a set of synthetic data generated by FDTD simulation for UWB radar detection of two trapped victims under debris at an earthquake site of collapsed buildings. Then, it is validated by laboratory experiments data. The results demonstrate that the combination of UWB radar as the hardware and advanced signal-processing algorithms as the software has potential for efficient vital sign detection and location in search and rescue for trapped victims in complex environment.
Learning Noun-Modifier Semantic Relations with Corpus-based and WordNet-based Features

We study the performance of two representations of word meaning in learning noun-modifier semantic relations. One representation is based on lexical resources, in particular WordNet, the other – on a corpus. We experimented with decision trees, instance-based learning and Support Vector Machines. All these methods work well in this learning task. We report high precision, recall and F-score, and small variation in performance across several 10-fold cross-validation runs. The corpus-based method has the advantage of working with data without word-sense annotations and performs well over the baseline. The WordNet-based method, requiring wordsense annotated data, has higher precision.
On physical-layer identification of wireless devices

Physical-layer device identification aims at identifying wireless devices during radio communication by exploiting unique characteristics of their analog (radio) circuitry. This work systematizes the existing knowledge on this topic in order to enable a better understanding of device identification, its implications on the analysis and design of security solutions in wireless networks and possible applications. We therefore present a systematic review of physical-layer identification systems and provide a summary of current state-of-the-art techniques. We further present a classification of attacks and discuss the feasibility, limitations, and implications in selected applications. We also highlight issues that are still open and need to be addressed in future work.
Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D skeletal data. A number of approaches have been proposed to extract representative features from 3D skeletal data, most commonly hard wired geometric or bio-inspired shape context features. We propose a hierarchial dynamic framework that first extracts high level skeletal joints features and then uses the learned representation for estimating emission probability to infer action sequences. Currently gaussian mixture models are the dominant technique for modeling the emission distribution of hidden Markov models. We show that better action recognition using skeletal features can be achieved by replacing gaussian mixture models by deep neural networks that contain many layers of features to predict probability distributions over states of hidden Markov models. The framework can be easily extended to include a ergodic state to segment and recognize actions simultaneously.
STRATEGIC PLANNING IN A TURBULENT ENVIRONMENT : EVIDENCE FROM THE OIL MAJORS

The long-running debate between the ‘rational design’ and ‘emergent process’ schools of strategy formation has involved caricatures of firms’ strategic planning processes, but little empirical evidence of whether and how companies plan. Despite the presumption that environmental turbulence renders conventional strategic planning all but impossible, the evidence from the corporate sector suggests that reports of the demise of strategic planning are greatly exaggerated. The goal of this paper is to fill this empirical gap by describing the characteristics of the strategic planning systems of multinational, multibusiness companies faced with volatile, unpredictable business environments. In-depth case studies of the planning systems of eight of the world’s largest oil companies identified fundamental changes in the nature and role of strategic planning since the end of the 1970s. The findings point to a possible reconciliation of ‘design’ and ‘process’ approaches to strategy formulation. The study pointed to a process of planned emergence in which strategic planning systems provided a mechanism for coordinating decentralized strategy formulation within a structure of demanding performance targets and clear corporate guidelines. The study shows that these planning systems fostered adaptation and responsiveness, but showed limited innovation and analytical sophistication.
Personas: practice and theory

ì Personasî is an interaction design technique with considerable potential for software product development. In three years of use, our colleagues and we have extended Alan Cooperís technique to make Personas a powerful complement to other usability methods. After describing and illustrating our approach, we outline the psychological theory that explains why Personas are more engaging than design based primarily on scenarios. As Cooper and others have observed, Personas can engage team members very effectively. They also provide a conduit for conveying a broad range of qualitative and quantitative data, and focus attention on aspects of design and use that other methods do not.
A deep auto-encoder based approach for intrusion detection system

One of the most challenging problems facing network operators today is network attacks identification due to extensive number of vulnerabilities in computer systems and creativity of attackers. To address this problem, we present a deep learning approach for intrusion detection systems. Our approach uses Deep Auto-Encoder (DAE) as one of the most well-known deep learning models. The proposed DAE model is trained in a greedy layer-wise fashion in order to avoid overfitting and local optima. The experimental results on the KDD-CUP'99 dataset show that our approach provides substantial improvement over other deep learning-based approaches in terms of accuracy, detection rate and false alarm rate.
StreamCloud: A Large Scale Data Streaming System

Data streaming has become an important paradigm for the real-time processing of continuous data flows in domains such as finance, telecommunications, networking, Some applications in these domains require to process massive data flows that current technology is unable to manage, that is, streams that, even for a single query operator, require the capacity of potentially many machines. Research efforts on data streaming have mainly focused on scaling in the number of queries or query operators, but overlooked the scalability issue with respect to the stream volume. In this paper, we present StreamCloud a large scale data streaming system for processing large data stream volumes. We focus on how to parallelize continuous queries to obtain a highly scalable data streaming infrastructure. StreamCloud goes beyond the state of the art by using a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. StreamCloud is implemented as a middleware and is highly independent of the underlying data streaming engine. We explore and evaluate different strategies to parallelize data streaming and tackle with the main bottlenecks and overheads to achieve scalability. The paper presents the system design, implementation and a thorough evaluation of the scalability of the fully implemented system.
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data from potentially untrustworthy sources, providing adversaries with the opportunity to manipulate them by inserting carefully crafted samples into the training set. Recent work has shown that this type of attack, called a poisoning attack, allows adversaries to insert backdoors or trojans into the model, enabling malicious behavior with simple external backdoor triggers at inference time and only a blackbox perspective of the model itself. Detecting this type of attack is challenging because the unexpected behavior occurs only when a backdoor trigger, which is known only to the adversary, is present. Model users, either direct users of training data or users of pre-trained model from a catalog, may not guarantee the safe operation of their ML-based system. In this paper, we propose a novel approach to backdoor detection and removal for neural networks. Through extensive experimental results, we demonstrate its effectiveness for neural networks classifying text and images. To the best of our knowledge, this is the first methodology capable of detecting poisonous data crafted to insert backdoors and repairing the model that does not require a verified and trusted dataset.