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One of the most important subconscious reactions, micro-expression (ME), is a spontaneous, subtle, and transient facial expression that reveals human beings’ genuine emotion. Therefore, automatically recognizing ME (MER) is becoming increasingly crucial in the field of affective computing, providing essential technical support for lie detection, clinical psychological diagnosis, and public safety. However, the ME data scarcity has severely hindered the development of advanced data-driven MER models. Despite the recent efforts by several spontaneous ME databases to alleviate this problem, there is still a lack of sufficient data. Hence, in this paper, we overcome the ME data scarcity problem by collecting and annotating a dynamic spontaneous ME database with the largest current ME data scale called DFME (Dynamic Facial Micro-expressions). Specifically, the DFME database contains 7,526 well-labeled ME videos spanning multiple high frame rates, elicited by 671 participants and annotated by more than 20 professional annotators over three years. Furthermore, we comprehensively verify the created DFME, including using influential spatiotemporal video feature learning models and MER models as baselines, and conduct emotion classification and ME action unit classification experiments. The experimental results demonstrate that the DFME database can facilitate research in automatic MER, and provide a new benchmark for this field. [ABSTRACT FROM AUTHOR]

Covert communication is an method that plays an important role in secure data transmission. The technology embeds covert information into data and propagates it through covert channels. The communication quality depends on the choice of channel and data embedding techniques. Recently, blockchain has emerged to become the preferred channel to carry out covert communication for its decentralization and anonymity features. Existing covert transaction methods are constructed transaction-by-transaction, which makes them immune to text analysis-based detection methods. However, it is easy to expose their features on the transaction graph level. Unfortunately, there is yet no method to detect covert transactions by the features of transaction graph. In this paper, we propose a covert transaction detection method based on graph structure. By analyzing the statistical features of graph structure for addresses, we can infer whether they are the participants of covert transactions. Furthermore, we design a protection method of covert transactions based on graph generation networks. By adjusting the structural features between different addresses, our method enhances the security of multiple interrelated covert transactions. Experimental analysis on the Bitcoin Testnet verifies the security and the efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

Computational experiments have been important since the 70s to find solutions to problems using computers, and even more now with the advancement of technology, especially artificial intelligence. The aim of this study is to know the level of interest on the part of researchers toward computational experiments in Computer Science. To find out this information, a Systematic Mapping Study has been carried out in IEEE, DBLP, Scopus, ACM and Web of Science databases. A total of 226 papers were selected as primary studies and 22 types of measurements were found, it was determined that the three most used measurements in computational experiments are performance at 51.33%, efficiency at 15.49%, and effectiveness at 15.04%. A total of 25 frameworks used for the computational experiments were determined, however 74.3% of the reports did not specify any framework used, 11.5% used a custom framework and the rest used a specific framework. It is concluded that performance is the most important measurement in computational experiments and that it is necessary for researchers to rely on a framework so that these experiments can be replicated or refined.

Software engineering (SE) and computer science (CS) programs in universities worldwide are marked by a gender gap, which subsequently translates into a gender gap at the industry level. However, there are positive activities that can help attract more women to these male-dominant professions. This study maps the literature related to the achievement of gender balance in SE and CS university-level education and identifies future research directions. More specifically, this article reports on a systematic mapping study of female-inclusive SE and CS tertiary education programs. The authors collected 882 publications between 2015 and 2022 from five databases (ACM, IEEE, Scopus, Web of Science, and Science Direct), selecting 143 peer-reviewed papers for further analysis. The results showed that the main academic contributors were researchers from the USA. The majority of the publications contained observations and explanations regarding the gender gap in computing education. However, an important part of the literature considered proposals and practical activities for achieving gender balance in SE and CS programs. Finally, the authors classified the literature related to female-inclusive SE and CS tertiary education programs, identified the main research focuses and regional distribution, and considered ideas for future research.

Threshold signature is a fundamental cryptographic primitive used in many practical applications. As proposed by Boneh and Komlo (CRYPTO’22), TAPS is a threshold signature that is a hybrid of privacy and accountability. It enables a combiner to combine $t$ signature shares while revealing nothing about the threshold $t$ or signing quorum to the public and asks a tracer to track a signature to the quorum that generates it. However, TAPS has three disadvantages: it 1) structures upon a centralized model, 2) assumes that both combiner and tracer are honest, and 3) leaves the tracing unnotarized and static. In this work, we introduce Decentralized, Threshold, dynamically Accountable and Private Signature (DeTAPS) that provides decentralized combining and tracing, enhanced privacy against untrusted combiners (tracers), and notarized and dynamic tracing. Specifically, we adopt Dynamic Threshold Public-Key Encryption (DTPKE) to dynamically notarize the tracing process, design non-interactive zero knowledge proofs to achieve public verifiability of notaries, and utilize the Key-Aggregate Searchable Encryption to bridge TAPS and DTPKE so as to awaken the notaries securely and efficiently. In addition, we formalize the definitions and security requirements for DeTAPS. Then we present a concrete construction and formally prove its security and privacy. To evaluate the performance, we build a prototype based on SGX2 and Ethereum. [ABSTRACT FROM AUTHOR]