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关键词： educational technologies   ChatGPT   GenAI   LLM   computer science education   database administration  

摘要： GenAI tools like ChatGPT have changed the educational landscape, and empirical experiences are needed to better understand how to use them to their fullest potential. This article empirically explores the usage of ChatGPT 3.5 in database administration education through a case study conducted with 40 computer science students. Specifically, it inspects how widespread the use of ChatGPT is and students' perceptions of this tool, how prior knowledge on a topic affects the use of ChatGPT, and the relationship between the usage of ChatGPT and success in solving practical problems. The student's grades in a computer practical exam, a set of theoretical tests to assess progression in knowledge acquisition, and a comprehensive questionnaire are employed as research instruments. The obtained results indicate that students use ChatGPT moderately but more frequently than traditional internet learning resources such as official documentation, Stack Overflow or googling. However, the usage is uneven among students, and those who end up getting better grades use ChatGPT more. Beyond prompting skills, one of the elements that is key to the students' productive use of this tool is their prior knowledge about database administration. This article concludes that ChatGPT is an excellent educational instrument in the context of database administration and that in order to use it properly, it is necessary for students to have good prompting skills as well as a sound theoretical basis. Training students in the use of GenAI tools like ChatGPT, for example, with a guided practice strategy where prompting and conducted step-by-step practice are employed is key to prevent the appearance of new digital trenches.

关键词： Feature extraction   Cloud computing   Fingerprint recognition   Training   Computational modeling   Accuracy   Transforms   Production   Costs   Computer science   Cloud storage   deduplication   resemblance detection  

摘要： Post-deduplication in traditional cloud environments primarily focuses on single-node, where delta compression is performed on the same deduplication node located on server side. However, with data explosion, the multi-node post-deduplication, also called global deduplication, has become a hot issue in research communities, which aims to simultaneously execute delta compression on data distributed across all nodes. Simply setting up single-node deduplication systems on multi-node environments would significantly affect storage utilization and incur secondary overhead from file migration. Nevertheless, existing global deduplication solutions suffer from lower data compression ratios and high computational overhead due to their resemblance detection's inherent limitations and overly coarse granularities. Similar blocks typically have high correlations between sub-blocks;inspired by this observation, we propose IBNR (Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication), which introduces a novel resemblance detection based on relationships between sub-blocks and determines the ownership of blocks in entry stage to achieve efficient global deduplication. Furthermore, the by-products of IBNR have shown powerful scalability by replacing internal resemblance detection scheme with existing solutions on practical workloads. Experimental results indicate that IBNR outperforms state-of-the-art solutions, achieving an average 1.99x data reduction ratio and varying degrees of improvement across other key metrics.

关键词： Unified modeling language   Feature extraction   Analytical models   Organizations   Stakeholders   Computer science   Syntactics   Codes   Planning   Guidelines   Feature modeling   software engineering processes   features   product lines   configurable systems  

摘要： Almost any software system needs to exist in multiple variants. While branching or forking-a.k.a. clone & own-are simple and inexpensive strategies, they do not scale well with the number of variants created. Software platforms-a.k.a. software product lines-scale and allow to derive variants by selecting the desired features in an automated, tool-supported process. However, product lines are difficult to adopt and to evolve, requiring mechanisms to manage features and their implementations in complex codebases. Such systems can easily have thousands of features with intricate dependencies. Feature models have arguably become the most popular notation to model and manage features, mainly due to their intuitive, tree-like representation. Introduced more than 30 years ago, thousands of techniques relying on feature models have been presented, including model configuration, synthesis, analysis, and evolution techniques. However, despite many success stories, organizations still struggle with adopting software product lines, limiting the usefulness of such techniques. Surprisingly, no modeling process exists to systematically create feature models, despite them being the main artifact of a product line. This challenges organizations, even hindering the adoption of product lines altogether. We present FM-PRO, a process to engineer feature models. It can be used with different adoption strategies for product lines, including creating one from scratch (pro-active adoption) and re-engineering one from existing cloned variants (extractive adoption). The resulting feature models can be used for configuration, planning, evolution, reasoning about variants, or keeping an overview understanding of complex software platforms. We systematically engineered the process based on empirically elicited modeling principles. We evaluated and refined it in a real-world industrial case study, two surveys with industrial and academic feature-modeling experts, as well as an open-source case st

关键词： Runtime   Accuracy   Fuzzy logic   Training   Predictive models   Servers   Robustness   Computer science   Artificial neural networks   Prediction algorithms   Deep neuro-fuzzy system   edge server   heterogeneous device   prediction  

摘要： Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.

关键词： Decoding   Reliability   Encoding   Codes   Reliability theory   Receivers   Hamming weight   Computer science   Channel capacity   Authentication   Multiple access   identification  

摘要： We introduce the problem of determining the identity of a byzantine user (internal adversary) in a communication system. We consider a two-user discrete memoryless multiple access channel where either user may deviate from the prescribed behaviour. Since small deviations may be indistinguishable from the effects of channel noise, it might be overly restrictive to attempt to detect all deviations. In our formulation, we only require detecting deviations which impede the decoding of the non-deviating user's message. When neither user deviates, correct decoding is required. When one user deviates, the decoder must either output a pair of messages of which the message of the non-deviating user is correct or identify the deviating user. The users and the receiver do not share any randomness. The results include a characterization of the set of channels where communication is feasible, and an inner and outer bound on the capacity region. We also show that whenever the rate region has non-empty interior, the capacity region is same as the capacity region under randomized encoding, where each user shares independent randomness with the receiver. We also give an outer bound for this randomized coding capacity region.

关键词： Codes   Codecs   Generators   Vectors   Linear codes   Parity check codes   Computer science   Reed-Solomon codes   Telecommunications   Sun   Extended code   MDS code   covering radius   deep hole   GRS code   extended GRS code  

摘要： For a given linear code C of length n over GF(q) and a nonzero vector u in GF(q)(n) , Sun, Ding and Chen defined an extended linear code C(u) of C , which is a generalisation of the classical extended code C(-1) of C and called the second kind of an extended code of C (see Finite Fields Appl., vol. 96, 102401, 2024 and Discrete Math., vol. 347, no. 9, 114080, 2024). They developed some general theory of the extended codes C(u) and studied the extended codes C(u) of several families of linear codes, including cyclic codes, projective two-weight codes, nonbinary Hamming codes, and a family of reversible MDS cyclic codes. The objective of this paper is to investigate the extended codes C(u) of MDS codes C over finite fields. The main result of this paper is that the extended code C(u) of an MDS [n,k] code C remains MDS if and only if the covering radius rho(C-perpendicular to)=k and the vector u is a deep hole of the dual code C-perpendicular to . As applications of this main result, an equivalent statement of MDS Conjecture is presented, the extended codes of the GRS codes and extended GRS codes are investigated, and the covering radii and some deep holes of several families of MDS codes are also determined.

关键词： Data models   Vectors   Anomaly detection   Sampling methods   Training   Computer science   Transformers   Semantics   Predictive models   Feature extraction   Deep learning-based log anomaly detection   data resampling methods   class imbalance   empirical analysis  

摘要： Numerous Deep Learning (DL)-based approaches have gained attention in software Log Anomaly Detection (LAD), yet class imbalance in training data remains a challenge, with anomalies often comprising less than 1% of datasets like Thunderbird. Existing DLLAD methods may underperform in severely imbalanced datasets. Although data resampling has proven effective in other software engineering tasks, it has not been explored in LAD. This study aims to fill this gap by providing an in-depth analysis of the impact of diverse data resampling methods on existing DLLAD approaches from two distinct perspectives. Firstly, we assess the performance of these DLLAD approaches across four datasets with different levels of class imbalance, and we explore the impact of resampling ratios of normal to abnormal data on DLLAD approaches. Secondly, we evaluate the effectiveness of the data resampling methods when utilizing optimal resampling ratios of normal to abnormal data. Our findings indicate that oversampling methods generally outperform undersampling and hybrid sampling methods. Data resampling on raw data yields superior results compared to data resampling in the feature space. These improvements are attributed to the increased attention given to important tokens. By exploring the resampling ratio of normal to abnormal data, we suggest generating more data for minority classes through oversampling while removing less data from majority classes through undersampling. In conclusion, our study provides valuable insights into the intricate relationship between data resampling methods and DLLAD. By addressing the challenge of class imbalance, researchers and practitioners can enhance DLLAD performance.

关键词： Target tracking   Cameras   Benchmark testing   Self-supervised learning   Training   Object tracking   Computer science   Multiple object tracking   human association   multi-view cameras   self-supervised learning  

摘要： Multi-view multi-human association and tracking (MvMHAT), is an emerging yet important problem for multi-person scene video surveillance, aiming to track a group of people over time in each view, as well as to identify the same person across different views at the same time, which is different from previous MOT and multi-camera MOT tasks only considering the over-time human tracking. This way, the videos for MvMHAT require more complex annotations while containing more information for self-learning. In this work, we tackle this problem with an end-to-end neural network in a self-supervised learning manner. Specifically, we propose to take advantage of the spatial-temporal self-consistency rationale by considering three properties of reflexivity, symmetry, and transitivity. Besides the reflexivity property that naturally holds, we design the self-supervised learning losses based on the properties of symmetry and transitivity, for both appearance feature learning and assignment matrix optimization, to associate multiple humans over time and across views. Furthermore, to promote the research on MvMHAT, we build two new large-scale benchmarks for the network training and testing of different algorithms. Extensive experiments on the proposed benchmarks verify the effectiveness of our method. We have released the benchmark and code to the public.

关键词： Software As A Service   Computer Science   Software Development Management   Project Management   Computer Science   E Infrastructure   Research And Development   Information Technology   Development Projects   Distribution System   Computer System   High Performance Computing   Digital Networks   Computer Usage   Computing Paradigm   Staff Members   Multidisciplinary Team   Funding Sources   Service Users   Computer Technology   Funding Agencies   Application Programming Interface   Computational Domain   National Science Foundation   Interdisciplinary Knowledge   Virtuous Cycle   Innovation Cycle   File System   Nanoelectronics   Modern Science   Virtualization Technology  

摘要： This article shares the author's experience and views of the creation, development, and operation of software-as-a-service cyberinfrastructures in an academic laboratory. A journey of three successive examples is reviewed: Purdue University Network Computing Hubs (PUNCH), In-Virtual Grid Organizations (In-ViGO) and Integrated Digitized Biocollections (iDigBio). The motivation and impact of each example are described along with their innovative technical characteristics that resulted from translating computer science results and experimental research into useful features. Reflections and recommendations inspired by the author's journey through these examples are discussed, including the need to fund and sustain different lifespans of translational computer science projects and the importance of creating and retaining interdisciplinary human capacity.