Set-Codes with Small Intersections and Small Discrepancies

We are concerned with the problem of designing large families of subsets over a common labeled ground set that have small pairwise intersections and the property that the maximum discrepancy of the label values within each of the sets is less than or equal to one. Our results, based on transversal designs, factorizations of packings and Latin rectangles, show that by jointly constructing the sets and labeling scheme, one can achieve optimal family sizes for many parameter choices. Probabilistic arguments akin to those used for pseudorandom generators lead to significantly suboptimal results when compared to the proposed combinatorial methods. The design problem considered is motivated by applications in molecular data storage and theoretical computer science

Re-calibrating DIY: Testing digital participation across dust sensors, fry pans and environmental pollution

An increasing number of low-cost and do-it-yourself (DIY) digital sensors for monitoring air quality are now in circulation. DIY technologies attempt to democratize environmental practices such as air quality sensing that might ordinarily be the domain of expert scientists. But in the process of setting up and using DIY sensors, citizens encounter just as many challenges for ensuring the accuracy of their devices and the validity of their data. In this article, we look specifically at the infrastructures and practices of DIY digital sensing. Through an analysis of urban sensing in London as an environmental media practice, we consider the specific techniques and challenges of calibrating DIY digital sensors for measuring air pollution to ensure the relative accuracy and validity of data. We ask, “How are DIY calibration practices expressive of particular political subjects and environmental relations—and not others?” “How might we re-calibrate DIY as a digital practice and political commitment through engagements with multiple genealogies and counter-genealogies of citizen-led inquiry?”ERC 31334

Incremental Information Gain Analysis of Input Attribute Impact on RBF-Kernel SVM Spam Detection

The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. Email spams can be detected through detecting senders’ behaviour, the contents of an email, subject and source address, etc, while SMS spam detection usually is based on the tokens or features of messages due to short content. However, a comprehensive analysis of email/SMS content may provide cures for users to aware of email/SMS spams. We cannot completely depend on automatic tools to identify all spams. In this paper, we propose an analysis approach based on information entropy and incremental learning to see how various features affect the performance of an RBF-based SVM spam detector, so that to increase our awareness of a spam by sensing the features of a spam. The experiments were carried out on the spambase and SMSSpemCollection databases in UCI machine learning repository. The results show that some features have significant impacts on spam detection, of which users should be aware, and there exists a feature space that achieves Pareto efficiency in True Positive Rate and True Negative Rate

The Risk Factors Associated with Grip Lock Injuries in Artistic Gymnasts: A Systematic Review

Artistic gymnastics (AG) is a sport that demands grace, strength, and flexibility, leading to a broad spectrum of injuries. The dowel grip (DG) is widely used by gymnasts to securely hold onto the high bar or uneven bars. However, incorrect usage of the DG can result in grip lock (GL) injuries. This systematic review aims to (1) identify studies that have investigated the risk factors related to GL injuries among gymnasts and (2) synthesize the key evidence. A comprehensive electronic search was conducted in the following databases: PubMed, ScienceDirect, Elsevier, SportDiscus, and Google Scholar, covering the period from their inception until November 2022. The data extraction and analysis were independently completed by two investigators. A total of 90 relevant studies were initially identified, out of which seven clinical trials met the eligibility criteria. For the quantitative synthesis, five studies were included. The details extracted from each article include: the sample characteristics (number, gender, age, and health status), the study design, the instrumentation or intervention used, and the final results. Our results revealed that the underlying causes of the risk factors of GL injuries were the irregular checking of the dowel grip and the mating surface of the bar, the tearing of the dowel of the leather strap, and the use of the dowel grip in different competition apparatuses. In addition, GL injuries may occur either as severe forearm fractures or mild injuries. Excessive flexion of the forearm and overpronation of the wrist during rotational movements, such as the swing or backward/forward giant circle, may increase the possibility of GL injury on the high bar. Future studies should focus on GL injury prevention strategy and rehabilitation protocol for GL injuries. Further high-quality research is required to establish the validity of these findings. © 2023 by the authors.European Commission, EC: .02.2.69/0.0/0.0/18_054/0014627Published with the financial support of the European Union, as part of the project entitled Development of capacities and environment for boosting the international, intersectoral, and interdisciplinary cooperation At UWB, project reg. no.CZ.02.2.69/0.0/0.0/18_054/0014627

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

© 2020 ACM. Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily rely on high-quality user generated content (including user profiles) or suffer from data insufficiency problem if only focusing on network topology, which brings researchers into an insoluble dilemma of model selection. In this paper, to address this problem, we propose a novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner. The proposed method overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information. Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks. Extensive experiments have been conducted on two large-scale real-life social networks. The experimental results demonstrate that the proposed method outperforms the state-of-the-art models with a big margin

The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence

Understanding the relation between early-life adversity and depression symptoms: The moderating role of sex and an interleukin-1β gene variant

Pro-inflammatory cytokines, such as interleukin (IL)-6 and tumor necrosis factor-α (TNF-α), are thought to play a fundamental role in the pathogenesis of depression within a subset of individuals. However, the involvement of IL-1β has not been as consistently linked to depression, possibly owing to difficulties in detecting this cytokine in blood samples or that changes in circulating levels might only be apparent in a subgroup of patients who have experienced early-life adversity. From this perspective, the association between early-life adversity and depressive illness might depend on genetic variants regulating IL-1β ac