nils-herrmann/clean_scirepeval huggingface dataset:
A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking

Carrying load alters normal walking, imposes additional stress to the musculoskeletal system, and results in an increase in energy consumption and a consequent earlier onset of fatigue. This phenomenon is largely due to increased work requirements in lower extremity joints, in turn requiring higher muscle activation. The aim of this work was to assess the biomechanical and physiological effects of a multi-joint soft exosuit that applies assistive torques to the biological hip and ankle joints during loaded walking. The exosuit was evaluated under three conditions: powered (EXO_ON), unpowered (EXO_OFF) and unpowered removing the equivalent mass of the device (EXO_OFF_EMR). Seven participants walked on an instrumented split-belt treadmill and carried a load equivalent to 30 % their body mass. We assessed their metabolic cost of walking, kinetics, kinematics, and lower limb muscle activation using a portable gas analysis system, motion capture system, and surface electromyography. Our results showed that the exosuit could deliver controlled forces to a wearer. Net metabolic power in the EXO_ON condition (7.5 ± 0.6 W kg−1) was 7.3 ± 5.0 % and 14.2 ± 6.1 % lower than in the EXO_OFF_EMR condition (7.9 ± 0.8 W kg−1; p = 0.027) and in the EXO_OFF condition (8.5 ± 0.9 W kg−1; p = 0.005), respectively. The exosuit also reduced the total joint positive biological work (sum of hip, knee and ankle) when comparing the EXO_ON condition (1.06 ± 0.16 J kg−1) with respect to the EXO_OFF condition (1.28 ± 0.26 J kg−1; p = 0.020) and to the EXO_OFF_EMR condition (1.22 ± 0.21 J kg−1; p = 0.007). The results of the present work demonstrate for the first time that a soft wearable robot can improve walking economy. These findings pave the way for future assistive devices that may enhance or restore gait in other applications.
Defeating script injection attacks with browser-enforced embedded policies

Web sites that accept and display content such as wiki articles or comments typically filter the content to prevent injected script code from running in browsers that view the site. The diversity of browser rendering algorithms and the desire to allow rich content make filtering quite difficult, however, and attacks such as the Samy and Yamanner worms have exploited filtering weaknesses. This paper proposes a simple alternative mechanism for preventing script injection called Browser-Enforced Embedded Policies (BEEP). The idea is that a web site can embed a policy in its pages that specifies which scripts are allowed to run. The browser, which knows exactly when it will run a script, can enforce this policy perfectly. We have added BEEP support to several browsers, and built tools to simplify adding policies to web applications. We found that supporting BEEP in browsers requires only small and localized modifications, modifying web applications requires minimal effort, and enforcing policies is generally lightweight.
Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors

The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.
Methods for computing state similarity in Markov Decision Processes

A popular approach to solving large probabilistic systems relies on aggregating states based on a measure of similarity. Many approaches in the literature are heuristic. A number of recent methods rely instead on metrics based on the notion of bisimulation, or behavioral equivalence between states (Givan et al., 2003; Ferns et al., 2004). An integral component of such metrics is the Kantorovich metric between probability distributions. However, while this metric enables many satisfying theoretical properties, it is costly to compute in practice. In this paper, we use techniques from network optimization and statistical sampling to overcome this problem. We obtain in this manner a variety of distance functions for MDP state aggregation that differ in the tradeoff between time and space complexity, as well as the quality of the aggregation. We provide an empirical evaluation of these tradeoffs.
Self-folding origami : shape memory composites activated by uniform heating 2 1

Self-folding is an approach used frequently in nature for the efficient fabrication of structures that is seldom used in engineered systems. Here, self-folding origami is presented, which consist of shape memory composites that are activated with uniform heating in an oven or a heated bath. These composites are rapidly fabricated using inexpensive materials and tools. The folding mechanism–based on the in-plane contraction of a sheet of shape memory polymer–is modeled, and parameters for the design of composites that self-fold into target shapes are characterized. Four selffolding shapes are demonstrated: a cube, an icosahedron, a prolate spheroid (egg) shape, and a Miura pattern; each of which is activated in an oven in <3 minutes. Self-sealing is also investigated using hot melt adhesive, and the resulting structures are found to beard up to twice the load of unsealed structures. Self-folding origami: shape memory composites activated by uniform heating 2
Improving Statistical Approach for Memory Leak Detection Using Machine Learning

Memory leaks are major problems in all kinds of applications, depleting their performance, even if they run on platforms with automatic memory management, such as Java Virtual Machine. In addition, memory leaks contribute to software aging, increasing the complexity of software maintenance. So far memory leak detection was considered to be a part of development process, rather than part of software maintenance. To detect slow memory leaks as a part of quality assurance process or in production environments statistical approach for memory leak detection was implemented and deployed in a commercial tool called Plumbr. It showed promising results in terms of leak detection precision and recall, however, even better detection quality was desired. To achieve this improvement goal, classification algorithms were applied to the statistical data, which was gathered from customer environments where Plumbr was deployed. This paper presents the challenges which had to be solved, method that was used to generate features for supervised learning and the results of the corresponding experiments.
So long, and no thanks for the externalities: the rational rejection of security advice by users

It is often suggested that users are hopelessly lazy and unmotivated on security questions. They chose weak passwords, ignore security warnings, and are oblivious to certificates errors. We argue that users' rejection of the security advice they receive is entirely rational from an economic perspective. The advice offers to shield them from the direct costs of attacks, but burdens them with far greater indirect costs in the form of effort. Looking at various examples of security advice we find that the advice is complex and growing, but the benefit is largely speculative or moot. For example, much of the advice concerning passwords is outdated and does little to address actual treats, and fully 100% of certificate error warnings appear to be false positives. Further, if users spent even a minute a day reading URLs to avoid phishing, the cost (in terms of user time) would be two orders of magnitude greater than all phishing losses. Thus we find that most security advice simply offers a poor cost-benefit tradeoff to users and is rejected. Security advice is a daily burden, applied to the whole population, while an upper bound on the benefit is the harm suffered by the fraction that become victims annually. When that fraction is small, designing security advice that is beneficial is very hard. For example, it makes little sense to burden all users with a daily task to spare 0.01% of them a modest annual pain.
User-defined gestures for free-hand TV control

As researchers and industry alike are proposing TV interfaces that use gestures in their designs, understanding users' preferences for gesture commands becomes an important problem. However, no rules or guidelines currently exist to assist designers and practitioners of such interfaces. The paper presents the results of the first study investigating users' preferences for free-hand gestures when controlling the TV set. By conducting an agreement analysis on user-elicited gestures, a set of gesture commands is proposed for basic TV control tasks. Also, guidelines and recommendations issued from observed user behavior are provided to assist practitioners interested in prototyping free-hand gestural designs for the interactive TV.
Face expression recognition with a 2-channel Convolutional Neural Network

A new architecture based on the Multi-channel Convolutional Neural Network (MCCNN) is proposed for recognizing facial expressions. Two hard-coded feature extractors are replaced by a single channel which is partially trained in an unsupervised fashion as a Convolutional Autoencoder (CAE). One additional channel that contains a standard CNN is left unchanged. Information from both channels converges in a fully connected layer and is then used for classification. We perform two distinct experiments on the JAFFE dataset (leave-one-out and ten-fold cross validation) to evaluate our architecture. Our comparison with the previous model that uses hard-coded Sobel features shows that an additional channel of information with unsupervised learning can significantly boost accuracy and reduce the overall training time. Furthermore, experimental results are compared with benchmarks from the literature showing that our method provides state-of-the-art recognition rates for facial expressions. Our method outperforms previously published methods that used hand-crafted features by a large margin.
Mining Discriminative Triplets of Patches for Fine-Grained Classification

Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions, therefore, accurate localization of discriminative regions remains a major challenge. We describe a patch-based framework to address this problem. We introduce triplets of patches with geometric constraints to improve the accuracy of patch localization, and automatically mine discriminative geometrically-constrained triplets for classification. The resulting approach only requires object bounding boxes. Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification.
LLR-Based Successive-Cancellation List Decoder for Polar Codes With Multibit Decision

Due to their capacity-achieving property, polar codes have become one of the most attractive channel codes. To date, the successive-cancellation list (SCL) decoding algorithm is the primary approach that can guarantee outstanding error-correcting performance of polar codes. However, the hardware designs of the original SCL decoder have a large silicon area and a long decoding latency. Although some recent efforts can reduce either the area or latency of SCL decoders, these two metrics still cannot be optimized at the same time. This brief, for the first time, proposes a general log-likelihood-ratio (LLR) based SCL decoding algorithm with multibit decision. This new algorithm, referred to as LLR - 2K b-SCL, can determine 2K bits simultaneously for arbitrary K with the use of LLR messages. In addition, a reduced-data-width scheme is presented to reduce the critical path of the sorting block. Then, based on the proposed algorithm, a VLSI architecture of the new SCL decoder is developed. Synthesis results show that, for an example (1024, 512) polar code with list size 4, the proposed LLR - 2K b - SCL decoders achieve a significant reduction in both area and latency as compared to prior works. As a result, the hardware efficiencies of the proposed designs with K = 2 and 3 are 2.33 times and 3.32 times of that of the state-of-the-art works, respectively.
The microsoft 2016 conversational speech recognition system

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task.
Neural methods for dynamic branch prediction

This article presents a new and highly accurate method for branch prediction. The key idea is to use one of the simplest possible neural methods, the perceptron, as an alternative to the commonly used two-bit counters. The source of our predictor's accuracy is its ability to use long history lengths, because the hardware resources for our method scale linearly, rather than exponentially, with the history length. We describe two versions of perceptron predictors, and we evaluate these predictors with respect to five well-known predictors. We show that for a 4 KB hardware budget, a simple version of our method that uses a global history achieves a misprediction rate of 4.6% on the SPEC 2000 integer benchmarks, an improvement of 26% over gshare. We also introduce a global/local version of our predictor that is 14% more accurate than the McFarling-style hybrid predictor of the Alpha 21264. We show that for hardware budgets of up to 256 KB, this global/local perceptron predictor is more accurate than Evers' multicomponent predictor, so we conclude that ours is the most accurate dynamic predictor currently available. To explore the feasibility of our ideas, we provide a circuit-level design of the perceptron predictor and describe techniques that allow our complex predictor to operate quickly. Finally, we show how the relatively complex perceptron predictor can be used in modern CPUs by having it override a simpler, quicker Smith predictor, providing IPC improvements of 15.8% over gshare and 5.7% over the McFarling hybrid predictor.
Building a Large-scale Software Programming Taxonomy from Stackoverflow

Taxonomy is becoming indispensable to a growing number of applications in software engineering such as software repository mining and defect prediction. However, the existing related taxonomies are always manually constructed. The sizes of these taxonomies are small and their depths are limited. In order to show the full potential of taxonomies in software engineering applications, in this paper, we present the first large-scale software programming taxonomy which is more comprehensive than any existing ones. It contains 38,205 concepts and 68,098 subsumption relations. Instead of learning from a open domain, we focus on taxonomy construction from Stackoverflow which is one of the largest QA websites about software programming. We propose a machine learning based method with novel features to create a taxonomy that captures the hierarchical semantic structure of tags in Stackoverflow. This method executes iteratively to find as many relations as possible. Experimental results show that our approach achieves much better accuracy than baselines. Compared with taxonomies related to software programming which are extracted from the general-purpose taxonomies such as WikiTaxonomy, Yago Taxonomy and Schema.org, our taxonomy has the widest coverage of concepts, contains the largest number of subsumption relations, and runs up to the deepest semantic hierarchy. Taxonomy Construction, Stackoverflow, Software Engineering
A fold-back current-limit circuit with load-insensitive quiescent current for CMOS low dropout regulator

A fold-back current-limit circuit, with load-insensitive quiescent current characteristic for CMOS low dropout regulator (LDO), is proposed in this paper. This method has been designed in 0.35 µm CMOS technology and verified by Hspice simulation. The quiescent current of the LDO is 5.7 µA at 100-mA load condition. It is only 2.2% more than it in no-load condition, 5.58 µA. The maximum current limit is set to be 197 mA, and the short-current limit is 77 mA. Thus, the power consumption can be saved up to 61% at the short-circuit condition, which also decreases the risk of damaging the power transistor. Moreover, the thermal protection can be simplified and the LDO will be more reliable.
Intervention Strategies for Increasing Engagement in Crowdsourcing: Platform, Predictions, and Experiments

Volunteer-based crowdsourcing depend critically on maintaining the engagement of participants. We explore a methodology for extending engagement in citizen science by combining machine learning with intervention design. We first present a platform for using real-time predictions about forthcoming disengagement to guide interventions. Then we discuss a set of experiments with delivering different messages to users based on the proximity to the predicted time of disengagement. The messages address motivational factors that were found in prior studies to influence users’ engagements. We evaluate this approach on Galaxy Zoo, one of the largest citizen science application on the web, where we traced the behavior and contributions of thousands of users who received intervention messages over a period of a few months. We found sensitivity of the amount of user contributions to both the timing and nature of the message. Specifically, we found that a message emphasizing the helpfulness of individual users significantly increased users’ contributions when delivered according to predicted times of disengagement, but not when delivered at random times. The influence of the message on users’ contributions was more pronounced as additional user data was collected and made available to the classifier.
IBM's Deep Blue Chess grandmaster chips

Creating the first World Championclass chess computer belongs among the oldest challenges in computer science. When World Chess Champion Garry Kasparov resigned the last game of a six-game match against IBM’s Deep Blue supercomputer on 11 May 1997, his loss marked achievement of this goal. The quest for a “chess machine” dates back to 1769 when the Turk—with a human player hidden inside—debuted in the Austrian court. The arrival of electronic computers in the late 1940s spurred new research interest in chess programs. Early programs emphasized the emulation of the human chess thought process. The Chess 4.5 program in the late 1970s first demonstrated that an engineering approach emphasizing hardware speed might be more fruitful. Belle, a specialpurpose hardwired chess machine from Bell Laboratories, became the first national master program in the early 1980s. Following the same trend, Cray Blitz running on a Cray supercomputer, and Hitech, another specialpurpose chess machine, became the top programs in the mid-1980s. For the next 10 years or so, chess machines based on a move generator of my design— ChipTest (1986-1987), Deep Thought (1988-1991), and Deep Thought II (19921995)—claimed spots as the top chess programs in the world. In 1988 the Deep Thought team won the second Fredkin Intermediate Prize for Grandmaster-level performance for Deep Thought’s 2650-plus rating on the USCF’s scale over 25 consecutive games. Deep Blue’s 1996 debut in the first Kasparov versus Deep Blue match in Philadelphia finally eclipsed Deep Thought II. The 1996 version of Deep Blue used a new chess chip designed at IBM Research over the course of three years. A major revision of this chip participated in the historic 1997 rematch between Kasparov and Deep Blue. This article concentrates mainly on the revised chip.
A hybrid particle swarm optimization – backpropagation algorithm for feedforward neural network training

The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm, also referred to as PSO–BP algorithm, is proposed to train the weights of feedforward neural network (FNN), the hybrid algorithm can make use of not only strong global searching ability of the PSOA, but also strong local searching ability of the BP algorithm. In this paper, a novel selection strategy of the inertial weight is introduced to the PSO algorithm. In the proposed PSO–BP algorithm, we adopt a heuristic way to give a transition from particle swarm search to gradient descending search. In this paper, we also give three kind of encoding strategy of particles, and give the different problem area in which every encoding strategy is used. The experimental results show that the proposed hybrid PSO–BP algorithm is better than the Adaptive Particle swarm optimization algorithm (APSOA) and BP algorithm in convergent speed and convergent accuracy.
Understanding Text Mining: A Pragmatic Approach

Making correct decisions often requires analysing large volumes of textual information. Text Mining is a budding new field that endeavours to garner meaningful information from natural language text. Text Mining is the process of applying automatic methods to analyse and structure textual data in order to create useable knowledge from previously unstructured information. Text Mining is inherently interdisciplinary, borrowing heavily from neighbouring fields such as data mining and computational linguistics. Some real application to define the state-of-the-art in Text Mining and to single out future needs and scenarios are collected.