nils-herrmann/clean_scirepeval huggingface dataset:
Regulators, Mount Up! Analysis of Privacy Policies for Mobile Money Services

Emerging digital financial services use mobile phones to provide access to populations traditionally excluded from the global economy. These “mobile money” services have proven extremely successful in their first ten years of deployment, and provide a powerful means of raising people out of poverty. Such services have access to a wealth of customer information, potentially including entire purchase histories, geolocation, and social network information. In this paper, we perform the first study of privacy policies in mobile money services, evaluating policies from 54 services and comparing them to 50 policies from traditional financial institutions. Because mobile money services are developed under a wide range of regulatory environments, we compare policies to the industry standard (the GSMA’s Mobile Privacy Principles) and to a traditional national standard (the FDIC’s Privacy Rule Handbook). Our analysis shows that almost half (44%) of these mobile money services do not have any privacy policy whatsoever. Of the services that do have privacy policies, roughly one-third (33%) fail to provide them in either of the two most common languages of their market. Furthermore, 50% of these policies do not ever identify to the user what data is actually being collected and stored. Finally, we find that where policies do exist, they are often incomplete and difficult to read by their target customers. These findings show that more work is needed to protect consumer privacy within these mobile money services.
Location-Based Services for Mobile Telephony: a Study of Users' Privacy Concerns

Context -aware computing often involves tracking peoples’ location. Many studies and applications highlight the importance of keeping people’s location information private. We discuss two types of locationbased services; location-tracking services that are based on other parties tracking the user’s location and position-aware services that rely on the device’s knowledge of its own location. We present an experimental case study that examines people’s concern for location privacy and compare this to the use of location-based services. We find that even though the perceived usefulness of the two different types of services is the same, locationtracking services generate more concern for privacy than posit ion-aware services. We conclude that development emphasis should be given to position-aware services but that location-tracking services have a potential for success if users are given a simple option for turning the location-tracking off.
Neural EEG-Speech Models

In this paper, we describe three neural network (NN) based EEG-Speech (NES) models that map the unspoken EEG signals to the corresponding phonemes. Instead of using conventional feature extraction techniques, the proposed NES models rely on graphic learning to project both EEG and speech signals into deep representation feature spaces. This NN based linear projection helps to realize multimodal data fusion (i.e., EEG and acoustic signals). It is convenient to construct the mapping between unspoken EEG signals and phonemes. Specifically, among three NES models, two augmented models (i.e., IANES-B and IANES-G) include spoken EEG signals as either bias or gate information to strengthen the feature learning and translation of unspoken EEG signals. A combined unsupervised and supervised training is implemented stepwise to learn the mapping for all three NES models. To enhance the computational performance, three way factored NN training technique is applied to IANES-G model. Unlike many existing methods, our augmented NES models incorporate spoken-EEG signals that can efficiently suppress the artifacts in unspoken-EEG signals. Experimental results reveal that all three proposed NES models outperform the baseline SVM method, whereas IANES-G demonstrates the best performance on speech recovery and classification task comparatively.
Integrating Text with Video and 3D Graphics: The Effects of Text Drawing Styles on Text Readability

There have been many studies of computer based text reading. However, only a few have considered text integrated with video and 3D graphics. This paper presents an investigation into the effects of varying (a) text drawing style (plain, billboard, Anti-Interference, shadow), (b) image polarity (positive and negative), and (c) background style (video and 3D) on text readability. Reading speed and accuracy were measured and subjective views of participants recorded.
 Results showed that: (a) there was little difference in reading performance for the video and 3D backgrounds; (b) the negative presentation outperformed the positive presentation; (c) the billboard drawing styles supported the best performance; subjective comments showed a preference for the billboard style. We therefore suggest, for reading tasks, that designers of interfaces for games, video, and augmented reality provide billboard style to maximize readability for the widest range of applications.
ACStream: Enforcing Access Control over Data Streams

In this demo proposal, we illustrate ACStream, a system built on top of Stream Base , to specify and enforce access control policies over data streams. ACStream supports a very flexible role-based access control model specifically designed to protect against unauthorized access to streaming data. The core component of ACStream is a query rewriting mechanism that, by exploiting a set of secure operators proposed by us in , rewrites a user query in such a way that it does not violate the specified access control policies during its execution. The demo will show how policies modelling a variety of access control requirements can be easily specified and enforced using ACStream.
The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility.

To: (1) determine the association between Internet addiction and depression, self-reported symptoms of attention deficit and hyperactivity disorder (ADHD), social phobia, and hostility for adolescents; and (2) evaluate the sex differences of association between Internet addiction and the above-mentioned psychiatric symptoms among adolescents.



A total of 2114 students (1204 male and 910 female) were recruited for the study. Internet addiction, symptoms of ADHD, depression, social phobia, and hostility were evaluated by the self-report questionnaire.



The results demonstrated that adolescents with Internet addiction had higher ADHD symptoms, depression, social phobia, and hostility. Higher ADHD symptoms, depression, and hostility are associated with Internet addiction in male adolescents, and only higher ADHD symptoms and depression are associated with Internet addiction in female students.



These results suggest that Internet addiction is associated with symptoms of ADHD and depressive disorders. However, hostility was associated with Internet addiction only in males. Effective evaluation of, and treatment for ADHD and depressive disorders are required for adolescents with Internet addiction. More attention should be paid to male adolescents with high hostility in intervention of Internet addiction.
Citation analysis as a tool in journal evaluation.

As a communications system, the network of journals that play a paramount role in the exchange of scientific and technical information is little understood. Periodically since 1927, when Gross and Gross published their study (1) of references in 1 year’s issues of the Journal of the American Chemical Socie/y, pieces of the network have been illuminated by the work of Bradford (2), Allen (3), Gross and Woodford (4), Hooker (5), Henkle (6), Fussier (7), Brown (8), and others (9). Nevertheless, there is still no map of the journal network as a whok. To date, studies of the network and of the interrelation of its components have been limited in the number of journak, the areas of scientific study, and the periods of time their authors were able to consider, Such shortcomings have not been due to any lack of purpose, insight, or energy on the part of investigators, but to the practical difficulty of compiling and manipulating manually the enormous amount of necessary data. A solution to this problem of data is available in the data base used to produce the Science Citation Index ( SCI ) (10). The coverage of the SCI is international and multidisciplinary; it has grown from 600 journals in 1964 to 2400 journals in 1972, and now includes the world’s most important scientific and technical journals in mow disciplines. The SCI is published quarterly and is cumulated annually and quinquennially, but the data base from which the volumes are compiled is maintained on magnetic tape and is updated weekly. At the end of 1971, this data base contained more than 27 mi[tion references to about 10 million different published items. These references appeared over the past decade in the footnotes and bibliographies of more than 2 million journal articles, communications, letters, and so on. The data base is, thus, not only multidisciplinary, it covers a substantial period of time and, being in machine-readable form, is amenable to extensive manipulation by computer. In 1971, the Institute for Scientfic Information (1S1) decided to undertake a systematic analysis of journal citation patterns across the whole of science and technology. It began by extracting from the data base all references pobIished during the last quarter of 1969 in the 2200 journals then covered by the SCL The resultant sample was about 1 million citations of journals, books, reports, theses, and so forth. To test whether this 3-month sample was representative of the year as a whole, it was matched against another sample made by selecting every 27th reference from the approximately 4 million references collected over the entire year. The two samples were similar enough in scope (number of diflerent items cited) and detail (relative frequency of their citation by different journals) to
Concurrent Hierarchical Reinforcement Learning for RoboCup Keepaway

Keepaway, originated from the RoboCup soccer simulation 2D challenge, has been widely used as a machine learning benchmark. In this paper, we present a concurrent hierarchical reinforcement learning approach to RoboCup Keepaway. Following the idea of hierarchies of abstract machines (HAMs), we write a partial policy as a HAM from the perspective of a single keeper, run multiple instances of the HAM, and use reinforcement learning to learn the optimal completion of the resulting joint HAM. Furthermore, we apply the idea of exploiting the intrinsic internal transitions within the HAM structure for more efficient learning. Experimental results confirm that the concurrent HAM approaches outperform the state of the art significantly on the very complex RoboCup Keepaway domain.
Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need
Greedy Function Approximation : A Gradient Boosting Machine

Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additive expansions based on any tting criterion. Speci c algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classi cation. Special enhancements are derived for the particular case where the individual additive components are decision trees, and tools for interpreting such \TreeBoost" models are presented. Gradient boosting of decision trees produces competitive, highly robust, interpretable procedures for regression and classi cation, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire 1996, and Friedman, Hastie, and Tibshirani 1998 are discussed. 1 Function estimation In the function estimation problem one has a system consisting of a random \output" or \response" variable y and a set of random \input" or \explanatory" variables x = fx1; ; xng. Given a \training" sample fyi;xig N 1 of known (y;x){values, the goal is to nd a function F (x) that maps x to y, such that over the joint distribution of all (y;x){values, the expected value of some speci ed loss function (y; F (x)) is minimized F (x) = argmin F (x) Ey;x (y; F (x)) = argmin F (x) Ex [Ey( (y; F (x)) jx] : (1) Frequently employed loss functions (y; F ) include squared{error (y F ) and absolute error jy F j for y 2 R (regression), and negative binomial log{likelihood, log(1 + e 2yF ), when y 2 f 1; 1g (classi cation). A common procedure is to take F (x) to be a member of a parameterized class of functions F (x;P), where P = fP1; P2; g is a set of parameters. In this paper we focus on \additive" expansions of the form
Metallic Glass Hemispherical Shell Resonators

By utilizing bulk metallic glasses' (BMGs) unique combination of amorphous structure, material properties, and fabrication opportunities, ultrasmooth and symmetric 3-D metallic glass resonators that are complimentary metal oxide semiconductor (CMOS) post-processing compatible are fabricated. Surface roughness to size ratio fabrication precision in the order of 100 parts per billion is demonstrated with a 3-mm diameter Pt57.5Cu14.7Ni5.3P22.5 BMG hemispherical shell with a thickness variation <;100 nm and a surface roughness of <;1 nm Ra. The resonator exhibits a resonant frequency of 13.9440 kHz ± 0.1 Hz with 0.035% frequency mismatch between degenerate N = 2 wineglass modes with a quality factor of 6200. This performance was obtained in the asmolded state without any device tuning or trimming. Another resonator with N = 2 resonant modes at 9.393 and 9.401 kHz, and quality factors of 7800 and 6500 was mounted into an integrated electrode system. Electrical readout by capacitive sensing in both time and frequency domains showed a resonance shift to 9.461 and 9.483 kHz, respectively. The quality factor was reduced to 5400 and 5300, respectively. This investigation demonstrates that BMG resonators may serve as a basis for robust microelectromechanical systems resonator devices with increased performance and low-cost fabrication techniques that exploits the atomic structure, unique softening behavior, strength, formability, and toughness of metallic glasses.
Prediction error estimation: a comparison of resampling methods

In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the 'true' prediction error of a prediction model in the presence of feature selection.



For small studies where features are selected from thousands of candidates, the resubstitution and simple split-sample estimates are seriously biased. In these small samples, leave-one-out cross-validation (LOOCV), 10-fold cross-validation (CV) and the .632+ bootstrap have the smallest bias for diagonal discriminant analysis, nearest neighbor and classification trees. LOOCV and 10-fold CV have the smallest bias for linear discriminant analysis. Additionally, LOOCV, 5- and 10-fold CV, and the .632+ bootstrap have the lowest mean square error. The .632+ bootstrap is quite biased in small sample sizes with strong signal-to-noise ratios. Differences in performance among resampling methods are reduced as the number of specimens available increase.



A complete compilation of results and R code for simulations and analyses are available in Molinaro et al. (2005) (http://linus.nci.nih.gov/brb/TechReport.htm).
Terror management theory and self-esteem revisited: the roles of implicit and explicit self-esteem in mortality salience effects.

Three studies tested the roles of implicit and/or explicit self-esteem in reactions to mortality salience. In Study 1, writing about death versus a control topic increased worldview defense among participants low in implicit self-esteem but not among those high in implicit self-esteem. In Study 2, a manipulation to boost implicit self-esteem reduced the effect of mortality salience on worldview defense. In Study 3, mortality salience increased the endorsement of positive personality descriptions but only among participants with the combination of low implicit and high explicit self-esteem. These findings indicate that high implicit self-esteem confers resilience against the psychological threat of death, and therefore the findings provide direct support for a fundamental tenet of terror management theory regarding the anxiety-buffering role of self-esteem.
Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis

The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10-13% and 3-5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.
From Buildings to Smart Buildings—Sensing and Actuation to Improve Energy Efficiency

A holistic approach to reducing the energy footprint of large commercial buildings is proposed. A detailed energy use breakdown within a modern building is presented, leading to the key insight that, in addition to the HVAC system, the energy used by miscellaneous plug loads which include IT equipment must also be addressed. An actuation framework is proposed to control plug-loads and the HVAC system for energy savings.
Obfuscator-LLVM -- Software Protection for the Masses

Software security with respect to reverse-engineering is a challenging discipline that has been researched for several years and which is still active. At the same time, this field is inherently practical, and thus of industrial relevance: indeed, protecting a piece of software against tampering, malicious modifications or reverse-engineering is a very difficult task. In this paper, we present and discuss a software obfuscation prototype tool based on the LLVM compilation suite. Our tool is built as different passes, where some of them have been open-sourced and are freely available, that work on the LLVM Intermediate Representation (IR) code. This approach brings several advantages, including the fact that it is language-agnostic and mostly independent of the target architecture. Our current prototype supports basic instruction substitutions, insertion of bogus control-flow constructs mixed with opaque predicates, control-flow flattening, procedures merging as well as a code tamper-proofing algorithm embedding code and data checksums directly in the control-flow flattening mechanism.
Violence Detection in Movies

As violence in movies has harmful influence on children, in this paper, we propose an algorithm to detect violent scene in movies. Under our definition of violence, the task of violent scene detection is decomposed into action scene detection and bloody frame detection. While previous approaches addressed on shot level of video structure only, our approach works on more semantic-complete scene structure of video. The input video (digital movie) is first segmented into several scenes. Based on the filmmaking characteristics of action scene, some features of the scene are extracted to feed into the support vector machine for classification. Finally, the face, blood and motion information are integrated to determine whether the action scene has violent content. Experimental results show that the proposed approach works reasonably well in detecting most of the violent scenes. Compared with related work, our approach is computationally simple yet effective.
Backpressure Delay Enhancement for Encounter-Based Mobile Networks While Sustaining Throughput Optimality

Backpressure routing, in which packets are preferentially transmitted over links with high queue differentials, offers the promise of throughput-optimal operation for a wide range of communication networks. However, when traffic load is low, backpressure methods suffer from long delays. This is of particular concern in intermittent encounter-based mobile networks which are already delay-limited due to the sparse and highly dynamic network connectivity. While state of the art mechanisms for such networks have proposed the use of redundant transmissions to improve delay, they do not work well when traffic load is high. In this paper we propose backpressure with adaptive redundancy (BWAR), a novel hybrid approach that provides the best of both worlds. This approach is robust, distributed, and does not require any prior knowledge of network load conditions. We also present variants of BWAR that remove redundant packets via a timeout mechanism, and that improve energy use. These algorithms are evaluated by mathematical analysis and by simulations of real traces of taxis in Beijing, China. The simulations confirm that BWAR outperforms traditional backpressure at low load, while outperforming encounter-routing schemes (Spray and Wait and Spray and Focus) at high load.
Structural Embedding of Syntactic Trees for Machine Comprehension

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.