nils-herrmann/clean_scirepeval huggingface dataset:
Assessing Grasp Stability Based on Learning and Haptic Data

An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machine-learning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements from fingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research.
Design and Characterization of a Lightweight and Fully Portable Remote Actuation System for Use With a Hand Exoskeleton

Enabling individuals who are living with reduced mobility of the hand to utilize portable exoskeletons at home has the potential to deliver rehabilitation therapies with a greater intensity and relevance to activities of daily living. Various hand exoskeleton designs have been explored in the past, however, devices have remained nonportable and cumbersome for the intended users. Here we investigate a remote actuation system for wearable hand exoskeletons, which moves weight from the weakened limb to the shoulders, reducing the burden on the user and improving portability. A push-pull Bowden cable was used to transmit actuator forces from a backpack to the hand with strict attention paid to total system weight, size, and the needs of the target population. We present the design and integration of this system into a previously presented hand exoskeleton, as well as its characterization. Integration of remote actuation reduced the exoskeleton weight by 56% to 113g without adverse effects to functionality. Total actuation system weight was kept to 754g. The loss of positional accuracy inherent with Bowden cable transmissions was compensated for through closed loop positional control of the transmission output. The achieved weight reduction makes hand exoskeletons more suitable to the intended user, which will permit the study of their effectiveness in providing long duration, high intensity, and targeted rehabilitation as well as functional assistance.
Bitcoin Beacon

We examine a protocol πbeacon that outputs unpredictable and publicly verifiable randomness, meaning that the output is unknown at the time that πbeacon starts, yet everyone can verify that the output is close to uniform after πbeacon terminates. We show that πbeacon can be instantiated via Bitcoin under sensible assumptions; in particular we consider an adversary with an arbitrarily large initial budget who may not operate at a loss indefinitely. In case the adversary has an infinite budget, we provide an impossibility result that stems from the similarity between the Bitcoin model and Santha-Vazirani sources. We also give a hybrid protocol that combines trusted parties and a Bitcoin-based beacon.
Convolutional Neural Networks for Age and Gender Classification

This paper focuses on the problem of gender and age classification for an image. I build off of previous work  that has developed efficient, accurate architectures for these tasks and aim to extend their approaches in order to improve results. The first main area of experimentation in this project is modifying some previously published, effective architectures used for gender and age classification . My attempts include reducing the number of parameters (in the style of [19]), increasing the depth of the network, and modifying the level of dropout used. These modifications actually ended up causing system performance to decrease (or at best, stay the same) as compared with the simpler architecture I began with. This verified suspicions I had that the tasks of age and gender classification are more prone to over-fitting than other types of classification. The next facet of my project focuses on coupling the architectures for age and gender recognition to take advantage of the gender-specific age characteristics and agespecific gender characteristics inherent to images. This stemmed from the observation that gender classification is an inherently easier task than age classification, due to both the fewer number of potential classes and the more prominent intra-gender facial variations. By training different age classifiers for each gender I found that I could improve the performance of age classification, although gender classification did not see any significant gains.
Self-organization in a perceptual network

The emergence of a feature-analyzing function from the development rules of simple, multilayered networks is explored. It is shown that even a single developing cell of a layered network exhibits a remarkable set of optimization properties that are closely related to issues in statistics, theoretical physics, adaptive signal processing, the formation of knowledge representation in artificial intelligence, and information theory. The network studied is based on the visual system. These results are used to infer an information-theoretic principle that can be applied to the network as a whole, rather than a single cell. The organizing principle proposed is that the network connections develop in such a way as to maximize the amount of information that is preserved when signals are transformed at each processing stage, subject to certain constraints. The operation of this principle is illustrated for some simple cases.<<ETX>>
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection

In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN  architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.
Automated and on-demand provisioning of virtual machines for database applications

Utility computing delivers compute and storage resources to applications as an 'on-demand utility', much like electricity, from a distributed collection of computing resources. There is great interest in running database applications on utility resources (e.g., Oracle's Grid initiative) due to reduced infrastructure and management costs, higher resource utilization, and the ability to handle sudden load surges. Virtual Machine (VM) technology offers powerful mechanisms to manage a utility resource infrastructure. However, provisioning VMs for applications to meet system performance goals, e.g., to meet service level agreements (SLAs), is an open problem. We are building two systems at Duke - Shirako and NIMO - that collectively address this problem.
 Shirako is a toolkit for leasing VMs to an application from a utility resource infrastructure. NIMO learns application performance models using novel techniques based on active learning, and uses these models to guide VM provisioning in Shirako. We will demonstrate: (a) how NIMO learns performance models in an online and automatic fashion using active learning; and (b) how NIMO uses these models to do automated and on-demand provisioning of VMs in Shirako for two classes of database applications - multi-tier web services and computational science workflows.
Inference and learning in probabilistic logic programs using weighted Boolean formulas

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs Expectation Maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state-of-the-art in probabilistic logic programming and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.
A Brief History of Generative Models for Power Law and Lognormal Distributions

Power law distributions are an increasingly common model for computer science applications; for example, they have been used to describe file size distributions and inand out-degree distributions for the Web and Internet graphs. Recently, the similar lognormal distribution has also been suggested as an appropriate alternative model for file size distributions. In this paper, we briefly survey some of the history of these distributions, focusing on work in other fields. We find that several recently proposed models have antecedents in work from decades ago. We also find that lognormal and power law distributions connect quite naturally, and hence it is not surprising that lognormal distributions arise as a possible alternative to power law distributions.
Playing Doom with SLAM-Augmented Deep Reinforcement Learning

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep QLearning Network (DQN), by adding details of objects and structural elements encountered, along with the agent’s localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction. We evaluate the efficacy of our approach in “Doom”, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies.
Strategyproof Peer Selection: Mechanisms, Analyses, and Experiments

We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowdsourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.
Event Detection and Factuality Assessment with Non-Expert Supervision

Events are communicated in natural language with varying degrees of certainty. For example, if you are “hoping for a raise,” it may be somewhat less likely than if you are “expecting” one. To study these distinctions, we present scalable, highquality annotation schemes for event detection and fine-grained factuality assessment. We find that non-experts, with very little training, can reliably provide judgments about what events are mentioned and the extent to which the author thinks they actually happened. We also show how such data enables the development of regression models for fine-grained scalar factuality predictions that outperform strong baselines.
A Survey of Algorithms for Dense Subgraph Discovery

In this chapter, we present a survey of algorithms for dense subgraph discovery. The problem of dense subgraph discovery is closely related to clustering though the two problems also have a number of differences. For example, the problem of clustering is largely concerned with that of finding a fixed partition in the data, whereas the problem of dense subgraph discovery defines these dense components in a much more flexible way. The problem of dense subgraph discovery © Springer Science+Business Media, LLC 2010 C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data, Advances in Database Systems 40, DOI 10.1007/978-1-4419-6045-0_10, 303 304 MANAGING AND MINING GRAPH DATA may wither be defined over single or multiple graphs. We explore both cases. In the latter case, the problem is also closely related to the problem of the frequent subgraph discovery. This chapter will discuss and organize the literature on this topic effectively in order to make it much more accessible to the reader.
Growth in a Time of Debt By

In this paper, we exploit a new multi-country historical dataset on public (government) debt to search for a systemic relationship between high public debt levels, growth and inflation. Our main result is that whereas the link between growth and debt seems relatively weak at “normal” debt levels, median growth rates for countries with public debt over roughly 90 percent of GDP are about one percent lower than otherwise; average (mean) growth rates are several percent lower. Surprisingly, the relationship between public debt and growth is remarkably similar across emerging markets and advanced economies. This is not the case for inflation. We find no systematic relationship between high debt levels and inflation for advanced economies as a group (albeit with individual country exceptions including the United States). By contrast, in emerging market countries, high public debt levels coincide with higher inflation. Our topic would seem to be a timely one. Public debt has been soaring in the wake of the recent global financial maelstrom, especially in the epicenter countries. This should not be surprising, given the experience of earlier severe financial crises. Outsized deficits and epic bank bailouts may be useful in fighting a downturn, but what is the long-run macroeconomic impact,
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News

In this paper we seek to understand the relationship between the online popularity of an article and its intrinsic quality. Prior experimental work suggests that the relationship between quality and popularity can be very distorted due to factors like social influence bias and inequality in visibility. We conduct a study of popularity on two different social news aggregators, Reddit and Hacker News. We define quality as the relative number of votes an article would have received if each article was shown, in a bias-free way, to an equal number of users. We propose a simple poisson regression method to estimate this quality metric from time-series voting data. We validate our methods on data from Reddit and Hacker News, as well the experimental data from prior work. This method works well even though the collected data is subject to common social media biases. Using these estimates, we find that popularity on Reddit and Hacker News is a stronger reflection of intrinsic quality than expected.
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

We propose a generic algorithm for computation of similarit y measures for sequential data. The algorithm uses generalized suffix trees f or efficient calculation of various kernel, distance and non-metric similarity func tions. Its worst-case run-time is linear in the length of sequences and independen t of the underlying embedding language, which can cover words, k-grams or all contained subsequences. Experiments with network intrusion detection, DN A analysis and text processing applications demonstrate the utility of distan ces and similarity coefficients for sequences as alternatives to classical kernel fu ctions.
Quantifying interactive user experience on thin clients

We describe an approach to quantifying the impact of network latency on interactive response and show that the adequacy of thin-client computing is highly variable and depend on both the application and available network quality. If near ideal network conditions (low latency and high bandwidth) can be guaranteed, thin clients offer a good computing experience. As network quality degrades, interactive performance suffers. It is latency - not bandwidth -that is the greater challenge. Tightly coupled tasks such as graphics editing suffer more than loosely coupled tasks such as Web browsing. The combination of worst anticipated network quality and most tightly coupled tasks determine whether a thin-client approach is satisfactory for an organization.
From motor babbling to hierarchical learning by imitation : a robot developmental pathway

How does an individual use the knowledge acquired through self exploration as a manipulable model through which to understand others and benefit from their knowledge? How can developmental and social learning be combined for their mutual benefit? In this paper we review a hierarchical architecture (HAMMER) which allows a principled way for combining knowledge through exploration and knowledge from others, through the creation and use of multiple inverse and forward models. We describe how Bayesian Belief Networks can be used to learn the association between a robot’s motor commands and sensory consequences (forward models), and how the inverse association can be used for imitation. Inverse models created through self exploration, as well as those from observing others can coexist and compete in a principled unified framework, that utilises the simulation theory of mind approach to mentally rehearse and understand the actions of others.
On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming

The augmented Lagrangian method (ALM) is a benchmark for solving a convex minimization model with linear constraints. We consider the special case where the objective is the sum of m functions without coupled variables. For solving this separable convex minimization model, it is usually required to decompose the ALM subproblem at each iteration into m smaller subproblems, each of which only involves one function in the original objective. Easier subproblems capable of taking full advantage of the functions’ properties individually could thus be generated. In this paper, we focus on the case where full Jacobian decomposition is applied to ALM subproblems, i.e., all the decomposed ALM subproblems are eligible for parallel computation at each iteration. For the first time, we show by an example that the ALM with full Jacobian decomposition could be divergent. To guarantee the convergence, we suggest combining an under-relaxation step and the output of the ALM with full Jacobian decomposition. A novel analysis is presented to illustrate how to choose refined step sizes for this under-relaxation step. Accordingly, a new splitting version of the ALM with full Jacobian decomposition is proposed. We derive the worst-case O(1/k) convergence rate measured by the iteration complexity (where k represents the iteration counter) in both the ergodic and a nonergodic senses for the new algorithm. Finally, an assignment problem is tested to illustrate the efficiency of the new algorithm.
Observing the Natural World with Flickr

The billions of public photos on online social media sites contain a vast amount of latent visual information about the world. In this paper, we study the feasibility of observing the state of the natural world by recognizing specific types of scenes and objects in large-scale social image collections. More specifically, we study whether we can recreate satellite maps of snowfall by automatically recognizing snowy scenes in geo-tagged, time stamped images from Flickr. Snow recognition turns out to be a surprisingly doff cult and under-studied problem, so we test a variety of modern scene recognition techniques on this problem and introduce a large-scale, realistic dataset of images with ground truth annotations. As an additional proof-of-concept, we test the ability of recognition algorithms to detect a particular species of flower, the California Poppy, which could be used to give biologists a new source of data on its geospatial distribution over time.
Practical leakage-resilient identity-based encryption from simple assumptions

We design the first Leakage-Resilient Identity-Based Encryption (LR-IBE) systems from static assumptions in the standard model. We derive these schemes by applying a hash proof technique from Alwen et.al. (Eurocrypt '10) to variants of the existing IBE schemes of Boneh-Boyen, Waters, and Lewko-Waters. As a result, we achieve leakage-resilience under the respective static assumptions of the original systems in the standard model, while also preserving the efficiency of the original schemes. Moreover, our results extend to the Bounded Retrieval Model (BRM), yielding the first regular and identity-based BRM encryption schemes from static assumptions in the standard model.
 The first LR-IBE system, based on Boneh-Boyen IBE, is only selectively secure under the simple Decisional Bilinear Diffie-Hellman assumption (DBDH), and serves as a stepping stone to our second fully secure construction. This construction is based on Waters IBE, and also relies on the simple DBDH. Finally, the third system is based on Lewko-Waters IBE, and achieves full security with shorter public parameters, but is based on three static assumptions related to composite order bilinear groups.