Guest Editorial: Intelligent solutions for green computing in internet of medical things

n/

Medical effects of poly-ethylene terephthalate (PET) non-woven fabrics treated with bamboo activated charcoal

In this study, bamboo activated charcoal was mixed with acrylic resin in various proportions and deposited on poly-ethylene terephthalate (PET) non-woven fabrics. A series of characterizations were carried out to estimate the performances of PET non-woven fabrics such as far infrared ray emission, heat retention, negative ions, deodorization of ammonia gas and tenacity. The results obtained indicate that the temperature difference on the surface of treated non-woven fabrics after exposure to a halogen lamp was between 4.28 to 8.26°C. The test for negative ions demonstrated that the concentration of negative ions released from treated non-woven fabrics was 420 to 630 ions/cm3. The deodorization rate of the treated non-woven fabrics was found to be between 85 to 92% and the rate was the same for 5 and 10 g/L of bamboo activated charcoal addition. An increase in resin concentration increased the abrasion strength and tensile strength; and reduced the tear strength of the treated non-woven fabrics. The bamboo activated charcoal concentration exhibited no effect on the physical properties of the treated non-woven fabrics.Key words: Poly-ethylene terephthalate (PET), non-woven fabrics, bamboo activated charcoal, far infrared ray, negative ions, deodorization

Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective

Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks.Comment: 19 pages, 4 figure

Persistent and multisite homophobic harassment during childhood and adolescence and its association with school difficulties in gay and bisexual men in Taiwan

Background: Homophobic harassment can compromise mental health of sexual minority youths. Objectives: This study examined the rates of persistent and multisite homophobic harassment and their associations with school difficulties during childhood and adolescence among gay and bisexual men in Taiwan. Methods: Participants were recruited through advertisements on the Facebook, Bulletin Board Systems, and the home pages of health promotion and counseling centers for the gay, lesbian, and bisexual community. The experiences of traditional and cyber harassment based on gender role nonconformity and sexual orientation of 500 gay or bisexual men were examined. The associations of multisite and persistent harassment victimization with school difficulties were evaluated. Results: A total of 239 (47.8%) and 131 (26.2%) participants experienced persistent and multisite harassment victimization, respectively. Harassment victimization was significantly associated with low satisfaction with academic performance in any stage of study. Moreover, the participants who were harassed in senior high schools were more likely to miss classes or be truant than those who were not harassed. The victims of multisite harassment at senior high schools were more likely to miss classes or be truant than those of school-only harassment. Discussion: Prevention and intervention programs are warranted to reduce homophobic harassment in sexual minority youths

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

Recently, quantum classifiers have been known to be vulnerable to adversarial attacks, where quantum classifiers are fooled by imperceptible noises to have misclassification. In this paper, we propose one first theoretical study that utilizing the added quantum random rotation noise can improve the robustness of quantum classifiers against adversarial attacks. We connect the definition of differential privacy and demonstrate the quantum classifier trained with the natural presence of additive noise is differentially private. Lastly, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples supported by experimental results.Comment: Submitted to IEEE ICASSP 202

On the private data synthesis through deep generative models for data scarsity of industrial Internet of Things

Due to the data-driven intelligence from the recent deep learning (DL)-based approaches, the huge amount of data collected from various kinds of sensors from industrial devices have the potential to revolutionize the current technologies used in the industry. To improve the efficiency and quality of machines, the machine manufacturer needs to acquire the history of the machine operation process. However, due to the business secrecy, the factories are not willing to do so. One promising solution to the above difficulty is the synthetic dataset and an informatic network structure, both through differentially private GANs (DP-GANs). Hence, this paper initiates the study of the utility difference between the above two kinds. We carry out an empirical study and find that the classifier generated by private informatic network structure is more accurate than the classifier generated by private synthetic data, with approximately 0.31\% ∼ 7.66\%

Gingyo-San Enhances Immunity and Potentiates Infectious Bursal Disease Vaccination

The purpose of the present study was to investigate the effects of Gingyo-san (GGS), a traditional Chinese medical formula, on peripheral lymphocyte proliferation and serum antibody titers in chickens vaccinated against the infectious bursal disease (IBD) virus. Treatment groups were fed one of three doses of GGS in their diet (0.5%, 1.0% and 2.0%, w/w), and the IBD vaccine was administered at 1 and 3 weeks of age. At Weeks 8, 12 and 16, changes in serum IBD antibody titers were measured via the micro-method and T cell proliferation. In gene expression experiments, GGS-treated peripheral T lymphocytes were stimulated with concanavalin A (ConA) for 24 h. The mRNA expression of interleukin-2 (IL-2), interferon-γ (IFN-γ), interleukin-4 (IL-4) and interleukin-12 (IL-12) was determined using a semi-quantitative RT-PCR assay. The results showed that a low dose of GGS could significantly raise the antibody titers. Medium and high doses of GGS enhanced IL-2 and IFN-γ production. GGS altered the expression of IL-4 and IL-12 in T lymphocytes. CD4+ T lymphocyte development was also skewed towards the Th1 phenotype. GGS enhanced cell-mediated immunity and augmented the effects of IBD vaccination in strengthening subsequent anti-viral responses

Acquiring Authentic Data in Unattended Wireless Sensor Networks

An Unattended Wireless Sensor Network (UWSN) can be used in many applications to collect valuable data. Nevertheless, due to the unattended nature, the sensors could be compromised and the sensor readings would be maliciously altered so that the sink accepts the falsified sensor readings. Unfortunately, few attentions have been given to this authentication problem. Moreover, existing methods suffer from different kinds of DoS attacks such as Path-Based DoS (PDoS) and False Endorsement-based DoS (FEDoS) attacks. In this paper, a scheme, called AAD, is proposed to Acquire Authentic Data in UWSNs. We exploit the collaboration among sensors to address the authentication problem. With the proper design of the collaboration mechanism, AAD has superior resilience against sensor compromises, PDoS attack, and FEDoS attack. In addition, compared with prior works, AAD also has relatively low energy consumption. In particular, according to our simulation, in a network with 1,000 sensors, the energy consumed by AAD is lower than 30% of that consumed by the existing method, ExCo. The analysis and simulation are also conducted to demonstrate the superiority of the proposed AAD scheme over the existing methods