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A vehicular ad hoc network (VANET) is essential for the autonomous vehicle industry, and with the advancement in VANET technology, security threats are increasing rapidly. Mitigation of these threats needs an intelligent security protocol that provides unbreakable security. In recent times, various three-factor authentication solutions for VANET were introduced that adopt the centralized Trusted Authority T A , which is responsible for assigning authentication parameters during vehicle registration, and the authentication process depends on these parameters. This article first explains the vulnerabilities of the recent three-factor (3F) authentication scheme presented by Xu et al. Our analysis proves that if an RSU is dishonest, it can easily bypass the T A and can create a session with OBU . Furthermore, this paper puts forward a new scheme that provides the 3F authentication for VANETs (TFPPASV) to resist RSU from bypassing the T A and to offer user privacy. The proposed scheme fulfills the security and performance requirements of the VANET. We use BAN-Logic analysis to perform a formal security analysis of the proposed scheme, in addition to the informal security feature discussion. Finally, we compare the security and performance of the proposed TFPPASV with some recent and related schemes.

To efficiently store and query a DBMS, administrators must select storage and indexing configurations. For example, one must decide whether data should be stored in rows or columns, in-memory or on disk, and which columns to index. These choices can be challenging to make for workloads that are mixed requiring hybrid transactional and analytical processing (HTAP) support. There is growing interest in system designs that can adapt how data is stored and indexed to execute these workloads efficiently. We present Tiresias , a predictor that learns the cost of data accesses and predicts their latency and likelihood under different storage scenarios. Tiresias makes these predictions by collecting observed latencies and access histories to build predictive models in an online manner, enabling autonomous storage and index adaptation. Experimental evaluation shows the benefits of predictive adaptation and the trade-offs for different predictive techniques.

Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

Systems continue to grow in complexity in response to the need to support vast quantities of data and a wide variety of workloads. Small changes in workloads and system configuration can result in significantly different system behaviour and performance characteristics. As a result, system administrators and developers spend many hours diagnosing and debugging performance problems in data systems and the applications that use them. In this paper, we present Sentinel, an analysis tool that assists these users by constructing fine-grained models of system behaviour and comparing these models to pinpoint differences in system behaviour for different workloads and system configurations. Importantly, Sentinel's insights are derived from built-in debug logging libraries without necessitating that their log messages be written to disk, thereby generalizing to all systems that use debug logging without incurring its overheads. Our experiments demonstrate Sentinel's superiority in analyzing the execution behaviour and performance characteristics of database systems, client applications, and web servers compared to prior approaches.

In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Ethiopian Kinits, Tizita, Bati, Ambassel and Anchihoye. Each Kinit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kinit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kinits Model (EKM), based on VGG, for classifying the EMIR clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram, Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for Kinit classification using EKM. MFCC was found to be superior and was therefore adopted for Experiment 2, where the performance of EKM models using MFCC was compared using three different audio sample lengths. 3s length gave the best results. In Experiment 3, EKM and four existing models were compared on the EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best accuracy (95.00%) as well as the fastest training time. We hope this work will encourage others to explore Ethiopian music and to experiment with other models for Kinit classification., 11 pages, 4 tables, 3 figures

Biologists consider variability during biological investigations. A robust quantitative understanding of variability is particularly important during data analysis, where statistics are used to quantify variation and draw conclusions about phenomena while accounting for variation. Many students struggle to correctly apply a quantitative understanding of variation to statistically analyze data. We present quantitative and qualitative analyses of introductory biology students’ responses on two pairs of multiple-choice questions querying two concepts related to the quantitative analysis of variation. More students correctly identify a mathematical expression of variation than correctly interpret it. Many students correctly interpret a nonsignificant p-value in the context of a very small sample size, but fewer students do so in the context of a large sample size. These results imply that many students have an incomplete quantitative understanding of variation. These findings suggest that instruction focusing on conceptual understanding, not procedural problem solving, may elevate students’ quantitative understanding of variation.

Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and assess these designs. In this paper, we show that the Yolov4-tiny architecture outperforms the Yolov4, and Yolov5s in terms of mAP@50 and computing time per image.

Crack inspections of automotive engine components are usually conducted manually; this is often tedious, with a high degree of subjectivity and cost. Therefore, establishing a robust and efficient method will improve the accuracy and minimize the subjectivity of the inspection. This paper presents a robust approach towards crack classification, using transfer learning and fine-tuning to train a pre-trained ConvNet model. Two deep convolutional neural network (DCNN) approaches to training a crack classifier—namely, via (1) a Light ConvNet architecture from scratch, and (2) fined-tuned and transfer learning top layers of the ConvNet architectures of AlexNet, InceptionV3, and MobileNet—are investigated. Data augmentation was utilized to minimize over-fitting caused by an imbalanced and inadequate training sample. Data augmentation improved the accuracy index by 4%, 5%, 7%, and 4%, respectively, for the proposed four approaches. The transfer learning and fine-tuning approach achieved better recall and precision scores. The transfer learning approach using the fine-tuned features of MobileNet attained better classification accuracy and is thus proposed for the training of crack classifiers. Moreover, we employed an up-to-date YOLOv5s object detector with transfer learning to detect the crack region. We obtained a mean average precision (mAP) of 91.20% on the validation set, indicating that the model effectively distinguished diverse engine part cracks.

The performance of data-driven, web-scale client applications is sensitive to access latency. To address this concern, enterprises strive to cache data on edge nodes that are closer to users, thereby avoiding expensive round-trips to remote data centers. However, these geo-distributed approaches are limited to caching static data. In this paper we present ChronoCache, a mid-tier caching system that exploits the presence of geo-distributed edge nodes to cache database query results closer to users. ChronoCache transparently learns and leverages client application access patterns to predictively combine query requests and cache their results ahead of time, thereby reducing costly round-trips to the remote database. We show that ChronoCache reduces query response times by up to 2/3 over prior approaches on multiple representative benchmark workloads. representative benchmark workloads.

The complexity of modern data systems and applications greatly increases the challenge in understanding system behaviour and diagnosing performance problems. When these problems arise, system administrators are left with the difficult task of remedying them by relying on large debug log files, vast numbers of metrics, and system-specific tooling. We demonstrate the Sentinel system, which enables administrators to analyze systems and applications by building models of system execution and comparing them to derive key differences in behaviour. The resulting analyses are then presented as system reports to administrators and developers in an intuitive fashion. Users of Sentinel can locate, identify and take steps to resolve the reported performance issues. As Sentinel's models are constructed online by intercepting debug logging library calls, Sentinel's functionality incurs little overhead and works with all systems that use standard debug logging libraries.

Single-master replicated database systems strive to be scalable by offloading reads to replica nodes. However, single-master systems suffer from the performance bottleneck of all updates executing at a single site. Multi-master replicated systems distribute updates among sites but incur costly coordination for multi-site transactions. We present DynaMast, a lazily replicated, multi-master database system that guarantees one-site transaction execution while effectively distributing both reads and updates among multiple sites. DynaMast benefits from these advantages by dynamically transferring the mastership of data, or remastering, among sites using a lightweight metadata-based protocol. DynaMast leverages remastering to adaptively place master copies to balance load and minimize future remastering. Using benchmark workloads, we demonstrate that DynaMast delivers superior performance over existing replicated database system architectures.

The amount and quality of land, water and atmospheric information is critical to plan, monitor, predict and mitigate the impacts of climate change, urbanization and population growth. To get reason...

© The Author(s) 2017. Diversity-generating retroelements (DGRs) are novel genetic elements that use reverse transcription to generate vast numbers of sequence variants in specific target genes. Here, we present a detailed comparative bioinformatic analysis that depicts the landscape of DGR sequences in nature as represented by data in GenBank. Over 350 unique DGRs are identified, which together form a curated reference set of putatively functional DGRs. We classify target genes, variable repeats and DGR cassette architectures, and identify two new accessory genes. The great variability of target genes implies roles of DGRs in many undiscovered biological processes. There is much evidence for horizontal transfers of DGRs, and we identify lineages of DGRs that appear to have specialized properties. Because GenBank contains data from only 10% of described species, the compilation may not be wholly representative of DGRs present in nature. Indeed, many DGR subtypes are present only once in the set and DGRs of the candidate phylum radiation bacteria, and Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea archaea, are exceptionally diverse in sequence, with little information available about functions of their target genes. Nonetheless, this study provides a detailed framework for classifying and studying DGRs as they are uncovered and studied in the future.

The ubiquity of distributed computing systems has led to an increased focus on distributed systems in the undergraduate curriculum. In this paper, we describe the design of, and our experiences with, a new distributed systems project where students implement the core components of a distributed file system called WatDFS. The WatDFS project enables students to meet learning objectives from across the distributed systems curriculum and interact with real systems, while providing a high-quality testing environment that yields actionable feedback to students on their submissions.

To meet growing application demands, distributed data systems replicate and partition data across multiple machines. Replication increases the resource and request processing capabilities of a system by spreading copies of the data across multiple machines, while partitioning splits data across machines to achieve the same objectives. Replication and partitioning present different trade-offs in the form of replication maintenance and multi-machine coordination costs, which system administrators must carefully evaluate. Traditionally, administrators made replication and partitioning decisions based on their understanding of the application workload, which results in suboptimal performance if the system is misconfigured or if the workload changes. However, systems that adaptively employ replication and partitioning can adjust these decisions based on workload observations and predictions, which improves performance and reduces complexity for administrators. In this tutorial, we present an overview of techniques used by systems to adaptively partition and replicate data and services. We focus on the decision-making strategies employed by these systems, and how these decisions are executed in an online environment. Finally, we identify opportunities for research in the area.

The already complex interrelationships between water, food, energy and the environment are facing both challenges and opportunities. Rising fuel costs and increasing concerns over the effects of climate change are reinvigorating policymakers’ interest in renewable energy sources such as hydropower and bio-energy—both from biofuels as well as biomass. Development of any of these sources has the potential to generate positive economic and environmental benefits, yet, at the same time, they can cause negative food and equity impacts. This obviously entails major tradeoffs between the food, energy and environmental goals of water and energy development, allocation and management. Using both a brief global overview as well as a closer review of four case studies from India, Ethiopia, Jordon and the USA, this paper tries to (i) a present the nature of the tradeoffs under different hydrological, energy, agricultural and environmental contexts and (ii) provide some anecdotal evidence and illustrative cases for the available policy options for minimizing conflicts but maximizing synergies between water, energy, food and environment.

Background Accurate and complete identification of mobile elements is a challenging task in the current era of sequencing, given their large numbers and frequent truncations. Group II intron retroelements, which consist of a ribozyme and an intron-encoded protein (IEP), are usually identified in bacterial genomes through their IEP; however, the RNA component that defines the intron boundaries is often difficult to identify because of a lack of strong sequence conservation corresponding to the RNA structure. Compounding the problem of boundary definition is the fact that a majority of group II intron copies in bacteria are truncated. Results Here we present a pipeline of 11 programs that collect and analyze group II intron sequences from GenBank. The pipeline begins with a BLAST search of GenBank using a set of representative group II IEPs as queries. Subsequent steps download the corresponding genomic sequences and flanks, filter out non-group II introns, assign introns to phylogenetic subclasses, filter out incomplete and/or non-functional introns, and assign IEP sequences and RNA boundaries to the full-length introns. In the final step, the redundancy in the data set is reduced by grouping introns into sets of ≥95% identity, with one example sequence chosen to be the representative. Conclusions These programs should be useful for comprehensive identification of group II introns in sequence databases as data continue to rapidly accumulate.