nils-herrmann/clean_scirepeval huggingface dataset:
Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study

The effects of age on functional connectivity (FC) of intrinsic connectivity networks (ICNs) have largely been derived from cross-sectional studies. Far less is known about longitudinal changes in FC and how they relate to ageing-related cognitive decline. We evaluated intra- and inter-network FC in 78 healthy older adults two or three times over a period of 4years. Using linear mixed modeling we found progressive loss of functional specialization with ageing, evidenced by a decline in intra-network FC within the executive control (ECN) and default mode networks (DMN). In contrast, longitudinal inter-network FC between ECN and DMN showed a u-shaped trajectory whereby functional segregation between these two networks initially increased over time and later decreased as participants aged. The rate of loss in functional segregation between ECN and DMN was associated with ageing-related decline in processing speed. The observed longitudinal FC changes and their associations with processing speed remained after correcting for longitudinal reduction in gray matter volume. These findings help connect ageing-related changes in FC with ageing-related decline in cognitive performance and underscore the value of collecting concurrent longitudinal imaging and behavioral data.
Double-spending fast payments in bitcoin

Bitcoin is a decentralized payment system that relies on Proof-of-Work (PoW) to verify payments. Nowadays, Bitcoin is increasingly used in a number of fast payment scenarios, where the time between the exchange of currency and goods is short (in the order of few seconds). While the Bitcoin payment verification scheme is designed to prevent double-spending, our results show that the system requires tens of minutes to verify a transaction and is therefore inappropriate for fast payments. An example of this use of Bitcoin was recently reported in the media: Bitcoins were used as a form of \emph{fast} payment in a local fast-food restaurant. Until now, the security of fast Bitcoin payments has not been studied. In this paper, we analyze the security of using Bitcoin for fast payments. We show that, unless appropriate detection techniques are integrated in the current Bitcoin implementation, double-spending attacks on fast payments succeed with overwhelming probability and can be mounted at low cost. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast payments are not always effective in detecting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we propose and implement a modification to the existing Bitcoin implementation that ensures the detection of double-spending attacks against fast payments.
Including Parent Training in the Early Childhood Special Education Curriculum for Children With Autism Spectrum Disorders

Volume 8, Number 2, Spring 2006, pages 79–87  Parent training has been shown to be a very effective method for promoting generalization and maintenance of skills in children with autism. However, despite its wellestablished benefits, few public school programs include parent training as part of the early childhood special education (ECSE) curriculum. Barriers to the provision of parent training include the need for parent education models that can be easily implemented in ECSE programs and the need for preparation of special educators in parent education strategies. This article describes a parent training model for children with autism developed for use in ECSE programs. The implementation of the program, teacher preparation, and preliminary outcomes and challenges will be discussed. Brooke Ingersoll Lewis & Clark College Hearing & Speech Institute
Learning Temporal Transformations From Time-Lapse Videos

Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.
EVOLUTION OF SOLID WASTE MANAGEMENT IN MALAYSIA

This paper seeks to examine the policy evolution of solid waste management in Malaysia and to determine its challenges and opportunities by assessing policy gaps, trends and stakeholders perception of solid waste management in Malaysia. Malaysian solid waste generation has been increasing drastically where solid waste generation was projected to increase from about 9.0 million tonnes in 2000 to about 10.9 million tonnes in 2010, to about 12.8 million tonnes in 2015 and finally to about 15.6 million tonnes in 2020 though national recycling rate is only about 3-5 %. This projected increasing rate of solid waste generation is expected to burden the country’s resources and environment in managing these wastes in a sustainable manner. Solid waste management policies in Malaysia has evolved from simple informal policies to supplementary provision in legislation such as the Local Government Act, 1976 and the Environmental Quality Act, 1974 to formal policies such as the National Strategic Plan for Solid Waste Management (NSP) 2005, Master Plan on National Waste Minimization (MWM) in 2006, National Solid Waste Management Policy 2006 and the Solid Waste and Public Cleansing Management Act (SWMA) 2007. Policy gap analysis indicates challenges in the area of policy implementation potentially due to lack of political will, weak stakeholder acceptance and policy impracticality due to direct adoption of policy practices from developed countries while potential opportunities are in the area of legislation for mandatory recycling and source separation as well as government green procurement initiatives. In conclusion, policy evolution of solid waste management in Malaysia may be shifting from a focus on basic solid waste management issues of proper collection, disposal and infrastructure requirements towards sustainable waste management.
Generalized State Coherence Transform for multidimensional localization of multiple sources

In our recent work an effective method for multiple source localization has been proposed under the name of cumulative State Coherence Transform (cSCT). Exploiting the physical meaning of the frequency-domain blind source separation and the sparse time-frequency dominance of the acoustic sources, multiple reliable TDOAs can be estimated with only two microphones, regardless of the permutation problem and of the microphone spacing. In this paper we present a multidimensional generalization of the cSCT which allows one to localize several sources in the P-dimensional space. An important approximation is made in order to perform a disjoint TDOA estimation over each dimension which reduces the localization problem to linear complexity. Furthermore the approach is invariant to the array geometry and to the assumed acoustic propagation model. Experimental results on simulated data show a precise 2-D localization of 7 sources by only using an array of three elements.
Learning Bayesian Networks is NP-Complete

Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reeecting the goodness-of-t of the structure to the data. The search procedure tries to identify network structures with high scores. Heckerman et al. (1995) introduce a Bayesian metric, called the BDe metric, that computes the relative posterior probability of a network structure given data. In this paper, we show that the search problem of identifying a Bayesian network|among those where each node has at most K parents|that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used. 12.1 Introduction Recently, many researchers have begun to investigate methods for learning Bayesian networks. Many of these approaches have the same basic components: a scoring metric and a search procedure. The scoring metric takes a database of observed cases D and a network structure B S , and returns a score reeecting the goodness-of-t of the data to the structure. A search procedure generates networks for evaluation by the scoring metric. These approaches use the two components to identify a network structure or set of structures that can be used to predict future events or infer causal relationships. Cooper and Herskovits (1992)|herein referred to as CH|derive a Bayesian metric, which we call the BD metric, from a set of reasonable assumptions about learning Bayesian networks containing only discrete variables. Heckerman et al. (1995)|herein referred to as HGC|expand upon the work of CH to derive a new metric, which we call the BDe metric, which has the desirable property of likelihood equivalence. Likelihood equivalence says that the data cannot help to discriminate equivalent structures. We now present the BD metric derived by CH. We use B h S to denote the hypothesis that B S is an I-map of the distribution that generated the database. 2 Given a belief-network structure B S , we use i to denote the parents of x i. We use r i to denote the number of states of variable x i , and q i = Q x l 2 i r l to denote the number of instances of i. We use the integer j to index these instances. That is, we write i = j to denote the observation of the jth instance of the parents of x i. 1996 Springer-Verlag. 2 There is an …
Continuous monitoring of distance-based outliers over data streams

Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria lead to different definitions for an outlier. In this work, we focus on distance-based outliers: an object x is an outlier if there are less than k objects lying at distance at most R from x. The problem offers significant challenges when a stream-based environment is considered, where data arrive continuously and outliers must be detected on-the-fly. There are a few research works studying the problem of continuous outlier detection. However, none of these proposals meets the requirements of modern stream-based applications for the following reasons: (i) they demand a significant storage overhead, (ii) their efficiency is limited and (iii) they lack flexibility. In this work, we propose new algorithms for continuous outlier monitoring in data streams, based on sliding windows. Our techniques are able to reduce the required storage overhead, run faster than previously proposed techniques and offer significant flexibility. Experiments performed on real-life as well as synthetic data sets verify our theoretical study.
A Hierarchical Genetic Algorithm for System Identification and Curve Fitting with a Supercomputer Implementation

This paper describes a hierarchical genetic algorithm (GA) framework for identifying closed form functions for multi-variate data sets. The hierarchy begins with an upper GA that searches for appropriate functional forms given a user defined set of primitives and the candidate independent variables. Each functional form is encoded as a tree structure, where variables, coefficients and functional primitives are linked. The functional forms are sent to the second part of the hierarchy, the lower GA, that optimizes the coefficients of the function according to the data set and the chosen error metric. To avoid undue complication of the functional form identified by the upper GA, a penalty function is used in the calculation of fitness. Because of the computational effort required for this sequential optimization of each candidate function, the system has been implemented on a Cray supercomputer. The GA code was vectorized for parallel processing of 128 array elements, which greatly speeded the calculation of fitness. The system is demonstrated on five data sets from the literature. It is shown that this hierarchical GA framework identifies functions which file the data extremely well, are reasonable in functional form, and interpolate and extrapolate well.
Virtual U: Defeating Face Liveness Detection by Building Virtual Models from Your Public Photos

In this paper, we introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, we show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. Our framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. The synthetic face of the user is displayed on the screen of the VR device, and as the device rotates and translates in the real world, the 3D face moves accordingly. To an observing face authentication system, the depth and motion cues of the display match what would be expected for a human face. We argue that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems: Unless they incorporate other sources of verifiable data, systems relying on color image data and camera motion are prone to attacks via virtual realism. To demonstrate the practical nature of this threat, we conduct thorough experiments using an end-to-end implementation of our approach and show how it undermines the security of several face authentication solutions that include both motion-based and liveness detectors.
Towards More Confident Recommendations : Improving Recommender Systems Using Filtering Approach Based on Rating Variance

In the present age of information overload, it is becoming increasingly harder to find relevant content. Recommender systems have been introduced to help people deal with these vast amounts of information and have been widely used in research as well as e-commerce applications. In this paper, we propose several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations. We then empirically demonstrate how these approaches work with various recommendation techniques. We also show how these approaches can generate more personalized recommendations, as measured by the coverage metric. As a result, users can be given a better control to choose whether to receive recommendations with higher coverage or higher accuracy.
Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste

Musicology students are engaged with music on an academic level and usually have an extensive musical background. They have a considerable knowledge of music history and theory and listening to music may be regarded as one of their primary occupations. Taken together, these factors qualify them as ≫expert listeners≪, who may be expected to exhibit a specific profile of musical taste: interest in a broad range of musical styles combined with a greater appreciation of ≫sophisticated≪ styles. The current study examined the musical taste of musicology students as compared to a control student group. Participants (n = 1003) completed an online survey regarding the frequency with which they listened to 22 musical styles. A factor analysis revealed six underlying dimensions of musical taste. A hierarchical cluster analysis then grouped all participants, regardless of their status, according to their similarity on these dimensions. The employed exploratory approach was expected to reveal potential differences between musicology students and controls. A three-cluster solution was obtained. Comparisons of the clusters in terms of musical taste revealed differences in the listening frequency and variety of appreciated music styles: the first cluster (51% musicology students/27% controls) showed the greatest musical engagement across all dimensions although with a tendency toward ≫sophisticated≪ musical styles. The second cluster (36% musicology students/46% controls) exhibited an interest in ≫conventional≪ music, while the third cluster (13% musicology students/27% controls) showed a strong liking of rock music. The results provide some support for the notion of specific tendencies in the musical taste of musicology students and the contribution of familiarity and knowledge toward musical omnivorousness. Further differences between the clusters in terms of social, personality, and sociodemographic factors are discussed.
2.4 GHz inkjet-printed RF energy harvester on bulk cardboard substrate

An experimental investigation on the inkjet-printed power harvester for 2.4GHz and review of RF characterization of substrate and printed conductors are presented in this paper. A one stage discrete rectifier based on a voltage doubler structure and a planar monopole antenna are fabricated on cardboard using inkjet printing. The performance of the whole system is examined by measuring the output voltage of the RF power harvester. By the utilization of the proposed idea, the fabrication of low-cost environmentally-friendly battery-less wireless modules is conceivable.
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework

Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural network structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. The compressed model generated by DeepIoT can directly use existing deep learning libraries that run on embedded and mobile systems without further modifications. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural networks on resource-constrained embedded devices.
Design of adaptive hypermedia learning systems: A cognitive style approach

In the past decade, a number of adaptive hypermedia learning systems have been developed. However, most of these systems tailor presentation content and navigational support solely according to students’ prior knowledge. On the other hand, previous research suggested that cognitive styles significantly affect student learning because they refer to how learners process and organize information. To this end, the study presented in this paper developed an adaptive hypermedia learning system tailored to students’ cognitive styles, with an emphasis on Pask’s Holist–Serialist dimension. How students react to this adaptive hypermedia learning system, including both learning performance and perceptions, was examined in this study. Forty-four undergraduate and postgraduate students participated in the study. The findings indicated that, in general, adapting to cognitive styles improves student learning. The results also showed that the adaptive hypermedia learning system have more effects on students’ perceptions than performance. The implications of these results for the design of adaptive hypermedia learning systems are discussed.
Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints

A program denotes computations in some universe of objects. Abstract interpretation of programs consists in using that denotation to describe computations in another universe of abstract objects, so that the results of abstract execution give some information on the actual computations. An intuitive example (which we borrow from Sintzoff [72]) is the rule of signs. The text -1515 * 17 may be understood to denote computations on the abstract universe {(+), (-), (±)} where the semantics of arithmetic operators is defined by the rule of signs. The abstract execution -1515 * 17 → -(+) * (+) → (-) * (+) → (-), proves that -1515 * 17 is a negative number. Abstract interpretation is concerned by a particular underlying structure of the usual universe of computations (the sign, in our example). It gives a summary of some facets of the actual executions of a program. In general this summary is simple to obtain but inaccurate (e.g. -1515 + 17 → -(+) + (+) → (-) + (+) → (±)). Despite its fundamentally incomplete results abstract interpretation allows the programmer or the compiler to answer questions which do not need full knowledge of program executions or which tolerate an imprecise answer, (e.g. partial correctness proofs of programs ignoring the termination problems, type checking, program optimizations which are not carried in the absence of certainty about their feasibility, …).
What do students want?: towards an instrument for students' evaluation of quality of learning analytics services

Quality assurance in any organization is important for ensuring that service users are satisfied with the service offered. For higher education institutes, the use of service quality measures allows for ideological gaps to be both identified and resolved. The learning analytic community, however, has rarely addressed the concept of service quality. A potential outcome of this is the provision of a learning analytics service that only meets the expectations of certain stakeholders (e.g., managers), whilst overlooking those who are most important (e.g., students). In order to resolve this issue, we outline a framework and our current progress towards developing a scale to assess student expectations and perceptions of learning analytics as a service.