Miracle-dz/UniFAR-FTUs huggingface dataset:
Vaping in the News: The Influence of News Exposure on Perceived e-Cigarette Use Norms

ABSTRACT Background: Research has documented the impact of descriptive norms on tobacco use, but few studies have investigated how media exposure shapes e-cigarette use norms. Purpose: The purpose of this study was to examine how exposure to e-cigarette-related news articles shapes individuals’ descriptive norm perceptions about real-world e-cigarette use. Method: The study implemented an experiment with a 2 normative direction (high vs low prevalence) × 2 exposure dosage (single vs double dose) between-subjects factorial design (N = 298). Analysis of variance and thematic analysis were conducted. Results: Normative direction and exposure dosage of prevalence information contained in the news articles interacted to influence perceived descriptive norms. Increasing the dosage of prevalence information enhanced descriptive norm perceptions in low-prevalence conditions only. Participants relied on institutional signals and behavioral cues to infer descriptive norms when prevalence information was absent in the news. Discussion: The study investigates the underlying mechanism of how news articles may influence normative perceptions. Translation to Health Educational Practice: Given that news media may inadvertently form social norms that are conducive to e-cigarette use behavior, Health Educators should pay attention to descriptive norms emanated from the news media environment. They should also consider norm debiasing strategies and the integration of dosage of low-prevalence information into social norm messages.
The Effect of Reactance on Satisfaction: A Study in the Context of Supplementary Health Care Systems

In a scenario in which the State is unable to serve the entire population, private health organizations have emerged, offering health plans and private assistance, called supplementary health systems. Given this context, the theme of this work is the study of the effect of psychological reactance on the satisfaction of users of supplementary health plans. The empirical phase was conducted through a survey with 145 university students. Data analysis, conducted through the macro PROCESS, indicated that the valuation of supplementary health care providers is closely related to the level of individual reactance of the user and the way the plan is contracted. Furthermore, freedom of choice of the operator minimizes negative evaluations from individuals with high psychological reactance.
Assessing handwitten digit segmentation algorithms

This work compares different segmentation algorithms for handwritten digits based on explicit segmentation. For this purpose, algorithms based on different concepts were implemented and evaluated under the same conditions. The algorithms were used to segment 2,369 pairs of touching digits of the NIST_SD19 database and were evaluated in terms of correct segmentation and computational time. We also discuss the complementarity of the segmentation algorithms. We have observed that independently of the individual performance of the algorithms, each method is able to segment samples that can not be segmented by any other method. Based on this observation, we conclude that the combination of different segmentation algorithms may be an interesting strategy to improve the correct segmentation rate.
A Low-Cost and Light-Weight Motion Tracking Suit

His work presents the design and implementation of a low-cost motion tracker system including a network of wired microelectromechanical systems (MEMS) based Inertial Measurement Units (IMUs) which fits into a suit for feasible capturing of human body motions. Using miniaturized IMUs and textile cables, the system is designed to be light-weight and easy to use providing maximal movement flexibility for the user. In order to make a long-term data capturing possible a low-power micro computer is deployed and a new data acquisition approach from multiple sensors is proposed which reduces the CPU load and the wiring complexity. While the complete system including hardware, software and methods for calibration and motion estimation is described, the contribution is mainly in the development of the sensory suit and data acquisition methods providing a novel low-cost, energy-efficient and convenient capturing platform for diverse applications.
A Comparison of Propositionalization Strategies for Creating Features from Linked Open Data

Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. In this paper, we compare different strategies for creating propositional features from Linked Open Data (a process called propositionalization), and present experiments on different tasks, i.e., classification, regression, and outlier detection. We show that the choice of the strategy can have a strong influence on the results.
Mismatched feature detection with finer granularity for emotional speaker recognition

The shapes of speakers’ vocal organs change under their different emotional states, which leads to the deviation of the emotional acoustic space of short-time features from the neutral acoustic space and thereby the degradation of the speaker recognition performance. Features deviating greatly from the neutral acoustic space are considered as mismatched features, and they negatively affect speaker recognition systems. Emotion variation produces different feature deformations for different phonemes, so it is reasonable to build a finer model to detect mismatched features under each phoneme. However, given the difficulty of phoneme recognition, three sorts of acoustic class recognition—phoneme classes, Gaussian mixture model (GMM) tokenizer, and probabilistic GMM tokenizer—are proposed to replace phoneme recognition. We propose feature pruning and feature regulation methods to process the mismatched features to improve speaker recognition performance. As for the feature regulation method, a strategy of maximizing the between-class distance and minimizing the within-class distance is adopted to train the transformation matrix to regulate the mismatched features. Experiments conducted on the Mandarin affective speech corpus (MASC) show that our feature pruning and feature regulation methods increase the identification rate (IR) by 3.64% and 6.77%, compared with the baseline GMM-UBM (universal background model) algorithm. Also, corresponding IR increases of 2.09% and 3.32% can be obtained with our methods when applied to the state-of-the-art algorithm i-vector.
Market intelligence and price adaptation

In the context of e-commerce, it is critical for a retailing company to be able to assess the market and respond quickly to changes in competition and/or service levels and availability of its products. If the company operates globally, and the geographical constraints on the supplier are different from those on the consumer, it is even more crucial to assess each market with respect to its characteristics and dynamically price every individual product accordingly. In this paper we report on an agent-based distributed system that was developed to support the retailing operations of one of the largest online book sellers in the UK, which has millions of books on offer and operates internationally in a number of market places. The system supports the collection of market data, processing of this data, and applying dynamically one of set of predefined pricing patterns. We describe the principles on which the price adaptation is based, the (long-running and proven successful) distributed scalable software architecture that supports it, illustrate how the mechanism copes with some typical situations system that arise and highlight some of the lessons learned from the development experience.
Brain Machine Interface — IEETA case study

The goal of the present paper is to report the recent advances in Electroencephalogram (EEG)-based Brain Machine Interface (BMI) developed at the Institute of Electrical Engineering and Telematics of Aveiro (IEETA). First, a short overview of the most successful BMI technologies is presented and then our ongoing research and protocol for motor imagery noninvasive BMI for a mobile robot control is discussed. The main EEG signal processing challenges as filtering, feature extraction and classification are also considered.
Developing smart campuses — A working model

In 2008 Birmingham City University (BCU) embarked on an ambitious new city centre campus development scheme. The scheme is materialising into two new campuses of circa 18000 and 24000 square metres respectively; as the main part of a £180m investment by BCU. The first campus is open and operating, the second is being built and due to open in September 2015. From the initial planning stages of the developments BCU wanted the campuses to be “intelligent-buildings”. This paper will explain and document how BCU have interwoven “intelligence” into its new campuses to improve business-processes, reduce energy use and carbon emissions and enhance the occupant experience. The focus of this paper will be upon integrating multi-protocol business and building-systems through an Enterprise Service Bus (ESB) to compliment an existing Service Orientated Architecture (SOA). The result is a university-wide suite of systems that are scalable, extensible, integrated and orchestrated. The resulting cost-savings and service enhancements will also be examined and discussed. In addition a roadmap of further developments will be outlined to compliment the developments already achieved.
Negotiating Spaces and the Public–Private Boundary: Language Policies Versus Language Use Practices in Odessa

While the so-called “end of public space” literature, focusing on encroachment of private interests and state surveillance, has contributed to critical thinking of access (or the lack thereof) to public space, and the loss of publicity of public space, the conceptual tools such literature offers to understand contestations in and over public space have remained underdeveloped or, at best, underexplored. This article builds on the above debates to provides further empirical evidence on the way actors of a country compete over, and negotiate, the use of public space and the way it should be regulated. Empirically, it illustrates competition and negotiation of the use of language in Odessa, the third largest city of Ukraine, where Ukrainian should be the official language but Russian is widely used. Theoretically, starting from the way public and private are negotiated, and the extent to which this happens, we will suggest that resistance to state measures, and policies, that do not suit a considerable portion of a population may happen not only formally but also informally. The practices, tactics, and mechanisms used may, however, remain “invisible” for some time and then surprise everyone by emerging, all of a sudden, one day. A possible way to notice these dynamics is to engage with an “everyday” approach, thus acknowledging that everyday practices are a meaningful, and useful, site for understanding sociopolitical developments in the process of the construction of “the political. ”
Function of Alternative Splicing

Alternative splicing is one of the most important mechanisms to generate a large number of mRNA and protein isoforms from the surprisingly low number of human genes. Unlike promoter activity, which primarily regulates the amount of transcripts, alternative splicing changes the structure of transcripts and their encoded proteins. Together with nonsense-mediated decay (NMD), at least 25% of all alternative exons are predicted to regulate transcript abundance. Molecular analyses during the last decade demonstrate that alternative splicing determines the binding properties, intracellular localization, enzymatic activity, protein stability and posttranslational modifications of a large number of proteins. The magnitude of the effects range from a complete loss of function or acquisition of a new function to very subtle modulations, which are observed in the majority of cases reported. Alternative splicing factors regulate multiple pre-mRNAs and recent identification of physiological targets shows that a specific splicing factor regulates pre-mRNAs with coherent biological functions. Therefore, evidence is now accumulating that alternative splicing coordinates physiologically meaningful changes in protein isoform expression and is a key mechanism to generate the complex proteome of multicellular organisms. D 2004 Published by Elsevier B.V.
Type-Safe Object Exchange Between Applications and a DSM Kernel

The Plurix project implements an object-oriented Operating System (OS) for PC clusters. Communication is achieved via shared objects in a Distributed Shared Memory (DSM) using restartable transactions and an optimistic synchronization scheme to guarantee memory consistency. We contend that coupling object orientation with the DSM property allows a type-consistent system bootstrapping, quick system startup and simplified development of distributed applications. It also facilitates checkpointing of the system state. The OS (including kernel and drivers) is written in Java using our proprietary Plurix Java Compiler (PJC) translating Java source code directly into Intel machine instructions. PJC is an integral part of the language-based OS and tailor-made for compiling in our persistent DSM environment. In this paper we briefly illustrate the architecture of our OS kernel which runs entirely in the DSM and the resulting opportunities for checkpointing and communication between applications and OS. We present issues of memory management related to the DSM-kernel and to strategies to avoid false-sharing .
Robust composite adaptive fuzzy identification and control for a class of MIMO nonlinear systems

A robust adaptive fuzzy identification and trajectory tracking control approach is developed for a class of multi-input-multi-output (MIMO) nonlinear systems in the presense of unmodeled uncertainties, parametric uncertainties, and external disturbances. A sliding mode-based fuzzy model identification/observer method is used to provide additional feedback of the unknown system dynamics. The model identification error is used along with the tracking error as a composite adaptive update law for a fuzzy logic based feedforward term. The fuzzy logic feedforward term is used in conjunction with a recently developed robust integral of the sign of the error (RISE) feedback method to yield a continuous controller that achieves semi-global asymptotic trajectory tracking.
Characteristics of the 60 GHz indoor wireless propagation based on ray tracing with human body movement

In this paper, an extended two-dimensional (2D) ray tracing algorithm is proposed to investigate the characteristics of indoor wireless propagation. A random variable, which serves as the extension to the classic 2D ray tracing algorithm, is introduced to study the human body movement effects to the propagation. Based on the proposed ray tracing algorithm, the path gains and delays are simulated under a variety of different settings. Simulation results and analysis show that the movement of human body degrades the transmission qualities.
A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.
Linear Statistical Models for Causation: A Critical Review

Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later refinements of the technique. Besides Yule, examples to be discussed here include Blau and Duncan [12] on stratification, as well as Gibson [28] on the causes of McCarthyism. Strong assumptions are required to infer causation from association by modeling. The assumptions are of two kinds: (a) causal, and (b) statistical. These assumptions will be formulated explicitly, with the help of response schedules in hypothetical experiments. In particular, parameters and error distributions must be stable under intervention. That will be hard to demonstrate in observational settings. Statistical conditions (like independence) are also problematic, and latent variables create further complexities. Causal modeling with path diagrams will be the primary topic. The issues are not simple, so examining them from several perspectives may be helpful. The article ends with a review of the literature and a summary.
Tempo: A Toolkit for The Timed Input/Output Automata Formalism

Abstract : Report developed under STTR contract for topic AFO4-T023. This Phase II project developed an integrated development environment, called Tempo, for specifying, analyzing, and verifying complex distributed system designs. This includes a modeling language, a graphical user interface, and computational support tools. This modeling and analysis framework provides an integrated suite of tools and methods. In more detail, this project developed: (a) a formal modeling language, called Tempo, based on the TIOA (Timed Input/Output Automata) formalism, for specifying timed, asynchronous, and interacting systems components, (b) the language processor for Tempo, incorporating syntax and type checking, and providing interfaces to computer-aided design tools, (c) a simulation tool allowing simulation of specifications and paired simulations of a specification and an abstract implementation, (d) an interface to model-checking tool UPAALL, and (e) theorem-proving link through an interface to PVS.
Design and Implementation of Music Information Retrieval and Gathering Engine (MIRAGE)

Query by Singing/Humming (QBSH) is a most natural way for music recognition. A QBSH system can help find songs by matching a part of melody users sing or hum. Many Music Information Retrieval (MIR) techniques have been used to carry out the QBSH goal. In the past, little studies had ever been revealed about how to design and implement a simple QBSH system. In this article, we combine Short-Time-Fourier-Transform, Fourier Transform and Spectrum Analysis techniques at the client end, and Dynamic Time Warping (DTW) and dynamic programming techniques at the server end to implement Music Information Retrieval And Gathering Engine (MIRAGE), which can take input queries issued by users from web-based client-ends toward MIRAGE deployed at the server-end. MIRAGE can identify melody when a user only knows the rhythm of a song without any other information.
An experimental study on the conversion between IFPUG and UCP functional size measurement units

The use of functional size measurement (FSM) methods in software development organizations is growing during the years. Also, object oriented (OO) techniques have become quite a standard to design the software and, in particular, Use Cases is one of the most used techniques to specify functional requirements. Main FSM methods do not include specific rules to measure the software functionality from its Use Cases analysis. To deal with this issue some other methods like Kramer’s functional measurement method have been developed. Therefore, one of the main issues for those organizations willing to use OO functional measurement method in order to facilitate the use cases count procedure is how to convert their portfolio functional size from the previously adopted FSM method towards the new method. The objective of this research is to find a statistical relationship for converting the software functional size units measured by the International Function Point Users Group (IFPUG) function point analysis (FPA) method into Kramer-Smith’s use cases points (UCP) method and vice versa. Methodologies for a correct data gathering are proposed and results obtained are analyzed to draw the linear and non-linear equations for this correlation. Finally, a conversion factor and corresponding conversion intervals are given to establish the statistical relationship.
An Analysis of the Scientific Status and Limitations of the Attitudinal Entropy Framework and an Initial Test of Some of Its Empirical Predictions

Dalege, Borsboom, van Harreveld, and van der Maas (2018) describe a novel framework for the conceptualization of attitudes that draws on principles from statistical mechanics. A core idea in their framework is that systems are often characterized by randomness (i.e., entropy) and that there is both heuristic and predictive value in applying the idea of entropy to the study of attitudes and related phenomena. We applaud their initiative: the attitudinal entropy framework provides an intriguing new perspective on theoretical questions and empirical findings in social psychology. It opens up new avenues for research in many areas and is a timely contribution given the growing popularity of predictive processing theories emphasizing entropy as an important factor in human cognition (for a recent overview see Metzinger & Wiese, 2017).
High-level online user attribution model based on human Polychronic-Monochronic tendency

User attribution process based on human inherent dynamics and preference is one area of research that is capable of elucidating and capturing human dynamics on the Internet. Prior works on user attribution concentrated on behavioral biometrics, 1-to-1 user identification process without consideration for individual preference and human inherent temporal tendencies, which is capable of providing a discriminatory baseline for online users, as well as providing a higher level classification framework for novel user attribution. To address these limitations, the study developed a temporal model, which comprises the human Polyphasia tendency based on Polychronic-Monochronic tendency scale measurement instrument and the extraction of unique human-centric features from server-side network traffic of 48 active users. Several machine-learning algorithms were applied to observe distinct pattern among the classes of the Polyphasia tendency, through which a logistic model tree was observed to provide higher classification accuracy for a 1-to-N user attribution process. The study further developed a high-level attribution model for higher-level user attribution process. The result from this study is relevant in online profiling process, forensic identification and profiling process, e-learning profiling process as well as in social network profiling process