Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation

Recent WiFi-based fall detection systems have drawn much attention due to their advantages over other sensory systems. Various implementations have achieved impressive progress in performance, thanks to machine learning and deep learning techniques. However, many of such high accuracy systems have low reliability as they fail to achieve robustness in unseen environments. To address that, this paper investigates a method of generalization through adversarial data augmentation. Our results show a slight improvement in deep learning-systems in unseen domains, though the performance is not significant.Comment: Will appear in Proceedings of the 54th Annual Conference on Information Sciences and Systems (CISS2020

Context modeling of agile software and a contextbased approach to support virtual enterprises

In the practice of software development contexts have been only implicitly modeled and transformed in fixed part of software. The information about context is dispersed in objects across the application. Each context change leads to modification or new development of the software. The context modeling helps developer to separate context objects from contextdependent objects. It allows better reuse of analysis, design and implementation models, if the context of certain objects is changed. The context modeling is interesting and necessary, when the software should be agile - i.e. when the environment and the condition of the software could be changed permanently, e.g. in case of platform for virtual enterprises. The paper introduces a novel approach to support processes within generic platforms for virtual enterprises: the context-based approach. The main advantage of the approach lies in its generic capacity, which allows the users to define processes flexibly to support their own enterprises. In this paper, we discuss further the phenomenon of extra-context logic, its modeling and its application case. The information of extra-context logic provides not only the better understanding of application domain, but also can be used by a wizard to support the interaction of users working with multiple systems

Stability investigations of isotropic and anisotropic exponential inflation in the Starobinsky-Bel-Robinson gravity

In this paper, we would like to examine whether a novel Starobinsky-Bel-Robinson gravity model admits stable exponential inflationary solutions with or without spatial anisotropies. As a result, we are able to derive an exact de Sitter inflationary to this Starobinsky-Bel-Robinson model. Furthermore, we observe that an exact Bianchi type I inflationary solution does not exist in the Starobinsky-Bel-Robinson model. However, we find that a modified Starobinsky-Bel-Robinson model, in which the sign of coefficient of $R^2$ term is flipped from positive to negative, can admit the corresponding Bianchi type I inflationary solution. Unfortunately, stability analysis using the dynamical system approach indicates that both of these inflationary solutions turn out to be unstable. Interestingly, we show that a stable de Sitter inflationary solution can be obtained in the modified Starobinsky-Bel-Robinson gravity.Comment: 26 pages, 2 figures. V2 with the abstract revised to improve its clarity, some relevant references added, and some typos fixed. All main calculations and conclusions remain unchanged. Comments are welcom

Properties of Concrete Containing Rubber Aggregate Derived From Discarded Tires

This study carried out the experiment to evaluate the effects of different contents and sizes of rubber particles derived from discarded tires used for replacing fine and coarse natural aggregates, on the workability of fresh rubberized concrete and the compressive and flexural strengths of hardened rubberized concrete. The study results showed that the workability of fresh rubberized concrete was improved when replacing natural fine aggregate (sand) with fine rubber particles (2.5-5 mm) at the replacing proportions of 30-50% by volume, and when replacing natural coarse aggregate (crushed stone) with coarse rubber particles (5-20 mm) at the replacing proportions of 10-30% by volume. With respect to the mechanical properties of hardened rubberized concrete, a larger reduction in the compressive and flexural strengths was generally found when the replacing proportions increased and when coarse aggregate rather than fine aggregate was replaced by rubber particles at all replacing proportions (10-50%). However, the study results also indicated that using fine rubber particles for replacing fine natural aggregate at the low replacing proportion (up to 10%) might not cause the significant effect on the compressive and flexural strength of rubberized concrete

Effects of protein levels of commercial diets on the growth performance and survival rate of rabbitfish (Siganus guttatus) at the nursing stage

This study aimed to determine the effect of a commercial diet's protein level on the fry-to-fingerling stage. Thirty days-old fries having the initial length and weight of 18.25 ± 0.15 mm fish-1 and 0.036 ± 0.50 g fish-1 respectively have been used in this study. Diet having three protein levels i.e. 30% (trial 1 as control), 35% (trial 2), 40% (trial 3), and 45% (trial 4), respectively, have been used to evaluate the effect of protein, and each trial has been repeated three times. During the study, stocking density was allocated to 1000 fish per composite tank with a volume of 1 m3. After 30 days of rearing, the weight of fingerlings in trial 1 reached up to 1.50 ± 0.02 g fish-1 and it was recorded as 1.52 ± 0.01g for trial 2, these two were lower than that of trials 3 and 4, where fingerling weight was reported 1.69 ± 0.01 and 1.58g fish-1 respectively and obtained the best weight compared to others. The length of fingerlings at the end of the experimental period was also changed in different trials and it was recorded 47.12; 46.92; 50.97; and 48.89 mm fish-1 for trail 1, 2, 3, and 4 respectively, among the tested combinations lower fingerlings length was recorded for trial 2 (35% CP), but it is not significantly different for trial 1 and 2 and a significant difference (P < 0.05) was reported for trail 2, 3, and 4. The survival rate of fingerlings ranged from 67.27 to 72.33%. Meanwhile, the herd distribution coefficient variation (CVW) in the treatment using 40% protein (trial 3) was the highest at 72.33% (p < 0.05). The results of the study can be concluded that the level of protein has a significant effect on the various growth parameters of fingerlings

Vai trò của thiết kế tổng thể trong ứng dụng tin học vào quản lí xí nghiệp

Vai trò của thiết kế tổng thể trong ứng dụng tin học vào quản lí xí nghiệp

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions