Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-sensor fusion mapping method based on kinect vision constraints

In order to meet the requirements of lightweight and precision for indoor mobile robots, this paper proposes a mapping method named as VFCDSA (Visual field constrained de-sampling algorithm) based on depth camera and 2D lidar. To save the computation, considering the obstacles outside the vertical active area of the robot having no affect on the robot's passage, the view angle of the depth camera is adjusted to eliminate the influence of obstacles above the robot. Meanwhile, the image coordinate transformation is introduced to remove the robot under the pixel coordinate system. The experiments results show that the new method allow mobile robots pass through the obstacles more effectively, and the point clouds being detected among the robot's active scene is generated before they are downsampled more efficiently, which greatly reduces the number of point clouds and saves more computation cost. Finally, the Bayesian method is combined to fuse the depth vision sensor data with the laser sensor data to establish a more reasonable uncertainty model.
Snap-Snap T-Shirt: Posture Awareness Through Playful and Somaesthetic Experience

In this pictorial we present a prototype of a novel personalized garment that provides rich haptic feedback for posture awareness in the context of repetitive strain injury (RSI). Unlike prior work concerned with posture correction, our aim was to design a garment that would allow the user to gain an awareness of his posture with a help of sensorial experiences. Collaboratively engaging the user as a co-designer in movement enactment, movement analysis and embodied co-design sessions enabled us to design a garment that offers posture awareness through playful and somaesthetic experience. We offer a reflective analysis of how our co-design approach enabled us to design for personalization, somaesthetics and playfulness in posture awareness.
Attentional Matrix Factorization with Document-context awareness and Implicit API Relationship for Service Recommendation

The rapid development of the mashup approach significantly plays a pivot role in building Web-based and mobile applications. Easy accessibility of data and functions are the main advantages for programmers to develop mashups from abundant sources of APIs. However, it simultaneously brings more difficulties to choose suitable APIs for a mashup, especially when the historical relations between APIs and mashups are very sparse. Existing probabilistic matrix factorization (PMF) recommender systems can effectively exploit the latent features of the invocations with the same weight. However, not all features are equally significant and predictive, and the useless features may bring noises to the model. Also, many current works explored the influence of mashups’ relationships, but few of them sheds lights on the relationship between APIs and their contextual interaction, which can be mined from their content description. This paper improves the PMF model by distinguishing the importance of latent feature interactions. We present an Attentional PMF model, which leverages a neural attention network to learn the significance of feature interactions and uses Doc2Vec technique for mining the contextual information. We also exploit the relationship between APIs from both their contextual similarities and invocation history and add them to the prediction model as a regularization part. Our experiments are performed with datasets from ProgrammableWeb. The results show that our model significantly outperforms some state-of-art recommender systems in mashup service applications.
Artificial Neural Networks Optimization and Convolution Neural Networks to Classifying Images in Remote Sensing: A Review

One of important functions of remote sensing data is producing the land-use/land-cover maps. Image classification is one of important applications for remote sensing imaginary. Machine learning (ML) techniques are the most widely used for this purpose in recent years. With the advent of computer vision thus, the need to deal with a large amount of data and avoiding any data redundancy, the deep learning techniques were appeared. Deep learning (DL) is a branch of machine learning that imitates the human brain structure and depends on the artificial neural networks (ANNs). Optimization of the neural networks is necessary for reduce the loss functions and avoiding any redundancy data in the training set, thus raise the accuracy. Genetic algorithms (GA) are the most widely used in the neural networks optimization, which considered as fully connected neural networks. Convolution neural networks (CNNs) are a branch of the artificial neural networks that are saving the computing cost and processing time. Thus, this paper presents a review of the deep learning algorithms specially the artificial neural networks, the genetic algorithm and the convolution neural networks. This paper also introduces a comparative study between the genetic algorithm and the convolution neural networks method. This comparison based on the overall accuracy (OA) and the kappa coefficient. This comparison shows that there are many conditions can affect the classifier accuracy. The results demonstrate that the CNNs algorithms are more accurate than the GA and in the other hand, the CNNs algorithms have lower computing cost.
Shape Memory Alloy Based Caudal Fin for a Robotic Fish: Design, Fabrication, Control and Characterization

A bio-mimetic fin embedded with shape memory alloy (SMA) wires has been developed. The design of the bio-mimetic fin is based on the musculature of a cuttlefish fin. The musculature of cuttlefish is studied. SMA is chosen as an actuator among the other smart material for its low actuation voltage and high power to weight ratio. A stepwise fabrication process of a bio-mimetic fin has been described for consistency and better performance. A series of tensile tests have been conducted to optimize the annealing temperature and the required pre-straining of the SMA wire actuating the developed fin. The optimum annealing temperature and the required pre-straining has been found to be 410° C and 4--6% respectively. The details of the heat treatment process followed have also been described. The maximum bending angle of 60° has been obtained for the developed bio-mimetic fin. The developed bio-mimetic fin has been integrated into a fish-inspired robotic structure to validate the capability of the developed bio-mimetic fin. The developed robotic fish can perform subcarangiform locomotion. The robot is capable of swimming in the forward direction as well as turning swimming mode. A control scheme for forward and turning swimming mode has been described. The developed robot is untethered and controlled wirelessly. The swimming speed and turning radius obtained are 7.6 cm/s and 45 cm respectively.
Feature identification for engineering model variants in cyber-physical production systems engineering

In Cyber-Physical Production System (CPPS) engineering, Assembly Sequence (AS) models of products are primary engineering artifacts. Product variants are often designed as Product-Process-Resource (PPR) AS models that are initiated with clone-and-own approaches and by the manual derivation of shared features. This paper introduces the PPR Feature Candidate Identification (PPR-FCI) approach for identifying features from PPR AS models of product variants. From these features our approach derives a superimposed PPR that describes design options for engineers planning the CPPS. The approach is based on existing feature extraction research which we adapted to the scope of PPR models in CPPS engineering. Based on a real-world product line, we evaluate our PPR-FCI approach for feasibility and usefulness by comparing our automated approach to the traditional manual approach with domain experts. Initial findings show that the approach can identify relevant features from PPR AS models and domain experts found the results useful. However, further research is required to improve the PPR-FCI approach regarding the optimization of PPR Assembly Sequence models.
Tracing from Sound to Movement with Mixture Density Recurrent Neural Networks

In this work, we present a method for generating sound-tracings using a mixture density recurrent neural network (MDRNN). A sound-tracing is a rendering of perceptual qualities of short sound objects through body motion. The model is trained on a dataset of single point sound-tracings with multimodal input data and learns to generate novel tracings. We use a second neural network classifier to show that the input sound can be identified from generated tracings. This is part of an ongoing research effort to examine the complex correlations between sound and movement and the possibility of modelling these relationships using deep learning.
Android Malware Detection Combined with Static and Dynamic Analysis

Android System has attracted not only constantly increasing number of smart device users, but also the serious attacks from explosive malicious apps. Consequently, the need to effectively detect Android malware is becoming more and more urgent. In the paper, combing the advantages of static analysis and dynamic analysis, we propose an Android malware detection method based on machine classification. Our experimental results show that the accuracy of the approach meets the requirements of Android malware detection. Subsequently, we apply this approach to perform an interesting detection on the popular apps of different user crowds, and provide some corresponding security advices.
Enabling Near Real-time Surveillance of Influenza-like Illness

Traditional public health surveillance systems would benefit from near real-time data integration and visualization that combines information from traditional and internet sources. In this paper, we describe a prototype system implemented to better automate the process of data collection, analysis and visualization of Influenza-Like Illness surveillance data. This approach enables timelier responses to abnormal events such as clusters, outbreaks and trends.
Deep learning chips: challenges and opportunities for ubiquitous power internet of things

Tasks of Ubiquitous Power Internet of Things run through the power generation, transmission, transformation, distribution, electricity use and other links, requiring advanced communication, artificial intelligence, big data and other technologies. Deep learning chips provide computational power for algorithm execution and data processing, which are indispensable foundations and basic components for intelligent terminals. Therefore, this paper summarizes the challenges and opportunities faced by deep learning chips in the construction of ubiquitous power Internet of Things. Firstly, the four parts of ubiquitous power Internet of Things including terminal layer, network layer, platform layer and application layer are described. Secondly, the key technologies of deep learning technology and deep neural network accelerator involved in deep learning chips are summarized. Finally, the research work of deep learning chips for ubiquitous power Internet of Things is surveyed. The main functions and existing problems are discussed, and the future research work is proposed.
GENDER AND NEW FAMILY PATTERNS IN EUROPE: ATTITUDES, BEHAVIOURS, AND OPINIONS

The great social changes experienced by many countries since the Second World War, and particularly throughout the most recent years, concern various aspects of individual and collective life, which also influence demographic behaviours: from the sexual and contraceptive behaviour to the models of couples’ formation and dissolution and to reproductive choices. Many scholars related this plurality of family forms also to a mutated attitude of men and women towards the family: the transformation in family formation appears therefore to be strongly influenced also by the changes in gender roles. The ideational system also seems to play a major role on the diffusion of new models of behaviour: opinions, attitudes and approaches towards ideal life styles represent one of the main determining factors of family and reproductive choices. The aim of this work is to explain the variability of family behaviours by adopting a gender approach: specifically, its originality consists in considering not only the gendered behaviours but also the opinions towards gender roles as main discriminant factors of the attitudes towards new family models. Particularly, we ask whether there is a correspondence between opinions favourable to egalitarian gender roles and the openness towards new family behaviour. Or conversely, is the effective gender equity in the division of roles to be more relevant in determining the cultural context at the basis of opinions’ formation of men and women? We did not observe a significant association between opinions towards gender roles and opinions on modern family behaviours. On the contrary, it emerges a clear connection between effective gender equality in the division of roles within the couple and a more openness towards new family behaviours.
Embedding Conversational Agents into AR: Invisible or with a Realistic Human Body?

Currently, (invisible) smart speech assistants, such as Siri, Alexa, and Cortana, are used by a constantly growing number of people. Moreover, Augmented Reality (AR) glasses are predicted to become widespread consumer devices in the future. Hence, smart assistants can easily become common applications of AR glasses, which allows for giving the assistant a visual representation as an embodied agent. While previous research on embodied agents found a user preference for a humanoid appearance, research on the uncanny valley suggests that simply designed humanoids can be favored over hyper-realistic humanoid characters. In a user study, we compared agents of simple versus more realistic appearance (seen through AR glasses) versus an invisible state-of-the-art speech assistants (see Figure 1). Our results indicate that a more realistic visualization is preferred as it provides additional communication cues, such as eye contact and gaze, which seem to be key features when talking to a smart assistant. But if the situation requires visual attention, e.g., when being mobile or in a multitask situation, an invisible agent can be more appropriate as they do not distract the visual focus, which can be essential during AR experiences.
The Effect of Smoothing Filter on CNN based AD Classification

Gaussian smoothing (GS) is a spatial low pass filtering method widely used in neuroimaging preprocessing. Full width at half maximum (FWHM) is a common parameter when the imaging data convolved with GS kernel. The convolutional neural networks (CNNs) can be considered as the feature extractor, which is implemented by applying a series of different filters. However, the influence of kernel size of GS for feature extraction remains unclear. In this study, we describe an automatic AD classification algorithm that is built on a pre-trained CNN model, AlexNet for feature extraction and support vector machine (SVM) for classification. The algorithm was trained and tested using the structural Magnetic Resonance Imaging (sMRI) data from Alzheimer's Disease Neuroimaging Initiative (ADNI). The data used in this study include 191 Alzheimer's disease (AD) patients and 103 normal control (NC) subjects. We evaluate the influence of FWHM on classification performance. When FWHM is 0mm, the classification accuracy obtained the highest value for AD and NC, which reached 91.5%, 92.4%, 89.0% for conv3, conv4 and conv5 of AlexNet respectively. The classification accuracy at each layer is relatively low when FWHM is 8mm. The result suggests that the higher smooth value may have a negative effect on feature extraction of CNNs during AD classification.
MuscleSense: Exploring Weight Sensing using Wearable Surface Electromyography (sEMG)

Strength training improves overall health, well-being, physical appearance, and sports performance.There are four major factors that affect training efficacy in a training session: exercise type, number of repetitions, movement velocity, and workload. Prior research has used wearable sensors to detect exercise type, number of repetitions, and movement velocity while training. However, detecting workload remains constrained to instrumented exercise equipment, such as smart exercise machines or RFID-tagged free weights.This paper presents MuscleSense, an approach that estimates exercise workload by using wearable Surface Electromyography (sEMG) sensors and regression analysis. We evaluated the accuracy of several regression models and the effects of sensor placement through a 20-person user study. Results showed that MuscleSense achieved an accuracy of 0.68kg (root mean square error, RMSE) in sensing workload using both forearm and arm sensors and support vector regression (SVR).
Reconstructing the intracellular pH microenvironment for enhancing photodynamic therapy

Acidic tumor microenvironments, characterized by weak acidity outside the cell (pH ∼ 6.5–7.0), have been revealed as a potential therapeutic target with specific acid-responsible materials. However, the intracellular pH of tumor cells is still slightly alkaline (pH ∼ 7.2–7.4), which is not conducive for nanomaterials that respond to acid but should destroy intracellular substances. Here, we proposed a new strategy for reconstructing the intracellular pH microenvironment to enhance photodynamic therapy. Nanomaterials (UCNP@ZIF + TPP + PA) were designed with an NaYF4:Yb:Tm (UCNP) inner core coated by a porous zeolitic imidazolate framework (ZIF-8) outside for photoacid (PA) and acid-responsive porphyrin (TPP) co-loading. Upon 980 nm laser irradiation, the emission light of UCNP activates PA for H+ release, which achieves reconstruction of the intracellular pH environment. In this new acid condition, TPP becomes protonated (TPP-H+), which reduces its aggregation by enhancing its water solubility, and in turn increases singlet oxygen (1O2) production for enhanced tumor killing. Both in vitro and in vivo experiments have proved that UCNP@ZIF + TPP + PA could reconstruct the intracellular pH environment and enhance therapeutic effect of photodynamic therapy (PDT). Reconstructing the intracellular pH by using light is of great significance for acid-responsive nanosystems to destroy intracellular substances and treat tumors. The results also signify the use of such acid-responsive materials for several other disease therapies.
Β Η ταυτοποίηση της Bartonella coopersplainsensis σε άγρια τρωκτικά ( Rattus norvegicus ) για πρώτη φορά στην Ελλάδα

This study was a preliminary attempt to detect Bartonella sp. in tissues (liver and heart) obtained from wild rodents {Rattus norvegicus) in central Greece. Eighteen (18) liver and eighteen (18) heart samples were examined, which were obtained from twenty three (23) animals. Two (2) liver samples were found positive, while the heart samples examined were found negative for Bartonella sp. 16S-23S intergenic spacer rDNA gene by PCR. Sequencing of the positive PCR products and comparison with those available in GenBank using the BLAST program revealed the same species of Bartonella in both positive samples with 100% sequence homology to Bartonella coopersplainsensis.
Developing a child-friendly Young Carers Questionnaire: A pilot study

A minimal amount of research has been conducted on the role of young carers in South Africa. Young carers are defined as children below 18 years of age who perform caring tasks for their ill parent(s) or guardian(s) which exceed the routine, household tasks expected to be performed by most children. These tasks range from medicating and bathing their parent to attending to their siblings. A team of researchers from Oxford University in England has designed the Young Carers Questionnaire to assess the social, health and educational impact on young carers managing parental illness. This will allow for the exploration of potential causal mechanisms concerning child well-being which may in the future be relevant for policy. The purpose of this study was to determine whether the questionnaire, interviewer behaviour and interview experience required refinement with the aim of increasing the validity of the overall questionnaire and the interview process in the South African context. This was determined through piloting the questionnaire. The sample included 10 male and female young carers between the ages of 12 and 18 from a township outside Paarl. Participants were recruited through referrals from the Stellenbosch Hospice and the Klapmuts Primary School. Cognitive interviewing was used to access feedback from the participants on the questionnaire. Their responses were analysed using thematic analysis. The results indicated a number of ways in which the format, wording and content of the questionnaire, the overall interview experience and the interviewer behaviour could be improved. The results were then presented to the parent study as recommendations for refining or improving the questionnaire.
Quantitative safety prediction of pile foundations

A necessity to use probability-based formats in design practice of pile foundations as highly-reliable structures is underlined. The specific features of the bearing capacity of piles in groups and principles of their structural safety prediction are discussed. Probability-based approaches and design equations for structural safety prediction of axially loaded piles in groups exposed to regular and extreme irregular variable actions are presented. The survival probabilities of single piles and pile group blocks are considered. It is shown that the total survival probability of pile foundations may be calculated by the unsophisticated method of transformed conditional probabilities. The numerical example illustrates a merit of the probability-based design of pile foundations and a conditionality of the deterministic method of partial safety factors.
BRG1 Activates Proliferation and Transcription of Cell Cycle-Dependent Genes in Breast Cancer Cells

Cancer malignancy is usually characterized by unlimited self-renewal. In some types of advanced tumors that are rapidly dividing, gene expression profiles depict elevations in pro-proliferative genes accompanied by coordinately elevated transcription of factors responsible for removal of DNA lesions. In our studies, fast proliferating breast cancer cell lines (MDA-MB-231 and MCF7), BRG1, a component of the SWI/SNF complex, emerges as an activator of functionally-linked genes responsible for activities such as mitotic cell divisions and DNA repair. Products of at least some of them are considerably overrepresented in breast cancer cells and BRG1 facilitates growth of MCF7 and MDA-MB-231 cell lines. BRG1 occurs at the promoters of genes such as CDK4, LIG1, and NEIL3, which are transcriptionally controlled by cell cycle progression and highly acetylated by EP300 in proliferating cells. As previously documented, in dividing cells BRG1 directly activates gene transcription by evicting EP300 modified nucleosomes from the promoters and, thereby, relaxing chromatin. However, the deficiency of BRG1 or EP300 activity for 48 h leads to cell growth arrest and to chromatin compaction, but also to the assembly of RB1/HDAC1/EZH2 complexes at the studied cell cycle-dependent gene promoters. Epigenetic changes include histone deacetylation and accumulation of H3K27me trimethylation, both known to repress transcription. Cell cycle arrest in G1 by inhibition of CDK4/6 phenocopies the effect of the long-term BRG1 inhibition on the chromatin structure. These results suggest that BRG1 may control gene transcription also by promoting expression of genes responsible for cell cycle progression in the studied breast cancer cells. In the current study, we show that BRG1 binding occurs at the promoters of functionally linked genes in proliferating breast cancer cells, revealing a new mechanism by which BRG1 defines gene transcription.