Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://github.com/yaobaimiao/CFPAM.

With the rapid development of deep learning, the use of object detection algorithms for aerial insulator image defect detection has become the main way. To address the problems of low detection accuracy for small targets, weak representation ability of feature maps, insufficient extracted key information, and the shortage of aerial insulator defect datasets, this paper proposes an improved insulator defect detection method named BS-YOLOv5s based on 3-D attention mechanism and Bi-Slim-neck using YOLOv5s as the base network. Additionally, to solve the problem of the shortage of aerial insulator datasets, this paper proposes a new aerial insulator dataset Weather-Insulator (WI) containing a variety of defect scenarios. The experimental results demonstrate that the proposed method not only greatly improves the detection accuracy, but also maintains a high detection speed, satisfying the engineering requirements for insulator defect detection. The dataset and code for this paper are publicly available at https://github.com/jspron/insulator-defect.

It is often difficult to separate the highly capable “experts” from the average worker in crowdsourced systems. This is especially true for challenge application domains that require extensive domain knowledge. The problem of stock analysis is one such domain, where even the highly paid, well-educated domain experts are prone to make mistakes. As an extremely challenging problem space, the “wisdom of the crowds” property that many crowdsourced applications rely on may not hold. In this article, we study the problem of evaluating and identifying experts in the context of SeekingAlpha and StockTwits, two crowdsourced investment services that have recently begun to encroach on a space dominated for decades by large investment banks. We seek to understand the quality and impact of content on collaborative investment platforms, by empirically analyzing complete datasets of SeekingAlpha articles (9 years) and StockTwits messages (4 years). We develop sentiment analysis tools and correlate contributed content to the historical performance of relevant stocks. While SeekingAlpha articles and StockTwits messages provide minimal correlation to stock performance in aggregate, a subset of experts contribute more valuable (predictive) content. We show that these authors can be easily identified by user interactions, and investments based on their analysis significantly outperform broader markets. This effectively shows that even in challenging application domains, there is a secondary or indirect wisdom of the crowds. Finally, we conduct a user survey that sheds light on users’ views of SeekingAlpha content and stock manipulation. We also devote efforts to identify potential manipulation of stocks by detecting authors controlling multiple identities.

In crowdsourced systems, it is often difficult to separate the highly capable "experts" from the average worker. In this paper, we study the problem of evaluating and identifying experts in the context of SeekingAlpha and StockTwits, two crowdsourced investment services that are encroaching on a space dominated for decades by large investment banks. We seek to understand the quality and impact of content on collaborative investment platforms, by empirically analyzing complete datasets of SeekingAlpha articles (9 years) and StockTwits messages (4 years). We develop sentiment analysis tools and correlate contributed content to the historical performance of relevant stocks. While SeekingAlpha articles and StockTwits messages provide minimal correlation to stock performance in aggregate, a subset of experts contribute more valuable (predictive) content. We show that these authors can be easily identified by user interactions, and investments using their analysis significantly outperform broader markets. Finally, we conduct a user survey that sheds light on users views of SeekingAlpha content and stock manipulation.

Even though mobile devices are ubiquitous, the conceptually simple endeavor of using co-located devices for multi-user experiences is cumbersome. It may not even be possible when certain apps are not widely available.

Today, most spectrum allocation algorithms use conflict graphs to capture interference conditions. The use of conflict graphs, however, is often questioned by the wireless community for two reasons. First, building accurate conflict graphs requires significant overhead, and hence does not scale to outdoor networks. Second, conflict graphs cannot properly capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand how severe these problems are and whether they can be overcome. We build “practical” conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. Calibrated models are imperfect, and we study the impact of their errors on multiple steps in the process, from calibrating propagation models, predicting signal strengths, to building conflict graphs. At each step, we analyze the introduction, propagation, and final impact of errors by comparing each intermediate result to its ground-truth counterpart. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these “estimated conflict graphs” lower spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference.

Mobile network traffic is set to explode in our near future, driven by the growth of bandwidth-hungry media applications. Current capacity solutions, including buying spectrum, WiFi offloading, and LTE picocells, are unlikely to supply the orders-of-magnitude bandwidth increase we need. In this paper, we explore a dramatically different alternative in the form of 60GHz mmwave picocells with highly directional links. While industry is investigating other mmwave bands (e.g. 28GHz to avoid oxygen absorption), we prefer the unlicensed 60GHz band with highly directional, short-range links (~100m). 60GHz links truly reap the spatial reuse benefits of small cells while delivering high per-user data rates and leveraging efforts on indoor 60GHz PHY technology and standards. Using extensive measurements on off-the-shelf 60GHz radios and system-level simulations, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths, and show that there are no fundamental physical barriers to high-capacity 60GHz outdoor picocells. We conclude by identifying open challenges and associated research opportunities.

Today's network control and management traffic are limited by their reliance on existing data networks. Fate sharing in this context is highly undesirable, since control traffic has very different availability and traffic delivery requirements. In this paper, we explore the feasibility of building a dedicated wireless facilities network for data centers. We propose Angora, a low-latency facilities network using low-cost, 60GHz beamforming radios that provides robust paths decoupled from the wired network, and flexibility to adapt to workloads and network dynamics. We describe our solutions to address challenges in link coordination, link interference and network failures. Our testbed measurements and simulation results show that Angora enables large number of low-latency control paths to run concurrently, while providing low latency end-to-end message delivery with high tolerance for radio and rack failures.

Mobile networking researchers have long searched for large-scale, fine-grained traces of human movement, which have remained elusive for both privacy and logistical reasons. Recently, researchers have begun to focus on geosocial mobility traces, e.g. Foursquare checkin traces, because of their availability and scale. But are we conceding correctness in our zeal for data? In this paper, we take initial steps towards quantifying the value of geosocial datasets using a large ground truth dataset gathered from a user study. By comparing GPS traces against Foursquare checkins, we find that a large portion of visited locations is missing from checkins, and most checkin events are either forged or superfluous events. We characterize extraneous checkins, describe possible techniques for their detection, and show that both extraneous and missing checkins introduce significant errors into applications driven by these traces.

Most spectrum distribution proposals today develop their allocation algorithms that use conflict graphs to capture interference relationships. The use of conflict graphs, however, is often questioned by the wireless community because of two issues. First, building conflict graphs requires significant overhead and hence generally does not scale to outdoor networks, and second, the resulting conflict graphs do not capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand just how severe these issues are in practice, and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. These propagation models are imperfect, and we study the impact of their errors by tracing the impact on multiple steps in the process, from calibrating propagation models to predicting signal strength and building conflict graphs. At each step, we analyze the introduction, propagation and final impact of errors, by comparing each intermediate result to its ground truth counterpart generated from measurements. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lose some spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique that addresses any remaining accumulative interference, the last missing piece in a practical spectrum distribution system using measurement-calibrated conflict graphs.

Mobile gaming is a big driver of app marketplaces. However, few mobile games deliver truly distinctive gameplay experiences for ad hoc collocated users. As an example of such an experience, consider a sword fight dual between two users facing each other where each user's phone simulates a sword. With phone in hand, the users' thrusts and blocks translate to attacks and counterattacks in the game. Such Phone-to-Phone Mobile Motion Games (MMG) represent interesting and novel gameplay for ad hoc users in the same location. One enabler for an MMG game like sword fight is continuous, accurate distance ranging. Existing ranging schemes cannot meet the stringent requirements of MMG games: speed, accuracy, and noise robustness. In this work, we design FAR, a new ranging scheme that can localize at 12 Hz with 2-cm median error while withstanding up to 0-dB noise, multipath, and Doppler effect issues. Our implementation runs on commodity smartphones and does not require any external infrastructure. Moreover, distance measurement accuracy is comparable to that of Kinect, a fixed-infrastructure motion capture system. Evaluation on users playing two prototype games indicate that FAR can fully support dynamic game motion in real time.

Dynamic spectrum access is a maturing technology that allows next generation wireless devices to make highly efficient use of wireless spectrum. Spectrum can be allocated on an on-demand basis for a given geographic location, time duration and frequency range. However, a major obstacle to adoption remains. There are no effective solutions to protect licensed users from spectrum misuse, where users transmit without properly licensing spectrum, and in doing so, interfere and disrupt legitimate flows to whom the spectrum is assigned. Given the flexibility of today's cognitive radios, an application can easily transmit on frequencies outside of its allocated range, either accidentally due to misconfiguration, or intentionally to avoid spectrum licensing costs. In this paper, we propose a system to secure dynamic spectrum transmissions, where authorized users embed secure spectrum permits into data transmissions, thus enabling patrolling trusted devices to detect devices transmitting without authorization. We focus our attention on the development of spectrum permits, and describe Gelato, a spectrum misuse detection system that minimizes both hardware costs and performance overhead on legitimate data transmissions.

Mobile gaming is a big driver of app marketplaces. However, few mobile games deliver truly distinctive gameplay experiences for ad hoc collocated users. As an example of such an experience, consider a sword fight dual between two users facing each other where each user's phone simulates a sword. With phone in hand, the users' thrusts and blocks translate to attacks and counterattacks in the game. Such Phone-to-Phone Mobile Motion Games (MMG) represent interesting and novel gameplay for ad hoc users in the same location. One enabler for an MMG game like sword fight is continuous, accurate distance ranging. Existing ranging schemes cannot meet the stringent requirements of MMG games: speed, accuracy and noise robustness. In this work, we design FAR, a new ranging scheme that can localize at 12Hz with 2cm median error while withstanding up to 0dB noise, multipath and Doppler effect issues. Our implementation runs on commodity smartphones and does not require any external infrastructure. Moreover, distance measurement accuracy is comparable to that of Kinect, a fixed-infrastructure motion capture system. Evaluation on users playing two prototype games indicate that FAR can fully support dynamic game motion in real-time.

Modern data centers are massive, and support a range of distributed applications across potentially hundreds of server racks. As their utilization and bandwidth needs continue to grow, traditional methods of augmenting bandwidth have proven complex and costly in time and resources. Recent measurements show that data center traffic is often limited by congestion loss caused by short traffic bursts. Thus an attractive alternative to adding physical bandwidth is to augment wired links with wireless links in the 60 GHz band.

Contrary to prior assumptions, recent measurements show that data center traffic is not constrained by network bisection bandwidth, but is instead prone to congestion loss caused by short traffic bursts. Compared to the cost and complexity of modifying data center architectures, a much more attractive option is to augment wired links with flexible wireless links in the 60 GHz band. Current proposals, however, are severely constrained by two factors. First, 60 GHz wireless links are limited by line-of-sight, and can be blocked by even small obstacles between the endpoints. Second, even beamforming links leak power, and potential interference will severely limit concurrent transmissions in dense data centers. In this paper, we explore the feasibility of a new wireless primitive for data centers, 3D beamforming. We explore the design space, and show how bouncing 60 GHz wireless links off reflective ceilings can address both link blockage and link interference, thus improving link range and number of current transmissions in the data center.

Today's WiFi access points (APs) are ubiquitous, and provide critical connectivity for a wide range of mobile networking devices. Many management tasks, e.g. optimizing AP placement and detecting rogue APs, require a user to efficiently determine the location of wireless APs. Unlike prior localization techniques that require either specialized equipment or extensive outdoor measurements, we propose a way to locate APs in real-time using commodity smartphones. Our insight is that by rotating a wireless receiver (smartphone) around a signal-blocking obstacle (the user's body), we can effectively emulate the sensitivity and functionality of a directional antenna. Our measurements show that we can detect these signal strength artifacts on multiple smartphone platforms for a variety of outdoor environments. We develop a model for detecting signal dips caused by blocking obstacles, and use it to produce a directional analysis technique that accurately predicts the direction of the AP, along with an associated confidence value. The result is Borealis, a system that provides accurate directional guidance and leads users to a desired AP after a few measurements. Detailed measurements show that Borealis is significantly more accurate than other real-time localization systems, and is nearly as accurate as offline approaches using extensive wireless measurements.

We present a demonstration of a phone-to-phone Sword Fight! game. It utilizes our solution for achieving high speed 3D continuous localization described in the accompanying conference paper [1]. The approach uses acoustic cues based on time-difference of arrival and power level. It assumes at least two microphones and one speaker per phone, which is common on new smartphones. Accelerometers and digital compasses assist in resolving ambiguous acoustic-only localization. Continuous localization is achieved with the aid of a loose time synchronization protocol and a Kalman filter. Lastly, practical gameplay issues are addressed.

Microblogging services, such as Twitter, are among the most important online social networks(OSNs). Different from OSNs such as Facebook, the topology of microblogging service is a directed graph instead of an undirected graph. Recently, due to the explosive increase of population size, graph sampling has started to play a critical role in measurement and characterization studies of such OSNs. However, previous studies have only focused on the unbiased sampling of undirected social graphs. In this paper, we study the unbiased sampling algorithm for directed social graphs. Based on the traditional Metropolis-Hasting Random Walk (MHRW) algorithm, we propose an unbiased sampling method for directed social graphs(USDSG). Using this method, we get the first, to the best of our knowledge, unbiased sample of directed social graphs. Through extensive experiments comparing with the ”ground truth ” (UNI, obtained through uniform sampling of directed graph nodes), we show that our method can achieve excellent performance in directed graph sampling and the error to UNI is less than 10%.

In recent research work of securing Network Coordinate (NC) system, they concentrate on the passive security defense mechanisms. In this paper we propose SLINCS, a social link based evaluation security system that utilizes information from existing social relationship networks to implement proactive security mechanisms for NC systems. The key idea is to eliminate suspicious nodes before they launch potential attacks.

Broadcatching is a promising mechanism to improve the experience of BitTorrent users by automatically downloading files advertised through RSS feeds. However, though widely used, the mechanism itself has not been well studied. In this paper, we conducted extensive experiments on PlanetLab to evaluate the performance of Broadcatching under different typical scenarios. The results demonstrated the effectiveness of the broadcatching: it reduces the average completion time and downloading failure ratio. It also improves the overall fairness of the system: the subscribers are encouraged to share more while downloading faster, which results in the increased share ratio. Our study is the first work to systematically evaluate the benefit of broadcatching and sheds lights on how to improve performance of BitTorrrent by manipulating peer's behavior like Broadcatching.