bluuebunny/crossref_metadata_2025 huggingface dataset:
How Strategic Trade Policy Leads to Mixed Cournot–Bertrand Competition?
Authors: Patrice Cassagnard , Mamadou Thiam
Published: December 2023

This paper revisits the link between strategic trade policies and the mode of competition in the product market, emphasizing the emergence of a different mode of competition if only one of the two governments implements such a policy. We show that with an endogenous mode of competition and an asymmetric strategic export policy we can lead to a mixed Cournot–Bertrand competition between firms. If the game is sequential and the second government wishes to react to this policy, we show that the final equilibrium will be even more unfavorable to the newly protected national leader: the second firm would not prefer this retaliation to be implemented. This reinforces the interest of the asymmetrical framework of this model.
Performance of Presidential Candidates in a Debate: A Game Theory Perspective
Authors: Schimit P. H. T.
Published: December 2023

In the last moments of the highly polarized and contentious 2022 presidential elections campaigns, two final debates took place for the second-round of the election. This paper proposes an analysis of these debates using an iterated game theory perspective, where the debates are sequential decision-making environments, with the candidates’ statements being classified as cooperative or defection. Cooperative actions denote constructive dialogue revolving around governmental plans, whereas defection encompasses negative campaigning tactics, including ad hominem attacks and the spread of misleading information. Through an exhaustive analysis of transcriptions from both debates, the candidates’ strategic moves are extracted and categorized. The findings offer new insights into the role of strategies in shaping electoral outcomes, emphasizing the nuanced interplay of policy discourse and personal character assessments in a politically charged environment. Additionally, the research underscores the profound implications of such strategies on voter perceptions and decisions, offering a new perspective to the existing literature on political communication and electoral game theory.
The Adjustable Bandgap of Novel Local Resonance Sandwich-Like Metamaterial Plates with Buckling Beam Oscillators
Authors: Guo Xiangying , Yang Dongshuo , Zhang Weixing , Cao Dongxing
Published: December 2023

Sandwich structures possess a high stiffness-to-mass ratio, which facilitates flexural wave propagation at lower frequencies and enhances their vibration isolation and sound insulation capabilities, therefore, sandwich-like metamaterial plates with the nonlinear local oscillator is constructed in this paper to obtain adjustable bandgap in low-frequency. The buckling beam oscillator consists of a small mass, linear support spring, and buckling elastic elements for nonlinear stiffness. First, governing equations of flexural vibrations of plates are deduced based on the Hamilton principle. By utilizing the harmonic balance method (HBM), the dispersion relation of a sandwich-like plate can be determined. Theoretical analysis reveals the presence of structural bandgap ranges at lower frequencies, which are subject to the excitation amplitudes. And in order to validate the theoretical findings, the finite element method (FEM) is employed. Moreover, the influence of structural parameters, including oscillator mass, linear spring stiffness, buckling beam angle, and Young’s modulus, are conducted to enhance the vibration control in the low-frequency. Interestingly, it is observed that the effectiveness of low-frequency suppression in the nonlinear metamaterial plate varies significantly across different boundary conditions. Therefore, the sandwich structures with nonlinear local oscillators can provide an effective way to suppress low-frequency vibration and maintain dynamic stability.
Detection of Silica in Rice Husks Using Laser-Induced Plasma and Studying the Effect of Laser Energy on the Parameters of the Produced Plasma
Authors: Abdalameer Nisreen Kh. , Yasoob N. A. , Mohammed A. Qahtan
Published: December 2023

This study aims to analyze the spectral properties of plasma produced from rice husk(Rh) using the laser breakdown spectroscopy (LIBS) method. The plasma generation process used the fundamental harmonic (1064 nm) of a Q-switched Nd:YAG laser. Yttrium aluminum garnet (YAG) is a man-made crystalline material. The laser fired pulses with a duration of 10 ns and a repetition rate of 6 Hz. Thus, the energy outputs achieved were 50–200 mJ at the wavelength of 1064 (nm). The silica content in the rice hulls was verified using an XRF measurement, which revealed the presence of silica in the rice hulls in a high percentage. Precise beam focusing was achieved by focusing the laser on the target material. This target material is placed within an atmospheric environment at standard pressure settings. The electron temperature was derived using the Boltzmann diagram method by harnessing experimental data for the linear properties associated with the neutral lines (Si II), (O II), and ion lines (Si I). The use of analytical methodology led to the determination of electron temperature values from 0.79 eV to 1.16 eV for the fundamental harmonic of the laser. At the same time, the electron (ne) density was determined by analyzing the Stark broadening profile associated with the neutral silica line. Furthermore, the study included an additional dimension by determining the plasma properties (electron temperature and electron density) by adjusting the laser energy on the target surface longitudinally along the path of the plasma plume.
Scope of Biological Property Activated Plant Extracted Nanoparticles for Human Immune Response — A Review
Authors: Hossain Nayem , Chowdhury Mohammad Asaduzzaman , Kchaou Mohamed , Sultana Sadia , Siddiky Abu Yousouf , Mobarak Md Hosne , Rahman Mohammed M.
Published: December 2023

Due to their small size, nanoparticles have brought a new era for researchers. They are utilized in different areas, including the food industry, pharmaceuticals, and medicals, for their outstanding properties. Many medicines are also used to treat patients with various diseases. Many researchers are continuing their research to find more applications of these materials. This paper reviews how nanoparticles synthesized from plants can help the immune response. Different nanoparticles have been initially studied with their source of origin, properties, and applications in the immune response. Then, how various nanoparticles work on the immune response has been briefly discussed. Different applications of nanoparticles on immune response with different perceptions have also been considered. The results of the applications of these particles have been debated as well. In the end, these particles’ potential challenges and future opportunities for immune response have been reviewed. This review paper will work as a guide for nanoparticle researchers in immunity.
PACMMOD Volume 1 Issue 4: Editorial
Authors: Agrawal Divyakant , Meliou Alexandra , Sudarshan S.
Published: December 2023

Welcome to this issue of the Proceedings of the ACM on Management of Data (Volume 1, Issue 4 (SIGMOD)). While this issue has papers from the SIGMOD track, PACMMOD will soon also have issues with papers from the newly created PODS track. Out of 189 submissions to the round of reviewing for the PACMMOD SIGMOD track whose submission deadline was April 15, 2023, a total of 49 articles were accepted, and are presented in this issue.
Cackle: Analytical Workload Cost and Performance Stability With Elastic Pools
Authors: Perron Matthew , Castro Fernandez Raul , DeWitt David , Cafarella Michael , Madden Samuel
Published: December 2023

Analytical query workloads are prone to rapid fluctuations in resource demands. These rapid, hard to predict resource demand changes make provisioning a challenge. Users must either over provision at excessive cost or suffer poor query latency when demand spikes. Prior work shows the viability of using cloud functions to match the supply of compute to the workload demand without provisioning resources ahead of time. For low query volumes, this approach is less costly at reasonable performance compared to provisioned systems, but as query volumes increase the cost overhead of cloud functions outweighs the benefit gained by rapid elasticity. In this work, we propose a novel strategy combining rapidly scalable but expensive resources with slow to start but inexpensive virtual machines to gain the benefit of elasticity without losing out on the cost savings of provisioned resources. We demonstrate a technique that minimizes cost over a wide range of workloads, environmental conditions, and compute costs while providing stable query performance. We implement these ideas in Cackle and demonstrate that it achieves similar performance and cost per query across a wide range of workloads, avoiding the cost and performance cliffs of alternative approaches.
Hierarchical Cut Labelling - Scaling Up Distance Queries on Road Networks
Authors: Farhan Muhammad , Koehler Henning , Ohms Robert , Wang Qing
Published: December 2023

Answering the shortest-path distance between two arbitrary locations is a fundamental problem in road networks. Labelling-based solutions are the current state-of-the-arts to render fast response time, which can generally be categorised into hub-based labellings, highway-based labellings, and tree decomposition labellings. Hub-based and highway-based labellings exploit hierarchical structures of road networks with the aim to reduce labelling size for improving query efficiency. However, these solutions still result in large search spaces on distance labels at query time, particularly when road networks are large. Tree decomposition labellings leverage a hierarchy of vertices to reduce search spaces over distance labels at query time, but such a hierarchy is generated using tree decomposition techniques, which may yield very large labelling sizes and slow querying. In this paper, we propose a novel solution hierarchical cut 2-hop labelling (HC2L) to address the drawbacks of the existing works. Our solution combines the benefits of hierarchical structures from both perspectives - reduce the size of a distance labelling at preprocessing time and further reduce the search space on a distance labelling at query time. At its core, we propose a new hierarchy, balanced tree hierarchy, which enables a fast, efficient data structure to reduce the size of distance labelling and to select a very small subset of labels to compute the shortest-path distance at query time. To speed up the construction process of HC2L, we further propose a parallel variant of our method, namely HC2L^p. We have evaluated our solution on 10 large real-world road networks through extensive experiments. The results show that our method is 1.5-4 times faster in terms of query processing while being comparable in terms of labelling construction time and achieving up to 60% smaller labelling size compared to the state-of-the-art approaches.
High-Ratio Compression for Machine-Generated Data
Authors: Zhang Jiujing , Shen Zhitao , Yang Shiyu , Meng Lingkai , Xiao Chuan , Jia Wei , Li Yue , Sun Qinhui , Zhang Wenjie , Lin Xuemin
Published: December 2023

Machine-generated data is rapidly growing and poses challenges for data-intensive systems, especially as the growth of data outpaces the growth of storage space. To cope with the storage issue, compression plays a critical role in storage engines, particularly for data-intensive applications, where a high compression ratio and efficient random access are essential. However, existing compression techniques tend to focus on general-purpose and data block approaches, but overlook the inherent structure of machine-generated data and hence result in low compression ratios or limited lookup efficiency. To address these limitations, we introduce the Pattern-Based Compression (PBC) algorithm, which specifically targets patterns in machine-generated data to achieve Pareto-optimality in most cases. Unlike traditional data block-based methods, PBC compresses data on a per-record basis, facilitating rapid random access. Our experimental evaluation demonstrates that PBC, on average, achieves a compression ratio twice as high as the state-of-the-art techniques while maintaining competitive compression and decompression speeds. We also integrate PBC to a production database system and achieve improvements on both comparison ratio and throughput.
Practical Dynamic Extension for Sampling Indexes
Authors: Rumbaugh Douglas B. , Xie Dong
Published: December 2023

The execution of analytical queries on massive datasets presents challenges due to long response times and high computational costs. As a result, the analysis of representative samples of data has emerged as an attractive alternative; this avoids the cost of processing queries against the entire dataset, while still producing statistically valid results. Unfortunately, the sampling techniques in common use sacrifice either sample quality or performance, and so are poorly suited for this task. However, it is possible to build high quality sample sets efficiently with the assistance of indexes. This introduces a new challenge: real-world data is subject to continuous update, and so the indexes must be kept up to date. This is difficult, because existing sampling indexes present a dichotomy; efficient sampling indexes are difficult to update, while easily updatable indexes have poor sampling performance. This paper seeks to address this gap by proposing a general and practical framework for extending most sampling indexes with efficient update support, based on splitting indexes into smaller shards, combined with a systematic approach to the periodic reconstruction. The framework's design space is examined, with an eye towards exploring trade-offs between update performance, sampling performance, and memory usage. Three existing static sampling indexes are extended using this framework to support updates, and the generalization of the framework to concurrent operations and larger-than-memory data is discussed. Through a comprehensive suite of benchmarks, the extended indexes are shown to match or exceed the update throughput of state-of-the-art dynamic baselines, while presenting significant improvements in sampling latency.
Rethinking the Encoding of Integers for Scans on Skewed Data
Authors: Prammer Martin , Patel Jignesh M.
Published: December 2023

Bit-parallel scanning techniques are characterized by their ability to accelerate compute through the process known as early pruning. Early pruning techniques iterate over the bits of each value, searching for opportunities to safely prune compute early, before processing each data value in its entirety. However, because of this iterative evaluation, the effectiveness of early pruning depends on the relative position of bits that can be used for pruning within each value. Due to this behavior, bit-parallel techniques have faced significant challenges when processing skewed data, especially when values contain many leading zeroes. This problem is further amplified by the inherent trade-off that bit-parallel techniques make between columnar scan and fetch performance: a storage layer that supports early pruning requires multiple memory accesses to fetch a single value. Thus, in the case of skewed data, bit-parallel techniques increase fetch latency without significantly improving scan performance when compared to baseline columnar implementations.

To remedy this shortcoming, we transform the values in bit-parallel columns using novel encodings. We propose the concept of forward encodings: a family of encodings that shift pruning-relevant bits closer to the most significant bit. Using this concept, we propose two particular encodings: the Data Forward Encoding and the Extended Data Forward Encoding. We demonstrate the impact of these encodings using multiple real-world datasets. Across these datasets, forward encodings improve the current state-of-the-art bit-parallel technique's scan and fetch performance in many cases by 1.4x and 1.3x, respectively.
Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite Networks
Authors: Zhang Fangyuan , Chen Dechuang , Wang Sibo , Yang Yin , Gan Junhao
Published: December 2023

A bipartite graph is a graph that consists of two disjoint sets of vertices and only edges between vertices from different vertex sets. In this paper, we study the counting problems of two common types of em motifs in bipartite graphs: (i) butterflies (2x2 bicliques) and (ii) bi-triangles (length-6 cycles). Unlike most of the existing algorithms that aim to obtain exact counts, our goal is to obtain precise enough estimations of these counts in bipartite graphs, as such estimations are already sufficient and of great usefulness in various applications. While there exist approximate algorithms for butterfly counting, these algorithms are mainly based on the techniques designed for general graphs, and hence, they are less effective on bipartite graphs. Not to mention that there is still a lack of study on approximate bi-triangle counting. Motivated by this, we first propose a novel butterfly counting algorithm, called one-sided weighted sampling, which is tailored for bipartite graphs. The basic idea of this algorithm is to estimate the total butterfly count with the number of butterflies containing two randomly sampled vertices from the same side of the two vertex sets. We prove that our estimation is unbiased, and our technique can be further extended (non-trivially) for bi-triangle count estimation. Theoretical analyses under a power-law random bipartite graph model and extensive experiments on multiple large real datasets demonstrate that our proposed approximate counting algorithms can reach high accuracy, yet achieve up to three orders (resp. four orders) of magnitude speed-up over the state-of-the-art exact butterfly (resp. bi-triangle) counting algorithms. Additionally, we present an approximate clustering coefficient estimation framework for bipartite graphs, which shows a similar speed-up over the exact solutions with less than 1% relative error.
Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes
Authors: Meng Zizhong , Cao Xin , Cong Gao
Published: December 2023

Selectivity estimation aims to estimate the size of query results size accurately and efficiently. Despite being a well-researched area for decades, most existing estimators are designed to handle comparison predicates over numeric and categorical data. Nevertheless, Set-valued data are ubiquitous in many applications such as information retrieval and recommendation systems. However, these estimators may not be effective for handling predicates over set-valued data. In this work, we presents novel techniques for selectivity estimation on queries involving predicates over set-valued attributes. We first propose the set-valued column factorization problem, whereby each each set-valued column is converted to multiple numeric subcolumns, and set containment predicates are converted to numeric comparison predicates. This enables us to leverage any existing estimator to perform selectivity estimation. We then develop two methods for column factorization and query conversion, namely ST and STH. We integrate ST and STH into three estimators, Postgres, Neurocard, and DeepDB. We then conduct a comprehensive empirical analysis by comparing our approach against three baselines across three different datasets. The experimental results demonstrate that our methods exhibit superior estimation accuracy while maintaining high efficiency compared to the baseline techniques.
Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach
Authors: Wang Qiming , Castro Fernandez Raul
Published: December 2023

Most deployed data discovery systems, such as Google Datasets, and open data portals only support keyword search. Keyword search is geared towards general audiences but limits the types of queries the systems can answer. We propose a new system that lets users write natural language questions directly. A major barrier to using this learned data discovery system is it needs expensive-to-collect training data, thus limiting its utility.

In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, Solo, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on well-known benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems.
Waffle: An Online Oblivious Datastore for Protecting Data Access Patterns
Authors: Maiyya Sujaya , Vemula Sharath Chandra , Agrawal Divyakant , El Abbadi Amr , Kerschbaum Florian
Published: December 2023

We present Waffle, a datastore that protects an application's data access patterns from a passive persistent adversary. Waffle achieves this without prior knowledge of the input data access distribution, making it the first of its kind to adaptively handle input sequences under a passive persistent adversary. Waffle maintains a constant bandwidth and client-side storage overhead, which can be adjusted to suit the application owner's preferences. This flexibility allows the owner to fine-tune system parameters and strike a balance between security and performance. Our evaluation, utilizing the Yahoo! Cloud Serving Benchmark (YCSB) benchmark and Redis as the backend storage, demonstrates promising results. The insecure baseline outperforms Waffle by a mere 5-6x, whereas Waffle outperforms Pancake-a state-of-the-art oblivious datastore under passive persistent adversaries-by 45-57%, and a concurrent ORAM system, TaoStore, by 102x.
VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation
Authors: Zhao Zhanhao , Pan Hexiang , Chen Gang , Du Xiaoyong , Lu Wei , Ooi Beng Chin
Published: December 2023

Cloud-native databases become increasingly popular while exposing to greater data security and correctness risks. Existing verifiable outsourced databases overlook either the correctness risk of transactions, or the disaggregation architecture: a key design consideration of cloud-native databases for performance and elasticity, or both. We present VeriTxn, a novel cloud-native database that efficiently provides verifiability of transaction correctness. VeriTxn relies on the trusted hardware (i.e., Intel SGX) to enable verifiable transaction processing. We build a page-structure cache in the trusted domain, where transactions can be verified with low, constant overhead. VeriTxn further optimizes the read-only transactions by exploiting disaggregation to fit the read-heavy workload in the cloud. We also integrate our proposal into MySQL, a popular open-source database. We conduct extensive experiments to compare VeriTxn against state-of-the-art verifiable databases and evaluate the performance of VeriTxn on MySQL. The results show that VeriTxn introduces tolerable performance degradation for verifiable transactions, while achieving up to 7.03× and 7.93× higher throughput than Litmus and LedgerDB, and its sustainable performance when integrated with MySQL.
Lightweight Acquisition and Ranging of Flows in the Data Plane
Authors: Monterubbiano Andrea , Langlet Jonatan , Walzer Stefan , Antichi Gianni , Reviriego Pedro , Pontarelli Salvatore
Published: December 2023

As networks get more complex, the ability to track almost all the flows is becoming of paramount importance. This is because we can then detect transient events impacting only a subset of the traffic. Solutions for flow monitoring exist, but it is getting very difficult to produce accurate estimations for every  tuple given the memory constraints of commodity programmable switches. Indeed, as networks grow in size, more flows have to be tracked, increasing the number of tuples to be recorded. At the same time, end-host virtualization requires more specific flowIDs, enlarging the memory cost for every single entry. Finally, the available memory resources have to be shared with other important functions as well (e.g., load balancing, forwarding, ACL).

To address those issues, we present FlowLiDAR (Flow Lightweight Detection and Ranging), a new solution that is capable of tracking almost all the flows in the network while requiring only a modest amount of data plane memory which is not dependent on the size of flowIDs. We implemented the scheme in P4, tested it using real traffic from ISPs and compared it against four state-of-the-art solutions: FlowRadar, NZE, PR-sketch, and Elastic Sketch. While those can only reconstruct up to 60% of the tuples, FlowLiDAR can track 98.7% of them with the same amount of memory.
Nautilus: A Framework for Cross-Layer Cartography of Submarine Cables and IP Links
Authors: Ramanathan Alagappan , Abdu Jyothi Sangeetha
Published: December 2023

Submarine cables constitute the backbone of the Internet. However, these critical infrastructure components are vulnerable to several natural and man-made threats, and during failures, are difficult to repair in remote oceans. In spite of their crucial role, we have a limited understanding of the impact of submarine cable failures on global connectivity, particularly on the higher layers of the Internet.

In this paper, we present Nautilus, a framework for cross-layer cartography of submarine cables and IP links. Using a corpus of public datasets and Internet cartographic techniques, Nautilus identifies IP links that are likely traversing submarine cables and maps them to one or more potential cables. Nautilus also gives each IP to cable assignment a prediction score that reflects the confidence in the mapping. Nautilus generates a mapping for 3.05 million and 1.43 million IPv4 and IPv6 links, respectively, spanning 91% of all active cables. In the absence of ground truth data, we validate Nautilus mapping using three techniques: analyzing past cable failures, using targeted traceroute measurements, and comparing with public network maps of two operators.
Near-Optimal Stochastic Bin-Packing in Large Service Systems with Time-Varying Item Sizes
Authors: Hong Yige , Xie Qiaomin , Wang Weina
Published: December 2023

In modern computing systems, jobs' resource requirements often vary over time. Accounting for this temporal variability during job scheduling is essential for meeting performance goals. However, theoretical understanding on how to schedule jobs with time-varying resource requirements is limited. Motivated by this gap, we propose a new setting of the stochastic bin-packing problem in service systems that allows for time-varying job resource requirements, also referred to as 'item sizes' in traditional bin-packing terms. In this setting, a job or 'item' must be dispatched to a server or 'bin' upon arrival. Its resource requirement may vary over time while in service, following a Markovian assumption. Once the job's service is complete, it departs from the system. Our goal is to minimize the expected number of active servers, or 'non-empty bins', in steady state.

Under our problem formulation, we develop a job dispatch policy, named Join-Reqesting-Server (JRS). Broadly, JRS lets each server independently evaluate its current job configuration and decide whether to accept additional jobs, balancing the competing objectives of maximizing throughput and minimizing the risk of resource capacity overruns. The JRS dispatcher then utilizes these individual evaluations to decide which server to dispatch each arriving job to. The theoretical performance guarantee of JRS is in the asymptotic regime where the job arrival rate scales large linearly with respect to a scaling factor r. We show that JRS achieves an additive optimality gap of O(√r) in the objective value, where the optimal objective value is Θ(r). When specialized to constant job resource requirements, our result improves upon the state-of-the-art o(r) optimality gap. Our technical approach highlights a novel policy conversion framework that reduces the policy design problem into a single-server problem.
Near-Optimal Packet Scheduling in Multihop Networks with End-to-End Deadline Constraints
Authors: Tsanikidis Christos , Ghaderi Javad
Published: December 2023

Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms were shown to achieve Ω(1/log(L)) fraction of the optimal objective value if the minimum link capacity in the network is Cmin =Ω(log (L)), where L is the maximum length of a packet's route in the network (which is bounded by the packet's maximum deadline). However, such guarantees can be quite pessimistic due to the strict worst-case traffic assumption and may not accurately reflect real-world settings. In this work, we aim to address this limitation by exploring whether it is possible to design algorithms that achieve a constant fraction of the optimal value while relaxing the worst-case traffic assumption. We provide a positive answer by demonstrating that in stochastic traffic settings, such as i.i.d. packet arrivals, near-optimal, (1-ε)-approximation algorithms can be designed if Cmin = Ω(log (L/ε)/ε2). To the best of our knowledge, this is the first result that shows this problem can be solved near-optimally under nontrivial assumptions on traffic and link capacity. We further present extended simulations using real network traces with non-stationary traffic, which demonstrate that our algorithms outperform worst-case-based algorithms in practical settings.
Towards Understanding and Characterizing the Arbitrage Bot Scam In the Wild
Authors: Li Kai , Guan Shixuan , Lee Darren
Published: December 2023

This paper presents the first comprehensive analysis of an emerging cryptocurrency scam named "arbitrage bot" disseminated on online social networks. The scam revolves around Decentralized Exchanges (DEX) arbitrage and aims to lure victims into executing a so-called "bot contract" to steal funds from them. To entice victims and convince them of this scheme, we found that scammers have flocked to publish YouTube videos to demonstrate plausible profits and provide detailed instructions and links to the bot contract.

To collect the scam at a large scale, we developed a fully automated scam detection system namedCryptoScamHunter, which continuously collects YouTube videos and automatically detects scams. Meanwhile,CryptoScamHunter can download the source code of the bot contract from the provided links and extract the associated scam cryptocurrency address. Through deployingCryptoScamHunter from Jun. 2022 to Jun. 2023, we have detected 10,442 arbitrage bot scam videos published from thousands of YouTube accounts. Our analysis reveals that different strategies have been utilized in spreading the scam, including crafting popular accounts, registering spam accounts, and using obfuscation tricks to hide the real scam address in the bot contracts. Moreover, from the scam videos we have collected over 800 malicious bot contracts with source code and extracted 354 scam addresses. By further expanding the scam addresses with a similar contract matching technique, we have obtained a total of 1,697 scam addresses. Through tracing the transactions of all scam addresses on the Ethereum mainnet and Binance Smart Chain, we reveal that over 25,000 victims have fallen prey to this scam, resulting in a financial loss of up to 15 million USD.

Overall, our work sheds light on the dissemination tactics and censorship evasion strategies adopted in the arbitrage bot scam, as well as on the scale and impact of such a scam on online social networks and blockchain platforms, emphasizing the urgent need for effective detection and prevention mechanisms against such fraudulent activity.
Change Point Detection with Adaptive Measurement Schedules for Network Performance Verification
Authors: Lindståhl Simon , Proutiere Alexandre , Johnsson Andreas
Published: December 2023

When verifying that a communications network fulfills its specified performance, it is critical to note sudden shifts in network behavior as quickly as possible. Change point detection methods can be useful in this endeavor, but classical methods rely on measuring with a fixed measurement period, which can often be suboptimal in terms of measurement costs. In this paper, we extend the existing framework of change point detection with a notion of physical time. Instead of merely deciding when to stop, agents must now also decide at which future time to take the next measurement. Agents must now minimize the necessary number of measurements pre- and post-change, while maintaining a trade-off between post-change delay and false alarm rate. We establish, through this framework, the suboptimality of typical periodic measurements and propose a simple alternative, called crisis mode agents. We show analytically that crisis mode agents significantly outperform periodic measurements schemes. We further verify this in numerical evaluation, both on an array of synthetic change point detection problems as well as on the problem of detecting traffic load changes in a 5G test bed through end-to-end RTT measurements.
MetaVRadar: Measuring Metaverse Virtual Reality Network Activity
Authors: Lyu Minzhao , Tripathi Rahul Dev , Sivaraman Vijay
Published: December 2023

The "metaverse", wherein users can enter virtual worlds to work, study, play, shop, socialize, and entertain, is fast becoming a reality, attracting billions of dollars in investment from companies such as Meta, Microsoft, and Clipo Labs. Further, virtual reality (VR) headsets from entities like Oculus, HTC, and Microsoft are rapidly maturing to provide fully immersive experiences to metaverse users. However, little is known about the network dynamics of metaverse VR applications in terms of service domains, flow counts, traffic rates and volumes, content location and latency, etc., which are needed to make telecommunications network infrastructure "metaverse ready" to support superlative user experience in the coming future.

This paper is an empirical measurement study of metaverse VR network behavior aimed at helping telecommunications network operators better provision and manage the network to ensure good user experience. Using illustrative hour-long network traces of metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, and undertake a detailed analysis of network traffic per state, identifying unique service domains, protocols, flow profiles, and volumetric patterns, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Armed with the network behavioral profiles, our second contribution develops a real-time methodMetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implementsMetaVRadar, evaluates its accuracy in our lab environment, and demonstrates its usability in a large university network so operators can better monitor and plan resources to support requisite metaverse user experience.
GuaNary: Efficient Buffer Overflow Detection In Virtualized Clouds Using Intel EPT-based Sub-Page Write Protection Support
Authors: Bitchebe Stella , Kone Yves , Olivier Pierre , Boukhobza Jalil , Bromberg Yérom-David , Hagimont Daniel , Tchana Alain
Published: December 2023

Write buffer overflow is a widespread and prevalent memory safety violation in C/C++, reported as the top vulnerability in 2022 and 2023. Secure memory allocators are generally used to protect systems against attacks that may exploit buffer overflows. Existing allocators mainly rely on two types of countermeasures to prevent or detect write overflows: canaries and guard pages, each with pros and cons in terms of detection latency and memory footprint.

For virtualized cloud applications, this paper follows the Out of Hypervisor (OoH) trend and introduces GuaNary, a safety guard against write overflows, allowing synchronous detection at a low memory footprint cost. OoH is a new virtualization research axis introduced in 2022 advocating the exposure of hardware features for virtualization to the guest OS so that its processes can take advantage of them. Based on the OoH principle, GuaNary leverages Intel Sub-Page write Permission (SPP), a recent hardware virtualization feature that allows to write-protect guest memory at the granularity of 128B (namely, sub-page) instead of 4KB. We implement a software stack, LeanGuard, which promotes the utilization of SPP from inside virtual machines by new secure allocators that use GuaNary. Our evaluation shows that for the same number of protected buffers, LeanGuard consumes 8.3× less memory than SlimGuard, a recent state-of-art secure allocator. Further, for the same memory consumption, LeanGuard allows protecting 25× more buffers than SlimGuard.
A Large Scale Study and Classification of VirusTotal Reports on Phishing and Malware URLs
Authors: Choo Euijin , Nabeel Mohamed , Kim Doowon , De Silva Ravindu , Yu Ting , Khalil Issa
Published: December 2023

VirusTotal (VT) is a widely used scanning service for researchers and practitioners to label malicious entities and predict new security threats. Unfortunately, it is little known to the end-users how VT URL scanners decide on the maliciousness of entities and the attack types they are involved in (e.g., phishing or malware-hosting websites). In this paper, we conduct a systematic comparative study on VT URL scanners' behavior for different attack types of malicious URLs, in terms of 1) detection specialties, 2) stability, 3) correlations between scanners, and 4) lead/lag behaviors. Our findings highlight that the VT scanners commonly disagree with each other on their detection and attack type classification, leading to challenges in ascertaining the maliciousness of a URL and taking prompt mitigation actions according to different attack types. This motivates us to present a new highly accurate classifier that helps correctly identify the attack types of malicious URLs at the early stage. This in turn assists practitioners in performing better threat aggregation and choosing proper mitigation actions for different attack types
Sampling for Remote Estimation of the Wiener Process over an Unreliable Channel
Authors: Pan Jiayu , Sun Yin , Shroff Ness B.
Published: December 2023

In this paper, we study a sampling problem where a source takes samples from a Wiener process and transmits them through a wireless channel to a remote estimator. Due to channel fading, interference, and potential collisions, the packet transmissions are unreliable and could take random time durations. Our objective is to devise an optimal causal sampling policy that minimizes the long-term average mean square estimation error. This optimal sampling problem is a recursive optimal stopping problem, which is generally quite difficult to solve. However, we prove that the optimal sampling strategy is, in fact, a simple threshold policy where a new sample is taken whenever the instantaneous estimation error exceeds a threshold. This threshold remains a constant value that does not vary over time. By exploring the structure properties of the recursive optimal stopping problem, a low-complexity iterative algorithm is developed to compute the optimal threshold. This work generalizes previous research by incorporating both transmission errors and random transmission times into remote estimation. Numerical simulations are provided to compare our optimal policy with the zero-wait and age-optimal policies.
Invertible Bloom Lookup Tables with Listing Guarantees
Authors: Mizrahi Avi , Bar-Lev Daniella , Yaakobi Eitan , Rottenstreich Ori
Published: December 2023

The Invertible Bloom Lookup Table (IBLT) is a probabilistic concise data structure for set representation that supports a listing operation as the recovery of the elements in the represented set. Its applications can be found in network synchronization and traffic monitoring as well as in error-correction codes. IBLT can list its elements with probability affected by the size of the allocated memory and the size of the represented set, such that it can fail with small probability even for relatively small sets. While previous works only studied the failure probability of IBLT, this work initiates the worst case analysis of IBLT that guarantees successful listing for all sets of a certain size. The worst case study is important since the failure of IBLT imposes high overhead. We describe a novel approach that guarantees successful listing when the set satisfies a tunable upper bound on its size. To allow that, we develop multiple constructions that are based on various coding techniques such as stopping sets and the stopping redundancy of error-correcting codes, and Steiner systems as well as new methodologies we develop. We analyze the sizes of IBLTs with listing guarantees obtained by the various methods as well as their mapping memory and runtime overheads. Lastly, we study lower bounds on the achievable sizes of IBLT with listing guarantees and verify the results in the paper by simulations.
Automated Backend Allocation for Multi-Model, On-Device AI Inference
Authors: Iyer Venkatraman , Lee Sungho , Lee Semun , Kim Juitem Joonwoo , Kim Hyunjun , Shin Youngjae
Published: December 2023

On-Device Artificial Intelligence (AI) services such as face recognition, object tracking and voice recognition are rapidly scaling up deployments on embedded, memory-constrained hardware devices. These services typically delegate AI inference models for execution on CPU and GPU computing backends. While GPU delegation is a common practice to achieve high speed computation, the approach suffers from degraded throughput and completion times under multi-model scenarios, i.e. concurrently executing services. This paper introduces a solution to sustain performance in multi-model, on-device AI contexts by dynamically allocating a combination of CPU and GPU backends per model. The allocation is feedback-driven, and guided by a knowledge of model-specific, multi-objective pareto fronts comprising inference latency and memory consumption. Primary contribution of this paper is a backend allocation algorithm that runs online per model, and achieves 25-100% improvement in throughput over static allocations as well as load-balancing scheduler solutions targeting multi-model scenarios. Other noteworthy contributions include a novel pareto front estimator that runs on-device, and also a software-based GPU profiler with a lightweight algorithm to detect changing GPU workloads. Specifically, the pareto front estimator outperforms state of the art algorithms NSGA-II and SPEA2 by 94% on pareto coverage, and by almost 2x on computational overhead.
A Developmental Social Neuroscience Perspective on Infant Autism Interventions
Authors: Dawson Geraldine , Rieder Amber D. , Johnson Mark H.
Published: December 2023

Research on early biomarkers and behavioral precursors of autism has led to interventions initiated during the infant period that could potentially change the course of infant brain and behavioral development in autism. This article integrates neuroscience and clinical perspectives to explore how knowledge of infant brain and behavioral development can inform the design of infant autism interventions. Focusing on infants ≤12 months, we review studies on behavioral precursors of autism and their neural correlates and clinical trials evaluating the efficacy of infant autism interventions. We then consider how contemporary developmental social neuroscience theories of autism can shed light on the therapeutic strategies used in infant autism interventions and offer a new perspective that emphasizes improving child outcome and well-being by enhancing infant–environment fit. Finally, we offer recommendations for future research that incorporates brain-based measures to inform individualized approaches to intervention and discuss ethical issues raised by infant autism interventions. Readers are referred to Supplemental Table 1 for a glossary of terms used in this article.
Paramecium Genetics, Genomics, and Evolution
Authors: Long Hongan , Johri Parul , Gout Jean-Francois , Ni Jiahao , Hao Yue , Licknack Timothy , Wang Yaohai , Pan Jiao , Jiménez-Marín Berenice , Lynch Michael
Published: December 2023

The ciliate genus Paramecium served as one of the first model systems in microbial eukaryotic genetics, contributing much to the early understanding of phenomena as diverse as genome rearrangement, cryptic speciation, cytoplasmic inheritance, and endosymbiosis, as well as more recently to the evolution of mating types, introns, and roles of small RNAs in DNA processing. Substantial progress has recently been made in the area of comparative and population genomics. Paramecium species combine some of the lowest known mutation rates with some of the largest known effective populations, along with likely very high recombination rates, thereby harboring a population-genetic environment that promotes an exceptionally efficient capacity for selection. As a consequence, the genomes are extraordinarily streamlined, with very small intergenic regions combined with small numbers of tiny introns. The subject of the bulk of Paramecium research, the ancient Paramecium aurelia species complex, is descended from two whole-genome duplication events that retain high degrees of synteny, thereby providing an exceptional platform for studying the fates of duplicate genes. Despite having a common ancestor dating to several hundred million years ago, the known descendant species are morphologically indistinguishable, raising significant questions about the common view that gene duplications lead to the origins of evolutionary novelties.
Electrochemical-Electromagnetic Coupled Physics-Based Mathematical Modeling of Li-Ion Batteries: A Theoretical Proof of Concept
Authors: Coman Paul T. , Mollah Rafid , Irish Nicholas , Karuppuswami Saranraj , Dix Sean T. , Johnson Wade , Garrick Taylor R. , White Ralph E.
Published: December 2023

This theoretical study presents a novel proof-of-concept modeling approach that couples a P2D physics-based electrochemical model with a 3D electromagnetic model to analyze the behavior of lithium-ion batteries under AC perturbations in the 0.1 Hz–100 kHz frequency range that generates an impedance response in a cylindrical Li-ion battery cell. The coupling between the two models was done by connecting them in series and adding the separate impedances from both models. By considering effective material properties for the electromagnetics model, and by using the Nyquist and Bode plots as figures of merit, it was shown that such a hybrid coupling can be used to predict the impedance of such a system. This research lays the groundwork for future experimental validation and optimization of battery designs in applications involving AC perturbations, offering insights for enhanced energy storage and conversion systems.