nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical Multi-class Iris Classification for Liveness Detection

In modern society, iris recognition has become increasingly popular. The security risk of iris recognition is increasing rapidly because of the attack by various patterns of fake iris. A German hacker organization called Chaos Computer Club cracked the iris recognition system of Samsung Galaxy S8 recently. In view of these risks, iris liveness detection has shown its significant importance to iris recognition systems. The state-of-the-art algorithms mainly rely on hand-crafted texture features which can only identify fake iris images with single pattern. In this paper, we proposed a Hierarchical Multi-class Iris Classification (HMC) for liveness detection based on CNN. HMC mainly focuses on iris liveness detection of multi-pattern fake iris. The proposed method learns the features of different fake iris patterns by CNN and classifies the genuine or fake iris images by hierarchical multi-class classification. This classification takes various characteristics of different fake iris patterns into account. All kinds of fake iris patterns are divided into two categories by their fake areas. The process is designed as two steps to identify two categories of fake iris images respectively. Experimental results demonstrate an extremely higher accuracy of iris liveness detection than other state-of-the-art algorithms. The proposed HMC remarkably achieves the best results with nearly 100% accuracy on ND-Contact, CASIA-Iris-Interval, CASIA-Iris-Syn and LivDet-Iris-2017-Warsaw datasets. The method also achieves the best results with 100% accuracy on a hybrid dataset which consists of ND-Contact and LivDet-Iris-2017-Warsaw datasets.
Multilingual representations for low resource speech recognition and keyword search

This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation.
Learning to rank for censored survival data

Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored. Utilizing this information is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions, namely partial likelihood methods, rank methods, and our classification method based on a Wasserstein metric (WM) and the nonparametric Kaplan Meier estimate of the probability density to impute the labels of censored examples, can take advantage of this information. The proposed method allows us to have a model that predict the probability distribution of an event. If a clinician had access to the detailed probability of an event over time this would help in treatment planning. For example, determining if the risk of kidney graft rejection is constant or peaked after some time. Also, we demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for ranking survival models.
An Association Thesaurus for Information Retrieval

Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent beneets for retrieval performance, and it is diicult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in IN-QUERY to evaluate diierent types of association thesauri, and thesauri constructed for a variety of collections.
Towards a global participatory platform : democratising open data , complexity science and collective intelligence

The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted 110 The European Physical Journal Special Topics government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of selforganization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed.
Reinterpreting No Free Lunch

Since its inception, the No Free Lunch theorem (NFL) has concerned the application of symmetry results rather than the symmetries themselves. In our view, the conflation of result and application obscures the simplicity, generality, and power of the symmetries involved. This paper separates result from application, focusing on and clarifying the nature of underlying symmetries. The result is a general set-theoretic version of NFL which speaks to symmetries when arbitrary domains and co-domains are involved. Although our framework is deterministic, we note situations where our deterministic set-theoretic results speak nevertheless to stochastic algorithms.
Hypothalamic Orexin Neurons Regulate Arousal According to Energy Balance in Mice

Mammals respond to reduced food availability by becoming more wakeful and active, yet the central pathways regulating arousal and instinctual motor programs (such as food seeking) according to homeostatic need are not well understood. We demonstrate that hypothalamic orexin neurons monitor indicators of energy balance and mediate adaptive augmentation of arousal in response to fasting. Activity of isolated orexin neurons is inhibited by glucose and leptin and stimulated by ghrelin. Orexin expression of normal and ob/ob mice correlates negatively with changes in blood glucose, leptin, and food intake. Transgenic mice, in which orexin neurons are ablated, fail to respond to fasting with increased wakefulness and activity. These findings indicate that orexin neurons provide a crucial link between energy balance and arousal.
Large scale biomedical texts classification: a kNN and an ESA-based approaches

With the large and increasing volume of textual data, automated methods for identifying significant topics to classify textual documents have received a growing interest. While many efforts have been made in this direction, it still remains a real challenge. Moreover, the issue is even more complex as full texts are not always freely available. Then, using only partial information to annotate these documents is promising but remains a very ambitious issue.



We propose two classification methods: a k-nearest neighbours (kNN)-based approach and an explicit semantic analysis (ESA)-based approach. Although the kNN-based approach is widely used in text classification, it needs to be improved to perform well in this specific classification problem which deals with partial information. Compared to existing kNN-based methods, our method uses classical Machine Learning (ML) algorithms for ranking the labels. Additional features are also investigated in order to improve the classifiers' performance. In addition, the combination of several learning algorithms with various techniques for fixing the number of relevant topics is performed. On the other hand, ESA seems promising for this classification task as it yielded interesting results in related issues, such as semantic relatedness computation between texts and text classification. Unlike existing works, which use ESA for enriching the bag-of-words approach with additional knowledge-based features, our ESA-based method builds a standalone classifier. Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach.



Experimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0.55 % while the ESA-based approach surprisingly yielded unsatisfactory results.



We have proposed simple classification methods suitable to annotate textual documents using only partial information. They are therefore adequate for large multi-label classification and particularly in the biomedical domain. Thus, our work contributes to the extraction of relevant information from unstructured documents in order to facilitate their automated processing. Consequently, it could be used for various purposes, including document indexing, information retrieval, etc.
DNS Unchained: Amplified Application-Layer DoS Attacks Against DNS Authoritatives

We present DNS Unchained, a new application-layer DoS attack against core DNS infrastructure that for the first time uses amplification. To achieve an attack amplification of 8.51, we carefully chain CNAME records and force resolvers to perform deep name resolutions— effectively overloading a target authoritative name server with valid requests. We identify 178 508 potential amplifiers, of which 74.3 % can be abused in such an attack due to the way they cache records with low Time-to-Live values. In essence, this allows a single modern consumer uplink to downgrade availability of large DNS setups. To tackle this new threat, we conclude with an overview of countermeasures and suggestions for DNS servers to limit the impact of DNS chaining attacks.
Relevance and lexical pragmatics *

The goal of lexical pragmatics is to explain how linguistically specified (‘literal’) word meanings are modified in use. While lexical-pragmatic processes such as narrowing, broadening and metaphorical extension are generally studied in isolation from each other, relevance theorists (Carston 2002, Wilson & Sperber 2002) have been arguing for a unified approach. I will continue this work by underlining some of the problems with more standard treatments, and show how a variety of lexicalpragmatic processes may be analysed as special cases of a general pragmatic adjustment process which applies spontaneously, automatically and unconsciously to fine-tune the interpretation of virtually every word.
A wearable computer system with augmented reality to support terrestrial navigation

To date augmented realities are typically operated in only a small defined area, in the order of a large room. This paper reports on our investigation into expanding augmented realities to outdoor environments. The project entails providing visual navigation aids to users. A wearable computer system with a see-through display, digital compass, and a differential GPS are used to provide visual cues while performing a standard orienteering task. This paper reports the outcomes of a set of trials using an off the shelf wearable computer, equipped with a custom built navigation software package, "map-in-the-hat".
Long short term memory for driver intent prediction

Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). The model is learnt using the position, heading and velocity fused from GPS, IMU and odometry data collected by the ego-vehicle. In this paper we focus on determining the earliest possible moment in which we can classify the driver's intention at an intersection. We consider the outcome of this work an essential component for all levels of road vehicle automation.
Efficient grid-based spatial representations for robot navigation in dynamic environments

In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees. In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online.
RNN-based sequence prediction as an alternative or complement to traditional recommender systems

The recurrent neural networks have the ability to grasp the temporal patterns within the data. This is a property that can be used in order to help a recommender system better taking into account the user past history. Still the dimensionality problem that raises within the recommender system field also raises here as the number of items the system have to be aware of is susceptibility high. Recent research have studied the use of such neural networks at a user’s session level. This thesis rather examines the use of this technique at a whole user’s past history level associated with techniques such as embeddings and softmax sampling in order to accommodate with the high dimensionality. The proposed method results in a sequence prediction model that can be used as is for the recommender task or as a feature within a more complex system.
Data Structures for Traveling Salesmen

The choice of data structure for tour representation plays a critical role in the efficiency of local improvement heuristics for the Traveling Salesman Problem. The tour data structure must permit queries about the relative order of cities in the current tour and must allow sections of the tour to be reversed. The traditional array-based representation of a tour permits the relative order of cities to be determined in small constant time, but requires worst-case Ω(N) time (where N is the number of cities) to implement a reversal, which renders it impractical for large instances. This paper considers alternative tour data structures, examining them from both a theoretical and experimental point of view. The first alternative we consider is a data structure based on splay trees, where all queries and updates take amortized time O( logN). We show that this is close to the best possible, because in the cell probe model of computation any data structure must take worst-case amortized time Ω( logN /log logN) per operation. Empirically (for random Euclidean instances), splay trees overcome their large constant-factor overhead and catch up to arrays by N = 10 , 000, pulling ahead by a factor of 4-10 (depending on machine) when N = 100 , 000. Two alternative tree-based data structures do even better in this range, however. Although both are asymptotically inferior to the splay tree representation, the latter does not appear to pull even with them until N ∼ 1 , 000 , 000. ________________ 1 Rutgers University, New Brunswick, NJ 08903, and University of California at San Diego, La Jolla, CA 92093 2 Room 2D-150, AT&T Bell Laboratories, Murray Hill, NJ 07974 3 Department of Mathematics and Computer Science, Amherst College, Amherst, MA 01002 4 Department of Mathematics, Rutgers University, New Brunswick, NJ 08903 * A preliminary version of this paper appeared under the same title  (1993), 145-154.
Learning Fair Representations

We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.
Mirror neurons and the simulation theory of mind-reading

A new class of visuomotor neuron has been recently discovered in the monkey's premotor cortex: mirror neurons. These neurons respond both when a particular action is performed by the recorded monkey and when the same action, performed by another individual, is observed. Mirror neurons appear to form a cortical system matching observation and execution of goal-related motor actions. Experimental evidence suggests that a similar matching system also exists in humans. What might be the functional role of this matching system? One possible function is to enable an organism to detect certain mental states of observed conspecifics. This function might be part of, or a precursor to, a more general mind-reading ability. Two different accounts of mind-reading have been suggested. According to `theory theory', mental states are represented as inferred posits of a naive theory. According to `simulation theory', other people's mental states are represented by adopting their perspective: by tracking or matching their states with resonant states of one's own. The activity of mirror neurons, and the fact that observers undergo motor facilitation in the same muscular groups as those utilized by target agents, are findings that accord well with simulation theory but would not be predicted by theory theory.
Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture

In this paper we introduce a novel approach to the combination of acoustic features and language information for a most robust automatic recognition of a speaker's emotion. Seven discrete emotional states are classified throughout the work. Firstly a model for the recognition of emotion by acoustic features is presented. The derived features of the signal-, pitch-, energy, and spectral contours are ranked by their quantitative contribution to the estimation of an emotion. Several different classification methods including linear classifiers, Gaussian mixture models, neural nets, and support vector machines are compared by their performance within this task. Secondly an approach to emotion recognition by the spoken content is introduced applying belief network based spotting for emotional key-phrases. Finally the two information sources are integrated in a soft decision fusion by using a neural net. The gain is evaluated and compared to other advances. Two emotional speech corpora used for training and evaluation are described in detail and the results achieved applying the propagated novel advance to speaker emotion recognition are presented and discussed.
Firmaster: Analysis Tool for Home Router Firmware

As the Internet has changed the way people communicate with each other in everyday life, the number of home Wi-Fi access routers has grown up significantly over the past few years. However, the security of routers used in every household is still in the low level. The common vulnerabilities in routers can be easily exploited by an attacker in order to obtain user’s sensitive information or even compromise the devices to be a part of the botnet network. Therefore, we developed one-stop service firmware analysis tool that can perform both static and dynamic analysis for the router firmware called “Firmaster”. textbfThe program is operated under graphical user interface (GUI) of Qt creator running on the Ubuntu Linux machine. textbfVulnerabilities of firmware analyzed by Firmaster program are based on OWASP’s Top 10 IoT Vulnerabilities 2014. Firmaster contains seven main functions: password cracking, SSL scanning, web static analysis, firmware update analysis, web dynamic analysis, port scanning and the summary report.