Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Contextual Anomaly Detection for Database Systems

Abnormal data access operations in database systems always hap-pen, which are typically incurred by misoperations or attacks, though these systems are enforced with strict access control policies. However, prior arts only focus on detecting abnormal data accesses by utilizing known attack patterns or identifying behaviors significantly deviated from normal behaviors. They cannot capture stealthy abnormal data access operations that are similar to normal ones. In this paper, we propose a novel unsupervised anomaly detection system UCAD, which aims to detect abnormal data access operations, by comparing operation's semantics with their contextual intent. However, it is non-trivial to obtain accurate semantics of operations for intent analysis because (i) the same operation may exhibit diverse semantics under different operation contexts and (ii) different operation sequences could have identical semantics due to heterogeneous user access patterns. To address this issue, we develop a new transformer model called Trans-DAS for UCAD. Trans-DAS learns the semantics of individual operations by utilizing the attention mechanism that analyzes the relevance between any pair of operations in sequence, and captures the contextual intent of operations inferred from the contexts. Specifically, Trans-DAS utilizes a particular embedding layer to embed the semantics of individual operations without the operation order information and a masking mechanism that allows Trans-DAS to learn the semantics according to the bidirectional contexts. Also, we define a new training objective for Trans-DAS to enlarge the difference among the embedded semantics. Furthermore, in order to effectively utilize Trans-DAS for detection, we develop two modules in UCAD, i.e., a data preprocessing module that allows Trans-DAS to accurately learn the normal semantic information by removing noisy data, and an anomaly detection module that learns the semantic information for intent comparison. We evaluate the performance of UCAD on real-world data traces under different settings (e.g., varied parameters and hybrid datasets). The results demonstrate that UCAD achieves the average F1-score of 0.94 in two scenarios, which significantly outperform baselines, and shows robustness to hybrid data and good transferability to different tasks.
Methodological Standards in Accessibility Research-PLXBCCR- on Motor Impairments: A Survey

The design and evaluation of accessibility technology is a core component of the computer science landscape, aiming to ensure that digital innovations are accessible to all. One of the most prominent and long-lasting areas of accessibility research focuses on motor impairments—deficiencies that affect the ability to move, manipulate objects, and interact with the physical world. In this survey article, we present an extensive overview of the past two decades of research into accessibility for people with motor impairments. Following a structured selection process, we analyzed the study details as reported in 177 relevant papers. Based on this analysis, we critically assess user representation, measurement instruments, and existing barriers that exist in accessibility research. Finally, we discuss future directions for accessibility research within the computer science domain.
Learned Cardinality Estimation: An In-depth Study

Learned cardinality estimation (CE) has recently gained significant attention for replacing long-studied traditional CE with machine learning, especially for deep learning. However, these estimators were developed independently and have not been fairly or comprehensively compared in common settings. Most studies use a subset of IMDB data which is too simple to measure their limits and determine whether they are ready for real, complex data. Furthermore, they are regarded as black boxes, without a deep understanding of why large errors occur. In this paper, we first provide a taxonomy and a unified workflow of learned estimators for a better understanding of estimators. We next comprehensively compare recent learned CE methods that support joins, from a subset of tables to full IMDB and TPC-DS datasets. Under the experimental results, we then demystify the black-box models and analyze critical components that cause large errors. We also measure their impact on query optimization. Finally, based on the findings, we suggest realizable research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide a more comprehensive and substantial framework for developing better estimators.
What Did My AI Learn? How Data Scientists Make Sense of Model Behavior

Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have learned. We frame this process as a form of sensemaking and derive a framework describing how data scientists develop mental models of AI behavior. To evaluate the framework, we show how existing AI analysis tools fit into this sensemaking process and use it to design AIFinnity, a system for analyzing image-and-text models. Lastly, we explored how data scientists use a tool developed with the framework through a think-aloud study with 10 data scientists tasked with using AIFinnity to pick an image captioning model. We found that AIFinnity’s sensemaking workflow reflected participants’ mental processes and enabled them to discover and validate diverse AI behaviors.
Halo: A Hybrid PMem-DRAM Persistent Hash Index with Fast Recovery

Hash index, a fundamental component in many data management systems, can benefit from the emerging persistent memory (PMem) to achieve high performance and instant recovery. However, existing persistent hash indexes are suboptimal in at least three aspects. First, their performance suffers from the mismatch between small random write and access granularity of PMem hardware. Second, none of them are aware of the significance of write amplification caused by memory allocators and synchronization primitives. Third, hybrid designs (PMem+DRAM) focus on improving throughput at the cost of extremely long recovery time. In this paper, we present the design and implementation of Halo, a hybrid hash index for PMem+DRAM environment, featuring a specifically designed volatile index and log-structured persistent storage layout. In order to suppress write amplification caused by memory allocators and to facilitate recovery, we propose Halloc, a highly-efficient memory manager for Halo. In addition, we propose mechanisms such as batched writes, prefetching for hybrid reads, and reactive snapshot to further optimize performance. We conduct extensive evaluations on a 32-core platform equipped with Intel Optane DC Persistent Memory Modules. The results show that Halo achieves up to 17.5x and 81.2x higher read and write throughput than state-of-the-art hash indexes under a wide range of workloads. Halo also outperforms current hybrid designs in recovery speed, which is 1 to 2 orders of magnitude faster.
Protecting Data Markets from Strategic Buyers

The growing adoption of data analytics platforms and machine learning-based solutions for decision-makers creates a significant demand for datasets, which explains the appearance of data markets. In a well-functioning data market, sellers share data in exchange for money, and buyers pay for datasets that help them solve problems. The market raises sufficient money to compensate sellers and incentivize them to keep sharing datasets. This low-friction matching of sellers and buyers distributes the value of data among participants. But designing online data markets is challenging because they must account for the strategic behavior of participants. In this paper, we introduce techniques to protect data markets from strategic participants, even when the asset traded is data. We combine those techniques into a pricing algorithm specifically designed to trade data. The evaluation includes a user study and extensive simulations. Together, the evaluation demonstrates how participants strategize and the effectiveness of our techniques.
Provenance-based Intrusion Detection Systems: A Survey

Traditional Intrusion Detection Systems (IDS) cannot cope with the increasing number and sophistication of cyberattacks such as Advanced Persistent Threats (APT). Due to their high false-positive rate and the required effort of security experts to validate them, incidents can remain undetected for up to several months. As a result, enterprises suffer from data loss and severe financial damage. Recent research explored data provenance for Host-based Intrusion Detection Systems (HIDS) as one promising data source to tackle this issue. Data provenance represents information flows between system entities as Direct Acyclic Graph (DAG). Provenance-based Intrusion Detection Systems (PIDS) utilize data provenance to enhance the detection performance of intrusions and reduce false-alarm rates compared to traditional IDS. This survey demonstrates the potential of PIDS by providing a detailed evaluation of recent research in the field, proposing a novel taxonomy for PIDS, discussing current issues, and potential future research directions. This survey aims to help and motivate researchers to get started in the field of PIDS by tackling issues of data collection, graph summarization, intrusion detection, and developing real-world benchmark datasets.
Relative Subboundedness of Contraction Hierarchy and Hierarchical 2-Hop Index in Dynamic Road Networks

Computing the shortest path for any two given vertices is an important problem in road networks. Since real road networks are dynamically updated due to real-time traffic conditions and it is costly to recompute the oracle O in use from scratch, O needs to be updated to reflect the changes in the network using incremental algorithms. An incremental algorithm is said to be bounded if its cost is polynomial in |CHANGED|, where CHANGED comprises both the changes to the graph and the resulting changes to O. An incremental problem is bounded if it has a bounded algorithm and is unbounded otherwise. We study the boundedness of the incremental counterparts of two state-of-the-art oracles, namely contraction hierarchy (CH) and hierarchical 2-hop index (H2H). We prove that under specific computational models, both CH and H2H are unbounded to maintain. Despite this fact, we introduce relative subboundedness as an alternative to boundedness. We prove that the state-of-the-art incremental algorithm for CH is relatively subbounded, and moreover, we propose a relatively subbounded algorithm for H2H. Our experimental study on real road networks shows that the algorithms studied are faster than recomputing from scratch even when 10% of the index needs to be updated, thereby verifying the effectiveness of relative subboundedness.
EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views

Advances in deep learning have led to a resurgence of interest in video analytics. In an exploratory video analytics pipeline, a data scientist often starts by searching for a global trend and then iteratively refines the query until they identify the desired local trend. These queries tend to have overlapping computation and often differ in their predicates. However, these predicates are computationally expensive to evaluate since they contain user-defined functions (UDFs) that wrap around deep learning models. In this paper, we present EVA, a video database management system (VDBMS) that automatically materializes and reuses the results of expensive UDFs to facilitate faster exploratory data analysis. It differs from the state-of-the-art (SOTA) reuse algorithms in traditional DBMSs in three ways. First, it focuses on reusing the results of UDFs as opposed to those of sub-plans. Second, it takes a symbolic approach to analyze predicates and identify the degree of overlap between queries. Third, it factors reuse into UDF evaluation cost and uses the updated cost function in critical query optimization decisions like predicate reordering and model selection. Our empirical analysis of EVA demonstrates that it accelerates exploratory video analytics workloads by 4x with a negligible storage overhead (1.001x). We demonstrate that the reuse algorithm in EVA complements the specialized filters adopted in SOTA VDBMSs.
Rank Aggregation with Proportionate Fairness

Given multiple individual rank orders over a set of candidates or items, where the candidates belong to multiple (non-binary) protected groups, we study the classical rank aggregation problem subject to proportionate fairness or p-fairness (RAPF in short), considering Kemeny distance. We first study the problem of producing the closest p-fair ranking to an individual ranked order IPF in short) considering Kendall-Tau distance, and present multiple solutions for IPF. We then present two computational frameworks(a randomized randpickperm and a deterministic algpickperm) to solve RAPF that leverages the solutions of IPF as a subroutine. We make several non-trivial algorithmic contributions: (i) we prove that when the group protected attribute is binary, IPF can be solved exactly using a greedy technique; (ii) we present two different solutions for IPF when the group protected attribute is multi-valued, algexact is optimal and algapprox admits a 2 approximation factor; (iii) we design a framework for RAPF solution with an approximation factor that is 2+ the approximation factor of the IPF solution. The resulting randpickperm and algpickperm solutions exhibit 3 and 4 approximation factors when designed using algexact and algapprox, respectively. We run extensive experiments using multiple real world and large scale synthetic datasets and compare our proposed solutions against multiple state-of-the-art related works to demonstrate the effectiveness and efficiency of our studied problem and proposed solution.
Determining acute physiological stress levels with wearable sensors based on movement quality and exhaustion during repetitive training exercises

Stress is considered to have a bidirectional relationship with exercise injuries. This PhD project addresses the lack of research on methodological and measurement standards for determining acute physiological stress in the challenging context of repetitive training exercises such as squats, push-ups, and crunches. It explores how stress levels can be determined in real-time with both inertial and heart rate sensors, and how stress levels correlate with movement quality and exhaustion. To this end, a systematic method is elaborated to build a model capable of classifying stress levels with only wearable sensors in real-time.
From Design Principles to Impacts: A Theoretical Framework and Research Agenda

In this paper, we integrate three streams of research in information systems (i.e., IS success, technology adoption, and human-centered design principles) to extend our understanding of technology use. We present a theoretical framework that incorporates the core ideas from these three streams of research. We leverage the proposed framework to present propositions that could guide future work. Specifically, the propositions we develop relate system-design principles to use and net benefits (i.e., job performance and job satisfaction) and rich use to job performance. We further suggest several broad potential future research directions.
Lucid Loop: Exploring the Parallels between Immersive Experiences and Lucid Dreaming

Lucid dreaming is the awareness of being in a dream, allowing dream control and living out fantasies. It also has benefits for growth and well-being. Yet, lucid dreaming is not accessible to most people. So, we created Lucid Loop—a neurofeedback-augmented immersive experience that utilizes AI-enhanced visuals and spatial audio in a virtual reality device for simulating lucid dreaming. We interviewed nine lucid dreamers who tried Lucid Loop and helped us propose design considerations: dreaming allusions, reality checks, focus points with neurofeedback, people in the scene, and immersion. Lucid Loop was like lucid dreaming because of its capacity for emotionality and fluidity between self and environment. Participants also noted several differences where technology might be limited. Lucid Loop appears to accurately simulate lucid dreaming, with implications for enhancing well-being and future applications for lucid dream training. Our research generalizes to technologically-mediated simulations of other emotive or internal experiences.
Complex signum non‐linearity for robust GNSS interference mitigation

The performance of a global navigation satellite system (GNSS) receiver can be significantly degraded in the presence of pulsed interference and jamming. In this study, the authors leverage on tools from robust statistics to enhance the receiver performance, with jamming signals treated as outliers to the nominal, interference-free model. Particularly, the signal samples are pre-processed with a zero-memory non-linearity (ZMNL), which limits the impact of pulsed inference in a principled way. A possible approach for the design of such ZMNL is provided by the M-estimator framework when the noise at the receiver input is modelled with a heavy-tailed distribution. This approach is adopted in this study and the complex signum non-linearity is analysed. This ZMNL is obtained by considering a complex Laplacian noise. This choice is discussed and analysed in the context of GNSS receivers under jamming. The impact of the complex signum non-linearity is theoretically analysed under nominal conditions, that is, in the absence of interference. Theoretical results are supported by Monte Carlo simulations. Real GNSS signals, collected in the presence of jamming, are used to demonstrate the advantages brought by the complex signum non-linearity. Theoretical and experimental results demonstrate the benefits of the proposed approach.
Analytical foundations for development of real-time supply chain capabilities

The emergence of real-time supply chain visibility technologies has raised challenges for organisations in developing the required management capabilities required to exploit enhanced visibility. The convergence of cloud computing, mobile technology, distributed computing, and data integration technologies has enabled managers for the first time to have real-time visibility of material flows in end to end supply chains, enhancing their ability to identify bottlenecks and disruptions of material flows anywhere in their network. To effectively harness these technologies, a new set of managerial decision-making capabilities as well as enhanced data governance disciplines will be required. In this research, we employ organisational information processing theory to explore the relationship of analytical capabilities, data quality, reporting quality, and real-time data capabilities on supply chain performance. Our research model suggests that the benefits of real-time information technologies are dependent on quality reporting and managerial analytical strengths to derive supply chain benefits. The implications for managerial applications and research are further described based on these findings.
Seeking New Measures for Gender Bias Effects in Open-Source Software

The problem of low gender diversity in open-source software (OSS) has been reported and studied in recent years. However, prior studies found that gender bias theories in social sciences cannot help us effectively identify gender bias effects in OSS. Our study takes the first step toward finding new measures for gender bias in OSS. This paper attempts to employ linguistic theories to identify different collaboration patterns between different genders. Our contributions are two-fold: we review linguistic literature on diversity and online collaboration, then we apply linguistic theories from our literature reviews to a random sample of code review conversations on GitHub.
TYPE 2 DIABETES MELLITUS , ANEMIA , AND HYPERTENSION AS THE RISK FACTORS FOR METASTATIC BREAST CANCER Diabetes

1 Master of Epidemiology, School of Postgraduate, Diponegoro University, Semarang, 50241, Indonesia elmaelmika11@gmail.com 2 Surgical Science Dept., Faculty of Medicine, Diponegoro University, Semarang, 50241, Indonesia, uud_dr@hotgmail.com 3 Occupational Safety and Health Dept., Faculty of Public Health, Diponegoro University, Semarang, 50241, Indonesia, arisuwondo57@gmail.com Corresponding Author: Elma Elmika, elmaelmika11@gmail.com, Master of Epidemiology, School of Postgraduate, Diponegoro University, Imam Bardjo, SH Street, Semarang City, Central Java, Indonesia, 50241
Selective Excitation of Characteristic Modes on an Electrically Large Antenna for MIMO Applications

The selective excitation of individual characteristic modes for MIMO applications on an electrically large antenna structure is investigated. The characteristic mode analysis yields that a large number of modes is significant and thus basically suited for use in a multi-mode antenna. In order to find appropriate excitation arrangements for the individual characteristic modes, they are grouped according to their symmetry properties. It is found that characteristic currents belonging to the same group are correlated and thus cannot be excited separately, resulting in a reduced set of uncorrelated antenna ports.
Exploring the Intention to Use Mobile Learning in Higher Education

: The transition to mobile devices and ubiquitous computing technology in education provides an unprecedented opportunity to help instructors influence ively deliver learning materials to learners anytime and anywhere (Cheng, 2013). The objective of the study is to determine the influence of students 'perception and personal innovativeness on students' intention to use mobile learning. It is a quantitative research. The variables of the study are students' perception, personal innovativeness, and intention to use mobile learning. The population of this research are Accounting students in the sixth semester at Unversitas Negeri Semarang. The samples of the research are 89 respondents taken by using simple random sampling technique and data are collected by questionnaires. Then, data are analyzed by descriptive analysis and multiple regression analysis with SPSS 21 application. The result of multiple regression analysis showed that simultaneously, social proof (X1) and purchase intention (X2) have significant influence on students' purchasing decision (Y) for 58.1% . Meanwhile, partially, the influence of students’ perception (X1) and personal innovativeness (X2) on intention to use mobile learning (Y) are 22% and 25% respectively. Thus, the model of the study is Y = 2.254 + 0.299X1 + 0.626X2 + e. The personal innovativeness variable shows a higher influence than the students’ perception. It is understandable that personal innovativeness is more likely to trigger concrete behavior to try new things about technology, as Lopez-Nicolas et al. (2008) argues that personal innovativeness in the use of information technology reflects a desire to try new technologies.
Employee Responses to Compensation Changes: Evidence from a Sales Firm

What are the long-term consequences of compensation changes? Using data from an inbound sales call center, we study employee responses to a compensation change that ultimately reduced take-home pay by 7% for the average affected worker. The change caused a significant increase in the turnover rate of the firm’s most productive employees, but the response was relatively muted for less productive workers. On the job performance changes were minimal among workers who remained at the firm. We quantify the cost of losing highly productive employees and find that their heightened sensitivity to changes in compensation limits managers’ ability to adjust incentives. Our results speak to a driver of compensation rigidity and the difficulty managers face when setting compensation.