nils-herrmann/clean_scirepeval huggingface dataset:
Effective techniques in healthy eating and physical activity interventions: a meta-regression.

Meta-analyses of behavior change (BC) interventions typically find large heterogeneity in effectiveness and small effects. This study aimed to assess the effectiveness of active BC interventions designed to promote physical activity and healthy eating and investigate whether theoretically specified BC techniques improve outcome.



Interventions, evaluated in experimental or quasi-experimental studies, using behavioral and/or cognitive techniques to increase physical activity and healthy eating in adults, were systematically reviewed. Intervention content was reliably classified into 26 BC techniques and the effects of individual techniques, and of a theoretically derived combination of self-regulation techniques, were assessed using meta-regression.



Valid outcomes of physical activity and healthy eating.



The 122 evaluations (N = 44,747) produced an overall pooled effect size of 0.31 (95% confidence interval = 0.26 to 0.36, I(2) = 69%). The technique, "self-monitoring," explained the greatest amount of among-study heterogeneity (13%). Interventions that combined self-monitoring with at least one other technique derived from control theory were significantly more effective than the other interventions (0.42 vs. 0.26).



Classifying interventions according to component techniques and theoretically derived technique combinations and conducting meta-regression enabled identification of effective components of interventions designed to increase physical activity and healthy eating.
IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application

The Internet of Things (IoT) is constantly evolving and is giving unique solutions to the everyday problems faced by man. “Smart City” is one such implementation aimed at improving the lifestyle of human beings. One of the major hurdles in most cities is its solid waste management, and effective management of the solid waste produced becomes an integral part of a smart city. This paper aims at providing an IoT based architectural solution to tackle the problems faced by the present solid waste management system. By providing a complete IoT based system, the process of tracking, collecting, and managing the solid waste can be easily automated and monitored efficiently. By taking the example of the solid waste management crisis of Bengaluru city, India, we have come up with the overall system architecture and protocol stack to give a IoT based solution to improve the reliability and efficiency of the system. By making use of sensors, we collect data from the garbage bins and send them to a gateway using LoRa technology. The data from various garbage bins are collected by the gateway and sent to the cloud over the Internet using the MQTT (Message Queue Telemetry Transport) protocol. The main advantage of the proposed system is the use of LoRa technology for data communication which enables long distance data transmission along with low power consumption as compared to Wi-Fi, Bluetooth or Zigbee.
Big data and e-government: issues, policies, and recommendations

The promises and potential of Big Data in transforming digital government services, governments, and the interaction between governments, citizens, and the business sector, are substantial. From "smart" government to transformational government, Big Data can foster collaboration; create real-time solutions to challenges in agriculture, health, transportation, and more; and usher in a new era of policy- and decision-making. There are, however, a range of policy challenges to address regarding Big Data, including access and dissemination; digital asset management, archiving and preservation; privacy; and security. This paper selectively reviews and analyzes the U.S. policy context regarding Big Data and offers recommendations aimed at facilitating Big Data initiatives.
Face versus non-face object perception and the ‘other-race’ effect: a spatio-temporal event-related potential study

To investigate a modulation of the N170 face-sensitive component related to the perception of other-race (OR) and same-race (SR) faces, as well as differences in face and non-face object processing, by combining different methods of event-related potential (ERP) signal analysis.



Sixty-two channel ERPs were recorded in 12 Caucasian subjects presented with Caucasian and Asian faces along with non-face objects. Surface data were submitted to classical waveforms and ERP map topography analysis. Underlying brain sources were estimated with two inverse solutions (BESA and LORETA).



The N170 face component was identical for both race faces. This component and its topography revealed a face specific pattern regardless of race. However, in this time period OR faces evoked significantly stronger medial occipital activity than SR faces. Moreover, in terms of maps, at around 170 ms face-specific activity significantly preceded non-face object activity by 25 ms. These ERP maps were followed by similar activation patterns across conditions around 190-300 ms, most likely reflecting the activation of visually derived semantic information.



The N170 was not sensitive to the race of the faces. However, a possible pre-attentive process associated to the relatively stronger unfamiliarity for OR faces was found in medial occipital area. Moreover, our data provide further information on the time-course of face and non-face object processing.
Automatic Number Plate Recognition

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. ANPR can be assisted in the detection of stolen vehicles. The detection of stolen vehicles can be done in an efficient manner by using the ANPR systems located in the highways. This paper presents a recognition method in which the vehicle plate image is obtained by the digital cameras and the image is processed to get the number plate information. A rear image of a vehicle is captured and processed using various algorithms. In this context, the number plate area is localized using a novel „feature-based number plate localization‟ method which consists of many algorithms. But our study mainly focusing on the two fast algorithms i.e., Edge Finding Method and Window Filtering Method for the better development of the number plate detection system
Practical Lessons From the Deployment and Management of a Smart City Internet-of-Things Infrastructure: The SmartSantander Testbed Case

The smart cities vision is inexorably turning into a reality. Among the different approaches used to realize more intelligent and sustainable environments, a common denominator is the role that information and communication technologies will play. Moreover, if there is one of these technologies that emerges among the rest, it is the Internet-of-Things (IoT). The ability to ubiquitously embed sensing and actuating capabilities that this paradigm enables is at the forefront of the technologies driving the urban environments transformation. However, there are very little practical experiences of the IoT infrastructure deployment at a large scale. This paper presents practical solutions to the main challenges faced during the deployment and management of a city-scale IoT infrastructure, which encompasses thousands of sensors and other information sources. The experience we have gained during the deployment and operation of the IoT-based smart city infrastructure carried out at Santander (Spain) has led to a number of practical lessons that are summarized in this paper. Moreover, the challenges and problems examples, excerpted from our own real-life experience, are described as motivators for the adopted solutions.
Hallmarks of Cancer: The Next Generation

The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
The Noise Clinic: a Blind Image Denoising Algorithm

This paper describes the complete implementation of a blind image denoising algorithm, that takes any digital image as input. In a first step the algorithm estimates a Signal and Frequency Dependent (SFD) noise model. In a second step, the image is denoised by a multiscale adaptation of the Non-local Bayes denoising method. We focus here on a careful analysis of the denoising step and present a detailed discussion of the influence of its parameters. Extensive commented tests of the blind denoising algorithm are presented, on real JPEG images and on scans of old photographs. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article1.
Web table column categorisation and profiling

Relational tables collected from HTML pages ("web tables") are used for a variety of tasks including table extension, knowledge base completion, and data transformation. Most of the existing algorithms for these tasks assume that the data in the tables has the form of binary relations, i.e., relates a single entity to a value or to another entity. Our exploration of a large public corpus of web tables, however, shows that web tables contain a large fraction of non-binary relations which will likely be misinterpreted by the state-of-the-art algorithms. In this paper, we propose a categorisation scheme for web table columns which distinguishes the different types of relations that appear in tables on the Web and may help to design algorithms which better deal with these different types. Designing an automated classifier that can distinguish between different types of relations is non-trivial, because web tables are relatively small, contain a high level of noise, and often miss partial key values. In order to be able to perform this distinction, we propose a set of features which goes beyond probabilistic functional dependencies by using the union of multiple tables from the same web site and from different web sites to overcome the problem that single web tables are too small for the reliable calculation of functional dependencies.
Virtual Worlds and Augmented Reality in Cultural Heritage Applications

Mixed Realities (Milgram & Kishino 1994) and their concept of cyber-real space interplay invoke such interactive digital narratives that promote new patterns of understanding. However, the "narrative” part, which refers to a set of events happening during a certain period of time and providing aesthetic, dramaturgical and emotional elements, objects and attitudes (Nandi & Marichal 2000, Tamura et al 2001) is still an early topic of research. Mixing such aesthetic ambiences with virtual character augmentations (Cavazza et al 2003) and adding dramatic tension has developed very recently these narrative patterns into an exciting new edutainment medium (Lindt 2003). Since recently, AR Systems had various difficulties to manage such a time-travel in a fully interactive manner, due to hardware & software complexities in AR ‘Enabling Technologies’ (Azuma et al 2001). Generally the setup of such systems was only operational in specific places (indoors-outdoors) or with specific objects which were used for training purposes rendering them not easily applicable in different sites. Furthermore, almost none of these systems feature full real-time virtual human simulation. With our approach, based on an efficient real-time tracking system, which require only a small pre-recorded sequence as a database, we can setup the AR experience with animated virtual humans anywhere, quickly. With the interplay of a modern real-time framework for integrated interactive virtual character simulation, we can enhance the experience with full virtual character simulations. Even if the environmental conditions are drastically altered, thus causing problems for the real-time camera tracker, we can re-train the camera tracker to allow it to continue its operation. The proposed set of algorithms and methodologies aim to extend the “AR Enabling Technologies” in order to further support real-time, mobile, dramaturgical and behavioured Mixed Reality simulations, as opposed to static annotations or rigid geometrical objects. Fig. 1 depicts fully simulated virtual humans (skin, clothes, face, body) augmenting a cultural heritage site.
NJAS: Sandboxing Unmodified Applications in non-rooted Devices Running stock Android

Malware poses a serious threat to the Android ecosystem. Moreover, even benign applications can sometimes constitute security and privacy risks to their users, as they might contain vulnerabilities, or they might perform unwanted actions. Previous research has shown that the current Android security model is not sufficient to protect against these threats, and several solutions have been proposed to enable the specification and enforcing of finer-grained security policies. Unfortunately, many existing solutions suffer from several limitations: they require modifications to the Android framework, root access to the device, to create a modified version of an existing app that cannot be installed without enabling unsafe options, or they cannot completely sandbox native code components. In this work, we propose a novel approach that aims to sandbox arbitrary Android applications. Our solution, called NJAS, works by executing an Android application within the context of another one, and it achieves sandboxing by means of system call interposition. In this paper, we show that our solution overcomes major limitations that affect existing solutions. In fact, it does not require any modification to the framework, does not require root access to the device, and does not require the user to enable unsafe options. Moreover, the core sandboxing mechanism cannot be evaded by using native code components.
Learning Deep Generative Models of Graphs

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph’s nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vectorand sequence-like knowledge representations, toward more expressive and flexible relational data structures.
Cosette: An Automated Prover for SQL

Deciding query equivalence is an important problem in data management with many practical applications. Solving the problem, however, is not an easy task. While there has been a lot of work done in the database research community in reasoning about the semantic equivalence of SQL queries, prior work mainly focuses on theoretical limitations. In this paper, we present COSETTE, a fully automated prover that can determine the equivalence of SQL queries. COSETTE leverages recent advances in both automated constraint solving and interactive theorem proving, and returns a counterexample (in terms of input relations) if two queries are not equivalent, or a proof of equivalence otherwise. Although the problem of determining equivalence for arbitrary SQL queries is undecidable, our experiments show that COSETTE can determine the equivalences of a wide range of queries that arise in practice, including conjunctive queries, correlated queries, queries with outer joins, and queries with aggregates. Using COSETTE, we have also proved the validity of magic set rewrites, and confirmed various real-world query rewrite errors, including the famous COUNT bug. We are unaware of any prior tool that can automatically determine the equivalences of a broad range of queries as COSETTE, and believe that our tool represents a major step towards building provably-correct query optimizers for real-world database systems.
Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.
Spectral Efficiency of CDMA with Random Spreading

The CDMA channel with randomly and independently chosen spreading sequences accurately models the situation where pseudonoise sequences span many symbol periods. Furthermore, its analysis provides a comparison baseline for CDMA channels with deterministic signature waveforms spanning one symbol period. We analyze the spectral efficiency (total capacity per chip) as a function of the number of users, spreading gain, and signal-to-noise ratio, and we quantify the loss in efficiency relative to an optimally chosen set of signature sequences and relative to multiaccess with no spreading. White Gaussian background noise and equal-power synchronous users are assumed. The following receivers are analyzed: a) optimal joint processing, b) single-user matched filtering, c) decorrelation, and d) MMSE linear processing.
Automated assessment and experiences of teaching programming

This article reports on the design, implementation, and usage of the CourseMarker (formerly known as CourseMaster) courseware Computer Based Assessment (CBA) system at the University of Nottingham. Students use CourseMarker to solve (programming) exercises and to submit their solutions. CourseMarker returns immediate results and feedback to the students. Educators author a variety of exercises that benefit the students while offering practical benefits. To date, both educators and students have been hampered by CBA software that has been constructed to assess text-based or multiple-choice answers only. Although there exist a few CBA systems with some capability to automatically assess programming coursework, none assess Java programs and none are as flexible, architecture-neutral, robust, or secure as the CourseMarker CBA system.
Drivers for Transactional B2C M-Commerce Adoption: Extended Theory of Planned Behavior

Although voluntary individual adoption of information technology is well studied in the literature, further theoretical development is needed to account for the specific characteristics of the mobile commerce artifact. In this study, we augment the theory of planned behavior to include evaluative variables that are not fully captured in attitude and to enhance the specificity of the model to mobile commerce. More specifically, we develop, operationalize and empirically test a model for explaining the adoption intention of transactional B2C mobile commerce. The model is empirically tested with mobile device users who have not adopted mobile commerce yet. The empirical results provide strong support for the theoretical model, shedding light on the significance and relative importance of specific adoption factors. The theoretical and empirical implications of these results are discussed.
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.