nils-herrmann/clean_scirepeval huggingface dataset:
Confessions that corrupt: evidence from the DNA exoneration case files.

Basic psychology research suggests the possibility that confessions-a potent form of incrimination-may taint other evidence, thereby creating an appearance of corroboration. To determine if this laboratory-based phenomenon is supported in the high-stakes world of actual cases, we conducted an archival analysis of DNA exoneration cases from the Innocence Project case files. Results were consistent with the corruption hypothesis: Multiple evidence errors were significantly more likely to exist in false-confession cases than in eyewitness cases; in order of frequency, false confessions were accompanied by invalid or improper forensic science, eyewitness identifications, and snitches and informants; and in cases containing multiple errors, confessions were most likely to have been obtained first. We believe that these findings underestimate the problem and have important implications for the law concerning pretrial corroboration requirements and the principle of "harmless error" on appeal.
SenSec: Mobile security through passive sensing

We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices. SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device. SenSec calculates the sureness that the mobile device is being used by its owner. Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security. In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors. We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments. User studies show that SenSec can achieve 75% accuracy in identifying the users and 71.3% accuracy in detecting the non-owners with only 13.1% false alarms.
A wall climbing robot using propulsive force of propeller

A robot capable of moving on a vertical wall of high-rise buildings can be used for rescue, wall inspection, fire fighting, etc.. A wall climbing robot using thrust force of propellers has been developed. The thrust force is inclined a little to the wall side to produce the frictional force between the wheels and wall surface. As the strong wind is predicted on the wall surface of buildings, the direction of thrust force is controlled to compensate the wind force acting on the robot. A frictional force augmentor is also considered, which is an airfoil to produce the lift force directed to the wall side by the cross wind. Its effect is tested in the wind tunnel. The overall performance of the robot is examined by computer simulation and a model was constructed and tested on the wall.<<ETX>>
Hierarchical recurrent neural network for skeleton based action recognition

Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers. In this paper, considering that recurrent neural network (RNN) can model the long-term contextual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the input, we divide the human skeleton into five parts according to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. The final representations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify the effectiveness of the proposed network, and also compare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.
Actions as space-time shapes

Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach by Gorelick et al. (2004) for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action recognition, detection and clustering. The method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action and low quality video
The Unified Accumulator Architecture: A Configurable, Portable, and Extensible Floating-Point Accumulator

Applications accelerated by field-programmable gate arrays (FPGAs) often require pipelined floating-point accumulators with a variety of different trade-offs. Although previous work has introduced numerous floating-point accumulation architectures, few cores are available for public use, which forces designers to use fixed-point implementations or vendor-provided cores that are not portable and are often not optimized for the desired set of trade-offs. In this article, we combine and extend previous floating-point accumulator architectures into a configurable, open-source core, referred to as the unified accumulator architecture (UAA), which enables designers to choose between different trade-offs for different applications. UAA is portable across FPGAs and allows designers to specialize the underlying adder core to take advantage of device-specific optimizations. By providing an extensible, open-source implementation, we hope for the research community to extend the provided core with new architectures and optimizations.
Evaluating the efficacy of test-driven development: industrial case studies

This paper discusses software development using the Test Driven Development (TDD) methodology in two different environments (Windows and MSN divisions) at Microsoft. In both these case studies we measure the various context, product and outcome measures to compare and evaluate the efficacy of TDD. We observed a significant increase in quality of the code (greater than two times) for projects developed using TDD compared to similar projects developed in the same organization in a non-TDD fashion. The projects also took at least 15% extra upfront time for writing the tests. Additionally, the unit tests have served as auto documentation for the code when libraries/APIs had to be used as well as for code maintenance.
10 Watt high efficiency GaAs MMIC power amplifier for space applications

This paper describes the design of a GaAs monolithic high power amplifier at Ku band. The chip delivers about 40 dBm of saturated output power, in CW operating conditions, at 11.7 GHz central frequency, with 17% of bandwidth. The saturated power gain is 12.4 dB with 2 dB gain flatness across the application bandwidth while the chip power added efficiency is estimated between 33% to 47%. The amplifier is designed to be used as final stage of a downlink satellite transmitter for Tracking Telemetry & Command system. A commercial power p-HEMT process capable of handling a power density higher than 1 W/mm has been selected for the MMIC design. Due to the space application, special attention must be put on the process and MMIC reliability: to this aim performances must be guaranteed in de-rated conditions respect to the process maximum ratings and, in addition, the channel temperature of the active devices must be kept within the value established by Space Requirements and carefully controlled. This makes the design objective very tight. The MMIC power amplifier design and some measurement results are presented in the paper.
Neural CRF Parsing

This paper describes a parsing model that combines the exact dynamic programming of CRF parsing with the rich nonlinear featurization of neural net approaches. Our model is structurally a CRF that factors over anchored rule productions, but instead of linear potential functions based on sparse features, we use nonlinear potentials computed via a feedforward neural network. Because potentials are still local to anchored rules, structured inference (CKY) is unchanged from the sparse case. Computing gradients during learning involves backpropagating an error signal formed from standard CRF sufficient statistics (expected rule counts). Using only dense features, our neural CRF already exceeds a strong baseline CRF model (Hall et al., 2014). In combination with sparse features, our system1 achieves 91.1 F1 on section 23 of the Penn Treebank, and more generally outperforms the best prior single parser results on a range of languages.
Bilingualism as a protection against the onset of symptoms of dementia

This study examined the effect of lifelong bilingualism on maintaining cognitive functioning and delaying the onset of symptoms of dementia in old age. The sample was selected from the records of 228 patients referred to a Memory Clinic with cognitive complaints. The final sample consisted of 184 patients diagnosed with dementia, 51% of whom were bilingual. The bilinguals showed symptoms of dementia 4 years later than monolinguals, all other measures being equivalent. Additionally, the rate of decline in Mini-Mental State Examination (MMSE) scores over the 4 years subsequent to the diagnosis was the same for a subset of patients in the two groups, suggesting a shift in onset age with no change in rate of progression.
The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism

The INTERSPEECH 2013 Computational Paralinguistics Challenge provides for the first time a unified test-bed for Social Signals such as laughter in speech. It further introduces conflict in group discussions as new tasks and picks up on autism and its manifestations in speech. Finally, emotion is revisited as task, albeit with a broader ranger of overall twelve emotional states. In this paper, we describe these four Sub-Challenges, Challenge conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants.
Latent semantic analysis

We now described an interesting application of SVD to text do cuments. Suppose we represent documents as a bag of words, soXij is the number of times word j occurs in document i, for j = 1 : W andi = 1 : D, where W is the number of words and D is the number of documents. To find a document that contains a g iven word, we can use standard search procedures, but this can get confuse d by ynonomy (different words with the same meaning) andpolysemy (same word with different meanings). An alternative approa ch is to assume that X was generated by some low dimensional latent representation X̂ ∈ IR, whereK is the number of latent dimensions. If we compare documents in the latent space, we should get improved retrie val performance, because words of similar meaning get mapped to similar low dimensional locations. We can compute a low dimensional representation of X by computing the SVD, and then taking the top k singular values/ vectors: 1
EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding

The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. This paper presents our Eesen framework which drastically simplifies the existing pipeline to build state-of-the-art ASR systems. Acoustic modeling in Eesen involves learning a single recurrent neural network (RNN) predicting context-independent targets (phonemes or characters). To remove the need for pre-generated frame labels, we adopt the connectionist temporal classification (CTC) objective function to infer the alignments between speech and label sequences. A distinctive feature of Eesen is a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that compared with the standard hybrid DNN systems, Eesen achieves comparable word error rates (WERs), while at the same time speeding up decoding significantly.
Intensive Lipid Lowering with Simvastatin and Ezetimibe in Aortic Stenosis

A ortic-valve stenosis is common in elderly persons, with a prevalence of 3 to 5% in the population over 75 years of age. The condition has been shown to be an inflammatory process associated with cardiovascular risk factors, with histopathological changes in the valve leaflets that are similar to those in other atherosclerotic diseases. Changes in the aortic valve are associated with an increased risk of death from cardiovascular causes and myocardial infarction, even in the absence of hemodynamic obstruction and signs of coronary disease. Hyperlipidemia has been suggested as a risk factor for stenosis of the aortic valve, but lipid-lowering studies have had conflicting results. Most studies have suggested a beneficial effect of statins, whereas one prospective, randomized study did not find any effect of lipidlowering therapy on the progression of aorticvalve stenosis. This study was designed by the steering committee on the basis of a protocol developed for the Simvastatin in Aortic Stenosis (SAS) study, which evaluated the effect of lipid-lowering therapy with simvastatin (at a dose of 40 to 80 mg) as compared with placebo on clinical and echocardiographic outcomes in patients with aortic stenosis. A total of 1873 patients underwent randomization, men and women between the ages of 45 and 85 years who had asymptomatic, mild-to-moderate aortic-valve stenosis, as assessed on echocardiography, with a peak aortic-jet velocity of 2.5 to 4 m per second. Patients were excluded if they had received a diagnosis or had symptoms of coronary artery disease, peripheral arterial disease, cerebrovascular disease, or diabetes mellitus or if they had any other condition requiring lipid-lowering therapy. To improve the lipid-lowering effect while decreasing the risk of myopathy, the steering committee decided to add ezetimibe (at a dose of 10 mg daily) to 40 mg of simvastatin. Echocardiography was performed at baseline and then annually and before valve surgery, according to a standardized echocardiographic protocol. The study was completed according to the protocol when all patients had been followed for a minimum of 4 years after randomization, at which point the primary outcome had occurred in at least 464 patients.
Automatic Steering Methods for Autonomous Automobile Path Tracking

This research derives, implements, tunes and compares selected path tracking methods for controlling a car-like robot along a predetermined path. The scope includes commonly used m ethods found in practice as well as some theoretical methods found in various literature from other areas of rese arch. This work reviews literature and identifies important path tracking models and control algorithms from the vast back ground and resources. This paper augments the literature with a comprehensive collection of important path tracking idea s, a guide to their implementations and, most importantly, an independent and realistic comparison of the perfor mance of these various approaches. This document does not catalog all of the work in vehicle modeling and control; only a selection that is perceived to be important ideas when considering practical system identification, ease of implementation/tuning and computational efficiency. There are several other methods that meet this criteria, ho wever they are deemed similar to one or more of the approaches presented and are not included. The performance r esults, analysis and comparison of tracking methods ultimately reveal that none of the approaches work well in all applications a nd that they have some complementary characteristics. These complementary characteristics lead to an idea that a combination of methods may be useful for more general applications. Additionally, applications for which the methods in this paper do not provide adequate solutions are identified.
A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks

The framework of cognitive wireless networks is expected to endow the wireless devices with the cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In many practical scenarios, the complexity of network dynamics makes it difficult to determine the network evolution model in advance. Thus, the wireless decision-making entities may face a black-box network control problem and the model-based network management mechanisms will be no longer applicable. In contrast, model-free learning enables the decision-making entities to adapt their behaviors based on the reinforcement from their interaction with the environment and (implicitly) build their understanding of the system from scratch through trial-and-error. Such characteristics are highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Therefore, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models on which those applications are based, a systematic overview of the learning algorithms in the domains of single-agent system, multiagent systems, and multiplayer games is provided. The applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the model-based, non-adaptive methods. Finally, a broad spectrum of challenges and open issues is discussed to offer a guideline for the future research directions.
A Compact High-Gain and Front-to-Back Ratio Elliptically Tapered Antipodal Vivaldi Antenna With Trapezoid-Shaped Dielectric Lens

A modified compact antipodal Vivaldi antenna is proposed with good performance for different applications including microwave and millimeter wave imaging. A step-by-step procedure is applied in this design including conventional antipodal Vivaldi antenna (AVA), AVA with a periodic slit edge, and AVA with a trapezoid-shaped dielectric lens to feature performances including wide bandwidth, small size, high gain, front-to-back ratio and directivity, modification on E-plane beam tilt, and small sidelobe levels. By adding periodic slit edge at the outer brim of the antenna radiators, lower-end limitation of the conventional AVA extended twice without changing the overall dimensions of the antenna. The optimized antenna is fabricated and tested, and the results show that S11 <; -10 dB frequency band is from 3.4 to 40 GHz, and it is in good agreement with simulation one. Gain of the antenna has been elevated by the periodic slit edge and the trapezoid dielectric lens at lower frequencies up to 8 dB and at higher frequencies up to 15 dB, respectively. The E-plane beam tilts and sidelobe levels are reduced by the lens.
Charging from sampled network usage

IP flows have heavy-tailed packet and byte size distributions. This make them poor candidates for uniform sampling---i.e. selecting 1 in N flows---since omission or inclusion of a large flow can have a large effect on estimated total traffic. Flows selected in this manner are thus unsuitable for use in usage sensitive billing. We propose instead using a size-dependent sampling scheme which gives priority to the larger contributions to customer usage. This turns the heavy tails to our advantage; we can obtain accurate estimates of customer usage from a relatively small number of important samples.The sampling scheme allows us to control error when charging is sensitive to estimated usage only above a given base level. A refinement allows us to strictly limit the chance that a customers estimated usage will exceed their actual usage. Furthermore, we show that a secondary goal, that of controlling the rate at which samples are produced, can be fulfilled provided the billing cycle is sufficiently long. All these claims are supported by experiments on flow traces gathered from a commercial network.
Open government and e-government: democratic challenges from a public value perspective

We consider open government (OG) within the context of e-government and its broader implications for the future of public administration. We argue that the current US Administration's Open Government Initiative blurs traditional distinctions between e-democracy and e-government by incorporating historically democratic practices, now enabled by emerging technology, within administrative agencies. We consider how transparency, participation, and collaboration function as democratic practices in administrative agencies, suggesting that these processes are instrumental attributes of administrative action and decision making, rather than the objective of administrative action, as they appear to be currently treated. We propose alternatively that planning and assessing OG be addressed within a "public value" framework. The creation of public value is the goal of public organizations; through public value, public organizations meet the needs and wishes of the public with respect to substantive benefits as well as the intrinsic value of better government. We extend this view to OG by using the framework as a way to describe the value produced when interaction between government and citizens becomes more transparent, participative, and collaborative, i.e., more democratic.