Automatic Search of Cubes for Attacking Stream Ciphers

Cube attack was proposed by Dinur and Shamir, and it has become an important tool for analyzing stream ciphers. As the problem that how to recover the superpolys accurately was resolved by Hao et al. in EUROCRYPT 2020, another important problem is how to find “good” superpolys, which is equivalent to finding “good” cubes. However, there are two difficulties in finding “good” cubes. Firstly, the number of candidate cubes is enormous and most of the cubes are not “good”. Secondly, it is costly to evaluate whether a cube is “good”.In this paper, we present a new algorithm to search for a kind of “good” cubes, called valuable cubes. A cube is called valuable, if its superpoly has (at least) a balanced secret variable. A valuable cube is “good”, because its superpoly brings in 1 bit of information about the key. More importantly, the superpolys of valuable cubes could be used in both theoretical and practical analyses. To search for valuable cubes, instead of testing a set of cubes one by one, the new algorithm deals with the set of cubes together, such that the common computations can be done only once for all candidate cubes and duplicated computations are avoided. Besides, the new algorithm uses a heuristic method to reject useless cubes efficiently. This heuristic method is based on the divide-and-conquer strategy as well as an observation.For verifications of this new algorithm, we applied it to Trivium and Kreyvium, and obtained three improvements. Firstly, we found two valuable cubes for 843-round Trivium, such that we proposed, as far as we know, the first theoretical key-recovery attack against 843-round Trivium, while the previous highest round of Trivium that can be attacked was 842, given by Hao et al. in EUROCRYPT 2020. Secondly, by finding many small valuable cubes, we presented practical attacks against 806- and 808-round Trivium for the first time, while the previous highest round of Trivium that can be attacked practically was 805. Thirdly, based on the cube used to attack 892-round Kreyvium in EUROCRYPT 2020, we found more valuable cubes and mounted the key-recovery attacks against Kreyvium to 893-round.

Affective computing in the context of music therapy: a systematic review

Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

Digital Technologies for Mental Health Improvements in the COVID-19 Pandemic: Systematic Review (Preprint)

BACKGROUND Digital technologies have been used to support mental health services for two decades, but the COVID-19 pandemic creates a unique opportunity for greater utilization and more data-driven assessment of these digital technologies. OBJECTIVE This research aims to provide a systematic review of the characteristics and effectiveness of digital inventions that were employed to improve mental health in the real context of COVID-19 pandemic. METHODS A combination of search terms was applied for automatic search of publications in the relevant databases. The key features of included studies were extracted, including the intervention, participant, and study details. RESULTS A total of 14 eligible studies were included in the final review, which were conducted across different geographic regions and among diverse cultural groups. Ten studies mainly examined the impact of digital technologies on healthy individuals, while only one study developed specific interventions for the isolated COVID-19 depressed patients in hospitals. Digital technologies identified in this study mainly include web-based interventions, smartphone or social media applications such as SMS messages, mobile games, and even video conferencing app. But only five of them were aligned with theoretical approaches from standardized psychological treatments. Most of the studies have reported positive effects of digital technologies, either on improving general mental and emotional well-being or addressing specific conditions (e.g., depression, loneliness). CONCLUSIONS This systematic review suggests that digital technologies hold promise in bridging the mental health-care gap during and after the COVID-19 pandemic, and calls for more rigorous studies to identify pertinent features that are likely to achieve more effective mental health outcomes.

Labour-law Basic Terminology Based on the Polish Labour Code

The paper discusses two approaches to compiling lists of labour-law basic terminology (BT): a frequency-based approach and a concept-based one. The purpose of the paper is to compare each of the methods based on two sets of basic terminology selected in accordance with them. Using the first method, terms are selected via an automatic search of keywords and terms and organised according to frequency with the use of Sketch Engine. The second means of term extraction is a concept-based approach in which terms are selected based on the table of contents of the Polish Labour Code, which, for the purposes of the study, is assumed to outline the terminological system of Polish labour law. The results of this research are reviewed from the viewpoint of terms’ frequency, the number of words they consist of, systemic relations between terms in the labour-law terminological system, and potential users and their needs. This has allowed the author to draw a few conclusions as to the characteristics of the approaches taken, and the applicability and usefulness of lists of BT compiled on their bases.

The Synthesis of an Adaptive Filter for Automatic Detection and Time Localization of Premotor EEG Patterns of the Human Brain

An adaptive low-pass filter has been synthesized for automatic patterns detection of arbitrary mental movements in records of multidimensional electroencephalograms (EEG). The filter is based on the multivariate singular spectrum analysis. The effective bandwidth of the filter corresponds to the spectrum of the sought patterns of mental activity on the observed EEG time interval. The use of the synthesized filter provided a reliable automatic search for patterns and the correct determination of their time boundaries. The correctness of the results has been confirmed in experiments with 24 volunteers.

Metaheuristics and Software Engineering: Past, Present, and Future

This work aims at giving an updated vision on the successful combination between Metaheuristics and Software Engineering (SE). Mostly during the 90s, varied groups of researchers dealing with search, optimization, and learning (SOL) met SE researchers, all of them looking for a quantified manner of modeling and solving problems in the software field. This paper will discuss on the construction, assessment, and exploitation tasks that help in making software programs a scientific object, subject to automatic study and control. We also want to show with several case studies how the quantification of software features and the automatic search for bugs can improve the software quality process, which eases compliance to ISO/IEEE standards. In short, we want to build intelligent automatic tools that will upgrade the quality of software products and services. Since we approach this new field as a cross-fertilization between two research domains, we then need to talk not only on metaheuristics for SE (well known by now), but also on SE for metaheuristics (not so well known nowadays). In summary, we will discuss here with three time horizons in mind: the old times [before the term search-based SE (SBSE) was used for this], the recent years on SBSE, and the many avenues for future research/development. A new body of knowledge in SOL and SE exists internationally, which is resulting in a new class of researchers able of building intelligent techniques for the benefit of software, that is, of modern societies.