nils-herrmann/clean_scirepeval huggingface dataset:
Facial age estimation using BSIF and LBP

Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP) histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database.
Abstract syntax graphs for domain specific languages

This paper presents a representation for embedded domain specific languages (EDSLs) using abstract syntax graphs (ASGs). The purpose of this representation is to deal with the important problem of defining operations that require observing or preserving sharing and recursion in EDSLs in an expressive, yet easy-to-use way. In contrast to more conventional representations based on abstract syntax trees, ASGs represent sharing and recursion explicitly as binder constructs. We use a functional representation of ASGs based on structured graphs, where binders are encoded with parametric higher-order abstract syntax. We show how adapt to this representation to well-typed ASGs. This is especially useful for EDSLs, which often reuse the type system of the host language. We also show an alternative class-based encoding of(well-typed) ASGs that enables extensible and modular well-typed EDSLs while allowing the manipulation of sharing and recursion.
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification

In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-theart methods on five benchmark datasets.
Relation Extraction Using Multi-Encoder LSTM Network on a Distant Supervised Dataset

Relation extraction techniques are used to find potential relational facts in textual content.  Extraction systems require huge amount of training data to model semantics of sentences and identify relations. Distant supervision, often used to construct training data for relation extraction, produces noisy alignments that can hurt the performance of relation extraction systems. To this end, we propose a simple, yet effective, technique to automatically compute confidence levels of alignments. We compute the confidence values of automatically labeled content using co-occurrence statistics of relations and dependency patterns of aligned sentences. Thereafter, we propose a novel multi-encoder bidirectional Long Short Term Memory (LSTM) model to identify relations in a given sentence. We use different features (words, part-of-speech (POS) tags and dependency paths) in each encoder and concatenate the hidden states of all the encoders to predict the relations. Our experiments show that a multi-encoder network can handle different features together to predict relations more accurately (~9% improvement over a single encoder model). We also conduct visualization experiments to show that our model learns intermediate representations effectively to identify relations in sentences.
A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using an RGB Camera

Recording and monitoring vital signs is an essential part of home-based healthcare. Using contact sensors to record physiological signals can cause discomfort to patients, especially after prolonged use. Hence, remote physiological measurement approaches have attracted considerable attention, as they do not require physical contact with the patient’s skin. Several studies proposed techniques to measure heart rate (HR) and heart rate variability by detecting the blood volume pulse from human facial video recordings while the subject is in a resting condition. In this paper, we focus on the measurement of HR. We adopt an algorithm that uses the independent component analysis (ICA) to separate the source (physiological) signal from noise in the RGB channels of a facial video. We generalize existing methods to support subject movement during video recording. Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. With respect to the ECG measurement ground truth, the proposed method decreases the RMSE by 27% compared with the state of the art in the stationary condition. When the subject is in motion, our proposed method achieves an RMSE of 1.12 bpm.
An approach to finite-time controller design for a class of T-S fuzzy systems

This paper studies the finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel non-Lipschitz continuous state feedback control scheme with augmented dynamics is developed. It is shown that finite-time convergence of the closed loop system can be achieved, and the potential controller singularity problem can be avoided. Finally, one example is given to illustrate the effectiveness of the proposed control approach.
Locally Weighted Regression : An Approach to Regression Analysis by Local Fitting

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On the tradeoff between privacy and utility in data publishing

In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility of the data. It is important to consider the tradeoff between privacy and utility. In a paper that appeared in KDD 2008, Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data, and concluded that "even modest privacy gains require almost complete destruction of the data-mining utility". This conclusion seems to undermine existing work on data anonymization. In this paper, we analyze the fundamental characteristics of privacy and utility, and show that it is inappropriate to directly compare privacy with utility. We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment, and propose an integrated framework for considering privacy-utility tradeoff, borrowing concepts from the Modern Portfolio Theory for financial investment. Finally, we evaluate our methodology on the Adult dataset from the UCI machine learning repository. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility.
Side-Channel Leakage of Masked CMOS Gates

There are many articles and patents on the masking of logic gates. However, the existing publications assume that a masked logic gate switches its output no more than once per clock cycle. Unfortunately, this assumption usually does not hold true in practice. In this article, we show that glitches occurring in circuits of masked gates make these circuits susceptible to classical first-order DPA attacks. Besides a thorough theoretical analysis of the DPA-resistance of masked gates in the presence of glitches, we also provide simulation results that confirm the theoretical elaborations. Glitches occur in every CMOS circuit. Consequently, the currently known masking schemes for CMOS gates do not prevent DPA attacks.
Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: status and recommendations.

The Movement Disorder Society Task Force for Rating Scales for Parkinson's disease (PD) prepared a critique of the Hoehn and Yahr scale (HY). Strengths of the HY scale include its wide utilization and acceptance. Progressively higher stages correlate with neuroimaging studies of dopaminergic loss, and high correlations exist between the HY scale and some standardized scales of motor impairment, disability, and quality of life. Weaknesses include the scale's mixing of impairment and disability and its non-linearity. Because the HY scale is weighted heavily toward postural instability as the primary index of disease severity, it does not capture completely impairments or disability from other motor features of PD and gives no information on nonmotor problems. Direct clinimetric testing of the HY scale has been very limited, but the scale fulfills at least some criteria for reliability and validity, especially for the midranges of the scale (Stages 2-4). Although a "modified HY scale" that includes 0.5 increments has been adopted widely, no clinimetric data are available on this adaptation. The Task Force recommends that: (1) the HY scale be used in its original form for demographic presentation of patient groups; (2) when the HY scale is used for group description, medians and ranges should be reported and analysis of changes should use nonparametric methods; (3) in research settings, the HY scale is useful primarily for defining inclusion/exclusion criteria; (4) to retain simplicity, clinicians should "rate what you see" and therefore incorporate comorbidities when assigning a HY stage; and (5) because of the wide usage of the modified HY scale with 0.5 increments, this adaptation warrants clinimetric testing. Without such testing, however, the original five-point scales should be maintained.
An efficient and generic reversible debugger using the virtual machine based approach

The reverse execution of programs is a function where programs are executed backward in time. A reversible debugger is a debugger that provides such a functionality. In this paper, we propose a novel reversible debugger that enables reverse execution of programs written in the C language. Our approach takes the virtual machine based approach. In this approach, the target program is executed on a special virtual machine. Our contribution in this paper is two-fold. First, we propose an approach that can address problems of (1) compatibility and (2) efficiency that exist in previous works. By compatibility, we mean that previous debuggers are not generic, i.e., they support only a special language or special intermediate code. Second, our approach provides two execution modes: the native mode, where the debuggee is directly executed on a real CPU, and the virtual machine mode, where the debuggee is executed on a virtual machine. Currently, our debugger provides four types of trade-off settings (designated by unit and optimization) to consider trade-offs between granularity, accuracy, overhead and memory requirement. The user can choose the appropriate setting flexibly during debugging without finishing and restarting the debuggee.
Deception detection using a multimodal approach

In this paper we address the automatic identification of deceit by using a multimodal approach. We collect deceptive and truthful responses using a multimodal setting where we acquire data using a microphone, a thermal camera, as well as physiological sensors. Among all available modalities, we focus on three modalities namely, language use, physiological response, and thermal sensing. To our knowledge, this is the first work to integrate these specific modalities to detect deceit. Several experiments are carried out in which we first select representative features for each modality, and then we analyze joint models that integrate several modalities. The experimental results show that the combination of features from different modalities significantly improves the detection of deceptive behaviors as compared to the use of one modality at a time. Moreover, the use of non-contact modalities proved to be comparable with and sometimes better than existing contact-based methods. The proposed method increases the efficiency of detecting deceit by avoiding human involvement in an attempt to move towards a completely automated non-invasive deception detection process.
Internet of Things: Vision, Applications and Challenges

The form of communication that we see now is either human-human or human-device, but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-machine (M2M). It aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. This paper aims to provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things and facilitate knowledge accumulation in efficiently.
The causes of welfare state expansion : deindustrialization or globalization ?

An influential line of argument holds that trade exposure causes economic uncertainty and spurs popular demands for compensatory and risk-sharing welfare state spending. The argument has gained renewed prominence through the recent work of Garrett (1998) and Rodrik (1997; 1998). This paper argues that the relationship between trade openness and welfare state expansion is spurious, and that the engine of welfare state expansion since the 1960s has been deindustrialization. Based on cross-sectional time-series data for 15 OECD countries we show that there is no relationship between trade exposure and the level of labor market risks (in terms of employment and wages), whereas the uncertainty and dislocations caused by deindustrialization have spurred electoral demands for compensating welfare state policies. Yet, while differential rates of deindustrialization explain differences in the overall size of the welfare state, its particular character -in terms of the share of direct government provision and the equality of transfer payments -is shaped by government partisanship. The argument has implications for the study, and the future,of thewelfare state thatarevery different from thosesuggested in the trade openness literature. Zusammenfassung In vielen einflußreichen Diskussionsbeiträgen wird die Meinung vertreten, daß die Liberalisierung des Handels ökonomische Verunsicherung zur Folge habe und damit zu Forderungen nach ausgleichenden wohlfahrtsstaatlichen Ausgaben führe. Die Arbeiten von Garrett (1998) und Rodrik (1997;1998) verliehen diesem Argument zusätzliche Relevanz. Gegenstand dieser Untersuchung ist die Beziehung zwischen Ausmaß an Offenheit einer Volkswirtschaft und der Ausdehnung des Wohlfahrtsstaates, dessen großzügige Entwicklung seit den 1960er Jahren durch zunehmende Deindustrialisierung ermöglicht wurde. Auf der Grundlage von Analysen länderübergreifender Zeitreihen und von 15 OECD-Ländern wird gezeigt, daß kein Zusammenhang zwischen einer Handelsliberalisierung und dem Grad der Arbeitsmarktrisiken (bezogen auf Löhne und Beschäftigung) besteht. Angesichts der durch die Deindustrialisierung verursachten Unsicherheit kommt es jedoch von seiten der Wähler zu Forderungen nach einer ausgleichenden Sozialpolitik. Während das Ausmaß der Deindustrialisierung die Größe und Ausstattung des Wohlfahrsstaates determiniert, wird sein spezifischer Charakter hinsichtlich der direkten Regierungsdienstleistungen und der ausgleichenden Transferzahlungen von den Regierungsparteien geprägt. Diese Argumentation ist von großer Tragweite für die Analyse und Zukunft des Wohlfahrtsstaates; sie weicht gravierend von der Literatur über offene Volkswirtschaften ab.
A new hybrid PSOGSA algorithm for function optimization

In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.
A New Technique for Characterization of Digital-to-Analog Converters in High-Speed Systems

In this paper, a new technique for characterization of digital-to-analog converters (DAC) used in wideband applications is described. Unlike the standard narrowband approach, this technique employs Least Square Estimation to characterize the DAC from dc to any target frequency. Characterization is performed using a random sequence with certain temporal and probabilistic characteristics suitable for intended operating conditions. The technique provides a linear estimation of the system and decomposes nonlinearity into higher-order harmonics and deterministic periodic noise. The technique can also be used to derive the impulse response of the converter, predict its operating bandwidth, and provide far more insight into its sources of distortion.
A Safe, Efficient Regression Test Selection Technique

Regression testing is an expensive but necessary maintenance activity performed on modified software to provide confidence that changes are correct and do not adversely affect other portions of the softwore. A regression test selection technique choses, from an existing test set, thests that are deemed necessary to validate modified software. We present a new technique for regression test selection. Our algorithms construct control flow graphs for a precedure or program and its modified version and use these graphs to select tests that execute changed code from the original test suite. We prove that, under certain conditions, the set of tests our technique selects includes every test from the original test suite that con expose faults in the modified procedfdure or program. Under these conditions our algorithms are safe. Moreover, although our algorithms may select some tests that cannot expose faults, they are at lease as precise as other safe regression test selection algorithms. Unlike many other regression test selection algorithms, our algorithms handle all language constructs and all types of program modifications. We have implemented our algorithms; initial empirical studies indicate that our technique can significantly reduce the cost of regression testing modified software.
HMM-based Address Parsing with Massive Synthetic Training Data Generation

Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches of synthetic training data generation to build robust models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses.
Toward a Theory of Situation Awareness in Dynamic Systems

Situational awareness has become an increasingly salient factor contributing to flight safety and operational performance, and the research has burgeoned to cope with the human performance challenges associated with the installation of advanced avionics systems in modern aircraft. The systematic study and application of situational awareness has also extended beyond the cockpit to include air traffic controllers and personnel operating within other complex, high consequence work domains. This volume offers a collection of essays that have made important contributions to situational awareness research and practice. To this end, it provides unique access to key readings that address the conceptual development of situational awareness, methods for its assessment, and applications to enhance situational awareness through training and design.
I/O-Complexity of Graph Algorithms

We show lower bounds of Q( $ Sort(V)) for the I/Ocomplexity of graph theoretic problems like connected components, biconnected components, and minimum spanning trees, where E and V are the number of edges and vertices in the input graph, respectively. We also present a deterministic O($ Sort(V) ’ max(l,loglog 9)) algorithm for the problem of graph connectivity, where B and D denote respectively the block size and number of disks. Our algorithm includes a breadth first search; this maybe of independent
Provable data possession at untrusted stores

We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage system.
 We present two provably-secure PDP schemes that are more efficient than previous solutions, even when compared with schemes that achieve weaker guarantees. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation.