nils-herrmann/clean_scirepeval huggingface dataset:
Commercialize quantum technologies in five years

Masoud Mohseni, Peter Read, Hartmut Neven and colleagues at Google’s Quantum AI Laboratory set out investment opportunities on the road to the ultimate quantum machines. From aspects of quantum entanglement to chemical reactions with large molecules, many features of the world cannot be described efficiently with conventional computers based on binary logic. The solution, as physicist Richard Feynman realized three decades ago, is to use quantum processors that adopt a blend of classical states simultaneously, as matter does. Many technical hurdles must be overcome for such quantum machines to be practical, however. These include noise control and improving the fidelity of operations acting on the quantum states that encode the information. The quantum-computing community is channelling most of its efforts towards building the ultimate machine: a digital quantum computer that tolerates noise and errors, and that in principle can be applied to any problem. In theory, such a machine — which will need large processors comprising many quantum bits, or qubits — should be able to calculate faster than a conventional computer. Such capability is at least a decade away. Correcting for errors requires redundancy, and the number of qubits needed quickly mounts. For example, factorizing a 2,000-bit number in one day, a task believed to be intractable using classical computers, would take 100 million qubits, even if individual quantum operations failed just once in every 10,000 operations. We have yet to assemble digital quantum processors with tens of qubits. This conservative view of quantum computing gives the impression that investors will benefit only in the long term. We contend that short-term returns are possible with the small devices that will emerge within the next five years, even though these will lack full error correction. A lack of theoretical guarantees need not preclude success. Heuristic ‘hybrid’ methods that blend quantum and classical approaches could be the foundation for powerful future applications. The recent success of neural networks in machine learning is a good example. In the 1990s, when the computing power required to train deep neural networks was unavailable, it was fashionable in the field to focus on ‘convex’ methods (based on functions with a clear minimum solution) that had a strong theoretical basis. Today, these methods are no match for deep learning. The underlying algorithms of neural networks Google’s cryostats reach temperatures of 10 millikelvin to run its quantum processors.
Biometric template security: Challenges and solutions

A biometric system is vulnerable to a variety of attacks aimed at undermining the integrity of the authentication process. These attacks are intended to either circumvent the security afforded by the system or to deter the normal functioning of the system. We describe the various threats that can be encountered by a biometric system. We specifically focus on attacks designed to elicit information about the original biometric data of an individual from the stored template. A few algorithms presented in the literature are discussed in this regard. We also examine techniques that can be used to deter or detect these attacks. Furthermore, we provide experimental results pertaining to a hybrid system combining biometrics with cryptography, that converts traditional fingerprint templates into novel cryptographic structures.
Understanding Pedestrian Behavior in Complex Traffic Scenes

Designing autonomous vehicles for urban environments remains an unresolved problem. One major dilemma faced by autonomous cars is understanding the intention of other road users and communicating with them. To investigate one aspect of this, specifically pedestrian crossing behavior, we have collected a large dataset of pedestrian samples at crosswalks under various conditions (e.g., weather) and in different types of roads. Using the data, we analyzed pedestrian behavior from two different perspectives: the way they communicate with drivers prior to crossing and the factors that influence their behavior. Our study shows that changes in head orientation in the form of looking or glancing at the traffic is a strong indicator of crossing intention. We also found that context in the form of the properties of a crosswalk (e.g., its width), traffic dynamics (e.g., speed of the vehicles) as well as pedestrian demographics can alter pedestrian behavior after the initial intention of crossing has been displayed. Our findings suggest that the contextual elements can be interrelated, meaning that the presence of one factor may increase/decrease the influence of other factors. Overall, our work formulates the problem of pedestrian-driver interaction and sheds light on its complexity in typical traffic scenarios.
A fast algorithm for finding most influential people based on the linear threshold model

Finding the most influential people is an NP-hard problem that has attracted many researchers in the field of social networks. The problem is also known as influence maximization and aims to find a number of people that are able to maximize the spread of influence through a target social network. In this paper, a new algorithm based on the linear threshold model of influence maximization is proposed. The main benefit of the algorithm is that it reduces the number of investigated nodes without loss of quality to decrease its execution time. Our experimental results based on two well-known datasets show that the proposed algorithm is much faster and at the same time more efficient than the state of the art
Security Evaluation of Support Vector Machines in Adversarial Environments

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository. Battista Biggio, Igino Corona, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123, Cagliari, Italy.  {fumera,giacinto,roli}@diee.unica.it Blaine Nelson Institut für Informatik, Universität Potsdam, August-Bebel-Straße 89, 14482 Potsdam, Germany.  blaine.nelson@gmail.com Benjamin I. P. Rubinstein IBM Research, Lvl 5 / 204 Lygon Street, Carlton, VIC 3053, Australia.  ben@bipr.net 1 ar X iv :1 40 1. 77 27 v1 [ cs .L G ] 3 0 Ja n 20 14 2 Biggio, Corona, Nelson, Rubinstein, Maiorca, Fumera, Giacinto,
Natural Hazards and Earth System Sciences Landslide tsunami case studies using a Boussinesq model and a fully nonlinear tsunami generation model

Case studies of landslide tsunamis require integration of marine geology data and interpretations into numerical simulations of tsunami attack. Many landslide tsunami generation and propagation models have been proposed in recent time, further motivated by the 1998 Papua New Guinea event. However, few of these models have proven capable of integrating the best available marine geology data and interpretations into successful case studies that reproduce all available tsunami observations and records. We show that nonlinear and dispersive tsunami propagation models may be necessary for many landslide tsunami case studies. GEOWAVE is a comprehensive tsunami simulation model formed in part by combining the Tsunami Open and Progressive Initial Conditions System (TOPICS) with the fully nonlinear Boussinesq water wave model FUNWAVE. TOPICS uses curve fits of numerical results from a fully nonlinear potential flow model to provide approximate landslide tsunami sources for tsunami propagation models, based on marine geology data and interpretations. In this work, we validate GEOWAVE with successful case studies of the 1946 Unimak, Alaska, the 1994 Skagway, Alaska, and the 1998 Papua New Guinea events. GEOWAVE simulates accurate runup and inundation at the same time, with no additional user interference or effort, using a slot technique. Wave breaking, if it occurs during shoaling or runup, is also accounted for with a dissipative breaking model acting on the wave front. The success of our case studies depends on the combination of accurate tsunami sources and an advanced tsunami propagation and inundation model.
Ensemble visualization for cyber situation awareness of network security data

Network security analysis and ensemble data visualization are two active research areas. Although they are treated as separate domains, they share many common challenges and characteristics. Both focus on scalability, time-dependent data analytics, and exploration of patterns and unusual behaviors in large datasets. These overlaps provide an opportunity to apply ensemble visualization research to improve network security analysis. To study this goal, we propose methods to interpret network security alerts and flow traffic as ensemble members. We can then apply ensemble visualization techniques in a network analysis environment to produce a network ensemble visualization system. Including ensemble representations provide new, in-depth insights into relationships between alerts and flow traffic. Analysts can cluster traffic with similar behavior and identify traffic with unusual patterns, something that is difficult to achieve with high-level overviews of large network datasets. Furthermore, our ensemble approach facilitates analysis of relationships between alerts and flow traffic, improves scalability, maintains accessibility and configurability, and is designed to fit our analysts' working environment, mental models, and problem solving strategies.
300-GHz Step-Profiled Corrugated Horn Antennas Integrated in LTCC

This paper presents 300-GHz step-profiled corrugated horn antennas, aiming at their integration in low-temperature co-fired ceramic (LTCC) packages. Using substrate integrated waveguide technology, the cavity inside the multi-layer LTCC substrate and a surrounding via fence are used to form a feeding hollow waveguide and horn structure. Owing to the vertical configuration, we were able to design the corrugations and stepped profile of horn antennas to approximate smooth metallic surface. To verify the design experimentally, the LTCC waveguides and horn antennas were fabricated with an LTCC multi-layer process. The LTCC waveguide exhibits insertion loss of 0.6 dB/mm, and the LTCC horn antenna exhibits 18-dBi peak gain and 100-GHz bandwidth with more than 10-dB return loss. The size of the horn antenna is only 5×5×2.8 mm3, which makes it easy to integrate it in LTCC transceiver modules.
The neosexual revolution.

The affluent societies of the Western world have witnessed a tremendous cultural and social transformation of sexuality during the 1980s and 1990s, a process I refer to as the neosexual revolution. Up to now, this recoding and reassessment of sexuality has proceeded rather slowly and quietly. Yet both its real and its symbolic effects may indeed be more consequential than those brought about in the course of the rapid, noisy sexual revolution of the 1960s and 1970s. The neosexual revolution is dismantling the old patterns of sexuality and reassembling them anew. In the process, dimensions, intimate relationships, preferences, and sexual fragments emerge, many of which had been submerged, were unnamed, or simply did not exist before. In general, sexuality has lost much of its symbolic meaning as a cultural phenomenon. Sexuality is no longer the great metaphor for pleasure and happiness, nor is it so greatly overestimated as it was during the sexual revolution. It is now widely taken for granted, much like egotism or motility. Whereas sex was once mystified in a positive sense--as ecstasy and transgression, it has now taken on a negative mystification characterized by abuse, violence, and deadly infection. While the old sexuality was based primarily upon sexual instinct, orgasm, and the heterosexual couple, neosexualities revolve predominantly around gender difference, thrills, self-gratification, and prosthetic substitution. From the vast number of interrelated processes from which neosexualities emerge, three empirically observable phenomena have been selected for discussion here: the dissociation of the sexual sphere, the dispersion of sexual fragments, and the diversification of intimate relationships. These processes go hand in hand with the commercialization and banalization of sexuality. They are looked upon as being controlled individually through the mechanisms of a fundamentally egotistical consensus morality. In conformity with the general principles at work in society, the outcome of the neosexual revolution could be described as self-disciplined and self-optimized lean sexuality.
Health outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances

Whilst the future for social media in chronic disease management appears to be optimistic, there is limited concrete evidence indicating whether and how social media use significantly improves patient outcomes. This review examines the health outcomes and related effects of using social media, while also exploring the unique affordances underpinning these effects. Few studies have investigated social media's potential in chronic disease, but those we found indicate impact on health status and other effects are positive, with none indicating adverse events. Benefits have been reported for psychosocial management via the ability to foster support and share information; however, there is less evidence of benefits for physical condition management. We found that studies covered a very limited range of social media platforms and that there is an ongoing propensity towards reporting investigations of earlier social platforms, such as online support groups (OSG), discussion forums and message boards. Finally, it is hypothesized that for social media to form a more meaningful part of effective chronic disease management, interventions need to be tailored to the individualized needs of sufferers. The particular affordances of social media that appear salient in this regard from analysis of the literature include: identity, flexibility, structure, narration and adaptation. This review suggests further research of high methodological quality is required to investigate the affordances of social media and how these can best serve chronic disease sufferers. Evidence-based practice (EBP) using social media may then be considered.
Bootstrapped Named Entity Recognition for Product Attribute Extraction

We present a named entity recognition (NER) system for extracting product attributes and values from listing titles. Information extraction from short listing titles present a unique challenge, with the lack of informative context and grammatical structure. In this work, we combine supervised NER with bootstrapping to expand the seed list, and output normalized results. Focusing on listings from eBay’s clothing and shoes categories, our bootstrapped NER system is able to identify new brands corresponding to spelling variants and typographical errors of the known brands, as well as identifying novel brands. Among the top 300 new brands predicted, our system achieves 90.33% precision. To output normalized attribute values, we explore several string comparison algorithms and found n-gram substring matching to work well in practice.
Language and space: some interactions

Is language linked to mental representations of space? There are several reasons to think that language and space might be separated in our cognitive systems, but they nevertheless interact in important ways. These interactions are evident in language viewed as a means of communication and in language considered a form of representation. In communication, spatial factors may be explicit in language itself, such as the spatial-gestural system of American Sign Language. Even the act of conversing with others is a spatial behavior because we orient to the locations of other participants. Language and spatial representations probably converge at an abstract level of concepts and simple spatial schemas.
Geo-spatial Event Detection in the Twitter Stream

The rise of Social Media services in the last years has created huge streams of information that can be very valuable in a variety of scenarios. What precisely these scenarios are and how the data streams can efficiently be analyzed for each scenario is still largely unclear at this point in time and has therefore created significant interest in industry and academia. In this paper, we describe a novel algorithm for geo-spatial event detection on Social Media streams. We monitor all posts on Twitter issued in a given geographic region and identify places that show a high amount of activity. In a second processing step, we analyze the resulting spatio-temporal clusters of posts with a Machine Learning component in order to detect whether they constitute real-world events or not. We show that this can be done with high precision and recall. The detected events are finally displayed to a user on a map, at the location where they happen and while they happen.
Human Behavior Modeling with Maximum Entropy Inverse Optimal Control

In our research, we view human behavior as a structured sequence of context-sensitive decisions. We develop a conditional probabilistic model for predicting human decisions given the contextual situation. Our approach employs the principle of maximum entropy within the Markov Decision Process framework. Modeling human behavior is reduced to recovering a context-sensitive utility function that explains demonstrated behavior within the probabilistic model. In this work, we review the development of our probabilistic model (Ziebart et al. 2008a) and the results of its application to modeling the context-sensitive route preferences of drivers (Ziebart et al. 2008b). We additionally expand the approach’s applicability to domains with stochastic dynamics, present preliminary experiments on modeling time-usage, and discuss remaining challenges for applying our approach to other human behavior modeling problems.
Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.
Scale Adaptive Dictionary Learning

Dictionary learning has been widely used in many image processing tasks. In most of these methods, the number of basis vectors is either set by experience or coarsely evaluated empirically. In this paper, we propose a new scale adaptive dictionary learning framework, which jointly estimates suitable scales and corresponding atoms in an adaptive fashion according to the training data, without the need of prior information. We design an atom counting function and develop a reliable numerical scheme to solve the challenging optimization problem. Extensive experiments on texture and video data sets demonstrate quantitatively and visually that our method can estimate the scale, without damaging the sparse reconstruction ability.
Online modeling for realtime facial animation

We present a new algorithm for realtime face tracking on commodity RGB-D sensing devices. Our method requires no user-specific training or calibration, or any other form of manual assistance, thus enabling a range of new applications in performance-based facial animation and virtual interaction at the consumer level. The key novelty of our approach is an optimization algorithm that jointly solves for a detailed 3D expression model of the user and the corresponding dynamic tracking parameters. Realtime performance and robust computations are facilitated by a novel subspace parameterization of the dynamic facial expression space. We provide a detailed evaluation that shows that our approach significantly simplifies the performance capture workflow, while achieving accurate facial tracking for realtime applications.
Secure Control Systems: A Quantitative Risk Management Approach

Critical infrastructures must continuously operate safely and reliably, despite a variety of potential system disturbances. Given their strict operating requirements, such systems are automated and controlled in real time by several digital controllers receiving measurements from sensors and transmitting control signals to actuators. Since these physical systems are often spatially distributed, there is a need for information technology (IT) infrastructures enabling the timely data flow between the system components. These networked control systems are ubiquitous in modern societies . Examples include the electric power network, intelligent transport systems, and industrial processes.
Effective Function Recovery for COTS Binaries using Interface Verification

Function recovery is a critical step in many binary analysis and instrumentation tasks. Existing approaches rely on commonly used function prologue patterns to recognize function starts, and possibly epilogues for the ends. However, this approach is not robust when dealing with different compilers, compiler versions, and compilation switches. Although machine learning techniques have been proposed, the possibility of errors still limits their adoption. In this work, we present a novel function recovery technique that is based on static analysis. Evaluations have shown that we can produce very accurate results that are applicable to a wider set of applications.