nils-herrmann/clean_scirepeval huggingface dataset:
Development of a Biomimetic Hand Exotendon Device (BiomHED) for Restoration of Functional Hand Movement Post-Stroke

Significant functional impairment of the hand is common among stroke survivors and restoration of hand function should be prioritized during post-stroke rehabilitation. The goal of this study was to develop a novel biomimetic device to assist patients in producing complex hand movements with a limited number of actuators. The Biomimetic Hand Exoskeleton Device (BiomHED) is actuated by exotendons that mimic the geometry of the major tendons of the hand. Ten unimpaired subjects and four chronic stroke survivors participated in experiments that tested the efficacy of the system. The exotendons reproduced distinct spatial joint coordination patterns similar to their target muscle-tendon units for both subject groups. In stroke survivors, the exotendon-produced joint angular displacements were smaller, but not significantly different, than those of unimpaired subjects (\mbi p = 0.15-0.84). Even with limited use of the BiomHED, the kinematic workspace of the index finger increased by 63%-1014% in stroke survivors. The device improved the kinematics of the tip-pinch task in stroke survivors and resulted in a significant reduction in the fingertip-thumb tip distance ( 17.9 ±15.3 mm). This device is expected to enable effective “task-oriented” training of the hand post-stroke.
Geometrically-Correct Projection-Based Texture Mapping onto a Deformable Object

Projection-based Augmented Reality commonly employs a rigid substrate as the projection surface and does not support scenarios where the substrate can be reshaped. This investigation presents a projection-based AR system that supports deformable substrates that can be bent, twisted or folded. We demonstrate a new invisible marker embedded into a deformable substrate and an algorithm that identifies deformations to project geometrically correct textures onto the deformable object. The geometrically correct projection-based texture mapping onto a deformable marker is conducted using the measurement of the 3D shape through the detection of the retro-reflective marker on the surface. In order to achieve accurate texture mapping, we propose a marker pattern that can be partially recognized and can be registered to an object's surface. The outcome of this work addresses a fundamental vision recognition challenge that allows the underlying material to change shape and be recognized by the system. Our evaluation demonstrated the system achieved geometrically correct projection under extreme deformation conditions. We envisage the techniques presented are useful for domains including prototype development, design, entertainment and information based AR systems.
An evolutionary algorithm that constructs recurrent neural networks

Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.
About relationship between business text patterns and financial performance in corporate data

This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.
Memsherlock: an automated debugger for unknown memory corruption vulnerabilities

Software vulnerabilities have been the main contributing factor to the Internet security problems such as fast spreading worms. Among these software vulnerabilities, memory corruption vulnerabilities such as buffer overflow and format string bugs have been the most common ones exploited by network-based attacks. Many security countermeasures (e.g., patching, automatic signature generation for intrusion detection systems) require vulnerability information to function correctly. However, despite many years of research, automatically identifying unknown software vulnerabilities still remains an open problem.
 In this paper, we present the development of a security debugging tool named MemSherlock, which can automatically identify unknown memory corruption vulnerabilities upon the detection of malicious payloads that exploit such vulnerabilities. MemSherlock provides critical information for unknown memory corruption vulnerabilities, including (1) the corruption point in the source code (i.e., the statement that allows the exploitation of memory corruption vulnerability), (2) the slice of source code that helps the malicious input to reach the corruption point, and (3) the description of how the malicious input exploits the unknown vulnerability. We evaluate MemSherlock with a set of 11 real-world applications that have buffer overflow, heap overflow, and format string vulnerabilities. The evaluation results indicate that MemSherlock is a useful tool to facilitate the automatic vulnerability analysis process.
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks

We present an approach for the verification of feed-forward neural networks in which all nodes have a piece-wise linear activation function. Such networks are often used in deep learning and have been shown to be hard to verify for modern satisfiability modulo theory (SMT) and integer linear programming (ILP) solvers. The starting point of our approach is the addition of a global linear approximation of the overall network behavior to the verification problem that helps with SMT-like reasoning over the network behavior. We present a specialized verification algorithm that employs this approximation in a search process in which it infers additional node phases for the non-linear nodes in the network from partial node phase assignments, similar to unit propagation in classical SAT solving. We also show how to infer additional conflict clauses and safe node fixtures from the results of the analysis steps performed during the search. The resulting approach is evaluated on collision avoidance and handwritten digit recognition case studies.
Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle

We hope to answer three questions: Has there been a structural break in postwar U.S. real GDP growth towards stabilization? If so, when? What is the nature of this structural break? We employ a Bayesian approach to identify a structural break at an unknown changepoint in a Markov-switching model of the business cycle. Empirical results suggest a break in GDP growth toward stabilization, with the posterior mode of the break date at 1984:1. Furthermore, we find a narrowing gap between growth rates during recessions and booms that is at least as important as any decline in the volatility of shocks.
A broadband transition between microstrip and coplanar stripline

A design for a broadband microstrip-to-coplanar-stripline transition using broadside-coupled strips is proposed in this letter. Two transitions are designed in back-to-back configuration and the insertion loss is optimized in X-band (8.2 GHz/spl sim/12.4 GHz). Both simulation and measurement are in good agreement. The measured bandwidth for 1 dB insertion loss and 20 dB return loss is from 6.9 GHz to 12.4 GHz.
A Cloud-Agnostic Container Orchestrator for Improving Interoperability

The last several years have seen a rapid increase in the use of containers and containerizing services, such as Docker, Kubernetes, and LXC. With such a rise comes a vast increase in opportunities for improved portability, security, and automation. While many of these opportunities are being taken advantage of in containers today, there remains many more possibilities that have not been explored. In this paper, we present a novel approach to container management that enables the rapid live migration of stateful containers between any hosts within private, public, or hybrid clouds, through a simple interface that provides a high-level view of the network of computing nodes and the containers running on them. Finally, we provide a vision for container management that allows greatly improved security and resiliency through autonomous and self-migrating containers existing across the scope of the Internet.
Will Your Project Get the Green Light? Predicting the Success of Crowdfunding Campaigns

Capital is always essential for a business project over times. After emerging in 2000, crowdfunding gradually becomes one of the most popular fundraising resources. However, the mechanism of crowdfunding significantly differs from traditional capital-collecting approaches. As long as the amount of pledged money reaches the goal in time, the project succeeds, its initiator receives the funds, the platform gains the revenue, and its backers acquire rewards. Reaching the goal by deadline becomes an important issue. The goal of our study is to develop an effective technique for predicting whether a crowdfunding campaign will succeed or fail. On the basis of a dataset collected from Kickstarter, our empirical evaluation results suggest that our proposed technique significantly outperforms the benchmark method. In addition, with the use of time-dependent factors, the prediction accuracy improves from 72.89% at day 0 to 87.13% at the first day and eventually to 89.62% at day 7.
Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems

In this paper, we propose an index that helps preserve the semantic interpretability of linguistic fuzzy models while a tuning of the membership functions (MFs) is performed. The proposed index is the aggregation of three metrics that preserve the original meanings of the MFs as much as possible while a tuning of their definition parameters is performed. Additionally, rule-selection mechanisms can be used to reduce the model complexity, which involves another important interpretability aspect. To this end, we propose a postprocessing multiobjective evolutionary algorithm that performs rule selection and tuning of fuzzy-rule-based systems with three objectives: accuracy, semantic interpretability maximization, and complexity minimization. We tested our approach on nine real-world regression datasets. In order to analyze the interaction between the fuzzy-rule-selection approach and the tuning approach, these are also individually proved in a multiobjective framework and compared with their respective single-objective counterparts. We compared the different approaches by applying nonparametric statistical tests for pairwise and multiple comparisons, taking into consideration three representative points from the obtained Pareto fronts in the case of the multiobjective-based approaches. Results confirm the effectiveness of our approach, and a wide range of solutions is obtained, which are not only more interpretable but are also more accurate.
Face Verification Across Age Progression

Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%
Tolerating hardware device failures in software

Hardware devices can fail, but many drivers assume they do not. When confronted with real devices that misbehave, these assumptions can lead to driver or system failures. While major operating system and device vendors recommend that drivers detect and recover from hardware failures, we find that there are many drivers that will crash or hang when a device fails. Such bugs cannot easily be detected by regular stress testing because the failures are induced by the device and not the software load. This paper describes Carburizer, a code-manipulation tool and associated runtime that improves system reliability in the presence of faulty devices. Carburizer analyzes driver source code to find locations where the driver incorrectly trusts the hardware to behave. Carburizer identified almost 1000 such bugs in Linux drivers with a false positive rate of less than 8 percent. With the aid of shadow drivers for recovery, Carburizer can automatically repair 840 of these bugs with no programmer involvement. To facilitate proactive management of device failures, Carburizer can also locate existing driver code that detects device failures and inserts missing failure-reporting code. Finally, the Carburizer runtime can detect and tolerate interrupt-related bugs, such as stuck or missing interrupts.
Dietary energy density is associated with obesity and the metabolic syndrome in U.S. adults.

Rising obesity rates have been linked to the consumption of energy-dense diets. We examined whether dietary energy density was associated with obesity and related disorders including insulin resistance and the metabolic syndrome.



We conducted a cross-sectional study using nationally representative data of U.S. adults > or =20 years of age from the 1999-2002 National Health and Nutrition Examination Survey (n = 9,688). Dietary energy density was calculated based on foods only. We used a series of multivariate linear regression models to determine the independent association between dietary energy density, obesity measures (BMI [in kilograms per meters squared] and waist circumference [in centimeters]), glycemia, or insulinemia. We used multivariate Poisson regression models to determine the independent association between dietary energy density and the metabolic syndrome as defined by the National Cholesterol and Education Program (Adult Treatment Panel III).



Dietary energy density was independently and significantly associated with higher BMI in women (beta = 0.44 [95% CI 0.14-0.73]) and trended toward a significant association in men (beta = 0.37 [-0.007 to 0.74], P = 0.054). Dietary energy density was associated with higher waist circumference in women (beta = 1.11 [0.42-1.80]) and men (beta = 1.33 [0.46-2.19]). Dietary energy density was also independently associated with elevated fasting insulin (beta = 0.65 [0.18-1.12]) and the metabolic syndrome (prevalence ratio = 1.10 [95% CI 1.03-1.17]).



Dietary energy density is an independent predictor of obesity, elevated fasting insulin levels, and the metabolic syndrome in U.S. adults. Intervention studies to reduce dietary energy density are warranted.
A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures

As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.
Trajectory Space: A Dual Representation for Nonrigid Structure from Motion

Existing approaches to nonrigid structure from motion assume that the instantaneous 3D shape of a deforming object is a linear combination of basis shapes. These bases are object dependent and therefore have to be estimated anew for each video sequence. In contrast, we propose a dual approach to describe the evolving 3D structure in trajectory space by a linear combination of basis trajectories. We describe the dual relationship between the two approaches, showing that they both have equal power for representing 3D structure. We further show that the temporal smoothness in 3D trajectories alone can be used for recovering nonrigid structure from a moving camera. The principal advantage of expressing deforming 3D structure in trajectory space is that we can define an object independent basis. This results in a significant reduction in unknowns and corresponding stability in estimation. We propose the use of the Discrete Cosine Transform (DCT) as the object independent basis and empirically demonstrate that it approaches Principal Component Analysis (PCA) for natural motions. We report the performance of the proposed method, quantitatively using motion capture data, and qualitatively on several video sequences exhibiting nonrigid motions, including piecewise rigid motion, partially nonrigid motion (such as a facial expressions), and highly nonrigid motion (such as a person walking or dancing).
Exploiting Social Information in Pairwise Preference Recommender System

There has been an explosion of social approaches to leverage recommender systems, mainly to deal with cold-start problems. However, most of the approaches are designed to handle explicit user’s ratings. We have envisioned Social PrefRec, a social recommender that applies user preference mining and clustering techniques to incorporate social information on the pairwise preference recommenders. Our approach relies on the hypothesis that user’s preference is similar to or influenced by their connected friends. This study reports experiments evaluating the recommendation quality of this method to handle the cold-start problem. Moreover, we investigate the effects of several social metrics on pairwise preference recommendations. We also show the effectiveness of our social preference learning approach in contrast to state-of-the-art social recommenders, expanding our understanding of how contextual social information affects pairwise recommenders.
A study of the documentation essential to software maintenance

Software engineering has been striving for years to improve the practice of software development and maintenance. Documentation has long been prominent on the list of recommended practices to improve development and help maintenance. Recently however, agile methods started to shake this view, arguing that the goal of the game is to produce software and that documentation is only useful as long as it helps to reach this goal.On the other hand, in the re-engineering field, people wish they could re-document useful legacy software so that they may continue maintain them or migrate them to new platform.In these two case, a crucial question arises: "How much documentation is enough?" In this article, we present the results of a survey of software maintainers to try to establish what documentation artifacts are the most useful to them.
A Metrics Suite for Object Oriented Design

Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field’s understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber, the theoretical base chosen for the metrics was the ontology of Bunge. Six design metrics are developed, and then analytically evaluated against Weyuker’s proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement. “A Metrics Suite For Object Oriented Design” Shyam R. Chidamber Chris F. Kemerer Index Terms CR