This paper addresses the problem of distributed detection in multi-agent networks. Agents receive private signals about an unknown state of the world. The underlying state is globally identifiable, ...yet informative signals may be dispersed throughout the network. Using an optimization-based framework, we develop an iterative local strategy for updating individual beliefs. In contrast to the existing literature which focuses on asymptotic learning, we provide a finite-time analysis. Furthermore, we introduce a Kullback-Leibler cost to compare the efficiency of the algorithm to its centralized counterpart. Our bounds on the cost are expressed in terms of network size, spectral gap, centrality of each agent and relative entropy of agents' signal structures. A key observation is that distributing more informative signals to central agents results in a faster learning rate. Furthermore, optimizing the weights, we can speed up learning by improving the spectral gap. We also quantify the effect of link failures on learning speed in symmetric networks. We finally provide numerical simulations for our method which verify our theoretical results.
Face-to-face interviews have long been the norm for conducting qualitative interviews in healthcare research. However, the Covid-19 pandemic has accelerated the need to explore alternative methods. ...This, along with the swift digitalization of healthcare, has led to video, telephone, and online interactions becoming increasingly used. The use of new techniques to carry out interviews through video, telephone, and online applications all come with benefits and drawbacks. In this article, three ways of collecting data through qualitative interviews are described and their uses exemplified through a project investigating the impact of a transition program for adolescents with congenital heart disease.
BiRank: Towards Ranking on Bipartite Graphs He, Xiangnan; Gao, Ming; Kan, Min-Yen ...
IEEE transactions on knowledge and data engineering,
2017-Jan.-1, 2017-1-1, 20170101, Volume:
29, Issue:
1
Journal Article
Peer reviewed
Open access
The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we study the problem ...of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector). We present a new solution, BiRank, which iteratively assigns scores to vertices and finally converges to a unique stationary ranking. In contrast to the traditional random walk-based methods, BiRank iterates towards optimizing a regularization function, which smooths the graph under the guidance of the query vector. Importantly, we establish how BiRank relates to the Bayesian methodology, enabling the future extension in a probabilistic way. To show the rationale and extendability of the ranking methodology, we further extend it to rank for the more generic n-partite graphs. BiRank's generic modeling of both the graph structure and vertex features enables it to model various ranking hypotheses flexibly. To illustrate its functionality, we apply the BiRank and TriRank (ranking for tripartite graphs) algorithms to two real-world applications: a general ranking scenario that predicts the future popularity of items, and a personalized ranking scenario that recommends items of interest to users. Extensive experiments on both synthetic and real-world datasets demonstrate BiRank's soundness (fast convergence), efficiency (linear in the number of graph edges), and effectiveness (achieving state-of-the-art in the two real-world tasks).
A locally recoverable code (LRC code) is a code over a finite alphabet, such that every symbol in the encoding is a function of a small number of other symbols that form a recovering set. In this ...paper, we derive new finite-length and asymptotic bounds on the parameters of LRC codes. For LRC codes with a single recovering set for every coordinate, we derive an asymptotic Gilbert-Varshamov type bound for LRC codes and find the maximum attainable relative distance of asymptotically good LRC codes. Similar results are established for LRC codes with two disjoint recovering sets for every coordinate. For the case of multiple recovering sets (the availability problem), we derive a lower bound on the parameters using expander graph arguments. Finally, we also derive finite-length upper bounds on the rate and the distance of LRC codes with multiple recovering sets.
It is without doubt that today the volume and sophistication of cyber attacks keeps consistently growing, militating an endless arm race between attackers and defenders. In this context, full-fledged ...frameworks, methodologies, or strategies that are able to offer optimal or near-optimal reaction in terms of countermeasure selection, preferably in a fully or semiautomated way, are of high demand. This is reflected in the literature, which encompasses a significant number of major works on this topic spanning over a time period of 5 years, that is, from 2012 to 2016. The survey at hand has a dual aim, namely, first, to critically analyze all the pertinent works in this field, and second to offer an in-depth discussion and side-by-side comparison among them based on seven common criteria. Also, a quite extensive discussion is offered to highlight on the shortcomings and future research challenges and directions in this timely area.
In this modern era, each and everything is computerized, and everyone has their own smart gadgets to communicate with others around the globe without any range limitations. Most of the communication ...pathways belong to smart applications, call options in smartphones, and other multiple ways, but e-mail communication is considered the main professional communication pathway, which allows business people as well as commercial and noncommercial organizations to communicate with one another or globally share some important official documents and reports. This global pathway attracts many attackers and intruders to do a scam with such innovations; in particular, the intruders generate false messages with some attractive contents and post them as e-mails to global users. This kind of unnecessary and not needed advertisement or threatening mails is considered as spam mails, which usually contain advertisements, promotions of a concern or institution, and so on. These mails are also considered or called junk mails, which will be reflected as the same category. In general, e-mails are the usual way of message delivery for business oriented as well as any official needs, but in some cases there is a necessity of transferring some voice instructions or messages to the destination via the same e-mail pathway. These kinds of voice-oriented e-mail accessing are called voice mails. The voice mail is generally composed to deliver the speech aspect instructions or information to the receiver to do some particular tasks or convey some important messages to the receiver. A voice-mail-enabled system allows users to communicate with one another based on speech input which the sender can communicate to the receiver via voice conversations, which is used to deliver voice information to the recipient. These kinds of mails are usually generated using personal computers or laptops and exchanged via general e-mail pathway, or separate paid and nonpaid mail gateways are available to deal with certain mail transactions. The above-mentioned e-mail spam is considered in many past researches and attains some solutions, but in case of voice-based e-mail aspect, there will be no options to manage such kind of security parameters. In this paper, a hybrid data processing mechanism is handled with respect to both text-enabled and voice-enabled e-mails, which is called Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP). This proposed approach provides a way of identifying the e-mail spam in both textual e-mails and speech-enabled e-mails. The proposed approach of GDTPNLP provides higher spam detection rate in terms of text extraction speed, performance, cost efficiency, and accuracy. These all will be explained in detail with graphical output views in the Results and Discussion.
With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing ...refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, <inline-formula><tex-math notation="LaTeX">g</tex-math> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2550041.gif"/> </inline-formula>-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
We proposed a VPN route control mechanism by extending the Outbound Route Filter (ORF). A new ORF type which is named VPN Prefix ORF is defined to carry the Route Distinguisher (RD), Source Provider ...Edge (PE) and other features of the "offending" VPN routes. The scenarios where a device simultaneously receives a large number of VPN routes exceeding the threshold in both intra-domain and inter-domain were simulated to verify the effect of VPN Prefix ORF mechanism. The CPU utilization, memory consumption, and routing convergence time were studied in detail to investigate whether the VPN mechanism can reduce the pressure on PE while not impose excessive burden on Route Reflector (RR). The results showed that VPN Prefix ORF mechanism can effectively prevent device resources from being occupied by a large number of unexpected VPN routes while not placing too much burden on RR.
As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and ...success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.
Background
Metabolic/bariatric procedures for the treatment of morbid obesity, as well as for type 2 diabetes, are among the most commonly performed gastrointestinal operations today, justifying ...periodic assessment of the numerical status of metabolic/bariatric surgery and its relative distribution of procedures.
Methods
An email questionnaire was sent to the leadership of the 50 nations or national groupings in the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO). Outcome measurements were numbers of metabolic/bariatric operations and surgeons, types of procedures performed, and trends from 2003 to 2008 to 2011 worldwide and in the regional groupings of Europe, USA/Canada, Latin/South America, and Asia/Pacific.
Results
Response rate was 84 %. The global total number of procedures in 2011 was 340,768; the global total number of metabolic/bariatric surgeons was 6,705. The most commonly performed procedures were Roux-en-Y gastric bypass (RYGB) 46.6 %; sleeve gastrectomy (SG) 27.8 %; adjustable gastric banding (AGB) 17.8 %; and biliopancreatic diversion/duodenal switch (BPD/DS) 2.2 %. The global trends from 2003 to 2008 to 2011 showed a decrease in RYGB: 65.1 to 49.0 to 46.6 %; an increase, followed by a steep decline, in AGB: 24.4 to 42.3 to 17.8 %; and a marked increase in SG: 0.0 to 5.3 to 27.89 %. BPD/DS declined: 6.1 to 4.9 to 2.1 %. The trends from the four IFSO regions differed, except for the universal increase in SG.
Conclusions
Periodic metabolic/bariatric surgery surveys add to the knowledge and understanding of all physicians caring for morbidly obese patients. The salient message of the 2011 assessment is that SG (0.0 % in 2008) has markedly increased in prevalence.
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