Miracle-dz/UniFAR-FTUs huggingface dataset:
Handling Coexistence of LoRa with Other Networks through Embedded Reinforcement Learning

The rapid growth of various Low-Power Wide-Area Network (LPWAN) technologies in the limited spectrum brings forth the challenge of their coexistence. Today, LPWANs are not equipped to handle this impending challenge. It is difficult to employ sophisticated media access control protocol for low-power nodes. Coexistence handling for WiFi or traditional short-range wireless network will not work for LPWANs. Due to long range, their nodes can be subject to an unprecedented number of hidden nodes, requiring highly energy-efficient techniques to handle such coexistence. In this paper, we address the coexistence problem for LoRa, a leading LPWAN technology. To improve the performance of a LoRa network under coexistence with many independent networks, we propose the design of a novel embedded learning agent based on a lightweight reinforcement learning at LoRa nodes. This is done by developing a Q-learning framework while ensuring minimal memory and computation overhead at LoRa nodes. The framework exploits transmission acknowledgments as feedback from the network based on what a node makes transmission decisions. To our knowledge, this is the first Q-learning approach for handling coexistence of low-power networks. Considering various coexistence scenarios of a LoRa network, we evaluate our approach through experiments indoors and outdoors. The outdoor results show that our Q-learning approach on average achieves an improvement of 46% in packet reception rate while reducing energy consumption by 66% in a LoRa network. In indoor experiments, we have observed some coexistence scenarios where a current LoRa network loses all the packets while our approach enables 99% packet reception rate with up to 90% improvement in energy consumption.
Peer to Peer Distributed Solar Energy Trading

Solar module prices have dramatically dropped in recent years, which in turn has facilitated distributed solar energy resources (DSERs) in smart grids. To manage electricity real time supply and demand, the utilities has shown the strong interest in deploying virtual power plants(VPPs) which enable solar generated energy trading to reduce the intermittent DSERs impact in electric grid. Unfortunately, the current energy trading approaches in residential VPPs typically require a trusted third party to take on the role of the middleman, DSER users are not allowed to trade their surplus solar energy independently and simultaneously to maximize potential benefits. In addition, these approaches do not achieve "fair" for both VPPs and DSERs users. To address this issue, we build a multi-agent deep reinforcement learning based peer to peer (P2P) new solar energy trading system-SolarTrader+, which enables unsupervised, distributed, and long term fair solar energy trading in residential VPPs. We apply deep reinforcement learning with neural networks as Q-value function approximator.We implement SolarTrader+ and evaluate it using data from U.S. residential VPP communities that are comprised of - 119 residential DSERs. Our results show that SolarTrader+ can reduce the aggregated VPP energy consumption by 83.8% when compared against a non-trading approach.
Link Topics from Q&A Platforms using Wikidata: A Tool for Cross-platform Hierarchical Classification

This paper proposes a novel rule-based topic classification tool for questions on Q&A platforms mediated by the Wikidata ontology – an open and accessible multilingual ontology curated by a large community of online users. Q&A platforms are important sources of information on the Web and often appear as part of Web search results. By adopting Wikidata taxonomic relations as references, our tool can categories the Web content from different platforms in a unified coarse-to-fine mode based on their domain coverage. To validate and demonstrate the potential applicability of our tool, a set of use cases and experiments are carried out on two popular Q&A platforms – Zhihu and Quora, where the impact of topic categories on question lifecycles is explored. Furthermore, we compare our results with the output generated by GPT-3 classifier. This tool sheds light on how structured knowledge bases can enable data interoperability and serve as a filtering functionality to mitigate classification bias of OpenAI.
On Approximate Symmetries of a Wave Equation with Quadratic Non-Linearity

Two different approximate symmetry methods and a proposed new one are contrasted using a wave equation with quadratic non-linearity. For each method, the approximate symmetries are calculated first. Then approximate solutions corresponding to some of the symmetries are calculated. It is found that a given specific approximate solution is attainable only by using the new proposed method.
Multitask Peer Prediction With Task-dependent Strategies

Peer prediction aims to incentivize truthful reports from agents whose reports cannot be assessed with any objective ground truthful information. In the multi-task setting where each agent is asked multiple questions, a sequence of mechanisms have been proposed which are truthful — truth-telling is guaranteed to be an equilibrium, or even better, informed truthful — truth-telling is guaranteed to be one of the best-paid equilibria. However, these guarantees assume agents’ strategies are restricted to be task-independent: an agent’s report on a task is not affected by her information about other tasks. We provide the first discussion on how to design (informed) truthful mechanisms for task-dependent strategies, which allows the agents to report based on all her information on the assigned tasks. We call such stronger mechanisms (informed) omni-truthful. In particular, we propose the joint-disjoint task framework, a new paradigm which builds upon the previous penalty-bonus task framework. First, we show a natural reduction from mechanisms in the penalty-bonus task framework to mechanisms in the joint-disjoint task framework that maps every truthful mechanism to an omni-truthful mechanism. Such a reduction is non-trivial as we show that current penalty-bonus task mechanisms are not, in general, omni-truthful. Second, for a stronger truthful guarantee, we design the matching agreement (MA) mechanism which is informed omni-truthful. Finally, for the MA mechanism in the detail-free setting where no prior knowledge is assumed, we show how many tasks are required to (approximately) retain the truthful guarantees.
Poster Abstract: O-RAN Signaling Optimizations for Improved IoT Handover Performance in 5G Networks

IoT systems require a wireless infrastructure that supports 5G devices, including handovers between heterogeneous and/or small cell radio access networks. These networks are subject to increased radio link failures and loss of IoT network function. 3GPP new radio (NR) applications include multihoming, i.e., simultaneously connecting devices, and handover, i.e., changing the point of access to the network. This work leverages the open radio access network (O-RAN) alliance, which specifies a new open architecture with intelligent controllers, to improve handover management. A new feedback-based time-to-trigger (TTT) handover mechanism is introduced. Improved throughput and reduced radio link failures over other techniques were achieved.
Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs

With the widespread development of algorithmic fairness, there has been a surge of research interest that aims to generalize the fairness notions from the attributed data to the relational data (graphs). The vast majority of existing work considers the fairness measure in terms of the low-order connectivity patterns (e.g., edges), while overlooking the higher-order patterns (e.g., k-cliques) and the dynamic nature of real-world graphs. For example, preserving triangles from graph cuts during clustering is the key to detecting compact communities; however, if the clustering algorithm only pays attention to triangle-based compactness, then the returned communities lose the fairness guarantee for each group in the graph. Furthermore, in practice, when the graph (e.g., social networks) topology constantly changes over time, one natural question is how can we ensure the compactness and demographic parity at each timestamp efficiently. To address these problems, we start from the static setting and propose a spectral method that preserves clique connections and incorporates demographic fairness constraints in returned clusters at the same time. To make this static method fit for the dynamic setting, we propose two core techniques, Laplacian Update via Edge Filtering and Searching and Eigen-Pairs Update with Singularity Avoided. Finally, all proposed components are combined into an end-to-end clustering framework named F-SEGA, and we conduct extensive experiments to demonstrate the effectiveness, efficiency, and robustness of F-SEGA.
A First Look at Public Service Websites from the Affordability Lens

Public service websites act as official gateways to services provided by governments. Many of these websites are essential for citizens to receive reliable information and online government services. However, the lack of affordability of mobile broadband services in many developing countries and the rising complexity of websites create barriers for citizens in accessing these government websites. This paper presents the first large-scale analysis of the affordability of public service websites in developing countries. We do this by collecting a corpus of 1900 public service websites, including public websites from nine developing countries and for comparison websites from nine developed countries. Our investigation is driven by website complexity analysis as well as evaluation through a recently proposed affordability index. Our analysis reveals that, in general, public service websites in developing countries do not meet the affordability target set by the UN’s Broadband Commission. However, we show that several countries can be brought within or closer to the affordability target by implementing webpage optimizations to reduce page sizes. We also discuss policy interventions that can help make access to public service website more affordable.
Coherent Topic Modeling for Creative Multimodal Data on Social Media

The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.
Pairwise-interactions-based Bayesian Inference of Network Structure from Information Cascades

An explicit network structure plays an important role when analyzing and understanding diffusion processes. In many scenarios, however, the interactions between nodes in an underlying network are unavailable. Although many methods for inferring a network structure from observed cascades have been proposed, they did not perceive the relationship between pairwise interactions in a cascade. Therefore, this paper proposes a Pairwise-interactions-based Bayesian Inference method (named PBI) to infer the underlying diffusion network structure. More specifically, to get more accurate inference results, we measure the weights of each candidate pairwise interaction in different cascades and add them to the likelihood of a contagion process. In addition, a pre-pruning work is introduced for candidate edges to further improve the inference efficiency. Experiments on synthetic and real-world networks show that PBI achieves significantly better results.
What Web Search Behaviors Lead to Online Purchase Satisfaction?

This study investigates the relationship between web information-seeking behavior and post-purchase satisfaction. We examine web search logs as a record of web information-seeking behaviors and product ratings in an e-commerce (EC) site as self-rated post-purchase satisfaction. Our analysis revealed that web search behaviors are different for satisfied and dissatisfied customers, and even for different types of customers and products. In particular, we found that (1) within a week prior to the purchase, satisfied users more frequently searched for a wider range of product-related information than dissatisfied users. (2) satisfied users searched with more specific queries than dissatisfied users prior to the purchase, especially when they are relatively familiar with web search and purchasing a relatively expensive product. (3) customers looking for opinions of others are more likely to be satisfied with their purchase. Furthermore, we also addressed the problem of predicting customers’ post-purchase satisfaction based on their web search behaviors before and after the purchase. This attempt demonstrated that customers are likely to have different levels of post-purchase satisfaction if they conducted different types of web search.
Are Mobile Advertisements in Compliance with App’s Age Group?

As smartphones and mobile apps permeate every aspect of people’s lives, children are accessing mobile devices at an increasingly younger age. The inescapable exposure of advertisements in mobile apps to children has grown alarmingly. Mobile advertisements are placed by advertisers and subsequently distributed by ad SDKs, under the rare control of app developers and app markets’ content ratings. Indeed, content that is objectionable and harmful to children’s mental health has been reported to appear in advertising, such as pornography. However, few studies have yet concentrated on automatically and comprehensively identifying such kid-unsuitable mobile advertising. In this paper, we first characterize the regulations for mobile ads relating to children. We then propose our novel automated dynamic analysis framework, named AdRambler, that attempts to collect ad content throughout the lifespan of mobile ads and identify their inappropriateness for child app users. Using AdRambler, we conduct a large-scale (25,000 mobile apps) empirical investigation and reveal the non-incidental presence of inappropriate ads in apps with child-included target audiences. We collected 11,270 ad views and identified 1,289 ad violations (from 775 apps) of child user regulations, with roughly half of the app promotions not in compliance with host apps’ content ratings. Our finding indicates that even certified ad SDKs could still propagate inappropriate advertisements. We further delve into the question of accountability for the presence of inappropriate advertising and provide concrete suggestions for all stakeholders to take action for the benefit of children.
Tracking Machine Learning Bias Creep in Traditional and Online Lending Systems with Covariance Analysis

Machine Learning (ML) algorithms are embedded within online banking services, proposing decisions about consumers’ credit cards, car loans, and mortgages. These algorithms are sometimes biased, resulting in unfair decisions toward certain groups. One common approach for addressing such bias is simply dropping the sensitive attributes from the training data (e.g. gender). However, sensitive attributes can indirectly be represented by other attributes in the data (e.g. maternity leave taken). This paper addresses the problem of identifying attributes that can mimic sensitive attributes by proposing a new approach based on covariance analysis. Our evaluation conducted on two different credit datasets, extracted from a traditional and an online banking institution respectively, shows how our approach: (i) effectively identifies the attributes from the data that encapsulate sensitive information and, (ii) leads to the reduction of biases in ML models, while maintaining their overall performance.
Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention

Businesses face a range of cyber risks, both external threats and internal vulnerabilities that continue to evolve over time. As cyber attacks continue to increase in complexity and sophistication, more organisations will experience them. For this reason, it is important that organisations seek timely consultancy from cyber professionals so that they can respond to and recover from cyber attacks as quickly as possible. However, huge surges in cyber attacks have long left cyber professionals short of what is required to cover the security needs. This problem is getting worse when an increasing number of people choose to work from home during the pandemic because this situation usually yields extra communication cost. In this paper, we propose to develop a cybersecurity-oriented dialogue system, called Dynalogue1, which can provide consultancy online as a cyber professional. For the first time, Dynalogue provides a promising solution to mitigate the need for cyber professionals via automatically generating problem-targeted conversions to victims of cyber attacks. In spite of many dialogue systems developed in the past, Dynalogue provides a distinct capability of handling long and complicated sentences that are common in cybersecurity-related conversations. It is challenging to have this capability because limited memory in dialogue systems can be hard to accommodate sufficient key information of long sentences. To overcome this challenge, Dynalogue utilises an attention mechanism that dynamically captures key semantics within a sentence instead of using fix window to cut off the sentence. To evaluate Dynalogue, we collect 67K real-world conversations (0.6M utterances) from Bleeping Computer2, which is one of the most popular cybersecurity consultancy websites in the world. The results suggest that Dynalogue outperforms all the existing dialogue systems with 1% ∼ 9% improvements on all different metrics. We further run Dynalogue on the public dataset WikiHow to validate its compatibility in other domains where conversations are also long and complicated. Dynalogue also outperforms all the other methods with at most 2.4% improvement.
Interactions between the Boreal Summer Intraseasonal Oscillation and Higher-Frequency Tropical Wave Activity

Abstract Interactions between the convection and circulation fields of the boreal summer intraseasonal oscillation (ISO) and two types of higher-frequency tropical wave activity are examined through a statistical analysis of 22 yr of data. During the convectively active phase of the ISO, westward-propagating mixed Rossby–gravity (MRG)–tropical depression (TD)-type wave activity is enhanced within the low-frequency ISO convective envelope, and is strongly correlated with low-frequency 850-hPa westerly anomalies. At the same time, eastward-propagating convectively coupled Kelvin wave activity is enhanced well to the east of the active ISO convection, in the central Pacific. A case study of an ISO event during July–September 1987 illustrates these statistically derived relationships. The enhanced phase of the ISO is shown to consist primarily of westward-propagating higher-frequency variability, including seven named tropical cyclones in the western Pacific, two of which project onto MRG–TD-type modes as the.
How to Pipeline Frame Transfer and Server Inference in Edge-assisted AR to Optimize AR Task Accuracy?

Edge-assisted AR supports high-quality AR on resource-constrained mobile devices by offloading high-rate camera-captured frames to powerful GPU edge servers to perform heavy vision tasks. Since the result of an offloaded frame may not come back in the same frame interval, edge-assisted AR designs resort to local tracking on the last server returned result to generate more accurate result for the current frame. In such an offloading+local tracking paradigm, reducing the staleness of the last server returned result is critical to improving AR task accuracy. In this paper, we present MPCP, an online offloading scheduling framework that minimizes the staleness of server-returned result in edge-assisted AR by optimally pipelining network transfer of frames to the edge server and the Deep Neural Network inference on the edge server. MPCP is based on model predictive control (MPC). Our evaluation results show that MPCP reduces the depth estimation error by up to 10.0% compared to several baseline schemes.
Automated WebAssembly Function Purpose Identification With Semantics-Aware Analysis

WebAssembly is a recent web standard built for better performance in web applications. The standard defines a binary code format to use as a compilation target for a variety of languages, such as C, C++, and Rust. The standard also defines a text representation for readability, although, WebAssembly modules are difficult to interpret by human readers, regardless of their experience level. This makes it difficult to understand and maintain any existing WebAssembly code. As a result, third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality themselves may not be feasible. To this end, we construct WASPur, a tool to automatically identify the purposes of WebAssembly functions. To build this tool, we first construct an extensive collection of WebAssembly samples that represent the state of WebAssembly. Second, we analyze the dataset and identify the diverse use cases of the collected WebAssembly modules. We leverage the dataset of WebAssembly modules to construct semantics-aware intermediate representations (IR) of the functions in the modules. We encode the function IR for use in a machine learning classifier, and we find that this classifier can predict the similarity of a given function against known named functions with an accuracy rate of 88.07%. We hope our tool will enable inspection of optimized and minified WebAssembly modules that remove function names and most other semantic identifiers.
Risk contagion among banks: evidence from Chinese data

ABSTRACT This study investigates risk contagion in the financial system using a sample of 140 Chinese banks. We create the risk contagion index, with and without bank WMPs (Wealth Management Products), and bank’s lending matrix. Based on the index, we find Industrial and Commercial Bank of China (ICBC), Industrial Bank, and Bank of Shanghai to have the highest contagion risk among, respectively, state-owned, joint-stock and city commercial banks. We simulate risk contagion with these three banks as the initial bankrupt banks and at a wide range of default loss rates. We find that their hypothetic bankruptcies do not cause other banks to follow suit, but smaller banks are more subject to the contagion effect of systemic risk. Further results show that banks with WMPs cause greater risk contagion than those without.
Distances on the Relative Spacecraft Motion Manifold

This paper establishes a methodology for obtaining the general solution to the spacecraft relative motion problem by utilizing the Cartesian conflguration space in conjunction with classical orbital elements. The geometry of the relative motion conflguration space is analyzed, and the relative motion manifold is determined. Most importantly, the geometric structure of the relative motion problem is used to derive useful metrics for quantiflcation of the minimum, maximum, and mean distance between spacecraft for commensurable and non-commensurable mean motions. A number of analytic solutions as well as useful examples are provided, illustrating the calculated bounds. A few particular cases are given which yield simple solutions.