nils-herrmann/clean_scirepeval huggingface dataset:
The group fused Lasso for multiple change-point detection

We present the group fused Lasso for detection of multiple ch ange-points shared by a set of co-occurring one-dimensional signals. Change-points are det cted by approximating the original signals with a constraint on the multidimensional total var iation, leading to piecewise-constant approximations. Fast algorithms are proposed to solve the res ulting optimization problems, either exactly or approximately. Conditions are given for consist ency of both algorithms as the number of signals increases, and empirical evidence is provided to su pport the results on simulated and array comparative genomic hybridization data.
Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as a feature representation. However, the information in this layer may be too coarse spatially to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To get the best of both worlds, we define the <italic>hypercolumn</italic> at a pixel as the vector of activations of all CNN units above that pixel. Using hypercolumns as pixel descriptors, we show results on three fine-grained localization tasks: simultaneous detection and segmentation, where we improve state-of-the-art from <italic>49.7</italic> mean <inline-formula><tex-math notation="LaTeX">$\mathrm{AP}^r$</tex-math><alternatives> <inline-graphic xlink:href="hariharan-ieq1-2578328.gif"/></alternatives></inline-formula> to <italic>62.4</italic>, keypoint localization, where we get a 3.3 point boost over a strong regression baseline using CNN features, and part labeling, where we show a 6.6 point gain over a strong baseline.
Attacking an Obfuscated Cipher by Injecting Faults

We study the strength of certain obfuscation techniques used to protect software from reverse engineering and tampering. We show that some common obfuscation methods can be defeated using a fault injection attack, namely an attack where during program execution an attacker injects errors into the program environment. By observing how the program fails under certain errors the attacker can deduce the obfuscated information in the program code without having to unravel the obfuscation mechanism. We apply this technique to extract a secret key from a block cipher obfuscated using a commercial obfuscation tool and draw conclusions on preventing this weakness.
Chest X-ray Image View Classification

The view information of a chest X-ray (CXR), such as frontal or lateral, is valuable in computer aided diagnosis (CAD) of CXRs. For example, it helps for the selection of atlas models for automatic lung segmentation. However, very often, the image header does not provide such information. In this paper, we present a new method for classifying a CXR into two categories: frontal view vs. lateral view. The method consists of three major components: image pre-processing, feature extraction, and classification. The features we selected are image profile, body size ratio, pyramid of histograms of orientation gradients, and our newly developed contour-based shape descriptor. The method was tested on a large (more than 8,200 images) CXR dataset hosted by the National Library of Medicine. The very high classification accuracy (over 99% for 10-fold cross validation) demonstrates the effectiveness of the proposed method.
High throughput multiple-precision GCD on the CUDA architecture

Investigation of the cryptanalytic strength of RSA cryptography requires computing many GCDs of two long integers (e.g., of length 1024 bits). This paper presents a high throughput parallel algorithm to perform many GCD computations concurrently on a GPU based on the CUDA architecture. The experiments with an NVIDIA GeForce GTX285 GPU and a single core of 3.0 GHz Intel Core2 Duo E6850 CPU show that the proposed GPU algorithm runs 11.3 times faster than the corresponding CPU algorithm.
Eigenface-domain super-resolution for face recognition

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.
Where Does TAM Reside in the Brain? The Neural Mechanisms Underlying Technology Adoption

Toward materializing the recently identified potential of cognitive neuroscience for IS research (Dimoka, Pavlou and Davis 2007), this paper demonstrates how functional neuroimaging tools can enhance our understanding of IS theories. Specifically, this study aims to uncover the neural mechanisms that underlie technology adoption by identifying the brain areas activated when users interact with websites that differ on their level of usefulness and ease of use. Besides localizing the neural correlates of the TAM constructs, this study helps understand their nature and dimensionality, as well as uncover hidden processes associated with intentions to use a system. The study also identifies certain technological antecedents of the TAM constructs, and shows that the brain activations associated with perceived usefulness and perceived ease of use predict selfreported intentions to use a system. The paper concludes by discussing the study’s implications for underscoring the potential of functional neuroimaging for IS research and the TAM literature.
Particle-based fluid simulation for interactive applications

Realistically animated fluids can add substantial realism to interactive applications such as virtual surgery simulators or computer games. In this paper we propose an interactive method based on Smoothed Particle Hydrodynamics (SPH) to simulate fluids with free surfaces. The method is an extension of the SPH-based technique by Desbrun to animate highly deformable bodies. We gear the method towards fluid simulation by deriving the force density fields directly from the Navier-Stokes equation and by adding a term to model surface tension effects. In contrast to Eulerian grid-based approaches, the particle-based approach makes mass conservation equations and convection terms dispensable which reduces the complexity of the simulation. In addition, the particles can directly be used to render the surface of the fluid. We propose methods to track and visualize the free surface using point splatting and marching cubes-based surface reconstruction. Our animation method is fast enough to be used in interactive systems and to allow for user interaction with models consisting of up to 5000 particles.
Contextual analysis of program logs for understanding system behaviors

Understanding the behaviors of a software system is very important for performing daily system maintenance tasks. In practice, one way to gain knowledge about the runtime behavior of a system is to manually analyze system logs collected during the system executions. With the increasing scale and complexity of software systems, it has become challenging for system operators to manually analyze system logs. To address these challenges, in this paper, we propose a new approach for contextual analysis of system logs for understanding a system's behaviors. In particular, we first use execution patterns to represent execution structures reflected by a sequence of system logs, and propose an algorithm to mine execution patterns from the program logs. The mined execution patterns correspond to different execution paths of the system. Based on these execution patterns, our approach further learns essential contextual factors (e.g., the occurrences of specific program logs with specific parameter values) that cause a specific branch or path to be executed by the system. The mining and learning results can help system operators to understand a software system's runtime execution logic and behaviors during various tasks such as system problem diagnosis. We demonstrate the feasibility of our approach upon two real-world software systems (Hadoop and Ethereal).
An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets

We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.
Approximate Frequency Counts over Data Streams

We present algorithms for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a user-specified parameter. Our algorithms can easily be deployed for streams of singleton items like those found in IP network monitoring. We can also handle streams of variable sized sets of items exemplified by a sequence of market basket transactions at a retail store. For such streams, we describe an optimized implementation to compute frequent itemsets in a single pass.
Deep residual learning for image steganalysis

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.
Banishing the homunculus: Making working memory work

The prefrontal cortex has long been thought to subserve both working memory and "executive" function, but the mechanistic basis of their integrated function has remained poorly understood, often amounting to a homunculus. This paper reviews the progress in our laboratory and others pursuing a long-term research agenda to deconstruct this homunculus by elucidating the precise computational and neural mechanisms underlying these phenomena. We outline six key functional demands underlying working memory, and then describe the current state of our computational model of the prefrontal cortex and associated systems in the basal ganglia (BG). The model, called PBWM (prefrontal cortex, basal ganglia working memory model), relies on actively maintained representations in the prefrontal cortex, which are dynamically updated/gated by the basal ganglia. It is capable of developing human-like performance largely on its own by taking advantage of powerful reinforcement learning mechanisms, based on the midbrain dopaminergic system and its activation via the basal ganglia and amygdala. These learning mechanisms enable the model to learn to control both itself and other brain areas in a strategic, task-appropriate manner. The model can learn challenging working memory tasks, and has been corroborated by several important empirical studies.
Lowering the barriers to large-scale mobile crowdsensing

Mobile crowdsensing is becoming a vital technique for environment monitoring, infrastructure management, and social computing. However, deploying mobile crowdsensing applications in large-scale environments is not a trivial task. It creates a tremendous burden on application developers as well as mobile users. In this paper we try to reveal the barriers hampering the scale-up of mobile crowdsensing applications, and to offer our initial thoughts on the potential solutions to lowering the barriers.
Fast Exact Search in Hamming Space With Multi-Index Hashing

There is growing interest in representing image data and feature descriptors using compact binary codes for fast near neighbor search. Although binary codes are motivated by their use as direct indices (addresses) into a hash table, codes longer than 32 bits are not being used as such, as it was thought to be ineffective. We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact k-nearest neighbor search in Hamming space. The approach is storage efficient and straight-forward to implement. Theoretical analysis shows that the algorithm exhibits sub-linear run-time behavior for uniformly distributed codes. Empirical results show dramatic speedups over a linear scan baseline for datasets of up to one billion codes of 64, 128, or 256 bits.
Identifying Non-Explicit Citing Sentences for Citation-Based Summarization

Identifying background (context) information in scientific articles can help scholars understand major contributions in their research area more easily. In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientific papers. We model the sentences in an article and their lexical similarities as aMarkov Random Fieldtuned to detect the patterns that context data create, and employ a Belief Propagationmechanism to detect likely context sentences. We also address the problem of generating surveys of scientific papers. Our experiments show greater pyramid scores for surveys generated using such context information rather than citation sentences alone.
Hybrid control for large swarms of aquatic drones

Maritime tasks, such as surveillance and patrolling, aquaculture inspection, and wildlife monitoring, typically require large operational crews and expensive equipment. Only recently have unmanned vehicles started to be used for such missions. These vehicles, however, tend to be expensive and have limited coverage, which prevents large-scale deployment. In this paper, we propose a scalable robotics system based on swarms of small and inexpensive aquatic drones. We take advantage of bio-inspired artificial evolution techniques in order to synthesize scalable and robust collective behaviors for the drones. The behaviors are then combined hierarchically with preprogrammed control in an engineeredcentric approach, allowing the overall behavior for a particular mission to be quickly configured and tested in simulation before the aquatic drones are deployed. We demonstrate the scalability of our hybrid approach by successfully deploying up to 1,000 simulated drones to patrol a 20 km long strip for 24 hours.
An Agent-Based Architecture for an Adaptive Human-Robot Interface

This paper describes an innovative agent-based architecture for mixed-initiative interaction between a human and a robot that interacts via a graphical user interface (GUI). Mixed-initiative interaction typically refers to a flexible interaction strategy between a human and a computer to contribute what is best-suited at the most appropriate time . In this paper, we extend this concept to human-robot interaction (HRI). When compared to pure humancomputer interaction, HRIs encounter additional difficulty, as the user must assess the situation at the robot’s remote location via limited sensory feedback. We propose an agent-based adaptive human-robot interface for mixed-initiative interaction to address this challenge. The proposed adaptive user interface (UI) architecture provides a platform for developing various agents that control robots and user interface components (UICs). Such components permit the human and the robot to communicate missionrelevant information.
Ultra-Fast and High-Reliability SOT-MRAM: From Cache Replacement to Normally-Off Computing

This paper deals with a new MRAM technology whose writing scheme relies on the Spin Orbit Torque (SOT). Compared to Spin Transfer Torque (STT) MRAM, it offers a very fast switching, a quasi-infinite endurance and improves the reliability by solving the issue of “read disturb”, thanks to separate reading and writing paths. These properties allow introducing SOT at all-levels of the memory hierarchy of systems and adressing applications which could not be easily implemented by STT-MRAM. We present this emerging technology and a full design framework, allowing to design and simulate hybrid CMOS/SOT complex circuits at any level of abstraction, from device to system. The results obtained are very promising and show that this technology leads to a reduced power consumption of circuits without notable penalty in terms of performance.
A Context-Dependent Attention System for a Social Robot

This paper presents part of an on-going project to integrate perception, attention, drives, emotions, behavior arbitration, and expressive acts for a robot designed to interact socially with humans. We present the design of a visual attention system based on a model of human visual search behavior from Wolfe (1994). The attention system integrates perceptions (motion detection, color saliency, and face popouts) with habituation effects and influences from the robot’s motivational and behavioral state to create a context-dependent attention activation map. This activation map is used to direct eye movements and to satiate the drives of the motivational system.
Decoding the visual and subjective contents of the human brain

The potential for human neuroimaging to read out the detailed contents of a person's mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimulus orientations the subject was seeing. Moreover, when subjects had to attend to one of two overlapping orthogonal gratings, feature-based attention strongly biased ensemble activity toward the attended orientation. These results demonstrate that fMRI activity patterns in early visual areas, including primary visual cortex (V1), contain detailed orientation information that can reliably predict subjective perception. Our approach provides a framework for the readout of fine-tuned representations in the human brain and their subjective contents.
Interactive query refinement

We investigate the problem of refining SQL queries to satisfy cardinality constraints on the query result. This has applications to the many/few answers problems often faced by database users. We formalize the problem of query refinement and propose a framework to support it in a database system. We introduce an interactive model of refinement that incorporates user feedback to best capture user preferences. Our techniques are designed to handle queries having range and equality predicates on numerical and categorical attributes. We present an experimental evaluation of our framework implemented in an open source data manager and demonstrate the feasibility and practical utility of our approach.
Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
On the Computational Power of Neural Nets

This paper deals with finite networks which consist of interconnections of synchronously evolving processors. Each processor updates its state by applying a “sigmoidal” scalar nonlinearity to a linear combination of the previous states of all units. We prove that one may simulate all Turing Machines by rational nets. In particular, one can do this in linear time, and there is a net made up of about 1,000 processors which computes a universal partial-recursive function. Products (high order nets) are not required, contrary to what had been stated in the literature. Furthermore, we assert a similar theorem about non-deterministic Turing Machines. Consequences for undecidability and complexity issues about nets are discussed too.