nils-herrmann/clean_scirepeval huggingface dataset:
SNR loss: A new objective measure for predicting the intelligibility of noise-suppressed speech

Most of the existing intelligibility measures do not account for the distortions present in processed speech, such as those introduced by speech-enhancement algorithms. In the present study, we propose three new objective measures that can be used for prediction of intelligibility of processed (e.g., via an enhancement algorithm) speech in noisy conditions. All three measures use a critical-band spectral representation of the clean and noise-suppressed signals and are based on the measurement of the SNR loss incurred in each critical band after the corrupted signal goes through a speech enhancement algorithm. The proposed measures are flexible in that they can provide different weights to the two types of spectral distortions introduced by enhancement algorithms, namely spectral attenuation and spectral amplification distortions. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train and street interferences). Highest correlation (r=-0.85) with sentence recognition scores was obtained using a variant of the SNR loss measure that only included vowel/consonant transitions and weak consonant information. High correlation was maintained for all noise types, with a maximum correlation (r=-0.88) achieved in street noise conditions.
Qualitative Easing : How it Works and Why it Matters

This paper is about the effectiveness of qualitative easing; a government policy that is designed to mitigate risk through central bank purchases of privately held risky assets and their replacement by government debt, with a return that is guaranteed by the taxpayer. Policies of this kind have recently been carried out by national central banks, backed by implicit guarantees from national treasuries. I construct a general equilibrium model where agents have rational expectations and there is a complete set of financial securities, but where agents are unable to participate in financial markets that open before they are born. I show that a change in the asset composition of the central bank’s balance sheet will change equilibrium asset prices. Further, I prove that a policy in which the central bank stabilizes fluctuations in the stock market is Pareto improving and is costless to implement.
SOCK: Rapid Task Provisioning with Serverless-Optimized Containers

Serverless computing promises to provide applications with cost savings and extreme elasticity. Unfortunately, slow application and container initialization can hurt common-case latency on serverless platforms. In this work, we analyze Linux container primitives, identifying scalability bottlenecks related to storage and network isolation. We also analyze Python applications from GitHub and show that importing many popular libraries adds about 100 ms to startup. Based on these findings, we implement SOCK, a container system optimized for serverless workloads. Careful avoidance of kernel scalability bottlenecks gives SOCK an 18× speedup over Docker. A generalized-Zygote provisioning strategy yields an additional 3× speedup. A more sophisticated three-tier caching strategy based on Zygotes provides a 45× speedup over SOCK without Zygotes. Relative to AWS Lambda and OpenWhisk, OpenLambda with SOCK reduces platform overheads by 2.8× and 5.3× respectively in an image processing case study.
Customer Purchase Intentions for Counterfeit Products Based on a Modified Theory of Planned Behavior

Counterfeits consumption has recently attracted considerable attention. This study examines factors affecting consumer purchase intention for counterfeit products. Specially, this study takes Vietnam, a developing country where consumer behavior is being affected by rising living standard, as a research object. Based on the Theory of Planned Behavior of Ajzen with the additional factor of perceived financial control, it is hoped that an improved framework can be obtained for predicting customer intention to purchase counterfeits. The hypotheses were tested via Structural Equation Modeling technique. The results indicated that subjective norms exert the greatest impact on customer intention to purchase counterfeits, while additional factor of perceived financial control negatively affects intentions. This investigation can help marketers and policy makers understand the key influences on consumer behavior towards counterfeits and thus to create effective marketing campaigns and anti-piracy strategies.
Continuous Media Sharing in Multimedia Database Systems

The timeliness and synchronization requirements of multimedia data demand e&ient buffer management and disk access schemes for multimedia database systems. The data rates involved are very high and despite the developmenl of eficient storage and retrieval strategies, disk I/O is a potential bottleneck, which limits the number of concurrent sessions supported by a system. This calls for more eficient use of data that has already been brought into the buffer. We introduce the notion of continuous media caching, which is a simple and novel technique where data that have been played back by a user are preserved in a controlled fashion for use by subsequent users requesting the same data. We present heuristics to determine when continuous media sharing is beneficial and describe the bufler management algorithms. Simulation studies indicate that our technique substantially improves the performance of multimedia database applications where data sharing is possible.
Understanding Information Security Culture: A Conceptual Framework

The importance of establishing an information security culture in an organization has become a well established idea. The aim of such a culture is to address the various human factors that can affect an organization’s overall information security efforts. However, understandingboth the various elements of an information security culture, as well as the relationships between these elements, can still be problematic. Schein’s definition of a corporateculture is often used to aid understanding of an information security culture. This paper briefly introduces Schein’s model. It then incorporates the important role knowledge plays in information security into this definition. Finally, a conceptual framework to aid understanding of the interactions between the various elements of such a culture, is presented. This framework is explained by means of illustrative examples, and it is suggested that this conceptual framework can be a useful aid to understanding information security culture.
Using Online Hotel Customer Reviews to Improve the Booking Process

The e-commerce field has developed to the point that more and more hotel companies provide online booking services to travelers as an integral part of their business model. Increasing numbers of hotel companies now provide such services as an integral part of their business model and their guests&apos; experiences with their hotel. Some third-party services allow customers to add comments on each hotel at the affiliated website. The current search tool features at hotel websites are based on fixed properties, allowing companies to take advantage of the huge number of available customer reviews to provide relevant information to consumers considering new services. The present research focuses on the possibility of linking customer reviews with search tools for online hotel booking and dividing the customers into categories based on their travel aims. This shall be accomplished by: 1) extracting customer reviews using opinion mining and finding hotel features that are frequently mentioned in the reviews, and 2) then analyzing those features to achieve the goal of enhancing booking processes by adding new characteristics, based on customer preferences. This research should improve online hotel booking by building a customized tool that utilizes available customer reviews at the Agoda website and matches them with users&apos; preferences based on survey results.
A Multilevel Model Primer Using SAS

This paper provides an introduction to specifying multilevel models using PROC MIXED. After a brief introduction to the field of multilevel modeling, users are provided with concrete examples of how PROC MIXED can be used to estimate (a) two-level organizational models, (b) two-level growth models, and (c) three-level organizational models. Both random intercept and random intercept and slope models are illustrated. Examples are shown using different real world data sources, including the publically available Early Childhood Longitudinal Study–Kindergarten cohort data. For each example, different research questions are examined through both narrative explanations and examples of the PROC MIXED code and corresponding output.
A multi-case study of agile requirements engineering and the use of test cases as requirements

It is an enigma that agile projects can succeed ‘without requirements’ when weak requirements engineering is a known cause for project failures. While agile development projects often manage well without extensive requirements test cases are commonly viewed as requirements and detailed requirements are documented as test cases.  We have investigated this agile practice of using test cases as requirements to understand how test cases can support the main requirements activities, and how this practice varies.  We performed an iterative case study at three companies and collected data through 14 interviews and 2 focus groups.  The use of test cases as requirements poses both benefits and challenges when eliciting, validating, verifying, and managing requirements, and when used as a documented agreement. We have identified five variants of the test-cases-as-requirements practice, namely de facto, behaviour-driven, story-test driven, stand-alone strict and stand-alone manual for which the application of the practice varies concerning the time frame of requirements documentation, the requirements format, the extent to which the test cases are a machine executable specification and the use of tools which provide specific support for the practice of using test cases as requirements.  The findings provide empirical insight into how agile development projects manage and communicate requirements. The identified variants of the practice of using test cases as requirements can be used to perform in-depth investigations into agile requirements engineering. Practitioners can use the provided recommendations as a guide in designing and improving their agile requirements practices based on project characteristics such as number of stakeholders and rate of change.
Keying Hash Functions for Message Authentication

The use of cryptographic hash functions like MD5 or SHA for message authentication has become a standard approach in many Internet applications and protocols. Though very easy to implement, these mechanisms are usually based on ad hoc techniques that lack a sound security analysis. We present new constructions of message authentication schemes based on a cryptographic hash function. Our schemes, NMAC and HMAC, are proven to be secure as long as the underlying hash function has some reasonable cryptographic strengths. Moreover we show, in a quantitative way, that the schemes retain almost all the security of the underlying hash function. In addition our schemes are efficient and practical. Their performance is essentially that of the underlying hash function. Moreover they use the hash function (or its compression function) as a black box, so that widely available library code or hardware can be used to implement them in a simple way, and replaceability of the underlying hash function is easily supported. Department of Computer Science & Engineering, Mail Code 0114, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093.  mihir@cs.ucsd.edu. http://www-cse.ucsd.edu/users/mihir. † IBM T.J. Watson Research Center, PO Box 704, Yorktown Heights, New York 10598.  canetti@ watson.ibm.com. Work done while author was at MIT, supported by a post-doctoral grant from the Rothschild Foundation. IBM T.J. Watson Research Center, PO Box 704, Yorktown Heights, New York 10598. Email:hugo@watson.ibm. com.
Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains

In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression. We hypothesize that syntactic information helps in making such models more robust across domains. We propose two major changes to the model: using explicit syntactic features and introducing syntactic constraints through Integer Linear Programming (ILP). Our evaluation shows that the proposed model works better than the original model as well as a traditional non-neural-network-based model in a cross-domain setting.
CONTRACT DURATION: EVIDENCE FROM FRANCHISING*

Economists generally view standard franchise contracts as efficient, while franchisee advocates view them as exploitive. Consistent with the economic view, we find that contract duration is positively and significantly related to the franchisee’s physical and human capital investments (which are often firm specific). In contrast to assertions by franchisee advocates, we find that these relations exist in subsamples containing only the most established franchisors (as measured by size and experience) and that larger, more experienced franchisors tend to offer longer-term contracts than do newer franchisors. Our evidence also suggests that there is learning across firms about optimal contract terms.
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

e proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diusion is known as early rumor detection, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. us, identifying trending rumors demands an ecient yet exible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classication algorithms to rumor detection in earliness since they rely on hand-craed features which require intensive manual eorts in the case of large amount of posts. is paper presents a deep aention model on the basis of recurrent neural networks (RNN) to learn selectively temporal hidden representations of sequential posts for identifying rumors. e proposed model delves so-aention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep aention based RNN model outperforms state-of-thearts that rely on hand-craed features; (2) the introduction of so aention mechanism can eectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.
Android Mobile Phone Controlled Bluetooth Robot Using 8051 Microcontroller

A robot is usually an electro-mechanical machine that is guided by computer and electronic programming. Many robots have been built for manufacturing purpose and can be found in factories around the world. Designing of the latest inverted ROBOT which can be controlling using an APP for android mobile. We are developing the remote buttons in the android app by which we can control the robot motion with them. And in which we use Bluetooth communication to interface controller and android. Controller can be interfaced to the Bluetooth module though UART protocol. According to commands received from android the robot motion can be controlled. The consistent output of a robotic system along with quality and repeatability are unmatched. Pick and Place robots can be reprogrammable and tooling can be interchanged to provide for multiple applications.
Parameter Estimation Method to Improve the Accuracy of Photovoltaic Electrical Model

This paper proposes an estimation method to identify the electrical model parameters of photovoltaic (PV) modules and makes a comparison with other methods already popular in the technical literature. Based on the full single-diode model, the mathematical description of the I-V characteristic of modules is generally represented by a coupled nonlinear equation with five unknown parameters, which is difficult to solve by an analytical approach. The aim of the proposed method is to find the five unknown parameters that guarantee the minimum absolute error between the P-V curves generated by the electrical model and the P-V curves provided by the manufacturers' datasheets for different external conditions such as temperature and irradiance. The first advantage of the proposed method is that the parameters are estimated using the P-V curves instead of I-V curves, since most of the applications that use the electrical model want to accurately estimate the extracted power. The second advantage is that the value ranges of each unknown parameter respect their physical meaning. In order to prove the effectiveness of the proposition, a comparison among methods is carried out using both types of P-V and I-V curves: those obtained by manufacturers' datasheets and those extracted experimentally in the laboratory.
TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)

We designed and implemented TAGME, a system that is able to efficiently and judiciously augment a plain-text with pertinent hyperlinks to Wikipedia pages. The specialty of TAGME with respect to known systems [5,8] is that it may annotate texts which are short and poorly composed, such as snippets of search-engine results, tweets, news, etc.. This annotation is extremely informative, so any task that is currently addressed using the bag-of-words paradigm could benefit from using this annotation to draw upon (the millions of) Wikipedia pages and their inter-relations.
Learning Task Grouping and Overlap in Multi-task Learning

In the paradigm of multi-task learning, multiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learning that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combination of a finite number of underlying basis tasks. The coefficients of the linear combination are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on the assumption that task parameters within a group lie in a low dimensional subspace but allows the tasks in different groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.
A topological approach for protein classification

Protein functionanddynamics are closely related to its sequence and structure.However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done throughmeasuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics. Persistent homology is a new branch of algebraic topology that has found its success in the topological data analysis in a variety of disciplines, including molecular biology. The present work explores the potential of using persistent homology as an independent tool for protein classification. To this end, we propose amolecular topological fingerprint based support vector machine (MTF-SVM) classifier. Specifically,we constructmachine learning feature vectors solely fromprotein topological fingerprints,which are topological invariants generated during the filtration process. To validate the presentMTF-SVMapproach, we consider four types of problems. First, we study protein-drug binding by using the M2 channel protein of influenza A virus. We achieve 96% accuracy in discriminating drug bound and unbound M2 channels. Secondly, we examine the use of MTF-SVM for the classification of hemoglobin molecules in their relaxed and taut forms and obtain about 80% accuracy. Thirdly, the identification of all alpha, all beta, and alpha-beta protein domains is carried out using 900 proteins.We have found a 85% success in this identification. Finally, we apply the present technique to 55 classification tasks of protein superfamilies over 1357 samples and 246 tasks over 11944 samples. Average accuracies of 82% and 73% are attained. The present study establishes computational topology as an independent and effective alternative for protein classification.
A Multigenerational View of Inequality

The study of intergenerational mobility and most population research are governed by a two-generation (parent-to-offspring) view of intergenerational influence, to the neglect of the effects of grandparents and other ancestors and nonresident contemporary kin. While appropriate for some populations in some periods, this perspective may omit important sources of intergenerational continuity of family-based social inequality. Social institutions, which transcend individual lives, help support multigenerational influence, particularly at the extreme top and bottom of the social hierarchy, but to some extent in the middle as well. Multigenerational influence also works through demographic processes because families influence subsequent generations through differential fertility and survival, migration, and marriage patterns, as well as through direct transmission of socioeconomic rewards, statuses, and positions. Future research should attend more closely to multigenerational effects; to the tandem nature of demographic and socioeconomic reproduction; and to data, measures, and models that transcend coresident nuclear families.
The Benefits of Being Yourself : An Examination of Authenticity , Uniqueness , and Well-Being

In the United States, many people are instructed about the value of “being yourself ” from a young age. However, what evidence is there to support this notion and what happens when “being yourself ” causes a person to stand out, or be different, from others? The field of positive psychology, with its focus on the science behind well-being, stands well-positioned to answer these questions. By reviewing the theories, measurements, and research behind the two constructs of authenticity and uniqueness, this paper aims to show how being oneself does relate positively to well-being, even when doing so sets a person apart from others. It shows that humans have a desire to be authentic and doing so correlates with higher levels of life satisfaction and well-being. It also finds that humans have sometimes competing needs to belong and be unique but that these can be jointly fulfilled by joining distinctive groups. Data supports the connection between authenticity and well-being, as well as the human desire to stand out from others. Because of this, it seems that openness and acceptance must be encouraged on a broader scale in order for individuals and societies to flourish.