FBI

Jui-Sheng Chou, Ngoc-Mai Nguyen

Abstract This study developed a novel optimization algorithm, called Forensic-Based Investigation (FBI), inspired by the suspect investigation–location–pursuit process that is used by police officers. Although numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters. The performance of parameter-free FBI was validated using four experiments: (1) The robustness and efficiency of FBI were compared with those of 12 representations of the top leading metaphors by using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling problem associated with a highway construction project. The experiment demonstrated that FBI yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness. (3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its performance was compared with those of the three winners in CEC to validate its effectiveness. (4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark functions to 1000. FBI is efficient because it requires a relatively short computational time for solving problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves high-dimensional problems. Given that the experiments demonstrated FBI’s robustness, efficiency, stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this study provided the scientific community with a metaheuristic optimization platform for graphically and logically manipulating optimization algorithms.

A. Islam, Ahmed Al Amin, Soo Young Shin

This letter presents a federated learning-basd data-accumulation scheme that combines drones and blockchain for remote regions where Internet of Things devices face network scarcity and potential cyber threats. The scheme contains a two-phase authentication mechanism in which requests are first validated using a cuckoo filter, followed by a timestamp nonce. Secure accumulation is achieved by validating models using a Hampel filter and loss checks. To increase the privacy of the model, differential privacy is employed before sharing. Finally, the model is stored in the blockchain after consent is obtained from mining nodes. Experiments are performed in a proper environment, and the results confirm the feasibility of the proposed scheme.

Mao-jian Chen, Chanchan Xiao, Wei-zhe Jiang, Weiping Yang, Q. Qin, Q. Tan, B. Lian, Zhijie Liang, Changyuan Wei

Background As a natural compound extracted from a variety of hot peppers, capsaicin has drawn increasing attention to its anti-cancer effects against multiple human cancers including breast cancer. FBI-1 is a major proto-oncogene negatively regulating the transcription of many tumor suppressor genes, and plays a vital role in tumorigenesis and progression. However, whether FBI-1 is involved in capsaicin-induced breast cancer suppression has yet to be ascertained. This study aimed to investigate the effects of capsaicin on proliferation and apoptosis and its association with FBI-1 expression in breast cancer. Methods CCK-8 and morphological observation assay were employed to detect cell proliferation. Flow cytometry and TUNEL assay were conducted to detect cell apoptosis. RNA interference technique was used to overexpress or silence FBI-1 expression. qRT-PCR and/or Western blot analysis were applied to detect the protein expression of FBI-1, Ki-67, Bcl-2, Bax, cleaved-Caspase 3, Survivin and NF-κB p65. Xenograft model in nude mice was established to assess the in vivo effects. Results Capsaicin significantly inhibited proliferation and induced apoptosis in breast cancer in vitro and in vivo, along with decreased FBI-1, Ki-67, Bcl-2 and Survivin protein expression, increased Bax protein expression and activated Caspase 3. Furthermore, FBI-1 overexpression obviously attenuated the capsaicin-induced anti-proliferation and pro-apoptosis effect, accompanied with the above-mentioned proteins reversed, whereas FBI-1 silencing generated exactly the opposite response. In addition, as a target gene of FBI-1, NF-κB was inactivated by p65 nuclear translocation suppressed with capsaicin treatment, which was perceptibly weakened with FBI-1 overexpression or enhanced with FBI-1 silencing. Conclusion This study reveals that FBI-1 is closely involved in capsaicin-induced anti-proliferation and pro-apoptosis of breast cancer. The underlying mechanism may be related to down-regulation of FBI-1-mediated NF-κB pathway. Targeting FBI-1 with capsaicin may be a promising therapeutic strategy in patients with breast cancer.

Jaeseok Byun, Sungmin Cha, Taesup Moon

We consider the challenging blind denoising problem for Poisson-Gaussian noise, in which no additional information about clean images or noise level parameters is available. Particularly, when only "single" noisy images are available for training a denoiser, the denoising performance of existing methods was not satisfactory. Recently, the blind pixelwise affine image denoiser (BP-AIDE) was proposed and significantly improved the performance in the above setting, to the extent that it is competitive with denoisers which utilized additional information. However, BP-AIDE seriously suffered from slow inference time due to the inefficiency of noise level estimation procedure and that of the blind-spot network (BSN) architecture it used. To that end, we propose Fast Blind Image Denoiser (FBI-Denoiser) for Poisson-Gaussian noise, which consists of two neural network models; 1) PGE-Net that estimates Poisson-Gaussian noise parameters 2000 times faster than the conventional methods and 2) FBI-Net that realizes a much more efficient BSN for pixelwise affine denoiser in terms of the number of parameters and inference speed. Consequently, we show that our FBI-Denoiser blindly trained solely based on single noisy images can achieve the state-of-the-art performance on several real-world noisy image benchmark datasets with much faster inference time (× 10), compared to BP-AIDE.

Jui-Sheng Chou, Ke-En Chen