Front-Running Protection for Distributed Exchanges using Tamper-Resistant Round Trip Time Measurements

In this paper we present ODIN, a front-running protection system that uses a novel algorithm to measure Round-Trip-Time (RTT) to untrusted servers. ODIN is the decentralized equivalent of THOR, a RTT-aware front-running protection system for trading on centralized exchanges. Unlike centralized exchanges, P2P exchanges have potentially malicious peers which makes reliable direct RTT measurement impossible. In order to prevent tampering by an arbitrarily malicious peer, ODIN performs an indirect RTT measurement that never interacts directly with the target machine. The RTT to the target is estimated by measuring the RTT to a randomized IP address that is known to be close to the target's IP address in the global routing network. We find that ODIN's RTT estimation algorithm provides an accurate, practical, and generic solution for collecting network latency data in a hostile network environment

Intelligent fault detection and classification based on hybrid deep learning methods for Hardware-in-the-Loop test of automotive software systems

Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%

The Drivers of Sustainable Apparel and Sportswear Consumption : A Segmented Kano Perspective

The steady increase of sustainable consumer behavior leads companies to strengthen their efforts to become socially and ecologically more sustainable. Particularly in the clothing and footwear industry, more and more companies are aware of their need to fundamentally adapt the way they create value. Sustainability offerings are developed, e.g., usage of upcycled materials (e.g., ocean plastic), circular business models (e.g., decomposition of returned products into components for new ones), as well as adapted product ranges (e.g., smaller or with fewer fashion cycles). However, it is frequently unclear in advance, which offerings will increase (or decrease) satisfaction and, consequently, drive (or not drive) sustainable consumption. The application of a segmented Kano perspective in an apparel and sportswear context that helps to answer these questions is presented: 17 potential offerings were assessed by a sample of 490 consumers. Our analysis demonstrates the usefulness of this methodology and that returning used products (to recycle them), discounts for buying sustainable products, sustainability level indicators, and biobased materials are highly attractive. However, the responsiveness varies across the derived consumer segments, from being decisive or attractive to indifferent or reverse. As assumed, gender and attitude towards sustainability are good predictors for segment membership

Nucleotide sequence of the luxA gene of Vibrio harveyi and the complete amino acid sequence of the alpha subunit of bacterial luciferase

The nucleotide sequence of the 1.85-kilobase EcoRI fragment from Vibrio harveyi that was cloned using a mixed-sequence synthetic oligonucleotide probe (Cohn, D. H., Ogden, R. C., Abelson, J. N., Baldwin, T. O., Nealson, K. H., Simon, M. I., and Mileham, A. J. (1983) Proc. Natl. Acad. Sci. U.S.A. 80, 120-123) has been determined. The alpha subunit-coding region (luxA) was found to begin at base number 707 and end at base number 1771. The alpha subunit has a calculated molecular weight of 40,108 and comprises a total of 355 amino acid residues. There are 34 base pairs separating the start of the alpha subunit structural gene and a 669-base open reading frame extending from the proximal EcoRI site. At the 3' end of the luxA coding region there are 26 bases between the end of the structural gene and the start of the luxB structural gene. Approximately two-thirds of the alpha subunit was sequenced by protein chemical techniques. The amino acid sequence implied by the DNA sequence, with few exceptions, confirmed the chemically determined sequence. Regions of the alpha subunit thought to comprise the active center were found to reside in two discrete and relatively basic regions, one from around residues 100-115 and the second from around residues 280-295

Runtime safety assurance of autonomous vehicles used for last-mile delivery in urban environments

Last-mile delivery of goods has gained a lot of attraction during the COVID-19 pandemic. However, current package delivery processes often lead to parking in the second lane, which in turn has negative effects on the urban environment in which the deliveries take place, i.e., traffic congestion and safety issues for other road users. To tackle these challenges, an effective autonomous delivery system is required that guarantees efficient, flexible and safe delivery of goods. The project LogiSmile, co-funded by EIT Urban Mobility, pilots an autonomous delivery vehicle dubbed the Autonomous Hub Vehicle (AHV) that works in cooperation with a small autonomous robot called the Autonomous Delivery Device (ADD). With the two cooperating robots, the project LogiSmile aims to find a possible solution to the challenges of urban goods distribution in congested areas and to demonstrate the future of urban mobility. As a member of Niedersächsische Forschungszentrum für Fahrzeugtechnik (NFF), the Institute for Software and Systems Engineering (ISSE) developed an integrated software safety architecture for runtime monitoring of the AHV, with (1) a dependability cage (DC) used for the on-board monitoring of the AHV, and (2) a remote command control center (CCC) which enables the remote off-board supervision of a fleet of AHVs. The DC supervises the vehicle continuously and in case of any safety violation, it switches the nominal driving mode to degraded driving mode or fail-safe mode. Additionally, the CCC also manages the communication of the AHV with the ADD and provides fail-operational solutions for the AHV when it cannot handle complex situations autonomously. The runtime monitoring concept developed for the AHV has been demonstrated in 2022 in Hamburg. We report on the obtained results and on the lessons learned

Phylogenetic analysis of condensation domains in NRPS sheds light on their functional evolution

<p>Abstract</p> <p>Background</p> <p>Non-ribosomal peptide synthetases (NRPSs) are large multimodular enzymes that synthesize a wide range of biologically active natural peptide compounds, of which many are pharmacologically important. Peptide bond formation is catalyzed by the Condensation (C) domain. Various functional subtypes of the C domain exist: An ^LC_Ldomain catalyzes a peptide bond between two L-amino acids, a ^DC_Ldomain links an L-amino acid to a growing peptide ending with a D-amino acid, a Starter C domain (first denominated and classified as a separate subtype here) acylates the first amino acid with a <it>β</it>-hydroxy-carboxylic acid (typically a <it>β</it>-hydroxyl fatty acid), and Heterocyclization (Cyc) domains catalyze both peptide bond formation and subsequent cyclization of cysteine, serine or threonine residues. The homologous Epimerization (E) domain flips the chirality of the last amino acid in the growing peptide; Dual E/C domains catalyze both epimerization and condensation.</p> <p>Results</p> <p>In this paper, we report on the reconstruction of the phylogenetic relationship of NRPS C domain subtypes and analyze in detail the sequence motifs of recently discovered subtypes (Dual E/C, ^DC_Land Starter domains) and their characteristic sequence differences, mutually and in comparison with ^LC_Ldomains. Based on their phylogeny and the comparison of their sequence motifs, ^LC_Land Starter domains appear to be more closely related to each other than to other subtypes, though pronounced differences in some segments of the protein account for the unequal donor substrates (amino vs. <it>β</it>-hydroxy-carboxylic acid). Furthermore, on the basis of phylogeny and the comparison of sequence motifs, we conclude that Dual E/C and ^DC_Ldomains share a common ancestor. In the same way, the evolutionary origin of a C domain of unknown function in glycopeptide (GP) NRPSs can be determined to be an ^LC_Ldomain. In the case of two GP C domains which are most similar to ^DC_Lbut which have ^LC_Lactivity, we postulate convergent evolution.</p> <p>Conclusion</p> <p>We systematize all C domain subtypes including the novel Starter C domain. With our results, it will be easier to decide the subtype of unknown C domains as we provide profile Hidden Markov Models (pHMMs) for the sequence motifs as well as for the entire sequences. The determined specificity conferring positions will be helpful for the mutation of one subtype into another, e.g. turning ^DC_Lto ^LC_L, which can be a useful step for obtaining novel products.</p

Group B Streptococcus colonization in pregnancy: prevalence and prevention strategies of neonatal sepsis

Early onset neonatal sepsis due to Group B streptococci (GBS) is responsible for severe morbidity and mortality of newborns. While different preventive strategies to identify women at risk are being recommended, the optimal strategy depends on the incidence of GBS-sepsis and on the prevalence of anogenital GBS colonization. We therefore aimed to assess the Group B streptococci prevalence and its consequences on different prevention strategies. We analyzed 1316 pregnant women between March 2005 and September 2006 at our institution. The prevalence of GBS colonization was determined by selective cultures of anogenital smears. The presence of risk factors was analyzed. In addition, the direct costs of screening and intrapartum antibiotic prophylaxis were estimated for different preventive strategies. The prevalence of GBS colonization was 21%. Any maternal intrapartum risk factor was present in 37%. The direct costs of different prevention strategies have been estimated as follows: risk-based: 18,500 CHF/1000 live births, screening-based: 50,110 CHF/1000 live births, combined screening- and risk-based: 43,495/1000 live births. Strategies to prevent GBS-sepsis in newborn are necessary. With our colonization prevalence of 21%, and the intrapartum risk profile of women, the screening-based approach seems to be superior as compared to a risk-based approac