nils-herrmann/clean_scirepeval huggingface dataset:
Use of kernel deep convex networks and end-to-end learning for spoken language understanding

We present our recent and ongoing work on applying deep learning techniques to spoken language understanding (SLU) problems. The previously developed deep convex network (DCN) is extended to its kernel version (K-DCN) where the number of hidden units in each DCN layer approaches infinity using the kernel trick. We report experimental results demonstrating dramatic error reduction achieved by the K-DCN over both the Boosting-based baseline and the DCN on a domain classification task of SLU, especially when a highly correlated set of features extracted from search query click logs are used. Not only can DCN and K-DCN be used as a domain or intent classifier for SLU, they can also be used as local, discriminative feature extractors for the slot filling task of SLU. The interface of K-DCN to slot filling systems via the softmax function is presented. Finally, we outline an end-to-end learning strategy for training the softmax parameters (and potentially all DCN and K-DCN parameters) where the learning objective can take any performance measure (e.g. the F-measure) for the full SLU system.
Array SSA Form and Its Use in Parallelization

Static single assignment (SSA) form for scalars has been a significant advance. It has simplified the way we think about scalar variables. It has simplified the design of some optimizations and has made other optimizations more effective. Unfortunately none of this can be be said for SSA form for arrays. The current SSA processing of arrays views an array as a single object. But the kinds of analyses that sophisticated compilers need to perform on arrays, for example those that drive loop parallelization, are at the element level. Current SSA form for arrays is incapable of providing the element-level data flow information required for such analyses.In this paper, we introduce an Array SSA form that captures precise element-level data flow information for array variables in all cases. It is general and simple, and coincides with standard SSA form when applied to scalar variables. It can also be used for structures and other variable types that can be modeled as arrays. An important application of Array SSA form is in automatic parallelization. We show how Array SSA form can enable parallelization of any loop that is free of loop-carried true data dependences. This includes loops with loop-carried anti and output dependences, unanalyzable subscript expressions, and arbitrary control flow within an iteration. Array SSA form achieves this level of generality by making manifest its ¿ functions as runtime computations in cases that are not amenable to compile-time analysis.
Infrastructure support for mobile computing

Mobile computing is emerging as the prime focus of next generation computing .One of the prime issues of mobile computing is to provide infrastructure support in terms of computing devices, seamless mobility, application middleware, data and user security, and user applications/services. Mobile commerce is one of the driving forces that has evinced enormous interest in mobile computing .The thought of conducting commerce on the go is what is driving the huge investments corporations are making in researching this area. This paper discusses the various challenges in providing infrastructure for wireless computing.
Space-time video completion

We present a method for space-time completion of large space-time "holes" in video sequences of complex dynamic scenes. The missing portions are filled-in by sampling spatio-temporal patches from the available parts of the video, while enforcing global spatio-temporal consistency between all patches in and around the hole. This is obtained by posing the task of video completion and synthesis as a global optimization problem with a well-defined objective function. The consistent completion of static scene parts simultaneously with dynamic behaviors leads to realistic looking video sequences. Space-time video completion is useful for a variety of tasks, including, but not limited to: (i) Sophisticated video removal (of undesired static or dynamic objects) by completing the appropriate static or dynamic background information, (ii) Correction of missing/corrupted video frames in old movies, and (iii) Synthesis of new video frames to add a visual story, modify it, or generate a new one. Some examples of these are shown in the paper.
An 82.4% efficiency package-bondwire-based four-phase fully integrated buck converter with flying capacitor for area reduction

Multi-phase converters have become a topic of great interest due to the high output power capacity and output ripple cancellation effect. They are even more beneficial to nowadays high-frequency fully integrated converters with output capacitor integrated on-chip. As one of the dominant chip area consumers, reducing the size of the on-chip decoupling capacitors directly leads to cost down. It is reported that a 5× capacitor area reduction can be achieved with a four-phase converter compared to a single-phase one . However, the penalty is obvious. Every extra phase comes with an inductor, which is also counted as cost and becomes more dominant with increase in the number of phases.
Mixed signal SoC based Bio-Sensor Node for long term health monitoring

Major requirements of wearable biosensor nodes include small size, light weight and low power consumption. It can be fulfilled by exploring various high integration technologies such as Application Specific Integrated Circuit (ASIC) or System on Chip (SoC) implementation. This paper presents SoC prototype of a Biosensor Node. Electrocardiogram (ECG), a vital body parameter, has been considered for prototype implementation. It is based on PSoC (Programmable System on Chip), a reconfigurable platform for SoC implementation. The main feature of PSoC is the programmable analog and digital blocks, which allow the implementation of a mixed signal circuit in single chip. Biosensor node comprises of data acquisition module implemented in PSoC 5 LP interfaced with Bluetooth module. It continuously acquires ECG data and wirelessly transmits the data to central node, which essentially is a smart phone. An android application has been used to plot received ECG signal data for monitoring over the long duration.
The Seattle Heart Failure Model: prediction of survival in heart failure.

Heart failure has an annual mortality rate ranging from 5% to 75%. The purpose of the study was to develop and validate a multivariate risk model to predict 1-, 2-, and 3-year survival in heart failure patients with the use of easily obtainable characteristics relating to clinical status, therapy (pharmacological as well as devices), and laboratory parameters.



The Seattle Heart Failure Model was derived in a cohort of 1125 heart failure patients with the use of a multivariate Cox model. For medications and devices not available in the derivation database, hazard ratios were estimated from published literature. The model was prospectively validated in 5 additional cohorts totaling 9942 heart failure patients and 17,307 person-years of follow-up. The accuracy of the model was excellent, with predicted versus actual 1-year survival rates of 73.4% versus 74.3% in the derivation cohort and 90.5% versus 88.5%, 86.5% versus 86.5%, 83.8% versus 83.3%, 90.9% versus 91.0%, and 89.6% versus 86.7% in the 5 validation cohorts. For the lowest score, the 2-year survival was 92.8% compared with 88.7%, 77.8%, 58.1%, 29.5%, and 10.8% for scores of 0, 1, 2, 3, and 4, respectively. The overall receiver operating characteristic area under the curve was 0.729 (95% CI, 0.714 to 0.744). The model also allowed estimation of the benefit of adding medications or devices to an individual patient's therapeutic regimen.



The Seattle Heart Failure Model provides an accurate estimate of 1-, 2-, and 3-year survival with the use of easily obtained clinical, pharmacological, device, and laboratory characteristics.
Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral.
 Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.
Barcode based Student Attendance System

Student attendance play significant role in order to justify academic outcome of a student and college as overall. Unfortunately, there is no automated attendance record keeping application available in colleges. There is a need for a tool to systematically keep the students attendance record due to increasing number of college students The project that we are going to make is to help the teachers in our college to avoid maintaining the registry book. This project uses a barcode scanner. B. B. S. A. S uses Barcode scanner to take the attendance of students entering the lab. Each student&apos;s ID card will have a barcode at the back side of it. This barcode contains unique data of the student such as roll number, branch and year. Etc. Student will scan their barcode at the end so that the student can&apos;t cheat. The display screen will show the attendance of the particular student after scanning his/her barcode.
Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

In a social network, nodes correspond topeople or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.
XenTune: Detecting Xen Scheduling Bottlenecks for Media Applications

Virtualization provides enormous economic and ecological benefits by enabling server consolidation and supporting low-cost green data centers. A key component of the hypervisor in a virtualized system is the scheduler. A typical scheduler provides parameters to influence the hypervisor's behavior. Specifically, an application's performance on the popular open-source Xen virtualization platform can be influenced by tuning its scheduler behavior using the weight, cap, and processor pinning variables. However, determining which parameters to tune and how to do that is non-trivial. In this paper, we introduce XenTune, a monitoring tool for the credit scheduler in Xen that helps in understanding application behavior in scheduler terms and assists in determining scheduler parameters. We demonstrate how the tool is used with application domains - specifically a media application domain. We demonstrate experimental results using a real work-load that shows the considerable benefits of correct scheduler parameter choices. XenTune had helped in the design of a recently proposed scheduler S and in this paper we show how scheduler S interacts with the media application to optimize its performance.
The Promise of Sustainable Happiness

Reference: Boehm, J. K., & Lyubomirsky, S. (in press). The promise of sustainable happiness. In S. J. Lopez (Ed.), Handbook of positive psychology (2 ed.).  The Promise of Sustainable Happiness Julia K. Boehm and Sonja Lyubomirsky Department of Psychology University of California, Riverside  From ancient history to recent times, philosophers, writers, self-help gurus, and now scientists have taken up the challenge of how to foster greater happiness. This chapter discusses why some people are happier than others, focusing on the distinctive ways that happy and unhappy individuals construe themselves and others, respond to social comparisons, make decisions, and self-reflect. We suggest that, despite several barriers to increased well-being, less happy people can strive successfully to be happier by learning a variety of effortful strategies and practicing them with determination and commitment. The sustainable happiness model (Lyubomirsky, Sheldon, & Schkade, 2005) provides a theoretical framework for experimental intervention research on how to increase and maintain happiness. According to this model, three factors contribute to an individual’s chronic happiness level – 1) the set point, 2) life circumstances, and 3) intentional activities, or effortful acts that are naturally variable and episodic. Such activities, which include committing acts of kindness, expressing gratitude or optimism, and savoring joyful life events, represent the most promising route to sustaining enhanced happiness. We describe a half dozen randomized controlled interventions testing the efficacy of each of these activities in raising and maintaining well-being, as well as the mediators and moderators underlying their effects. Future researchers must endeavor not only to learn which particular practices make people happier, but how and why they do so.
Human , Social , and Now Positive Psychological Capital Management : INVESTING IN PEOPLE FOR COMPETITIVE ADVANTAGE

Despite the substantial support for the importance of the human factor in achieving sustainable performance, human resources continue to primarily be given lip service, partly owing to the commonly perceived difficulties of quantifying their impact on bottom-line performance and competitive advantage. This article uses well-established business criteria to compare the contribution of human resources to sustainable competitive advantage to that of traditionally recognized resources such as financial, structural, and technological capital. The article presents support for three dimensions of human resources that warrant its inimitablity and sustainability for competitive advantage. Two of these dimensions are human capital (explicit and tacit knowledge) and social capital (networks, norms/ values, and trust). Then, a new dimension of human resources, positive psychological capital, which involves measurable, developable psychological capacities that can be readily enhanced and managed for performance improvement, is introduced. These capacities include self-efficacy/ confidence, hope, optimism, and resiliency. There is growing evidence that human resources are crucial to organizational success, and may offer the best return on investment for sustainable competitive advantage. Jeffery Pfeffer's extensive work, summarized in his book The Human Equation, discusses substantially supported but unfortunate findings that only about half of today's organizations and their managers believe that human resources really do matter. Moreover, only half of those organizations act upon their beliefs beyond paying lip service to this vital resource. Few organizations have adopted high performance work practices, such as 360-degree feedback, pay-for-performance, self-managed teams, employee empowerment, and other human-oriented initiatives. Furthermore, Pfeffer shows that about half of those who " believe " that human resources are their most important asset and " do something about it " actually " stick to " their beliefs and commit to these high performance work practices over time. Pfeffer carefully documents that the resulting " one-eighth " are the world-class that may vary in size and technology, and across industries and countries, but share worldwide superiority in profitability, productivity, innovation, quality, customer satisfaction, and bottom-line profitability. As Carly Fiorina of Hewlett-Packard Co. recently observed: " the most magical and tangible and ultimately the most important ingredient in the transformed landscape is people. " Other well known business leaders such as Intel Corp.'s Andy Grove and Microsoft's 4 Bill Gates further support this claim in their observation that " our most important asset walks out the door every night. " In other words, there finally seems to be the realization that human …
A comparative study of game mechanics and control laws for an adaptive physiological game

We present an adaptive biofeedback game that aims to maintain the player’s arousal by modifying game difficulty in response to the player’s physiological state, as measured with wearable sensors. Our approach models the interaction between human physiology and game difficulty during gameplay as a control problem, where game difficulty is the system input and player arousal its output. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use (1) car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty, (2) electrodermal activity as physiological correlate of arousal, and (3) two types of control law: proportional (P) control, and proportional-integral-derivative (PID) control. We also propose quantitative measures to characterize the effectiveness of these game adaptations and controllers in manipulating the player’s arousal. Experimental trials with 25 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences in effectiveness among the three game mechanics and also between the two control laws. Specifically, manipulating car speed provides higher control of arousal levels than changing road visibility or vehicle steering. Our results also confirm that PID control leads to lower error and reduced oscillations in the closed-loop response compared to proportional-only control. Finally, we discuss the theoretical and practical implications of our approach.
Higher Lower Bounds from the 3SUM Conjecture

The 3SUM conjecture has proven to be a valuable tool for proving conditional lower bounds on dynamic data structures and graph problems. This line of work was initiated by Pǎtraşcu  who reduced 3SUM to an offline SetDisjointness problem. However, the reduction introduced by Pǎtraşcu suffers from several inefficiencies, making it difficult to obtain tight conditional lower bounds from the 3SUM conjecture. In this paper we address many of the deficiencies of Pǎtraşcu’s framework. We give new and efficient reductions from 3SUM to offline SetDisjointness and offline SetIntersection (the reporting version of SetDisjointness) which leads to polynomially higher lower bounds on several problems. Using our reductions, we are able to show the essential optimality of several algorithms, assuming the 3SUM conjecture. • Chiba and Nishizeki’s O(mα)-time algorithm (SICOMP 1985) for enumerating all triangles in a graph with arboricity/degeneracy α is essentially optimal, for any α. • Bjørklund, Pagh, Williams, and Zwick’s algorithm (ICALP 2014) for listing t triangles is essentially optimal (assuming the matrix multiplication exponent is ω = 2). • Any static data structure for SetDisjointness that answers queries in constant time must spend Ω(N2−o(1)) time in preprocessing, where N is the size of the set system. These statements were unattainable via Pǎtraşcu’s reductions. We also introduce several new reductions from 3SUM to pattern matching problems and dynamic graph problems. Of particular interest are new conditional lower bounds for dynamic versions of Maximum Cardinality Matching, which introduce a new technique for obtaining amortized lower bounds.
A comparison of trading algorithms based on machine learning classifiers : application on the S & P 500

In this paper we analyse whether different machine learning classifiers can be useful when it comes to financial data prediction. We first measure the accuracy and the volatility of our algorithm on the stocks included in the S&P 500 stock index. We then back-test it against the same benchmark for two different periods. Overall, our results show that an algorithm that trades just according to the predictions of the classifiers, is not profitable even if the accuracy obtained by the algorithm is higher than the accuracy that should be obtained according to the random walk theory. However, it can boost the results obtained by other strategies, maintaining the same level of volatility.
Accuracy of differential shift estimation by correlation and split-bandwidth interferometry for wideband and delta-k SAR systems

Estimation of differential shift of image elements between two synthetic aperture radar (SAR) images is the basis for many applications, like digital elevation model generation or ground motion mapping. The shift measurement can be done nonambiguously on the macro scale at an accuracy depending on the range resolution of the system or on the micro scale by employing interferometric methods. The latter suffers from phase cycle ambiguities and requires phase unwrapping. Modern wideband high-resolution SAR systems boast resolutions as small as a few tens of a wavelength. If sufficiently many samples are used for macro-scale shift estimation, the accuracy can be increased to a small fraction of a resolution cell and even in the order of a wavelength. Then, accurate absolute ranging becomes precise enough to support phase unwrapping or even make it obsolete. This letter establishes a few fundamental equations on the accuracy bounds of shift estimation accuracy for several algorithms: coherent speckle correlation, incoherent speckle correlation, split-band interferometry, a multifrequency approach, and correlation of point scatterers in clutter. It is shown that the performance of split-band interferometry is close to the Crame/spl acute/r-Rao bound for a broad variety of bandwidth ratios. Based on these findings, Delta-k systems are proposed to best take advantage of the available radar bandwidth.
A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events

State-of-the-art event encoding approaches rely on sentence or phrase level labeling, which are both time consuming and infeasible to extend to large scale text corpora and emerging domains. Using a multiple instance learning approach, we take advantage of the fact that while labels at the sentence level are difficult to obtain, they are relatively easy to gather at the document level. This enables us to view the problems of event detection and extraction in a unified manner. Using distributed representations of text, we develop a multiple instance formulation that simultaneously classifies news articles and extracts sentences indicative of events without any engineered features. We evaluate our model in its ability to detect news articles about civil unrest events (from Spanish text) across ten Latin American countries and identify the key sentences pertaining to these events. Our model, trained without annotated sentence labels, yields performance that is competitive with selected state-of-the-art models for event detection and sentence identification. Additionally, qualitative experimental results show that the extracted event-related sentences are informative and enhance various downstream applications such as article summarization, visualization, and event encoding.