Miracle-dz/UniFAR-FTUs huggingface dataset:
Design your room: adding virtual objects to a real indoor scenario

This paper presents an interior design application that uses photos from real indoor spaces and rooms as input. The user is encouraged to take a picture of a certain place and virtually reshape it with furniture, different colors, textures and materials. The system analyses the still image of the physical space through image processing and computer vision algorithms to detect the world orientation relative to the camera. Knowing this, it is possible to create a natural interface where objects are laid on the floor and pushed around as in real life.
Activity and Gait Recognition with Time-Delay Embeddings

Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination of feature extraction using time-delay embedding and supervised learning. The computational requirements are considerably lower than existing approaches, so the processing can be done in real time on a low-powered portable device such as a mobile phone. We evaluate the performance of our algorithm on a large, noisy data set comprising over 50 hours of data from six different subjects, including activities such as running and walking up or down stairs. We also demonstrate the ability of the system to accurately classify an individual from a set of 25 people, based only on the characteristics of their walking gait. The system requires very little parameter tuning, and can be trained with small amounts of data.
Smoking affects diagnostic salivary periodontal disease biomarker levels in adolescents.

BACKGROUND The effects of smoking on periodontal biomarkers in adolescents are unknown. This study investigates matrix metalloproteinase (MMP)-8 and polymorphonuclear leukocyte elastase levels in saliva together with periodontal health indices accounting for body mass index and smoking in a birth cohort from Finland. METHODS The oral health of boys (n = 258) and girls (n = 243) aged 15 to 16 years was examined clinically. Health habits were assessed by questionnaire. Saliva samples were collected and analyzed by immunofluorometric and peptide assays for MMP-8 levels and polymorphonuclear leukocyte elastase activities, and investigated statistically with the background factors. RESULTS Median MMP-8 values of male smokers were 112.03 microg/l compared to 176.89 microg/l of non-smokers (P = 0.05). For female smokers corresponding values were 170.88 microg/l versus 177.92 microg/l in non-smokers (not statistically significant). Elastase values in male smokers were 5.88 x 10(-3) Delta OD(405)/h versus 11.0 x 10(-3) Delta OD(405)/h in non-smokers (P = 0.02), and in female smokers 9.16 x 10(-3) Delta OD(405)/h versus 10.88 x 10(-3) Delta OD(405)/h in non-smokers (P = 0.72). The effect was strengthened by high pack-years of smoking (MMP-8, P = 0.04; elastase, P = 0.01). Both biomarkers increased with gingival bleeding. However, statistically significant associations were observed with bleeding on probing and MMP-8 (P = 0.04); MMP-8 was suggestively associated with probing depth (P = 0.09) in non-smoking boys. In smokers with calculus, MMP-8 increased after adjusting with body mass index (P = 0.03). No corresponding differences were seen in girls. CONCLUSIONS Smoking significantly decreased both biomarkers studied. Compared to girls, boys seem to have enhanced susceptibility for periodontitis as reflected in salivary MMP-8 values.
PeerDB: peering into personal databases

In this demonstration, we present a prototype peer-topeer (P2P) application called PeerDB[2] that provides database capabilities. This system has been developed at the National University of Singapore in collaboration with Fudan University, and is being enhanced with more features and applications. The concept behind PeerDB is similar to the analogy of publishing personal web sites, except that it is now applied to personal databases. Unlike personal web sites which are usually hosted together in a central web server, personal databases are stored in the person’s own PC. In addition, it is increasingly common for people to keep their data in common personal DBMS like MySQL, and MSAccess. Therefore, a PeerDB node allows an user to index and publish his/her personal database for other peers to query. PeerDB builds on and extends BestPeer [1] for DBMS applications. Briefly, BestPeer is a generic P2P system designed to serve as a platform to develop P2P applications easily and efficiently. It has the following features: (1) It employs mobile agents; (2) It shares data at a finer granularity as well as computational power; (3) It can dynamically reconfigure the BestPeer network so that a node is always directly connected to peers that provide the best service; (4) It employs a set of location independent global name lookup (LIGLO) servers to uniquely recognize nodes whose IP addresses may change as a result. In the PeerDB network, a set of PeerDB nodes communicate or share resources with each other. Each node comprises four components that are loosely integrated: (a) a data management system (we used MySQL in our implementation) that facilitates storage, manipulation and retrieval of the data at the node, and the associated local and export dictionaries that reflect the meta-data (schema and keywords); (b) a database agent system called DBAgent that provides the environment for mobile agents to operate on; (c) a cache manager for managing remote meta-data and data in secondary storage; and (d) a user-friendly user interface. PeerDB has several distinguishing features. First, it allows users to query data without knowing the schema of data in other nodes. To address this issue, we adopt an Informa-
Heterogeneous Feeding Patterns of the Dengue Vector, Aedes aegypti, on Individual Human Hosts in Rural Thailand

Background Mosquito biting frequency and how bites are distributed among different people can have significant epidemiologic effects. An improved understanding of mosquito vector-human interactions would refine knowledge of the entomological processes supporting pathogen transmission and could reveal targets for minimizing risk and breaking pathogen transmission cycles. Methodology and principal findings We used human DNA blood meal profiling of the dengue virus (DENV) vector, Aedes aegypti, to quantify its contact with human hosts and to infer epidemiologic implications of its blood feeding behavior. We determined the number of different people bitten, biting frequency by host age, size, mosquito age, and the number of times each person was bitten. Of 3,677 engorged mosquitoes collected and 1,186 complete DNA profiles, only 420 meals matched people from the study area, indicating that Ae. aegypti feed on people moving transiently through communities to conduct daily business. 10–13% of engorged mosquitoes fed on more than one person. No biting rate differences were detected between high- and low-dengue transmission seasons. We estimate that 43–46% of engorged mosquitoes bit more than one person within each gonotrophic cycle. Most multiple meals were from residents of the mosquito collection house or neighbors. People ≤25 years old were bitten less often than older people. Some hosts were fed on frequently, with three hosts bitten nine times. Interaction networks for mosquitoes and humans revealed biologically significant blood feeding hotspots, including community marketplaces. Conclusion and significance High multiple-feeding rates and feeding on community visitors are likely important features in the efficient transmission and rapid spread of DENV. These results help explain why reducing vector populations alone is difficult for dengue prevention and support the argument for additional studies of mosquito feeding behavior, which when integrated with a greater understanding of human behavior will refine estimates of risk and strategies for dengue control.
The Influence of Media Coverage on the Stock Returns and Momentum Profits

Media coverage not only provides much firm-specific information to the investor public intuitionally, but also is informative to the stock price. This study tries to examine whether media coverage is an important factor to stock returns on the Taiwan stock market. Our empirical results support this finding on the Taiwan stock market, and find investors can utilize momentum strategies to earn abnormal returns. These findings can provide some meaningful insights to e-business corporate manager, while releasing some news to the public.
Towards Personalized Drug Delivery - Preparation of an Encapsulated Multicompartment System

Single liposomes and vesicles are successfully utilized as delivery vehicles of pharmaceuticals. However limitations of these unilamellar, single compartments led to the development of encapsulated multicompartment systems that establishes the prospect of multicomponent or multifunctional drug delivery systems. So far compartmentalization is restricted to binary systems. To realize a personalized drug delivery, a programmable linkage of n-entities of different content will be needed. Here we present both a programmable DNA-mediated linkage of three distinct vesicle populations and a novel encapsulation protocol. We discuss how the techniques established in this study might be used in personalized healthcare based on customtailored encapsulated multicompartment vesicular drug delivery systems.
All-pole modeling technique based on the Weighted Sum of the LSP polynomials

With the order of prediction equal to m, conventional linear prediction (LP) yields an all-pole filter that matches exactly the autocorrelation function of the input signal between indices 0 and m. This study presents a new technique, Weighted-Sum Line Spectrum Pair (WLSP), where an all-pole filter is defined by using a sum of weighted LSP polynomials. WLSP yields a stable all-pole filter of order m, whose autocorrelation function coincides to that of the input signal between indices 0 and m −1. By sacrificing the exact matching of the autocorrelations at index m, WLSP models the autocorrelation of the input signal at the indices above m more accurately than conventional LP. The current paper presents mathematical properties of WLSP together with preliminary results on modeling of vowels. The results indicate that WLSP, in comparison to conventional LP is able to yield all-pole filters that model especially the upper formants with a larger dynamic range between formant peaks and spectral valleys.
Lightweight capability domains: towards decomposing the Linux kernel

Despite a number of radical changes in how computer systems are used, the design principles behind the very core of the systems stack---an operating system kernel---has remained unchanged for decades. We run monolithic kernels developed with a combination of an unsafe programming language, global sharing of data structures, opaque interfaces, and no explicit knowledge of kernel protocols. Today, the monolithic architecture of a kernel is the main factor undermining its security, and even worse, limiting its evolution towards a safer, more secure environment. Lack of isolation across kernel subsystems allows attackers to take control over the entire machine with a single kernel vulnerability. Furthermore, complex, semantically rich monolithic code with globally shared data structures and no explicit interfaces is not amenable to formal analysis and verification tools. Even after decades of work to make monolithic kernels more secure, over a hundred serious kernel vulnerabilities are still reported every year. Modern kernels need decomposition as a practical means of confining the effects of individual attacks. Historically, decomposed kernels were prohibitively slow. Today, the complexity of a modern kernel prevents a trivial decomposition effort. We argue, however, that despite all odds modern kernels can be decomposed. Careful choice of communication abstractions and execution model, a general approach to decomposition, a path for incremental adoption, and automation through proper language tools can address complexity of decomposition and performance overheads of decomposed kernels. Our work on lightweight capability domains (LCDs) develops principles, mechanisms, and tools that enable incremental, practical decomposition of a modern operating system kernel.
Automatic detection of dining plates for image-based dietary evaluation

An automatic detector that finds circular dining plates in chronically recorded images or videos is reported for the study of food intake and obesity. We first detect edges from input images. After a number of processing steps that convert edges into curves, arc filtering and grouping algorithms are applied. Then, convex hulls are identified and the ones that fit the description of ellipses corresponding to dining plates are determined. Our experiments using real-world images indicate that this detector is highly reliable and robust even when the input images contain complex background scenes and the dining plates are severely occluded.
Text Analytics: the convergence of Big Data and Artificial Intelligence

The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers� comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain�s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics.
Fruit over sunbed: Carotenoid skin colouration is found more attractive than melanin colouration

Skin colouration appears to play a pivotal part in facial attractiveness. Skin yellowness contributes to an attractive appearance and is influenced both by dietary carotenoids and by melanin. While both increased carotenoid colouration and increased melanin colouration enhance apparent health in Caucasian faces by increasing skin yellowness, it remains unclear, firstly, whether both pigments contribute to attractiveness judgements, secondly, whether one pigment is clearly preferred over the other, and thirdly, whether these effects depend on the sex of the face. Here, in three studies, we examine these questions using controlled facial stimuli transformed to be either high or low in (a) carotenoid colouration, or (b) melanin colouration. We show, firstly, that both increased carotenoid colouration and increased melanin colouration are found attractive compared to lower levels of these pigments. Secondly, we show that carotenoid colouration is consistently preferred over melanin colouration when levels of colouration are matched. In addition, we find an effect of the sex of stimuli with stronger preferences for carotenoids over melanin in female compared to male faces, irrespective of the sex of the observer. These results are interpreted as reflecting preferences for sex-typical skin colouration: men have darker skin than women and high melanization in male faces may further enhance this masculine trait, thus carotenoid colouration is not less desirable, but melanin colouration is relatively more desirable in males compared to females. Taken together, our findings provide further support for a carotenoid-linked health-signalling system that is highly important in mate choice.
An improved technique for fluorescence staining of fungal nuclei and septa

Nuclei and septa of fungal mycelium grown on glass slides coated with potato-dextrose agar stained very clearly with fluorescent Hoechst Dye 33258 in buffer of pH 7.8 and 10.5, respectively. The technique is simple, rapid, and gives excellent and clear staining.
A General, Abstract Model of Incremental Dialogue Processing

We present a general model and conceptual framework for specifying architectures for incremental processing in dialogue systems, in particular with respect to the topology of the network of modules that make up the system, the way information flows through this network, how information increments are 'packaged', and how these increments are processed by the modules. This model enables the precise specification of incremental systems and hence facilitates detailed comparisons between systems, as well as giving guidance on designing new systems.
Long‐Term Trends in Phytoplankton Chlorophyll
 a
 and Size Structure in the Benguela Upwelling System

The Benguela Upwelling System (BUS) is among the most productive ecosystems globally, supporting numerous fisheries and ecosystem services in Southern Africa. Sea‐viewing Wide Field‐of‐view Sensor and Moderate‐resolution Imaging Spectroradiometer‐Aqua chlorophyll a (Chla) concentrations between September 1997 and February 2018 were used to investigate long‐term trends in phytoplankton biomass and size structure (microphytoplankton [>20 μm], nanophytoplankton [2–20 μm], and picophytoplankton [<2 μm]) in the Northern Benguela, Southern Benguela (SB), and Agulhas Bank (AB) shelf and open ocean regions of the BUS. Trends in upwelling and correlations with Chla and size structure were examined. Increasing Chla and microphytoplankton trends occurred in the Northern Benguela shelf and open ocean, while decreases were evident on the SB shelf in all seasons. In the SB open ocean, small increases occurred during austral winter, with a decrease in spring. On the AB shelf, increases in Chla and microphytoplankton occurred in summer with decreases during the other seasons. Patterns differed in the AB open ocean, with increases in winter and spring and decreases in summer and autumn. Although R values indicated that linear trends accounted for a reasonable portion of the variance, and most trends were statistically significant, they showed only small changes on the shelf domains and little to no change in the open ocean. Strong correlations between upwelling, Chla, and the size classes were observed, but distinct seasonal differences occurred in each region. This is the first 20‐year analysis of phytoplankton biomass and community structure in the BUS and provides a baseline against which future changes can be monitored. Plain Language Summary Two satellite ocean color data products were combined and used to examine trends in chlorophyll a and the proportions of microphytoplankton, nanophytoplankton, and picophytoplankton in three subregions of the Benguela Upwelling System, namely, the northern Benguela, southern Benguela, and Agulhas Bank. These trends were related to trends in upwelling‐favorable winds and significant correlations were observed but with distinct seasonal differences in each region. Linear trends were mostly significant and accounted for a reasonable portion of the variance, but they showed only small changes on the shelf domains and little to no change in the open ocean regions. This study provides the first 20‐year analysis of phytoplankton biomass and community structure in the Benguela Upwelling System and provides a baseline against which future changes can be monitored.
The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes

The quaternion least mean square (QLMS) algorithm is introduced for adaptive filtering of three- and four-dimensional processes, such as those observed in atmospheric modeling (wind, vector fields). These processes exhibit complex nonlinear dynamics and coupling between the dimensions, which make their component-wise processing by multiple univariate LMS, bivariate complex LMS (CLMS), or multichannel LMS (MLMS) algorithms inadequate. The QLMS accounts for these problems naturally, as it is derived directly in the quaternion domain. The analysis shows that QLMS operates inherently based on the so called ldquoaugmentedrdquo statistics, that is, both the covariance E{ xx H} and pseudocovariance E{ xx T} of the tap input vector x are taken into account. In addition, the operation in the quaternion domain facilitates fusion of heterogeneous data sources, for instance, the three vector dimensions of the wind field and air temperature. Simulations on both benchmark and real world data support the approach.
Hardware realization of DC embedding video watermarking technique based on FPGA

In this paper, a video watermark technique is proposed. The proposed technique depends on inserting invisible watermark in Low Frequency DCT domain using pseudo random number (PN) sequence generator for the video frames instead of high or mid band frequency components. This technique has been realized using Matlab and VHDL. The system has been implemented on Xilinx chip XC5VLX330T. The result of implementation shows that maximum frequency 13.61 MHZ. The experimental and implementation results has been demonstrated and discussed.
Fuzzy Model-Based Servo and Model Following Control for Nonlinear Systems

This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.
Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness

There has recently been a great deal of interest in inferring network connectivity from the spike trains in populations of neurons. One class of useful models that can be fit easily to spiking data is based on generalized linear point process models from statistics. Once the parameters for these models are fit, the analyst is left with a nonlinear spiking network model with delays, which in general may be very difficult to understand analytically. Here we develop mean-field methods for approximating the stimulus-driven firing rates (in both the time-varying and steady-state cases), auto- and cross-correlations, and stimulus-dependent filtering properties of these networks. These approximations are valid when the contributions of individual network coupling terms are small and, hence, the total input to a neuron is approximately gaussian. These approximations lead to deterministic ordinary differential equations that are much easier to solve and analyze than direct Monte Carlo simulation of the network activity. These approximations also provide an analytical way to evaluate the linear input-output filter of neurons and how the filters are modulated by network interactions and some stimulus feature. Finally, in the case of strong refractory effects, the mean-field approximations in the generalized linear model become inaccurate; therefore, we introduce a model that captures strong refractoriness, retains all of the easy fitting properties of the standard generalized linear model, and leads to much more accurate approximations of mean firing rates and cross-correlations that retain fine temporal behaviors.