Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-Empowered Data Offloading in MEC-Enabled IoV Networks

Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network. This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Afterward, this article discusses challenges and future perspectives for IoV technologies.
Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather

Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non-stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather conditions especially with the traditional statistical approaches. Drawing on three-month smart card data in Brisbane, Australia, this research adopts and assesses a suite of machine-learning algorithms, i.e., random forest, eXtreme Gradient Boosting (XGBoost) and Tweedie XGBoost, to model and predict near real-time bus ridership in relation to sudden change of weather conditions. The study confirms that there indeed exists a significant level of spatio-temporal variability of weather-ridership relationship, which produces equally dynamic patterns of prediction errors. Further comparison of model performance suggests that Tweedie XGBoost outperforms the other two machine-learning algorithms in generating overall more accurate prediction outcomes in space and time. Future research may advance the current study by drawing on larger data sets and applying more advanced machine and deep-learning approaches to provide more enhanced evidence for real-time operation of transit systems.
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge the improvements in reliability that can be gained from their closer integration. Results derived using the popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation protocol is extended with spoofed trials. %subjected to spoofing attacks. However, even the straightforward integration of ASV and CM systems in the form of score-sum and deep neural network-based fusion strategies reduce the EER to 1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification (SASV) challenge has been formed to encourage greater attention to the integration of ASV and CM systems as well as to provide a means to benchmark different solutions.
Extensible Database Simulator for Fast Prototyping In-Database Algorithms

With the rapid increasing of data scale, in-database analytics and learning has become one of the most studied topics in data science community, because of its significance on reducing the gap between the management and the analytics of data. By extending the capability of database on analytics and learning, data scientists can save much time on exchanging data between databases and external analytic tools. For this goal, researchers are attempting to integrate more data science algorithms into database. However, implementing the algorithms in mainstream databases is super time-consuming, since the developers often have to make a deep dive into the database kernels. Thus there are demands for an easy-to-extend database simulator to help fast prototype and verify the in-database algorithms before implementing them in real databases. In this demo, we present such an extensible relational database simulator, DBSim, to help data scientists prototype their in-database analytics and learning algorithms and verify the effectiveness of their ideas with minimal cost. DBSim simulates a real relational database with all the major components of the mainstream RDBMS, including SQL parser, relational operators, query optimizer, etc. In addition, DBSim provides various interfaces for users to flexibly plug their custom extension modules into any of the major components, without modifying the kernel. By those interfaces, DBSim supports easy extensions on SQL syntax, relational operators, query optimizer rules and cost models, and physical plan execution. To enable accurate evaluation on users' extensions, DBSim supports connecting to real RDBMS and using their real-world cost estimators to calculate the query plan cost. Furthermore, DBSim provides utilities to facilitate users' developing and debugging, like query plan visualizer and interactive analyzer on optimization rules. We develop DBSim using pure Python to support seamless implementation of most data science algorithms into it, since many of them are written in Python.
Matrix-Analytic Methods for Solving Poisson's Equation with Applications to Markov Chains of GI/G/1-Type

In this paper, we are devoted to developing matrix-analytic methods for solving Poisson's equation for irreducible and positive recurrent discrete-time Markov chains (DTMCs). Two special solutions, including the deviation matrix D and the expected additive-type functional matrix K, will be considered. The results are applied to Markov chains of GI/G/1-type and MAP/G/1 queues with negative customers. Further extensions to continuous-time Markov chains (CTMCs) are also investigated.
Vehicle Models and Optimal Control on a Nonplanar Surface

We present a 10 DoF dynamic vehicle model for model-based control on nonplanar road surfaces. A parametric surface is used to describe the road surface, allowing the surface parameterization to describe the pose of the vehicle. We use the proposed approach to compute minimum-time vehicle trajectories on nonplanar surfaces and compare planar and nonplanar models.
Working memory inspired hierarchical video decomposition with transformative representations

Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e.g., extracting moving contrast-filled vessels from the complex and noisy backgrounds of X-ray coronary angiography (XCA). However, the challenges caused by dynamic backgrounds, overlapping heterogeneous environments and complex noises still exist in video decomposition. To solve these problems, this study is the first to introduce a flexible visual working memory model in video decomposition tasks to provide interpretable and high-performance hierarchical deep architecture, integrating the transformative representations between sensory and control layers from the perspective of visual and cognitive neuroscience. Specifically, robust PCA unrolling networks acting as a structure-regularized sensor layer decompose XCA into sparse/low-rank structured representations to separate moving contrast-filled vessels from noisy and complex backgrounds. Then, patch recurrent convolutional LSTM networks with a backprojection module embody unstructured random representations of the control layer in working memory, recurrently projecting spatiotemporally decomposed nonlocal patches into orthogonal subspaces for heterogeneous vessel retrieval and interference suppression. This video decomposition deep architecture effectively restores the heterogeneous profiles of intensity and the geometries of moving objects against the complex background interferences. Experiments show that the proposed method significantly outperforms state-of-the-art methods in accurate moving contrast-filled vessel extraction with excellent flexibility and computational efficiency.
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph

Building query graphs from natural language questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctly answered if its query graph is built correctly and the right answer is then retrieved by issuing the query graph against the KG. Therefore, this paper focuses on query graph generation from natural language questions. Existing approaches for query graph generation ignore the semantic structure of a question, resulting in a large number of noisy query graph candidates that undermine prediction accuracies. In this paper, we define six semantic structures from common questions in KGQA and develop a novel Structure-BERT to predict the semantic structure of a question. By doing so, we can first filter out noisy candidate query graphs by the predicted semantic structures, and then rank the remaining candidates with a BERT-based ranking model. Extensive experiments on two popular benchmarks MetaQA and WebQuestionsSP (WSP) demonstrate the effectiveness of our method as compared to state-of-the-arts.
What a designer can change: a proposal for a categorisation of artefact-related aspects

This paper proposes a categorisation of artefact-related aspects that in different ways set people’s preconditions for acting with technology. The categorisation can serve as a starting point for discussing which aspects are relevant to consider from a Design for Sustainable Behaviour perspective. The categorisation sorts these aspects into different layers: from the over-arching layer of enabled activity, through artefact type(s), operative functions, interactive functions, and finally communicative functions. Using examples from research studies, and a selection of theory and methods, we argue for an increased focus on all artefact- related aspects. Moreover, it is essential to move between layers to ensure a consistent design that in every layer provides preconditions for sustainable behaviour. The paper also discusses benefits of redesigning the artefacts people use in their everyday activities, instead of designing artefacts that stimulate new types of conservation activities.
Coverage Control for a Multi-robot Team with Heterogeneous Capabilities using Block Coordinate Descent (BCD) Method

In this paper, we propose a coverage control system for a multi-robot team with heterogeneous capabilities to patrol or monitor a bounded environment. The capability could be defined as any criterion of robots like remaining power or mobile speed, depending on the purpose. The proposed control system aims to allocate different portions of the environment to the robots according to their capabilities, i.e., the robot with higher capability takes a larger portion of the environment while the robot with lower capability takes a smaller one. We use the block coordinate descent (BCD) method to optimize the location of portions and the partitioning method alternately. A centralized machine is used to synchronize the robots and the gradient of each robot can be computed in a distributed manner. Simulation results are provided to illustrate the performance of the proposed control system.
Realtime dynamic binary instrumentation

We present a novel technique and framework for decreasing instrumentation overhead in software systems that utilize dynamic binary instrumentation. First, we introduce a lightweight networking framework combined with an easily extensible BSON implementation as a heavy analysis routine replacement. Secondly, we bind instrumentation and analysis threads to non-overlapping cpu cores--allowing analysis threads to execute faster. Lastly, we utilize a lock-free buffering system to bridge the gap between instrumentation and analysis threads, and minimize the overhead to the instrumentation threads. Using this combination, we managed to write a dynamic binary instrumentation tool (DBI) in Pin using Pin++ that is 1100% faster than its counterpart DBI tool with no buffering, and less than 500% slower than a similar tool with no analysis routine.
Interior estimates for the Virtual Element Method

We analyze the local accuracy of the virtual element method. More precisely, we prove an error bound similar to the one holding for the finite element method, namely, that the local $H^1$ error in a interior subdomain is bounded by a term behaving like the best approximation allowed by the local smoothness of the solution in a larger interior subdomain plus the global error measured in a negative norm.
Phase transition dimensionality crossover from two to three dimensions in a trapped ultracold atomic Bose gas

The equilibrium properties of a weakly interacting atomic Bose gas across the Berezinskii-Kosterlitz-Thouless (BKT) and Bose-Einstein condensation (BEC) phase transitions are numerically investigated through a dimensionality crossover from two to three dimensions. The crossover is realised by conﬁning the gas in an experimentally feasible hybridised trap which provides homo-geneity along the planar xy -directions through a box potential in tandem with a harmonic transverse potential along the transverse z -direction. The dimensionality is modiﬁed by varying the frequency of the harmonic trap from tight to loose transverse trapping. Our ﬁndings, based on a stochastic (projected) Gross-Pitaevskii equation, showcase a continuous shift in the character of the phase transition from BKT to BEC, and a monotonic increase of the identiﬁed critical temperature as a function of dimensionality, with the strongest variation exhibited for small chemical potential values up to approximately twice the transverse conﬁning potential.
Fairness in Graph Mining: A Survey

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we discuss current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.
The average size of maximal matchings in graphs

We investigate the ratio $\avM(G)$ of the average size of a maximal matching to the size of a maximum matching in a graph $G$. If many maximal matchings have a size close to $\maxM(G)$, this graph invariant has a value close to 1. Conversely, if many maximal matchings have a small size, $\avM(G)$ approaches $\frac{1}{2}$. We propose a general technique to determine the asymptotic behavior of $\avM(G)$ for various classes of graphs. To illustrate the use of this technique, we first show how it makes it possible to find known asymptotic values of $\avM(G)$ which were typically obtained using generating functions, and we then determine the asymptotic value of $\avM(G)$ for other families of graphs, highlighting the spectrum of possible values of this graph invariant between $\frac{1}{2}$ and $1$.
Inductive Coupling between Wires in Cables with a Grounded Conductor

The arrangement of conductors in a multi-core power cable leads to a situation where various conductor loops are built up. One or several loops are formed by the phase and neutral conductors with the operational current ∞owing in these conductors. A further loop is built up by the protective earth conductor which is connected to the equipotential bonding system at several locations. The area of this loop is essentially arranged outside of the power cable. The inductive coupling from the phase conductor loops into that loop causes common mode voltages in the protective earth system with consequent common mode currents. It can be demonstrated that this efiect takes place even in case of balanced phase currents in the cable. Numerical simulations and parameter studies were carried out in order to describe this efiect quantitatively and to investigate the in∞uence of difierent cable parameters onto the resulting common mode voltages.
Investigations of strength and leak tightness of wave-ring gaskets

The paper deals with stress-strain analysis and estimation of leak tightness of wave-ring gaskets. The investigations are carried out using the simplified analytical approach. A cylindrical shell of constant mean thickness is introduced to simulate the gasket. It is assumed that the shell is simply supported at the inner surface of the seat. The influence of certain geometric and assembly parameters on the strength and leak tightness of the closure is analytically investigated. The results are presented in dimensionless variables in order to generalise the conclusions. The analytical solution is verified by FEM calculations and compared with the experimental results.
A Systematic Review of the Epidemiology of Nonfatal A Systematic Review of the Epidemiology of Nonfatal Strangulation, a Human Rights and Health Concern Strangulation, a Human Rights and Health Concern

Abstract We reviewed the literature on the epidemiology of nonfatal strangulation (also, albeit incorrectly, called choking) by an intimate partner. We searched 6 electronic databases to identify cross-sectional, primary research studies from 1960 to 2014 that reported national prevalence estimates of nonfatal strangulation by an intimate partner among community-residing adults. Of 7260 identified references, 23 articles based on 11 self-reported surveys in 9 countries met the inclusion criteria. The percentage of women who reported ever having been strangled by an intimate partner ranged from 3.0% to 9.7%; past-year prevalence ranged from 0.4% to 2.4%, with 1.0% being typical. Although many epidemiological surveys inquire about strangulation, evidence regarding its prevalence is scarce. Modifying or adding a question to ongoing national surveys, particularly the Demographic and Health Surveys, would remedy the lack of data for low- and middle-income countries. In addition, when questions about strangulation are asked, findings should be reported rather than only combined with other questions to form broader categories (e.g., severe violence). Such action is merited because of the multiple negative short- and long-term sequelae of strangulation.
Using Mitogenomes to Explore the Social and Ecological Contexts of Crocodile Mummification in Ancient Egypt

Abstract We used isotopic and genomic data to explore the ecological and social context of cultural practices associated with the mummification of crocodiles in ancient Egypt. Ancient DNA was recovered from four mummified crocodile hatchlings held in the collections of the Peabody Museum of Natural History, Yale University. Previous genetic analyses of crocodile mummies have indicated that most mummies represent the newly resurrected taxon, Crocodylus suchus Geoffroy Saint-Hilaire, 1807. However, mitogenomic data for the Yale Peabody Museum mummies indicates that these specimens represent the first genomically authenticated representatives of the Nile crocodile (Crocodylus niloticus Laurenti, 1768) in museum collections. We explore these findings within the broader context of modern and historical distributions of both crocodile species and the potential implications for our understanding of funerary practices involving crocodiles in ancient Egypt.
Exclusion of Overabundant White-tailed Deer (Odocoileus virginianus) Results in Shifts in Soil Microbial Communities and Abiotic Soil Condition in a Northeastern Deciduous Forest

Abstract. Past and current anthropogenic practices have resulted in dramatic alterations to ungulate population densities worldwide. When ungulate populations are overabundant, they can alter the dynamics, structure, and function of ecosystems. White-tailed deer (Odocoileus virginianus), specifically, can occur at densities far greater than their historical records in parts of their native range, which includes forests in the northeastern U.S. They have been shown to alter community structure of native plants, indirectly impact animal communities, and promote the success of invasive species. Despite much research into the effects of overabundant ungulates and deer in particular, less is known about the effects of deer on soil microbial communities. Here, we utilized soil samples from inside and outside of six deer exclosures located in a regional second growth mixed hardwood forest on the Binghamton University campus in Vestal NY, U.S.A. A metagenomic analysis was conducted on DNA extracted from the soil to identify the microbes present. Soil characteristics, including soil organic matter, soil moisture, pH, and electrical conductivity were also measured. Soil samples from inside exclosures had on average lower pH, higher soil moisture and organic matter, and higher electrical conductivity. The microbial communities across all samples were dominated by Proteobacteria, Acidobacteria, and Actinobacteria. However, the structure of the microbial soil community appeared to differ between samples taken inside and outside the exclosures, with those taken outside more closely resembling other outside samples and those sampled inside soils showing more variability in community structure. Overall, our results suggest that overabundant deer may have a homogenization effect on the soil abiotic environment and the soil microbial community.