Recently, Wireless Rechargeable Sensor Networks (WRSNs) based platforms have become promising for broad applications. However, if an adversary disrupts the wireless charging process in WRSNs, sensors may die due to lack of timely energy supply, compromising the reliability and availability of systems relying on sensing tasks. In this paper, we develop a zero-cost power jamming attack in WRSNs, termed rEflection-based jaMmIng aTtack (EMIT), which introduces an off-the-shelf and inconspicuous reflector such as a Coca-Cola can that intentionally reflects the wave from the charger to destructively interfere with the charging wave at the target sensor. Our approach lifts the limitations of traditional charging attacks, including high cost, complex implementation and ease of detection. We conduct extensive field experiments to evaluate EMIT attack in different types of WRSNs. The results show that on average, the success rate of EMIT attack is 90% in WRSNs with fixed charging locations, and 75% in WRSNs with dynamic charging locations. Finally, we build a real-world WRSN on university campus to study the effectiveness of EMIT attack in complex scenarios. In total, EMIT attack causes 134 sensor deaths over 66 days.

Hardware transports such as RoCE deliver high performance with minimal host CPU, but are best suited to special-purpose deployments that limit their use, e.g., backend networks or Ethernet with Priority Flow Control (PFC). We introduce Falcon, the first hardware transport that supports multiple Upper Layer Protocols (ULPs) and heterogeneous application workloads in general-purpose Ethernet datacenter environments (with losses and without special switch support). Key design elements include: delay-based congestion control with multipath load balancing; a layered design with a simple request-response transaction interface for multi-ULP support; hardware-based retransmissions and error-handling for scalability; and a programmable engine for flexibility. The first Falcon hardware implementation delivers a peak performance of 200 Gbps, 120 Mops/sec, with near-optimal operation completion times that are up to 8× lower than CX-7 RoCE under network congestion, and up to 65% higher goodput under lossy conditions.

Lifelong machine learning (LML) has driven the development of extensive web applications, enabling the learning systems deployed on web servers to deal with a sequence of tasks in an incremental fashion. Such systems can retain knowledge from learned tasks in a knowledge base and seamlessly apply it to improve the future learning. Unfortunately, most existing LML methods require labels in every task, whereas providing persistent human labeling for all future tasks is costly, onerous, error-prone, and hence impractical. Motivated by this situation, we propose a new paradigm named unsupervised lifelong learning with curricula (ULLC), where only one task needs to be labeled for initialization and the system then performs lifelong learning for subsequent tasks in an unsupervised fashion. A main challenge of realizing this paradigm lies in the occurrence of negative knowledge transfer, where partial old knowledge becomes detrimental for learning a given task yet cannot be filtered out by the learner without the help of labels. To overcome this challenge, we draw insights from the learning behaviors of humans. Specifically, when faced with a difficult task that cannot be well tackled by our current knowledge, we usually postpone it and work on some easier tasks first, which allows us to grow our knowledge. Thereafter, once we go back to the postponed task, we are more likely to tackle it well as we are more knowledgeable now. The key idea of ULLC is similar – at any time, a pool of candidate tasks are organized in a curriculum by their distances to the knowledge base. The learner then starts from the closer tasks, accumulates knowledge from learning them, and moves to learn the faraway tasks with a gradually augmented knowledge base. The viability and effectiveness of our proposal are substantiated through extensive empirical studies on both synthetic and real datasets.

Learning with streaming data has received extensive attention during the past few years. Existing approaches assume that the feature space is fixed or changes by following explicit regularities, limiting their applicability in real-time applications. For example, in a smart healthcare platform, the feature space of the patient data varies when different medical service providers use nonidentical feature sets to describe the patients' symptoms. To fill the gap, we in this article propose a novel learning paradigm, namely, Generative Learning With Streaming Capricious (GLSC) data, which does not make any assumption on the feature space dynamics. In other words, GLSC handles the data streams with a varying feature space, where each arriving data instance can arbitrarily carry new features and/or stop carrying partial old features. Specifically, GLSC trains a learner on a universal feature space that establishes relationships between old and new features, so that the patterns learned in the old feature space can be used in the new feature space. The universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve the performance with theoretical guarantees. The experimental results demonstrate that GLSC achieves conspicuous performance on both synthetic and real data sets.

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks ( WRNs ). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of the vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. We formalize the effective charging problem as a charging reward maximization problem ( CRMP ), where the amount of reward obtained by charging a device is inversely proportional to the residual lifetime of the device. Then, we prove that CRMP is NP-hard. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning ( RL ) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

With the maturation of wireless charging technology, Wireless Rechargeable Sensor Networks (WRSNs) has become a promising solution for prolong network lifetimes. Recently studies propose to employ a mobile charger (MC) to simultaneously charge multiple sensors within the same charging range, such that the charging performance can be improved. In this paper, we aim to jointly optimize the number of dead sensors and the energy usage effectiveness in such multi-node charging scenarios. We achieve this by introducing the partial charging mechanism, meaning that instead of following the conventional way that each sensor gets fully charged in one time step, our work allows MC to fully charge a sensor by multiple times. We show that the partial charging mechanism causes minimizing the number of dead sensors and maximizing the energy usage effectiveness to conflict with each other. We formulate this problem and develop a multi-node temporal spatial partial-charging algorithm (MTSPC) to solve it. The optimality of MTSPC is proved, and extensive simulations are carried out to demonstrate the effectiveness of MTSPC.

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks (WRNs). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. Then, we formalize the effective charging problem as a charging reward maximization problem (CRMP), where the amount of reward obtained by charging a de-vice is inversely proportional to the residual lifetime of the device. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning (RL) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

Learning with streaming data has received extensive attention during the past few years. Existing approaches assume the feature space is fixed or changes by following explicit regularities, limiting their applicability in dynamic environments where the data streams are described by an arbitrarily varying feature space. To handle such capricious data streams, we in this paper develop a novel algorithm, named OCDS (Online learning from Capricious Data Streams), which does not make any assumption on feature space dynamics. OCDS trains a learner on a universal feature space that establishes relationships between old and new features, so that the patterns learned in the old feature space can be used in the new feature space. Specifically, the universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve performance with theoretical analysis. The experimental results demonstrate that OCDS achieves conspicuous performance on synthetic and real datasets.

Type inference is an important part of functional programming languages and has been increasingly adopted to imperative programming. However, providing effective error messages in response to type inference failures (due to type errors in programs) continues to be a challenge. Type error messages generated by compilers and existing error debugging approaches often point to bogus error locations or lack sufficient information for removing the type error, making error debugging ineffective. Counter-factual typing (CFT) addressed this problem by generating comprehensive error messages with each message includes a rich set of information. However, CFT has a large response time, making it too slow for interactive use. In particular, our recent study shows that programmers usually have to go through multiple iterations of updating and recompiling programs to remove a type error. Interestingly, our study also reveals that program updates are minor in each iteration during type error debugging. We exploit this fact and develop eCFT, an efficient version of CFT, which doesn't recompute all error fixes from scratch for each updated program but only recomputes error fixes that are changed in response to the update. Our key observation is that minor program changes lead to minor error suggestion changes. eCFT is based on principal typing, a typing scheme more amenable to reuse previous typing results. We have evaluated our approach and found it is about 12.4× faster than CFT in updating error fixes.

In C programs, error specifications, which specify the value range that each function returns to indicate failures, are widely used to check and propagate errors for the sake of reliability and security. Various kinds of C analyzers employ error specifications for different purposes, e.g., to detect error handling bugs, yet a general approach for generating precise specifications is still missing. This limits the applicability of those tools. In this paper, we solve this problem by developing a machine learning-based approach named MLPEx. It generates error specifications by analyzing only the source code, and is thus general. We propose a novel machine learning paradigm based on transfer learning, enabling MLPEx to require only one-time minimal data labeling from us (as the tool developers) and zero manual labeling efforts from users. To improve the accuracy of generated error specifications, MLPEx extracts and exploits project-specific information. We evaluate MLPEx on 10 projects, including 6 libraries and 4 applications. An investigation of 3,443 functions and 17,750 paths reveals that MLPEx generates error specifications with a precision of 91% and a recall of 94%, significantly higher than those of state-of-the-art approaches. To further demonstrate the usefulness of the generated error specifications, we use them to detect 57 bugs in 5 tested projects.

Device-to-device (D2D) is a new paradigm in cellular networks that enhances network performance by introducing increased spectral efficiency and reduced communication delay. Efficient data dissemination is indispensable for supporting many D2D applications such as content distribution and location-aware advertisement. In this work, we investigate a new and interesting data dissemination problem where the receivers are not explicitly known and data must be disseminated to the receivers within a probabilistic delay budget. We propose to exploit data depositories, which can temporarily house data and deliver them to interested receivers upon requests. We formally formulate the delay-constrained profit maximization problem for data deposition in D2D networks and show its NP-hardness. Under the unique mobile opportunistic network setting, a practical solution must be distributed, localized, and online. To this end, we introduce three algorithms for Direct Online Selection of 1-Depository, Direct Online Selection of L-Depositories, and Mixed Online Selection of L-Depositories. To demonstrate and evaluate the system, we implement a prototype using Google Nexus handsets and conduct experiments for five weeks. We further carry out simulations based on real-world mobility traces for evaluation of large-scale networks and various network settings that are impractical to experiment.

Providing better supports for debugging type errors has been an active research area in the last three decades. Numerous approaches from different perspectives have been developed. Most approaches work well under certain conditions only, for example, when type errors are caused by single leaves and when type annotations are correct. However, the research community is still unaware of which conditions hold in practice and what the real debugging situations look like. We address this problem with a study of 3 program data sets, which were written in different years, using different compilers, and were of diverse sizes. They include more than 55,000 programs, among which more than 2,700 are ill typed. We investigated all the ill-typed programs, and our results indicate that current error debugging support is far from sufficient in practice since only about 35% of all type errors were caused by single leaves. In addition, type annotations cannot always be trusted in error debuggers since about 30% of the time type errors were caused by wrong type annotations. Our study also provides many insights about the debugging behaviors of students in functional programming, which could be exploited for developing more effective error debuggers.

Type inference is convenient by allowing programmers to elide type annotations, but this comes at the cost of often generating very confusing and opaque type error messages that are of little help to fix type errors. Though there have been many successful attempts at making type error messages better in the past thirty years, many classes of errors are still difficult to fix. In particular, current approaches still generate imprecise and uninformative error messages for type errors arising from errors in grouping constructs like parentheses and brackets. Worse, a recent study shows that these errors usually take more than 10 steps to fix and occur quite frequently (around 45% to 60% of all type errors) in programs written by students learning functional programming. We call this class of errors, nonstructural errors . We solve this problem by developing Learnskell, a type error debugger that uses machine learning to help diagnose and deliver high quality error messages, for programs that contain nonstructural errors. While previous approaches usually report type errors on typing constraints or on the type level, Learnskell generates suggestions on the expression level. We have performed an evaluation on more than 1,500 type errors, and the result shows that Learnskell is quite precise. It can correctly capture 86% of all nonstructural errors and locate the error cause with a precision of 63%/87% with the first 1/3 messages, respectively. This is several times more than the precision of state-of-the-art compilers and debuggers. We have also studied the performance of Learnskell and found out that it scales to large programs.

In wireless rechargeable sensor networks (WRSNs), prior studies mainly focus on the optimization of power transfer efficiency. In this work, we consider the cost for building and operating WRSNs. In the network, sensor nodes can be charged by mobile chargers, that have limited energy which is used for charging and moving. We introduce a novel concept called “shuttling” and introduce an optimal charging algorithm, which is proven to achieve the minimum number of chargers in theory. We also point out the limitations of the optimal algorithm, which motivates the development of solutions named Push-Shuttle-Back (PSB). We formally prove that PSB achieves the minimum number of chargers and the optimal shuttling distance in a 1D scenario with negligible energy loss. When the loss in wireless charging is non-negligible, we propose to exploit detachable battery pack (DBP) and propose a DBP-PSB algorithm to avoid energy loss. We further extend the solution to 2D scenarios and introduce a new circle-based “shortcutting” scheme that improves charging efficiency and reduces the number of chargers needed to serve the sensor network. We carry out extensive simulations to demonstrate the performance of the proposed algorithms, and the results show the proposed algorithms achieve a low overall cost.

The authors of "Low-Cost Collaborative Mobile Charging for Large-Scale Wireless Sensor Networks" which appeared in August issue of this journal [ibid., vol. 16, no. 8, pp. 2213–2227, Aug. 2017] would like to correct a typo that occurred in Fig. 1. The numbers above the X axis were wrong. The corrected Fig. 1 is provided