Activating Constructive Employee Behavioural Responses in a Crisis: Examining the Effects of Pre-crisis Reputation and Crisis Communication Strategies on Employee Voice Behaviours

This study explores how organizational management can promote employee voice behaviours, as positive behavioural reactions with constructive ideas, in responding to organizational crisis. Using an experimental study (N = 640) among full-time employees in the United States, the study found that pre-crisis internal reputation and crisis communication strategies—accommodative response and stealing thunder—positively and directly affected constructive employee voice behaviours in a crisis situation. Furthermore, the study revealed how post-crisis internal reputation mediates the influences of pre-crisis internal reputation and stealing thunder on positive/constructive and negative/destructive employee voice behaviours. The findings of this study contribute to the theoretical development of crisis communication in the internal context of an organization, especially with respect to employee voice behaviours. The study also highlights an important practical implication for crisis managers who can activate and promote positive employee behaviour voices, thereby influencing leadership\u27s strategic decision-making in an organizational crisis

Insights From Three Online Art Educators: Strategies for Instruction, Interaction, and Assessment

Currently, the entire world is experiencing an unprecedented threat due to the outbreak of COVID-19, which requires the majority of K-16 education to be temporarily taught online. The three authors have been teaching virtual courses with a studio art focus for a number of years. We share our collective insights for approaches to instruction, interaction, and assessment in virtual courses that might help other art educators to achieve successful learning outcomes for their students. We learned that building a learning community and peer connections is of the utmost importance; we propose mixing asynchronous and synchronous methods and providing prompt and comprehensive feedback on students’ artwork. The authors encourage other art educators to stay open-minded to new and flexible teaching environments, transforming this crisis into an opportunity to incorporate innovations into their teaching that even more effectively meet every student’s needs

Deep Learning based Cryptanalysis of Lightweight Block Ciphers, Revisited

Cryptanalysis is to infer the secret key of cryptography algorithm. There are brute-force attack, differential attack, linear attack, and chosen plaintext attack. With the development of artificial intelligence, deep learning-based cryptanalysis has been actively studied. There are works in which known-plaintext attacks against lightweight block ciphers, such as S-DES, have been performed. In this paper, we propose a cryptanalysis method based on the-state-of-art deep learning technologies (e.g. residual connections and gated linear units) for lightweight block ciphers (e.g. S-DES and S-AES). The number of parameters required for training is significantly reduced by 93.16~\% and the average of bit accuracy probability increased by about 5.3~\%, compared with previous work

Transformer encoder-based Crypto-Ransomware Detection for Low-Power Embedded Processors

Crypto-ransomware has a process to encrypt the victim\u27s files, and crypto-ransomware requests the victim for money for a key to decrypt the encrypted file. In this paper, we present new approaches to prevent crypto-ransomware by detecting block cipher algorithms for Internet of Things (IoT) platforms. The generic software of the AVR package and the lightweight block cipher library (FELICS) written in C language was trained through the neural network, and then we evaluated the result. Unlike the previous technique, the proposed method does not extract sequence and frequency characteristics, but considers opcodes and opcode sequences as words and sentences, performs word embedding, and then inputs them to the neural network based on the encoder structure of the transformer model. Through this approach, the file size was reduced by 0.5 times while maintaining a similar level of classification performance compared to the previous method. The detection success rate for the proposed method was evaluated with the F-measured value, which is the harmonic mean of precision and recall. In addition to achieving 98% crypto-ransomware detection success rates, classification by benign firmware and lightweight cryptography algorithm, Substitution-Permutation-Network (SPN) structure, Addition-Rotation-eXclusive-or structure (ARX) and normal firmware classification are also possible

Quantum Neural Network based Distinguisher for Differential Cryptanalysis on Simplified Block Ciphers

Differential cryptanalysis is a block cipher analysis technology that infers a key by using the difference characteristics. Input differences can be distinguished using a good difference characteristic, and this distinguishing task can lead to key recovery. Artificial neural networks are a good solution for distinguishing tasks. For this reason, recently, neural distinguishers have been actively studied. We propose a distinguisher based on a quantum-classical hybrid neural network by utilizing the recently developed quantum neural network. To our knowledge, we are the first attempt to apply quantum neural networks for neural distinguisher. The target ciphers are simplified ciphers (S-DES, S-AES, S-PRESENT-[4]), and a quantum neural distinguisher that classifies the input difference from random data was constructed using the Pennylane library. Finally, we obtained quantum advantages in this work: improved accuracy and reduced number of parameters. Therefore, our work can be used as a quantum neural distinguisher with high reliability for simplified ciphers

Quantum Artificial Intelligence on Cryptanalysis

With the recent development of quantum computers, various studies on quantum artificial intelligence technology are being conducted. Quantum artificial intelligence can improve performance in terms of accuracy and memory usage compared to deep learning on classical computers. In this work, we proposed an attack technique that recovers keys by learning patterns in cryptographic algorithms by applying quantum artificial intelligence to cryptanalysis. Cryptanalysis was performed in the current practically usable quantum computer environment, and this is the world\u27s first study to the best of our knowledge. As a result, we reduced 70 epochs and reduced the parameters by 19.6%. In addition, higher average BAP (Bit Accuracy Probability) was achieved despite using fewer epochs and parameters. For the same epoch, the method using a quantum neural network achieved a 2.8% higher BAP with fewer parameters. In our approach, quantum advantages in accuracy and memory usage were obtained with quantum neural networks. It is expected that the cryptanalysis proposed in this work will be better utilized if a larger-scale stable quantum computer is developed in the future

Quantum Implementation of AIM: Aiming for Low-Depth

Security vulnerabilities in the symmetric-key primitives of a cipher can undermine the overall security claims of the cipher. With the rapid advancement of quantum computing in recent years, there is an increasing effort to evaluate the security of symmetric-key cryptography against potential quantum attacks. This paper focuses on analyzing the quantum attack resistance of AIM, a symmetric-key primitive used in the AIMer digital signature scheme. We presents the first quantum circuit implementation of AIM and estimates its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover\u27s search algorithm. For Grover\u27s key search, the most important optimization metric is the depth, especially when considering parallel search. Our implementation gathers multiple methods for a low-depth quantum circuit of AIM in order to reduce the Toffoli depth and full depth

Ultrastructure of a Mobile Threadlike Tissue Floating in a Lymph Vessel

Observations of the primo vascular system (PVS) floating in lymph ducts were reported by various groups. There have been, however, no studies on the ultrastructure of the entire cross section of a primo vessel (PV) inside a lymph vessel with a transmission electron microscope (TEM). In the current study we took the TEM images of a cross section of the PV inside a lymph vessel. We used the Alcian blue staining method for the finding of the target PV in a lymphatic vessel by injecting the dye into the inguinal lymph nodes. The stained PV was harvested together with the lymph vessel and some parts of the specimens were used for studying with optical microscopes. Some other parts were treated according to a standard protocol for TEM. As the results the TEM study revealed the loosely distributed collagen fibers with plenty of empty spaces and the lumens with the endothelial nuclei. It turned out to be very similar to the ultrastructure of the PVs observed on the surfaces of internal organs. It also showed how compactly the PV is surrounded with lymphocytes. In conclusion, the detailed morphological features like the distribution of fibers in the PV were revealed and shown to be similar to another kind of the PV on the surfaces of internal organs

Establishment of a piglet model for peritoneal metastasis of ovarian cancer

Background : A piglet model for peritoneal metastasis (PM) of ovarian cancer was developed. It will contribute to establishing innovative chemotherapeutical and surgical strategies without any limitation on rodent models. Methods : A total of 12 four- to five-week-old piglets of 7 to 8 kg were used. Two phases of ovarian cancer cell injections were performed with laparoscopic surgery. In phase I trial, 5.0 × 106 SK-OV-3 cells in 0.1 ml suspension were inoculated into the omentum, peritoneum, and uterine horns of two piglets twice with a one-week interval. In the phase II trial, 5.0 × 106 SNU-008 cells in 0.1 ml suspension were injected only into uterine horns within the same time frame because tumor implantation after inoculation of SK-OV-3 cells was not observed at the omentum or peritoneum in the phase I trial. Modified peritoneal cancer index (PCI) score was used to monitor tumorigenesis up to 4 weeks after inoculation. Tumor tissues disseminated in the peritoneum 4 weeks after injection were used for histological examination with hematoxylin and eosin (H&E) and paired-box gene 8 (PAX-8) staining. Results : In the phase I trial, two piglets showed PM with modified PCI scores of 5 and 4 at 3 weeks after the first inoculation, which increased to 14 and 15 after 4 weeks, respectively. In the phase II trial, PM was detected in eight of ten piglets, which showed modified PCI scores of 6 to 12 at 4 weeks after the first inoculation. The overall incidence of PM from the total of 12 piglets after inoculation was 75%. Immunohistochemical H&E and PAX-8 staining confirmed metastatic tumors. Conclusions : This study provides strong evidence that piglets can be employed as a model for PM by inoculating ovarian cancer cell lines from humans. Using two cell lines, the PM rate is 75%.This research was supported by a grant from Seoul National University (No,800–20190437). Moreover, Commercializations Promotion Agency for R&D Outcomes supported this research with a grant funded by the Korea government (the Ministry of Science and ICT; No. 1711151316)