Bounded Abstract Effects

Effect systems have been a subject of active research for nearly four decades, with the most notable practical example being checked exceptions in programming languages such as Java. While many exception systems support abstraction, aggregation, and hierarchy (e.g., via class declaration and subclassing mechanisms), it is rare to see such expressive power in more generic effect systems. We designed an effect system around the idea of protecting system resources and incorporated our effect system into the Wyvern programming language. Similar to type members, a Wyvern object can have effect members that can abstract lower-level effects, allow for aggregation, and have both lower and upper bounds, providing for a granular effect hierarchy. We argue that Wyvern’s effects capture the right balance of expressiveness and power from the programming language design perspective. We present a full formalization of our effect-system design, showing that it allows reasoning about authority and attenuation. Our approach is evaluated through a security-related case study.

Adversary Models for Mobile Device Authentication

Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.

Age and Gender Impact on Password Hygiene

Password hygiene plays an essential part in securing systems protected with single-factor authentication. A significant fraction of security incidents happen due to weak or reused passwords. The reasons behind differences in security vulnerable behaviour between various user groups remains an active research topic. The paper aims to identify the impact of age and gender on password strength using a large password dataset. We recovered previously hashed passwords of 102,120 users from a leaked customer database of a car-sharing company. Although the measured effect size was small, males significantly had stronger passwords than females for all age groups. Males aged 26–45 were also significantly different from all other groups, and password complexity decreased with age for both genders equally. Overall, very weak password hygiene was observed, 72% of users based their password on a word or used a simple sequence of digits, and passwords of over 39% of users were found in word lists of previous leaks.

Fourier Image Watermarking: Print-Cam Application

Digital image watermarking is an active research field since it provides protection, security, and authenticity of data. This paper presents development and implementation of a blind and robust watermarking application for ID images under a print-cam system. In the present case, the images are watermarked and printed on ID cards and then detected freehandedly with a smartphone camera. In order to design an efficient and robust image watermarking application, the attacks produced in print-cam processes, such as geometric distortions, must be resolved. Accordingly, the proposed watermarking approach is applied in the Fourier domain. Then, a frame-based projective rectification is integrated to deal with geometric distortions by using detection of Hough lines. Moreover, better robustness against print-cam watermarking attacks was achieved compared with the existing methods, and an Android application was designed and implemented based on the proposed scheme.

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

Challenges in Predictive Modeling of Single Pass Tangential Flow Filtration for Continuous Biomanufacturing

Opportunities for process intensification and increased productivity have made the field of Continuous Biomanufacturing an area of high interest and active research. Within the purification train of producing biologics, Tangential Flow Filtration (TFF) is typically employed after chromatographic separations, to increase drug substance concentration, making the process more economical and further meeting dosage specifications. In a batch operation, concentration occurs via recirculation of the feed material where desired output concentration is attained through multiple pump-passes over the TFF membrane, while steadily excluding the buffer. Single-Pass Tangential Flow Filtration (SPTFF) enables continuity of this process by achieving similar concentration factors through a single – pass over these membranes while operating at low feed flow rates. Our work elucidates the development of a mechanistic process model to predict SPTFF performance across a relatively wide design space using a first principles approach. The developed model is found to be accurate for a range of high feed flow rates but is inaccurate at flow rates below 25 L/m2/hr. At very low flow rates, small differences in the mass transfer coefficient have been observed to significantly alter the prediction of the retentate concentration. We thus describe the challenges in predictive process modeling of SPTFF in antibody biomanufacturing.

Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics

Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data are one of the most commonly used modes of data in diverse applications such as healthcare and finance. Much of the existing interpretability methods used for tabular data only report feature-importance scores—either locally (per example) or globally (per model)—but they do not provide interpretation or visualization of how the features interact. We address this limitation by introducing Feature Vectors, a new global interpretability method designed for tabular datasets. In addition to providing feature-importance, Feature Vectors discovers the inherent semantic relationship among features via an intuitive feature visualization technique. Our systematic experiments demonstrate the empirical utility of this new method by applying it to several real-world datasets. We further provide an easy-to-use Python package for Feature Vectors.

Conceptualization and digitalization of the heritage of soviet architects: A.V. Makhrovskaya, V.I. Kochedamov and A.I. Naumov within the Preserved Culture project

Проект «Сохраненная культура» уже более десяти лет занимается изучением и продвижением в сети Интернет достижений отечественной науки и культуры ХХ века. Статья описывает и систематизирует уникальный опыт проекта по исследованию и актуализации творческого наследия выдающихся советских архитекторов: подготовку и публикацию воспоминаний об ученом-градостроителе, члене-корреспонденте РААСН А.В. Махровской, оцифровку личного архива историка градостроительства, декана архитектурного факультета Академии художеств В.И. Кочедамова и выпуск 4-томного издания его трудов с комментариями современных ученых, а также создание документального фильма «Архитектура блокады», посвященного памяти А.И. Наумова, доктора архитектуры, члена-корреспондента Академии строительства и архитектуры СССР, автора трех генеральных планов развития Ленинграда, организатора маскировки города в годы Великой Отечественной войны. Особое внимание в статье уделено проблеме цифрового разрыва и прикладным подходам и методам его преодоления, позволяющим сохранять и популяризировать памятники «бумажной» культуры прошлого века в условиях информационного общества через создание активного исследовательского сообщества. The Preserved Culture project has been researching and promoting the achievements of Russian science and culture of the 20th century on the Internet for more than ten years. This article describes and systematizes a unique experience on the study and update of the creative heritage of the distinguished Soviet architects. This includes the preparation and publication of the memories about scientist-urban planner, corresponding member of the Russian Academy of Architecture and Building Sciences A.V. Makhrovskaya; the digitization of the personal archive of the urban development historian, dean of the faculty of architecture of the Academy of Arts V.I. Kochedamov, as well as the release of the four-volume edition of his works with commentaries of modern scientists. The article also presents the documentary film “Architecture of the Blockade” which is dedicated to the memory of A.I. Naumov, the Doctor of Architecture, corresponding member of the Academy of Construction and Architecture of the USSR, author of three Leningrad master plans, organizer of the city masking during the Great Patriotic War. Particular attention in the article is paid to the problem of the digital divide and applied approaches and methods of overcoming it, which make it possible to preserve and popularize non-digital cultural monuments of the last century in the context of the information society through the creation of an active research community.

A Survey on Formal Specification and Verification of Smart Mass Transit Railway Interlocking System

Nowadays interest in Smart Mass Transit Rail has grown-up to a large extent in a metropolitan area as the need for urban mobility has increased steadily. The reliability of software being used in such mass transit rail is crucial for us, specifically when software crashes may lead to catastrophic loss of human life and assets. For example, when we travel by metro it is essential for us that the interlocking system software controlling the metros are accurate so collisions and derailment are prevented. The reliability and safety of such interlocking systems are made on the precise functional requirements specification and verification respectively. Therefore, the precise functional requirements specification and verification of such interlocking systems represent a challenge in an active research area, so in this paper, we survey various articles in this field and discuss their consequences.

Review of Recent Offshore Wind Turbine Research and Optimization Methodologies in Their Design

As international efforts to address climate change grow, an increasing number of countries and companies have put forward a clear “net zero” goal through accelerated renewable-energy development. As a renewable energy source, offshore wind energy has received particular attention from many countries and is a highly active research area. However, the design of offshore wind turbine structures faces challenges due to the large and complex design parameter space as well as different operational requirements and environmental conditions. Advanced optimization technology must be employed to address these challenges. Using an efficient optimization algorithm, it is possible to obtain optimized parameters for offshore wind turbine structures, balancing energy generation performance and the life of the floating wind turbine. This paper presents a review of the types and fundamental principles of several critical optimization technologies along with their application in the design process, with a focus on offshore wind turbine structures. It concludes with a discussion of the future prospects of optimization technology in offshore wind research.