nils-herrmann/clean_scirepeval huggingface dataset:
A real-time face tracker

We present a real-time face tracker in this paper The system has achieved a rate of 30% frameshecond using an HP-9000 workstation with a framegrabber and a Canon VC-CI camera. It can track a person 'sface while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system. First, we present a stochastic model to characterize skin-color distributions of human faces. The information provided by the model is sufJicient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second, a motion model e's used to estimate image motion and to predict search window. Third, a camera model is used toprediet and to compensate for camera motion. The system can be applied to tele-conferencing and many HCI applications including lip-reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.
Evolving boxes for fast vehicle detection

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9–13 FPS detection speed on a moderate commercial GPU.
Learning Structured Inference Neural Networks with Label Relations

Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with fine-grained labels that describe major components, coarse-grained labels that depict high level abstraction, or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.
A Short History of Computational Complexity

It all started with a machine. In 1936, Turing developed his theoretical computational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40’s and 50’s, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960’s by Hartmanis and Stearns. And thus computational complexity was born. In the early days of complexity, researchers just tried understanding these new measures and how they related to each other. We saw the first notion of efficient computation by using time polynomial in the input size. This led to complexity’s most important concept, NP-completeness, and its most fundamental question, whether P = NP. The work of Cook and Karp in the early 70’s showed a large number of combinatorial and logical problems were NP-complete, i.e., as hard as any problem computable in nondeterministic polynomial time. The P = NP question is equivalent to an efficient solution of any of these problems. In the thirty years hence this problem has become one of the outstanding open questions in computer science and indeed all of mathematics. In the 70’s we saw the growth of complexity classes as researchers tried to encompass different models of computations. One of those models, probabilistic computation, started with a probabilistic test for primality, led to probabilistic complexity classes and a new kind of interactive proof system that itself led to hardness results for approximating certain NP-complete problems. We have also seen strong evidence that we can remove the randomness from computations and most recently a deterministic algorithm for the original primality problem. In the 80’s we saw the rise of finite models like circuits that capture computation in an inherently different way. A new approach to problems like P = NP arose from these circuits and though they have had limited success in separating complexity classes, this approach brought combinatorial techniques into the area and led to a much better understanding of the limits of these devices.  http://www.cs.bu.edu/faculty/homer.  Supported in part by the NSF under grant NSF-CCR-998310 and by the ARO under grant DAAD19-02-1-0058.
The ProM Framework: A New Era in Process Mining Tool Support

Under the umbrella of buzzwords such as “Business Activity Monitoring” (BAM) and “Business Process Intelligence” (BPI) both academic (e.g., EMiT, Little Thumb, InWoLvE, Process Miner, and MinSoN) and commercial tools (e.g., ARIS PPM, HP BPI, and ILOG JViews) have been developed. The goal of these tools is to extract knowledge from event logs (e.g., transaction logs in an ERP system or audit trails in a WFM system), i.e., to do process mining. Unfortunately, tools use different formats for reading/storing log files and present their results in different ways. This makes it difficult to use different tools on the same data sets and to compare the mining results. Furthermore, some of these tools implement concepts that can be very useful in the other tools but it is often difficult to combine tools. As a result, researchers working on new process mining techniques are forced to build a mining infrastructure from scratch or test their techniques in an isolated way, disconnected from any practical applications. To overcome these kind of problems, we have developed the ProM framework, i.e., an “plugable” environment for process mining. The framework is flexible with respect to the input and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining ideas. This paper introduces the ProM framework and gives an overview of the plug-ins that have been developed.
Citances : Citation Sentences for Semantic Analysis of Bioscience Text

We propose the use of the text of the sentences surrounding citations as an important tool for semantic interpretation of bioscience text. We hypothesize several different uses of citation sentences (which we call citances), including the creation of training and testing data for semantic analysis (especially for entity and relation recognition), synonym set creation, database curation, document summarization, and information retrieval generally. We illustrate some of these ideas, showing that citations to one document in particular align well with what a hand-built curator extracted. We also show preliminary results on the problem of normalizing the different ways that the same concepts are expressed within a set of citances, using and improving on existing techniques in automatic paraphrase generation.
Platforms rules : multi-sided platforms as regulators

In 1983, the videogame market in the USA collapsed, leading to bankruptcy for more than 90 percent of game developers, as well as Atari, manufacturer of the dominant game console at the time. The main reason was a ‘lemons’ market failure: because it had not developed a technology for locking out unauthorized games, Atari was unable to prevent the entry of opportunistic developers, who flooded the market with poor-quality games. At a time when consumers had few ways to distinguish good from bad games, bad games drove out good ones. The videogame market was resurrected six years later only when Nintendo entered with a set of draconian policies to regulate third-party developers more tightly. Central to Nintendo’s strategy was the use of a security chip designed to lock out any game not directly approved by Nintendo. Twenty-five years later, in the summer of 2008, Apple launched the iPhone store (a digital store of third-party applications for its immensely popular iPhone) at a time when lemons problems had become less of an issue, with widely available reviews and ratings available on the Internet. Even so, Apple reserved the right to verify and exclude any third-party application it did not deem appropriate. And it exerted that right swiftly by taking down an application named ‘I Am Rich’, which cost $999 (the maximum price allowed by Apple), while doing nothing more than presenting a glowing ruby on the buyer’s iPhone screen. Apple also kicked out Podcaster, an application that would allow users to download podcasts without going through iTunes store. The Atari, Nintendo and Apple examples illustrate instances in which non-price instruments were a critical part of strategy for multi-sided platforms (MSPs) – platforms that enable interactions between multiple groups of surrounding consumers and ‘complementors’.2 This chapter provides a general and basic conceptual framework for interpreting non-price instruments, which analogizes MSPs as private regulators; and
Empirical Findings in Agile Methods

In recent years, the use of, interest in, and controversy about Agile methodologies have realized dramatic growth. Anecdotal evidence is rising regarding the effectiveness of agile methodologies in certain environments and for specified projects. However, collection and analysis of empirical evidence of this effectiveness and classification of appropriate environments for Agile projects has not been conducted. Researchers from four institutions organized an eWorkshop to synchronously and virtually discuss and gather experiences and knowledge from eighteen Agile experts spread across the globe. These experts characterized Agile Methods and communicated experiences using these methods on small to very large teams. They discussed the importance of staffing Agile teams with highly skilled developers. They shared common success factors and identified warning signs of problems in Agile projects. These and other findings and heuristics gathered through this valuable exchange can be useful to researchers and to practitioners as they establish an experience base for better decision making. 1. The rise of Agile Methods Plan-driven methods are those in which work begins with the elicitation and documentation of a “complete” set of requirements, followed by architectural and high level-design development and inspection. Examples of plan-driven methods include various waterfall and iterative approaches, such as the Personal Software Process (PSP) . Beginning in the mid-1990’s, some practitioners found these initial requirements documentation, and architecture and design development steps frustrating and, perhaps, impossible . As Barry Boehm  suggests, these plan-driven methods may well start to pose difficulties when change rates are still relatively low. The In Proceedings of Extreme Programming and Agile Methods XP/Agile Universe 2002, pp. 197-207.
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
A Survey on Botnet Architectures, Detection and Defences

Botnets are known to be one of the most serious Internet security threats. In this survey, we review botnet architectures and their controlling mechanisms. Botnet infection behavior is explained. Then, known botnet models are outlined to study botnet design. Furthermore, Fast-Flux Service Networks (FFSN) are discussed in great details as they play an important role in facilitating botnet traffic. We classify botnets based on their architecture. Our classification criterion relies on the underlying C&C (Command and Control) protocol and thus botnets are classified as IRC (Internet Relay Chat), HTTP (HyperText Transfer Protocol), P2P (Peer-to-Peer), and POP3 (Post Office Protocol 3) botnets. In addition, newly emerging types of botnets are surveyed. This includes SMS & MMS mobile botnet and the botnets that abuse the online social networks. In term of detection methods, we categorize detection methods into three main groups, namely: (1) traffic behavior detection -in which we classify botnet traffic into; C&C traffic, bot generated traffic, and DNS traffic, (2) botmaster traceback detection, and (3) botnet detection using virtual machines. Finally, we summarize botnet defence measures that should be taken after detecting a botnet.
Meta-Learning with Temporal Convolutions

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. Recent work in meta-learning seeks to overcome this shortcoming by training a meta-learner on a distribution of similar tasks; the goal is for the meta-learner to generalize to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, most recent approaches to meta-learning are extensively hand-designed, either using architectures that are specialized to a particular application, or hard-coding algorithmic components that tell the meta-learner how to solve the task. We propose a class of simple and generic meta-learner architectures, based on temporal convolutions, that is domainagnostic and has no particular strategy or algorithm encoded into it. We validate our temporal-convolution-based meta-learner (TCML) through experiments pertaining to both supervised and reinforcement learning, and demonstrate that it outperforms state-of-the-art methods that are less general and more complex.
Learning to Extract Signature and Reply Lines from Email

We describe methods for automatically identifying signature blocks and reply lines in plaintext email messages. This analysis has many potential applications, such as preprocessing email for text-to-speech systems; anonymization of email corpora; improving automatic content-based mail classifiers; and email threading. Our method is based on applying machine learning methods to a sequential representation of an email message, in which each email is represented as a sequence of lines, and each line is represented as a set of features. We compare several state-of-the-art sequential and non-sequential machine learning algorithms on different feature sets, and present experimental results showing that the presence of a signature block in a message can be detected with accuracy higher than 97%; that signature block lines can be identified with accuracy higher than 99%; and that signature block and reply lines can be simultaneously identified with accuracy of higher than 98%.
KYPO Cyber Range: Design and Use Cases

The physical and cyber worlds are increasingly intertwined and exposed to cyber attacks. The KYPO cyber range provides complex cyber systems and networks in a virtualized, fully controlled and monitored environment. Time-efficient and cost-effective deployment is feasible using cloud resources instead of a dedicated hardware infrastructure. This paper describes the design decisions made during it’s development. We prepared a set of use cases to evaluate the proposed design decisions and to demonstrate the key features of the KYPO cyber range. It was especially cyber training sessions and exercises with hundreds of participants which provided invaluable feedback for KYPO platform development.
Directed diffusion: a scalable and robust communication paradigm for sensor networks

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.
Flat Luneburg Lens via Transformation Optics for Directive Antenna Applications

The great flexibility offered by transformation optics for controlling electromagnetic radiation by virtually re-shaping the electromagnetic space has inspired a myriad of dream-tailored electromagnetic devices. Here we show a 3D-transformed microwave Luneburg lens antenna which demonstrates high directivity, low side-lobe level, broadband response and steerable capabilities. A conventional Luneburg lens is redesigned accounting for dielectric materials that implement a coordinate transformation, modifying the lens geometry to accommodate its size and shape for easy integration with planar microwave antenna applications. An all dielectric lens is manufactured following a thorough holistic analysis of ceramic materials with different volume fractions of bi-modal distributed titanate fillers. Fabrication and measurements of a 3-D flat Luneburg lens antenna validate the design and confirm a high-directivity performance. A directivity of 17.96 dBi, low side-lobe levels for both main planes ~ -26 dB, excellent directivity performance within the X-band and beam-steering up to 34 ° were achieved.
Locally Scale-Invariant Convolutional Neural Networks

Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classification tasks. With the exception of ImageNet, these datasets are carefully crafted such that objects are well-aligned at similar scales. Naturally, the feature learning problem gets more challenging as the amount of variation in the data increases, as the models have to learn to be invariant to certain changes in appearance. Recent results on the ImageNet dataset show that given enough data, ConvNets can learn such invariances producing very discriminative features . But could we do more: use less parameters, less data, learn more discriminative features, if certain invariances were built into the learning process? In this paper we present a simple model that allows ConvNets to learn features in a locally scale-invariant manner without increasing the number of model parameters. We show on a modified MNIST dataset that when faced with scale variation, building in scale-invariance allows ConvNets to learn more discriminative features with reduced chances of over-fitting.
Developer Behavior and Sentiment from Data Mining Open Source Repositories

Developer sentiment may wax and wane as a project progresses. Open-source projects that attract and retain developers tend to be successful. It may be possible to predict project success, in part, if one can measure developer behavior and sentiment -- projects with active, happy developers are more likely to succeed. We have analyzed GitHub.com projects in an attempt to model these concepts. We have data mined 124 projects from GitHub.com. The projects were automatically mined using sequence mining methods to derive a behavioral model of developer activities. The projects were also mined for developer sentiment. Finally, a regression model shows how sentiment varies with behavioral differences -- a change in behavior is correlated with a change in sentiment. The relationship between sentiment and success is not directly explored, herein. This research project is a preliminary step in a larger research project aimed at understanding and monitoring FLOSS projects using a process modeling approach.
How we know our own minds: the relationship between mindreading and metacognition.

Four different accounts of the relationship between third-person mindreading and first-person metacognition are compared and evaluated. While three of them endorse the existence of introspection for propositional attitudes, the fourth (defended here) claims that our knowledge of our own attitudes results from turning our mindreading capacities upon ourselves. Section 1 of this target article introduces the four accounts. Section 2 develops the "mindreading is prior" model in more detail, showing how it predicts introspection for perceptual and quasi-perceptual (e.g., imagistic) mental events while claiming that metacognitive access to our own attitudes always results from swift unconscious self-interpretation. This section also considers the model's relationship to the expression of attitudes in speech. Section 3 argues that the commonsense belief in the existence of introspection should be given no weight. Section 4 argues briefly that data from childhood development are of no help in resolving this debate. Section 5 considers the evolutionary claims to which the different accounts are committed, and argues that the three introspective views make predictions that are not borne out by the data. Section 6 examines the extensive evidence that people often confabulate when self-attributing attitudes. Section 7 considers "two systems" accounts of human thinking and reasoning, arguing that although there are introspectable events within System 2, there are no introspectable attitudes. Section 8 examines alleged evidence of "unsymbolized thinking". Section 9 considers the claim that schizophrenia exhibits a dissociation between mindreading and metacognition. Finally, section 10 evaluates the claim that autism presents a dissociation in the opposite direction, of metacognition without mindreading.
Wrappers for Feature Subset Selection

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes. @ 1997 Elsevier Science B.V.