A new form of human trafficking has emerged across Chinese borders, where individuals are lured to Southeast Asia with fraudulent job offers and then coerced into operating online scams. Despite its massive economic and human toll, this scam-driven trafficking remains underexplored in academic research. Through qualitative analysis of 158 RedNote posts, we examined how Chinese online communities respond to this threat. Our findings reveal that perpetrators exploit cultural ties to recruit victims for cybercriminal roles within self-sustaining compounds, using sophisticated manipulation tactics. Survivors face serious reintegration barriers, including family rejection, as the cultural values that enable trafficking also hinder their recovery. While communities present protective strategies, efforts are complicated by doubts about the reliability of support and cross-border coordination. We discuss key implications for prevention, platform governance, and international cooperation against scam-driven trafficking. Warning: This paper contains descriptions of physical, psychological, and sexual abuse.

Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.

This paper studies the problem of selecting queries to efficiently crawl a deep web data source using a set of sample documents. Crawling deep web is the process of collecting data from search interfaces by issuing queries. One of the major challenge

Crawling deep web is the process of collecting data from search interfaces by issuing queries. With wide availability of programmable interface encoded in Web services, deep web crawling has received a large variety of applications. One of the major challenges crawling deep web is the selection of the queries so that most of the data can be retrieved at a low cost. We propose a general method in this regard. In order to minimize the duplicates retrieved, we reduced the problem of selecting an optimal set of queries from a sample of the data source into the well-known set-covering problem and adopt a classical algorithm to resolve it. To verify that the queries selected from a sample also produce a good result for the entire data source, we carried out a set of experiments on large corpora including Wikipedia and Reuters. We show that our sampling-based method is effective by empirically proving that 1) The queries selected from samples can harvest most of the data in the original database; 2) The queries with low overlapping rate in samples will also result in a low overlapping rate in the original database; and 3) The size of the sample and the size of the terms from where to select the queries do not need to be very large.

Abstract Common logical design errors in communication protocols are typically detected through reachability analysis or state‐based model checking techniques. These techniques suffer from a state explosion problem and a variety of methods have been explored to deal with it. In this paper, two transformation rules are proposed to be applied specifically on the specifications of communication protocols to reduce their sizes while preserving common logical design errors. These rules are applied on the specification of each individual process in the protocols before state exploration and thus can be combined with most of the existing reachability analysis or model checking techniques applied during state exploration. Copyright © 2007 John Wiley & Sons, Ltd.

Modern biological studies rely on life science Web databases as well as sophisticated Web-based software tools (e.g., homology search tools, modeling and visualization tools). These tools or Web services often have to be combined and integrated in order to support a given study (i.e. an in-silico experiment). The large number of the available Web services makes a service discovery process that can identify the set of services that satisfy a given number of constraints from the pool of all the available Web services crucial. Without such an automated service discovery process the user will have to have a thorough knowledge of all of the existing Web services, a task-which is impractical in the life science domain. In this paper, a scalable approach to service discovery in the biological domain is presented. The approach is based on and guided by a domain ontology.

In this paper, we present our study on statically analyzing design artifacts in multithreaded systems to check the correctness with respect to the nondeterministic behavior of the systems. The description of an abstract behavior of a multithreaded system on design stage can be naturally decomposed into the descriptions of the behavior of each thread and the description of the interactions among these threads. We assume that the behavior of each thread is described in terms of synchronizing finite state machine, a special finite state machine whose transitions may contain information about thread synchronization. Such information is expressed by way of some well-known synchronization mechanism from implementation languages. For the moment, we consider synchronization among multiple threads via shared objects, governed by Java monitors. The operational semantics for a network of such synchronizing finite state machines is provided in terms of labeled transition systems. The defined formal model is the basis for formally reasoning about the correctness of the design against certain properties that, due to the nondeterminism involved, may be hard to detect by testing final code.