Miracle-dz/UniFAR-FTUs huggingface dataset:
TCNS: Node Selection With Privacy Protection in Crowdsensing Based on Twice Consensuses of Blockchain

With the rapid growth of smart terminals in recent years, crowdsensing which utilizes the human intelligence to solve complicated problems have gained considerable interest and exploit. The majority of the existing crowdsensing systems rely on a trusted third-party platform to complete sensing tasks and collect large-scale data. However, the platform cannot completely ensure trust in the real world. The issues of security and privacy caused by the center platform should not be ignored. In this paper, we propose a decentralized privacy-preserving model based on twice verifications and consensuses of blockchain (TCNS). In the prototype of TCNS, an anonymity strategy which can be verified based on the elliptic curve algorithm is proposed to protect the user identity privacy. Then, we propose a twice consensus mechanism, which ensures that the data can be traced and avoids data from being impersonated, tampered with, and denied. Moreover, we propose a user attribute protection scheme based on the lightweight homomorphic encryption algorithm. Finally, considering various influencing factors comprehensively, TCNS uses fuzzy theories to select the candidate mobile nodes. Further, we implement the prototype with real-world datasets, the experimental analysis of privacy protection and safety shows that TCNS can effectively prevent association analysis attacks and background knowledge attacks. More gratifying, the time overhead for generating a new block is acceptable.
A current-mode gain-control scheme with constant bandwidth and propagation delay for a transimpedance preamplifier

This paper presents a current-mode gain-control scheme that significantly increases the input dynamic range of a wideband optoelectronic receiver without affecting its bandwidth or delay or deteriorating its noise properties. A current buffer with variable attenuation is placed between the photodetector and the transimpedance preamplifier. In this way, the input dynamic range of the receiver can be increased, or alternatively, the signal dynamics can be reduced, by over 20 dB. A BiCMOS test circuit designed for a pulsed time-of-flight laser rangefinder has a measured bandwidth of 170 MHz and an input dynamic range of /spl sim/80 dB. The delay varies only /spl plusmn/5 ps when the gain is varied by 24 dB (1:15).
Time interval measurements using time-to-voltage conversion with built-in dual-slope A/D conversion

An integrated CMOS circuit for measuring a time interval given as the width of a pulse is presented. The circuit converts the time interval to be measured to a longer interval which can be digitized more accurately using a clock than the initial one. The circuit is a combination of a time-to-voltage converter (TVC) and a dual-slope A/D converter. In the time-to-voltage conversion a capacitor is discharged with a constant current during the time interval to be measured. The use of dual-slope A/D conversion reduces the effects of current and capacitance value drifting due to temperature changes. The measurement range (50-500 ns) can be scaled with the discharging current (12-90 mu A). The resolution of the time-to-voltage conversion is 200 ps or better. The A/D conversion uses a 10 MHz clock for digitization, giving an overall resolution of 400 ps. <<ETX>>
A Review of Bioinformatics Model and Computational Software of Next Generation Sequencing

In the past decade it has become increasingly the effort for researcher to surpass the bioinformatics challenges foremost in next generation sequencing (NGS). This review paper gives an overview of the computational software and bioinformatics model that has been used for next generation sequencing. In this paper, the description on functionalities, source type and website of the program or software are provided. These computational software and bioinformatics model are differentiating into three types of bioinformatics analysis stages including alignment, variant calling and filtering and annotation. Besides, we discuss the future work and the development for new bioinformatics tool to be advanced.
An Efficient Character-Level and Word-Level Feature Fusion Method for Chinese Text Classification

In order to extract semantic feature information between texts more efficiently and reduce the effect of text representation on classification results, we propose a features fusion model C_BiGRU_ATT based on deep learning. The core task of our model is to extract the context information and local information of the text using Convolutional Neural Network(CNN) and Attention-based Bidirectional Gated Recurrent Unit(BiGRU) at character-level and word-level. Our experimental results show that the classification accuracies of C_BiGRU_ATT reach 95.55% and 95.60% on two Chinese datasets THUCNews and WangYi respectively. Meanwhile, compared with the single model based on character-level and word-level for CNN, the classification accuracies of C_BiGRU_ATT is increased by 1.6%, 2.7% on the THUCNews, and is increased by 0.6%, 5.2% on the WangYi. The results show that the proposed model C_BiGRU_ATT can extract text features more effectively.
The Impact of Careers Guidance for Employed Adults in Continuing Education

The validity of the matching estimator in programme evaluation depends on the completeness of the set of variables used for matching. When an attitudinal variable is relevant for the participation decision, but is either unmeasured or measured only after entry to the programme, estimates of effects may be biased or hard to interpret. This issue is investigated with data from an evaluation study of careers guidance for employed adults, which utilised the method of propensity score matching. Job satisfaction, measured shortly after entry to the programme, was found to be strongly associated with participa-tion, but may itself have been influenced by the early experience of careers guidance. Estimates of the impacts of guidance on several post-programme education and training outcomes are considered, both including and exclud-ing the job satisfaction measure from the participation model. Data experiments with adjusted values of job satisfaction are also performed. It is found that estimates of treatment effects are highly sensitive to these variants, and respond in a non-monotonic fashion. The implications for evaluation methodology are discussed.
Using Formative Research to Develop the Healthy Me, Healthy We Campaign

Although social marketing principles have been successfully employed in school-based obesity prevention interventions, their use in early care and education (ECE) settings has been limited. This paper reports on formative research to develop Healthy Me, Healthy We (HMHW), an innovative social marketing campaign that encourages partnership between parents and providers to foster healthy eating and physical activity in preschoolers. To guide campaign development, we consulted existing literature and identified useful theories. In addition, three focus groups with providers (n = 17) and four with parents (n = 20) were conducted to understand strategies used to instill healthy habits in children, perceptions of how their personal behaviors influence children, and the usefulness of parent–provider partnerships. Parents and providers recognized healthy eating and physical activity as important to the development of the “whole child. ” Both groups expressed feeling great responsibility for shaping children’ habits and being a role model for healthy behaviors, particularly for healthy eating. While parents and providers felt partnership was important, areas of conflict emerged with how partnerships should be executed. There is great potential for such partnerships, but careful communication is needed to avoid triggering feelings of guilt among parents and perceptions of superiority among providers. These findings informed campaign development, which was pretested in an ECE setting. The resulting HMHW campaign is delivered by the childcare center and includes materials for classroom and home use. The campaign helps strengthen parent–provider partnerships to encourage healthy eating and physical activity habits during early childhood, a critical period in the development of lifelong health habits.
Link prediction and link detection in sequences of large social networks using temporal and local metrics

This dissertation builds upon the ideas introduced by Liben-Nowell and Kleinberg in The Link Prediction Problem for Social Networks [42]. Link prediction is the problem of predicting between which unconnected nodes in a graph a link will form next, based on the current structure of the graph. The following research contributions are made: • Highlighting the difference between the link prediction and link detection problems, which have been implicitly regarded as identical in current research. Despite hidden links and forming links having very highly significant differing metric values, they could not be distinguished from each other by a machine learning system using traditional metrics in an initial experiment. However, they could be distinguished from each other in a "simple" network (one where traditional metrics can be used for prediction successfully) using a combination of new graph analysis approaches. • Defining temporal metric statistics by combining traditional statistical measures with measures commonly employed in financial analysis and traditional social network analysis. These metrics are calculated over time for a sequence of sociograms. It is shown that some of the temporal extensions of traditional metrics increase the accuracy of link prediction. • Defining traditional metrics using different radii to those at which they are normally calculated. It is shown that this approach can increase the individual prediction accuracy of certain metrics, marginally increase the accuracy of a group of metrics, and greatly increase metric computation speed without sacrificing information content by computing metrics using smaller radii. It also solves the “distance-three task” (that common neighbour metrics cannot predict links between nodes at a distance greater than three). • Showing that the combination of local and temporal approaches to link prediction can lead to very high prediction accuracies. Furthermore in “complex” networks (ones where traditional metrics cannot be used for prediction successfully) local and temporal metrics become even more useful.
Estimativa do consumo máximo de oxigênio e análise de concordância entre medida direta e predita por diferentes testes de campo

OBJETIVO: Verificar la estimativa de la potencia aerobica y la concordancia entre la medida directa y la prevista, segun tres diferentes pruebas de campo en jovenes universitarios. METODOS: Participaron en el estudio 12 individuos (23,1 ± 2,8 anos), siete hombres y cinco mujeres, quienes fueron sometidos a medidas antropometricas de masa corporal, estatura y espesura de dobleces cutaneos. Ademas de eso, las personas realizaron una prueba maxima directa (MD) en estera y tres pruebas de campo para verificacion del consumo maximo de oxigeno (VO2max), en valores relativos. Las pruebas de campo que se utilizaron fueron: carrera/caminata de 12 minutos de Cooper (COOPER), carrera/caminata de una milla (MILLA) [1,6 quilometro] y shuttle run de 20 metros (SR-20m). Los datos fueron analizados mediante procedimientos descriptivos, y se utilizo la prueba t en pares para las comparaciones entre la medida directa y cada una de las pruebas de campo. La regresion linear simple suministro informaciones cuanto al desempeno en las pruebas y sus respectivas ecuaciones, mientras que la concordancia entre los metodos se hizo por el analisis de Bland-Altman, con determinacion de la tendencia de medida. RESULTADOS: No se encontraron diferencias significativas entre el MD y las tres pruebas de campo. Los errores estandares de estimativas variaron desde, aproximadamente, 5,8 a 6,0 ml.kg-1.min-1, y las correlaciones de r = 0,61 - 0,64. Los limites de concordancia fueron considerados como siendo amplios para las tres pruebas, no obstante, sin bies ni tendencia de estimativa. CONCLUSION: Aun considerando la similitud entre los valores promedios obtenidos en las comparaciones, entre las pruebas de campo y la medida de referencia, la prueba de la MILLA fue la que presento mejores resultados de desempeno y concordancia para la estimativa del VO2max.
EarLocalizer

With much concern over security, it has become essential to maintain the identity and track of an individual's activities in the modern healthcare sector. Although there are biometric authentication systems based on different modalities, recognition of a person using the ear has gained much attention as ears are unique. Ear localization is a first step for ear-based biometric authentication systems, and this needs to be accurate, since it plays a crucial role in the overall performance of the system. The localization of ear in the side face images captured in the wild possess great challenges due to varying angles, light, scale, background clutter, blur and occlusion, etc. In this chapter, the authors have proposed EarLocalizer model to localize the ear, which is inspired by Faster-RCNN. The model is evaluated on two wild ear databases, UBEAR-II and USTB-III, and has achieved an accuracy of 95% and 99.08%, respectively, at IOU (Intersection over Union) = 0.5. The results of the proposed model signify that the model is invariant to the environmental conditions.
A critical perspective of engagement in online health communities

ABSTRACT Many individuals go to online health communities to obtain emotional and information support. This study employs critical discourse analysis (CDA) to examine the discourse in five online childhood immunization forums to identify online practices related to engagement and emancipation. We identify four online engagement discourse types in online health communities: cognitive, emotional, behavioural, and political. Consistent with our critical perspective, we identify drivers and obstacles to emancipation. The obstacles to emancipation include the adverse consequences of building online knowledge and collective intelligence, shifts in patient–physician relationships, and “Big” institutional power. Members’ suggestions for empowering community members include encouraging members to conduct their own research on childhood immunization and to differentiate between facts and opinions. These findings suggest that sensemaking is one way to emancipate online health community members, while highlighting the challenges of using online communities to promote emancipation.
Mining Big Data With Computational Methods

When studying online communication, researchers are confronted with vast amounts of unstructured text data and experience severe limitations to the established methods of manual quantitative content analysis. Text mining methods developed in computational natural language processing (NLP) allow the automatic capture of semantics in massive populations of texts. In this chapter, we present state of the art NLP methods and discuss potential applications and limitations for communication research. Unsupervised methods such as cooccurrence analysis or topic modeling enhance explorative research whereas supervised methods such as machine-learning classification support a deductive research strategy similar to traditional content analysis.
Cognitive–Behavioral Therapies for Young People in Outpatient Treatment for Nonopioid Drug Use

Objectives: This review evaluates the evidence on the effects of cognitive–behavioral therapy (CBT) on drug use reduction for young people in treatment for nonopioid drug use. Method: We followed Campbell Collaboration guidelines to conduct a systematic review of randomized and nonrandomized trials. Meta-analytic methods were used to quantitatively synthesize study results. Results: The search yielded seven studies that met inclusion criteria. Each of the seven studies compared CBT to another intervention. Authors’ Conclusions: The review found that there was no evidence that CBT interventions perform better or worse than the comparison interventions and additional research is needed. The number of studies included in this review was limited, and therefore should the overall results be interpreted with caution.
Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma

Yapay sinir aglari ve makine ogrenmesi, uzun yillardir bircok problemin cozumunde kullanilmistir. Problemlerin ve modellerin karmasiklasmasi ve veri sayisindaki artis hesaplama yukunu de beraberinde getirmistir. Bu calismada yapay sinir aglarindan derin ogrenmeye tum gecis sureci, modeller ve pratik uygulamalar kisa ve oz gosterilmistir. Ayrica donanim, yazilim ve kullanilan kutuphaneler hakkinda da bilgiler verilmistir. Ozel olarak kalabalik analizi icin kullanilan geleneksel yontemler ozetlenmistir. Kalabalik analizi icin literaturdeki derin ogrenme yaklasimlari detaylica anlatilmis ve veri kumeleri tanitilmistir. Ayrica son yillarda yapilmis calismalar analiz edilmis ve karsilastirilmistir. Sonuc olarak, kalabalik analizi, derin ogrenme yardimiyla basarili sonuclar alinan hem akademik hem de pratik bir calisma alanidir.
Toward sequential segregation of speech sounds based on spatial cues

In a context of competing sound sources, the auditory scene analysis aims to draw an accurate and useful representation of the perceived sounds. Solving such a scene consists of grouping sound events which come from the same source and segregating them from the other sounds. This PhD work intended to further our understanding of how the human auditory system processes these complex acoustic environments, with a particular emphasis on the potential influence of spatial cues on perceptual stream segregation. All the studies conducted during this PhD endeavoured to rely on realistic configurations.In a real environment, the diffraction and reflection properties of the room and the head lead to distortions of the sounds depending on the source and receiver positions. This phenomenon is named colouration. Speechshaped noises, as a first approximation of speech sounds, were used to evaluate the effect of this colouration on stream segregation. The results showed that the slight monaural spectral differences induced by head and room colouration can induce segregation. Moreover, this segregation was enhanced by adding the binaural cues associated with a given position (ITD, ILD). Especially, a second study suggested that the monaural intensity variations across time at each ear were more relevant for stream segregation than the interaural level differences. The results also indicated that the percept of lateralization associated with a given ITD helped the segregation when the lateralization was salient enough. Besides, the ITD per se could also favour segregation.The natural ability to perceptually solve an auditory scene is relevant for speech intelligibility. The main idea was to replicate the first experiments with speech items instead of frozen noises. A characteristic of running speech is a high degree of acoustical variability used to convey information. Thus, as a first step, we investigated the robustness of stream segregation based on a frequency difference to variability on the same acoustical cue (i.e., frequency). The second step was to evaluate the fundamental frequency difference that enables to separate speech items. Indeed, according to the limited effects measured in the two first experiments, it was assumed that spatial cues might be relevant for stream segregation only in interaction with another “stronger” cue such as a F0 difference.The results of these preliminary experiments showed first that the introduction of a large spectral variability introduced within pure tone streams can lead to a complicated percept, presumably consisting of multiple streams. Second, the results suggested that a fundamental frequency difference comprised between 3 and 5 semitones enables to separate speech item. These experiments provided results that will be used to design the next experiment investigating how an ambiguous percept could be biased toward segregation by introducing spatial cues.
The Application of the Topic Modeling to Question Answer Retrieval

— Topic modeling (TM) is used for the extraction of the information from unstructured documents. The aim of this study is to investigate the application of the Latent Dirichlet Allocation Topic modeling algorithm to question answer retrieval. The most appropriate answer is automatically selected from a database of answers based on a combination of several similarity measures. The primary hypothesis assumed in this study is that a question and its correct answer are thematically similar. All TM results were compared to a simple word count approach, employed as the reference model. Results show that the topic modeling approach performs better than the reference model as the number of the documents increase. It is also proved that the difference in results is statistically significant. Nevertheless, basic LDA turned out to be insufficient for efficient question answering. It is therefore hypothesized that additional expert knowledge would greatly improve its performance.
Optimal Power and Resource Allocation for Transmit Power Minimization in OFDMA-based NOMA Networks

In this article, we address the problem of optimal joint power and resource allocation when Non-Orthogonal Multiple Access (NOMA) and Orthogonal Frequency Multiple Access (OFDMA) are combined for a hybrid downlink multiple access. First, an optimal solution of joint power and resource allocation minimizing the transmit power consumption, is obtained by rewriting the original optimization problem into an equivalent convex one and then by solving it by means of the well-known interior-point method. Second, we analyze the properties of the optimum, which are twofold; we show that at the optimum, the order of the users is preserved from one channel to another. Then, we derive a closed-form expression for the optimal power allocation in the particular case where all users have positive non-zero transmit powers in all channels.
Improving the Power, Performance and Usability of Datalog by Pushing Constraints into Recursion

We introduce a novel query optimization method based on pushing extrema and other integrity constraints into recursion. This optimization produces a more efficient computation that preserves the soundness and completeness for the predicates specified by the integrity constraints. Complex algorithms, including greedy algorithms, can now be specified via simpler programs that are stratified with respect to min and max, and then implemented via optimized rules that use these aggregates in the seminaive fixpoint computation to achieve more efficient execution. This rewriting is not limited to extrema, but in many situations a similar optimization is applicable to other constraints as well. The relationships of this optimization technique with recent works on monotonic aggregates and their combined efficient implementation in the seminaive fixpoint method are also elucidated.
MODELLING UNCERTAINTY OF SINGLE IMAGE INDOOR LOCALISATION USING A 3D MODEL AND DEEP LEARNING

Abstract. Many current indoor localisation approaches need an initial location at the beginning of localisation. The existing visual approaches to indoor localisation perform a 3D reconstruction of the indoor spaces beforehand, for determining this initial location, which is challenging for large indoor spaces. In this research, we present a visual approach for indoor localisation that is eliminating the requirement of any image-based reconstruction of indoor spaces by using a 3D model. A deep Bayesian convolutional neural network is fine-tuned with synthetic images generated from a 3D model to estimate the camera pose of real images. The uncertainty of the estimated camera poses is modelled by sampling the outputs of the Bayesian network fine-tuned with synthetic images. The results of the experiments indicate that a localisation accuracy of 2 metres can be achieved using the proposed approach.