Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring a probabilistic model of semantic memory from word association norms

In this paper, we introduce a method of data-analysis for word association norms. The defining characteristic of this method is that is based upon the inference of a probabilistic generative model of word-associations. The inferred model can in principle provide a clear and intuitive representation of the semantic knowledge inherent in word association data, facilitate an understanding of the process by which word associations are generated, extrapolate beyond the observed data to make reasonable inferences about new word associations, and facilitate an understanding of the process underlying false recall in memory experiments. Finally, the nature and form of the probabilistic model inferred using this method is directly comparable to the so-called Topicsmodel of Griffiths, Steyvers, and Tenenbaum (2007). As such, a potential future application of this work is the analysis and validation of the semantic knowledge inferred from the distributional statistics of text by direct comparison with the semantic knowledge inherent in association norms.
Streaming transformation of XML to RDF using XPath-based mappings

The Extensible Markup Language (XML) has become a widely adopted data interchange format. With the rise of Linked Data published using the Resource Description Framework (RDF), a number of tools for transforming XML to RDF have been developed. Specifying XML→RDF mappings for these tools often requires skills in programming languages such as XSLT or XQuery. Moreover, these tools are rarely able to deal with large XML inputs. We introduce an XML to RDF transformation approach, which is based on mappings comprising RDF triple templates that employ simple XPath expressions. Thanks to the restricted XPath expressions, which can be evaluated against a stream of XML data, our implementation can handle extremely large input XML files. To process the XML input efficiently, we employ XML filtering techniques and a strategy for selecting relevant XML nodes to generate RDF triples from. We show that the time complexity of our mapping algorithm is linear in the size of the XML input and also prove its practical efficiency with an evaluation on large real-world data.
Improved diagonal tensor approximation (DTA) and hybrid DTA/BCGS–FFT method for accurate simulation of 3D inhomogeneous objects in layered media

This paper presents an improved diagonal tensor approximation (DTA) and its hybridization with the stabilized biconjugate-gradient fast Fourier transform (BCGS–FFT) algorithm to solve a volume integral equation for three-dimensional (3D) objects in layered media. The improvement in DTA is obtained for lossy media through a higher-order approximation. The interaction between the dyadic Green's function and the contrast source is efficiently evaluated by the (FFT) algorithm through the convolution and correlation theorems. For the hybrid implementation, the DTA solution is used as an initial estimate and a preconditioner in the BCGS–FFT algorithm in order to solve the forwards modelling problem accurately with fewer iterations than the conventional BCGS–FFT algorithm. The accuracy and convergence of the DTA, BCGS–FFT and hybrid DTA/BCGS–FFT methods are compared extensively with several numerical examples. Numerical results show that (a) the improved DTA formulation enhances the accuracy and (b) the DTA/BCGS–FFT method can produce results as accurate as the conventional BCGS–FFT but with fewer iterations if the contrast is moderate. For very high contrasts, the hybrid method does not seem to improve further on the BCGS–FFT iteration convergence.
Status of Nature Reserves in Inner Mongolia, China

Nature reserves are an important component of the strategy to halt biodiversity loss caused by habitat fragmentation and loss, climate change and other anthropogenic factors. In the past decades, 184 nature reserves were designed for biodiversity conservation in Inner Mongolia. However, no studies have quantified the general condition of these reserves. In this paper, we summarized the history, distribution and effects of human interference on these reserves in Inner Mongolia. The results showed that: (1) The total area of nature reserves is 138,047 km 2 in Inner Mongolia. This constitutes 11.7% of its geographic area, which is lower than the national (14.9%), and the global average (13%). These reserves are mainly forest (68) and inland wetland (31) ecosystems. However, in terms of area, desert (40,948 km 2 ), forest (26,141 km 2 ) and inland wetland ecosystems (25,540 km 2 ) are predominant; (2) nature reserves have increased rapidly in number and area since 1995, especially at the province, city, and county levels; (3) the evergreen coniferous (26.4%), wetland (20.2%) and deciduous needle-leaf forests (19.6%) were sufficiently protected according to the 2020 target of 17% set by the Convention on Biological Diversity, while the other eight natural vegetation types, i.e., evergreen broad-leaved forests (14.2%), shrubs (13.5%), meadow vegetation (12.5%), typical steppe (10.2%), open forests (8.9%), desert vegetation (6.2%), desert steppe (2.9%), and sand vegetation (1.6%) were insufficiently protected; (4) the effects of human activities on these vegetation types were different. Open forest, sand vegetation, shrub, typical steppe, meadow steppe, evergreen broad-leaved, and evergreen coniferous forest were more affected than other vegetation types. Our results indicated that a more scientific approach is needed to effectively manage nature reserves in Inner Mongolia.
A shockable rhythm detection algorithm for automatic external defibrillators by combining a slope variability analyzer with a band-pass digital filter

To make automatic external defibrillators (AEDs) easy to use by the public who is not familiar with emergency treatment and electrocardiogram (ECG) analysis, it is critical to have an accurate shockable rhythm recognition algorithm. This paper presents a novel compositive algorithm by combining a slope variability analyzer with a band-pass digital filter so as to accurately distinguish shockable rhythms from non-shockable rhythms for automatic external defibrillators (AEDs). A total of 35 ECG records from the widely recognized Creighton University Ventricular Tachyarrhythmia Database (CUDB) were used to test the performance of the proposed algorithm. The obtained sensitivity of 94.2% and the specificity of 96.6% both satisfy requirements by the AHA rules on the arrhythmias detection for AEDs, and show a higher performance comparing with the previous HILB algorithm and the slope variability method only. As a conclusion, the proposed compositive algorithm would potentially provide a useful tool for AED systems with a higher accuracy and lower computation requirements.
An Intelligent Telecardiology System Using a Wearable and Wireless ECG to Detect Atrial Fibrillation

This study presents a novel wireless, ambulatory, real-time, and autoalarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world. This system consists of a lightweight and power-saving wireless ECG device equipped with a built-in automatic warning expert system. This device is connected to a mobile and ubiquitous real-time display platform. The acquired ECG signals are instantaneously transmitted to mobile devices, such as netbooks or mobile phones through Bluetooth, and then, processed by the expert system. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel tracking cardiac-rhythm disorders. The proposed system also activates an emergency medical alarm system when problems occur. Clinical testing reveals that the proposed system is approximately 94% accurate, with high sensitivity, specificity, and positive prediction rates for ten normal subjects and 20 AF patients. We believe that in the future a business-card-like ECG device, accompanied with a mobile phone, can make universal cardiac protection service possible.
Multimodal Dialogue System Grammars

We describe how multimodal grammars for dialogue systems can be written using the Grammatical Framework (GF) formalism. A proof-of-concept dialogue system constructed using these techniques is also presented. The software engineering problem of keeping grammars for different languages, modalities and systems (such as speech recognizers and parsers) in sync is reduced by the formal relationship between the abstract and concrete syntaxes, and by generating equivalent grammars from GF grammars.
Information Provenance and Semantic Publishing A New Vision for Publishing in Science

Science is facing many organisational challenges. Online technologies are opening new ways for scientists to communicate and exchange knowledge. Technological advances in scientific instrumentation are allowing scientists to gather large quantities of data that place increasing demands on storage, processing and data management. New knowledge, data and compute resource technologies are being developed to cope with these demands. These trends have lead to unprecedented capacity for the creation of scientific knowledge. I argue, however, that the area of scientific publishing, though it has seen significant improvements, has not fully capitalised on the available knowledgemanagement and networking technologies. I present a general strategy and some initial steps toward a new semantic publishing paradigm that draws together technologies and ideas from knowledge representation, grid technologies, web 2.0 and the Semantic Web. At the core of semantic publishing is detailed semantic representation of scientific ideas and arguments. With such representation, the concept of theory provenance— a record of the arguments and dependencies of published ideas—becomes feasible. When theory provenance is combined with data provenance (the history of acquisition and processing of data) published science can achieve full transparency, allowing effective verification and review. As an initial forray into semantic representation of scientific arguments, I present a framework for representing arguments based on evidence. Such arguments are fundamental to science, and are essential to detailed semantic representation of scientific arguments. I argue that a semantically published body of knowledge can be exposed to automatic and semi-automatic analysis such as accurate citation and impact analysis, adapted social bookmarking as a quality and interest measure and provision of different presentations or views of scientific knowledge for diverse audiences. Advances in natural language processing also suggest the future possibility of automatic semantic translation of existing published works. With improved computing power and artificial intelligence technologies, automated reasoning could conceivably be applied to a semantically published body of knowledge as a tool for consistency checking and an aid to discovering new scientific theories. Finally, I summarise the social changes, technological gaps and missing knowledge that would need to be filled to achieve this vision, and argue that these challenges are not insurmountable.
A comprehensive review of energy efficiency in cloud computing environment

High energy consumption in the cloud has become a huge problem in the data center. Energy represents direct significant cost in the operation of the data center. In Information Technology, infrastructure, Internet applications are in more demand. Cloud computing provides IT resources in the form of infrastructure, platform and application by providing services through the Internet Technology. This leads to more energy being consumed as cloud is used to provide IT services from the IT resources to the IT industry and to the Organizations. To analyze power consumed in the data center, applications are deployed in cloud and tested using different workload conditions. Virtualization depicts more energy utilization in the cloud data center. In this paper discussed about the comparison of cloud and cloud computing, cloud type providers, component performance through secured shell. Identified the various levels of energy consumptions in the cloud. the different techniques which is used to reduce the power consumption in the server and workload consolidation using various parameters are considered.
Event tracking based on domain dependency

This paper proposes a method for event tracking on broadcast news stories based on distinction between a topic and an event. A topic and an event are identified using a simple criterion called domain dependency of words: how greatly a word features a given set of data. The method was tested on the TDT corpus which has been developed by the TDT Pilot Study and the result can be regarded as promising the usefulness of the method.
Selective control-flow abstraction via jumping

We present jumping, a form of selective control-flow abstraction useful for improving the scalability of goal-directed static analyses. Jumping is useful for analyzing programs with complex control-flow such as event-driven systems. In such systems, accounting for orderings between certain events is important for precision, yet analyzing the product graph of all possible event orderings is intractable. Jumping solves this problem by allowing the analysis to selectively abstract away control-flow between events irrelevant to a goal query while preserving information about the ordering of relevant events. We present a framework for designing sound jumping analyses and create an instantiation of the framework for per- forming precise inter-event analysis of Android applications. Our experimental evaluation showed that using jumping to augment a precise goal-directed analysis with inter-event reasoning enabled our analysis to prove 90–97% of dereferences safe across our benchmarks.
Mobile Health: Design of Flexible and Stretchable Electrophysiological Sensors for Wearable Healthcare Systems

Flexible and stretchable sensors have recently emerged as an active area of research for designing wearable, implantable and ingestible systems that allow pervasive and unobtrusive health monitoring in mobile health (m-Health) applications. Nevertheless, there lacks systematical study that compares the performances of these new types of sensors with their conventional counterparts. In this study, we reported the fabrication of serpentine-shape, flexible and stretchable electrodes that can be conformally attached to the skin via a printing-transfer process, and we introduced Area Density (AD) as a key parameter to guide the future design of these sensors. Sensors of AD 10, 20 and 40 percentages were designed and fabricated. The performances of these sensors for capturing one type of electrophysiological signals, i.e. electrocardiogram (ECG), were compared against ECG obtained by conventional gold and stainless steel metal clips. The results of this study found that flexible sensors designed with larger AD resulted in ECG of higher signal-to-noise ratio (SNR). In particular, ECG with SNR comparable to those measured by conventional metal clips (SNR>25dB) can be achieved when this new type of flexible sensors were designed with AD=40%. The results of this study shed light on the design of novel wearable and flexible electrodes, which can be used not only for on-body sensing but also in-body measurement inside the gastrointestinal tract.
The influence of prior knowledge on experiment design guidance in a science inquiry context

ABSTRACT Designing and conducting sound and informative experiments is an important aspect of inquiry learning. Students, however, often design experiments that do not allow them to reach conclusions. Considering the difficulties students experience with the process of designing experiments, additional guidance in the form of an Experiment Design Tool (EDT) was developed, together with reflection questions. In this study, 147 pre-university students worked in an online inquiry learning environment on buoyancy and Archimedes’ principle. Students were randomly assigned to one of three conditions, each of which contained a different version of the EDT. Since students’ prior knowledge has been found to influence the amount and type of guidance they need, the versions of the tool differed with respect to the level of guidance provided. A pre- and post-test were administered to assess students’ conceptual knowledge. No overall differences between conditions were found. In a subsequent analysis, students were classified as either low, low-intermediate-, high-intermediate, or high prior knowledge students. For Archimedes’ principle we found that low-intermediate prior knowledge students gained significantly more conceptual knowledge than low prior knowledge students in the fully guided condition. It is hypothesised that students need at least some prior knowledge in order to fully benefit from the guidance offered.
LinTraP: Primitive Operators for the Execution of Model Transformations with LinTra

The problems addressed by Model-Driven Engineering (MDE) approaches are increasingly complex, hence performance and scalability of model transformations are gaining importance. In previous work, we introduced LinTra, which is a platform for executing out-place model transformations in parallel. The parallel execution of LinTra is based on the Linda coordination language, where high-level model transformation languages (MTLs) are compiled to LinTra and eventually executed through Linda. In order to dene the compilation modularly, this paper presents a minimal, yet sucient, collection of primitive operators that can be composed to (re)construct any out-place, unidirectional MTL.
To share the fame in a fair way, hm modifies h for multi-authored manuscripts

The h-index has been introduced by Hirsch as a useful measure to characterize the scientific output of a researcher. I suggest a simple modification in order to take multiple co-authorship appropriately into account, by counting each paper only fractionally according to (the inverse of) the number of authors. The resulting hm-indices for eight famous physicists lead to a different ranking from the original h-indices.
LinkedIn to classroom community: assessing classroom community on the basis of social media usage

ABSTRACT Recently, many popular social media platforms have been incorporated into educational settings to supplement classroom teaching and improve the student learning experience. Considering the degree to which social media has been adopted by the sport industry, and the popularity of social media among college students, the current study examines if social media can be utilised as a viable pedagogical tool for sport management education. While the foundations of education are multifaceted, one medium that has been shown to influence education is classroom community. The purpose of this study was to investigate the ability of social media to either influence or hinder classroom community. To adequately address the current study’s research questions, the authors prepared a longitudinal experimental design, measuring classroom community amongst two classes (n= 89), where each class differed only in their usage/non-usage of social media. Results indicate mixed results, as the non-social media group exhibited a greater sense of classroom community than did the social media-centric group. Pedagogical and practical implications are forwarded.
An efficient wavelet-based motion estimation algorithm

In this paper, we propose a wavelet-based fast motion estimation algorithm for video sequence encoding with a low bit-rate. By using one of the properties of the wavelet transform, multi-resolution analysis (MRA), and the spatial interpolation of an image, we can simultaneously reduce the prediction error and the computational complexity inherent in video sequence encoding. In addition, by defining a significant block (SB) based on the differential information of the wavelet coefficients between successive frames, the proposed algorithm enables us to make up for the increase in the number of motion vectors when the MRME algorithm is used. As a result, we are not only able to improve the peak signal-to-noise ratio (PSNR), but also reduce the computational complexity by up to 67%.
Feedback Control in the Presence of Noisy Channels: “Bode-Like” Fundamental Limitations of Performance

This paper addresses fundamental limitations of feedback using information theoretic conservation laws and flux arguments. The paper has two parts. In the first part, we derive a conservation law dictating that causal feedback cannot reduce the differential entropy inserted in the loop by external sources. An interpretation of this result is that the total randomness induced by disturbances, as measured by differential entropy, cannot be reduced by causal feedback; it can only be re-allocated in time or in frequency (if well defined). Under asymptotic stationarity assumptions, this result has a spectral representation which constitutes an extension of Bode's inequality for arbitrary feedback. Our proofs make clear the role of causality, as well as how stability assumptions impact the final result. In the second part, we derive an inequality unveiling that the feedback loop must be able to convey information originating from two independent sources: 1) initial states of the physical plant; 2) exogenous disturbance signals. By using such principle, we construct a variety of information rate (information flux) inequalities. Furthermore, we derive a universal performance bound which is parameterized solely by the feedback capacity and the parameters of the plant. The latter is a new fundamental limitation, which is different from Bode's classical result, indicating that finite feedback capacity brings a new type of performance bound.
The Epistemology of scheduling problems Conference Item

Scheduling is a knowledge-intensive task spanning over many activities in day-to-day life. It deals with the temporallybound assignment of jobs to resources. Although scheduling has been extensively researched in the AI community for the past 30 years, efforts have primarily focused on specific applications, algorithms, or 'scheduling shells' and no comprehensive analysis exists on the nature of scheduling problems, which provides a formal account of what scheduling is, independently of the way scheduling problems can be approached. Research on KBS development by reuse makes use of ontologies, to provide knowledge-level specifications of reusable KBS components. In this paper we describe a task ontology, which formally characterises the nature of scheduling problems, independently of particular application domains and independently of how the problems can be solved. Our results provide a comprehensive, domain-independent and formally specified reference model for scheduling applications. This can be used as the basis for further analyses of the class of scheduling problems and also as a concrete reusable resource to support knowledge acquisition and system development in scheduling applications.