Application Programming Interface (API) recommendation based on graphs is a valuable task in the fields of data visualization and software engineering. However, this task was previously undefined until a recently published paper coining the task as Plot2API and utilizing a deep learning-based method named SPGNN. Compared to general image classification methods, this dedicated approach uses semantic parsing to exploit deep features and yields better performance. However, its performance declines sharply in unbalanced datasets, thus limiting its generalizability. To address this issue, we propose a method named Multi-cue Shape and software engineering-aware Transferable Imbalance Learning (MSTIL), consisting of three major components: Cross-Language Shape-Aware Plot Transfer Learning (CLSAPTL), Cross-Language API Semantic Similarity-based Data Augmentation (CLASSDA), and Imbalance Plot2API Learning (IPL). Motivated by the hierarchical classification of the graphs, CLSAPTL guides the model to learn the graphs’ class hierarchy and thereby enabling the model to learn more transferable visual features. Given that a graph can be associated with multiple APIs and motivated by the fact that many APIs that exert similar functions in different languages have semantically similar names, CLASSDA leverages the samples of APIs with semantically similar names to assist in feature learning. Finally, inspired by the essence of softmax cross entropy loss, IPL alleviates the imbalances between positive and negative samples during training. We conduct our experiments on two public datasets. Extensive experimental results shows that MSTIL improves the performance of classic CNNs along with the state-of-the-art method, demonstrating its effectiveness. Specifically, MSTIL has an average relative mAP improvement of 12.94% across the models on all datasets.

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced.Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories.We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

In today's blockchain system, designing a secure and high throughput blockchain on par with a centralized payment system is a difficult task. Sharding is one of the most worthwhile emerging technologies for improving the system throughput while maintain high-security level. However, previous sharding-related designs have two main limitations. First, the security and throughput of their random-based sharding system are not high enough as they did not leverage the heterogeneity among validators. Second, to design an incentive mechanism that promotes cooperation could incur a huge overhead on their system. In this article, we propose RepChain, a reputation-based secure and fast blockchain system via sharding, which also provides high incentive to stimulate node cooperation. RepChain utilizes reputation to explicitly characterize the heterogeneity among the validators and lay the foundation for the incentive mechanism. We propose a new double-chain architecture-a transaction chain and a reputation chain. For the transaction chain, an efficient Raft-based synchronous consensus has been presented. For the reputation chain, the synchronous Byzantine fault tolerance consensus that combines collective signing has been utilized to prevent the attack on both reputation score and the related transaction blocks. It supports a high throughput transaction chain with moderate generation speed. Moreover, we propose a reputation-based sharding and leader selection scheme. To analyze the security of RepChain, we propose a recursive formula to calculate the epoch security within only $\mathcal {O}(km^{2})$ time. Furthermore, we implement and evaluate RepChain on the Amazon Web Service platform. The results show our solution can enhance both throughout and security level of the existing sharding-based blockchain system.

Visible light positioning (VLP) is a promising direction for indoor localization. VLP depends on Visible light communication (VLC) to receive location anchors sent by light bulbs. In order to decode high-frequency VLC signals, today's VLP systems require the receiver to equip either rolling shutter cameras or high-frequency light sensors, which bring considerable overhead or are even unavailable on many mobile devices. This paper introduces ALS-P, a lightweight VLP approach which only requires the commercially widely available ambient light sensor (ALS). ALS is conventionally not treated as a feasible VLC receiver as its sampling rate is far less than that of VLC signals. Our basic idea is to leverage the property of frequency aliasing. Through dynamically adjusting the sampling rate of the ALS sensor, the down-converted signals can be uniquely distinguished. To realize this idea, we propose novel designs to address challenges which stem from ALS hardware, high-order light aliasing, and environmental interference cancellation. Besides, ALS can also enable a lightweight VLC via changing LED frequencies. We implement the ALS-P on commercial LED bulbs and the ALS of existing smartphones. The evaluation shows that the ALSP can enable robust and efficient LED frequency decoding with at least 4LEDs bulbs in practical scenarios. As a result, ALS-P can enable VLC at a data rate of 5bit/s per each LED bulb and achieve a sub-meter level indoor localization accuracy.

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden for receiving with cameras. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for devices with a camera. This paper presents the VLP system PIXEL, which realizes our idea. In PIXEL, we developed 1) a novel color-based modulation scheme to handle user's mobility and 2) a sensor-assisted positioning algorithm to ease user's burden in capturing multiple location anchors. Our experiments based on the prototype show that PIXEL can provide 10 cm-level real-time VLP for wearables and smartphones with camera resolution as coarse as 60 pixel × 80 pixel and CPU frequency as low as 300 MHz.

Since the mobile camera is often small in size and easy to conceal, existing anti-piracy solutions are inefficient in defeating mobile-camera-based piracy, thus it remains a serious threat to copyright of Intellectual property. This paper presents Rainbow, a low-cost lighting system which prevents mobile-camera-based piracy attacks on intellectual property in the physical world, e.g., art paintings. Through embedding invisible illuminance flickers and chromatic changes into the light, our system can significantly degrade the imaging quality of a camera while maintaining a good visual experience for human eyes. Extensive objective evaluations under different scenarios demonstrate that Rainbow is robust to different confounding factors and can effectively defeat piracy attacks on various mobile devices. Subjective tests on volunteers further evidence that our system can not only significantly pollute pirated photos but also be able to provide good lighting conditions.

Internet of Things (IoT) devices are largely embedded devices which lack a sophisticated user interface, e.g., touch screen, keyboard, etc. As a consequence, traditional Pre-Shared Key (PSK) based authentication for mobile devices becomes difficult to apply. For example, according to our study on home automation devices which leverage smartphone for PSK input, the current process does not protect against active impersonating attack and also leaks the Wi-Fi password to eavesdroppers, i.e., currently these IoT devices can be exploited to enter into critical infrastructures, e.g., home networks. Motivated by this real-world security vulnerability, in this paper we propose a novel proximity-based mechanism for IoT device authentication, called Move2Auth, for the purpose of enhancing IoT device security. In Move2Auth, we require user to hold smartphone and perform one of two hand-gestures (moving towards and away, and rotating) in front of IoT device. By combining (1) large RSS-variation and (2) matching between RSS-trace and smartphone sensor-trace, Move2Auth can reliably detect proximity and authenticate IoT device accordingly. Based on our implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, we prove Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.

This paper presents Shadow, a novel display-to-device communication system working in radio frequency. It leverages the Electromagnetic Radiation (EMR) emanated from display to transmit information. We modulate the signal transmitted to display to make the corresponding EMR signal fall into the FM band. In this way. when users' devices with FM ability are approaching to the display, it can automatically receive information from the display. Since Shadow does not rely on camera, wearable devices can also obtain information from display. In addition to the lightweight communication ability, Shadow keeps the communication truly invisible by only transmitting in the period that will not be shown on the display panel. Our system requires no modification on hardware, and we implement it with commodity display system and mobile devices. Results show it can achieve 1.5 kbps at distances of up to 20cm from the display panel.

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose SEER, a frame-level wideband spectrum adaptation solution which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the desired transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. SEER can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped SEER on the GNURadio/USRP platform to demonstrate its feasibility. Furthermore, using 1.6GHz channel measurements and trace-driven simulations, we have evaluated the merits of SEER over state-of-the-art approaches.

Performance anomaly is a well-known problem in 802.11 networks. It refers to the case where the aggregated throughput of the multirate network is degraded by slow rate links due to the fair sharing of transmission opportunities. As a result of growing heterogeneity in 802.11 hardware, the coexistence of slow rate and fast rate links are more common and performance anomaly is considered severer. In this paper, we propose to leverage the emerging full duplex technology to relief the penalty of performance anomaly for 802.11 network. Although in general cases, full duplex gain is limited by the uplink-downlink interference (UDI), we find in performance anomaly, it is possible to eliminate UDI and achieve full duplex communication in slow rate links through successive interference cancellation (SIC). To realize such idea, we design a novel MAC protocol for full duplex AP and half duplex clients. When performance anomaly occurs, the protocol leverages a distributed pairing algorithm to select slow rate links for full duplex communication to increase the network throughput. We implement the full MAC design in NS3 network simulator, results show it has an average throughput gain of 30% over legacy protocol.

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose Seer, a frame-level wideband spectrum adaptation system which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the intended transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. Seer can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped Seer on the GNURadio/USRP platform and tested it under various environments. Furthermore, our evaluation using 1.6GHz spectrum measurements shows that Seer largely improves system throughput over fixed channel configuration and state-of-the-art spectrum adaptation approaches.

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls.

Visible light positioning (VLP) is an emerging positioning technique that utilizes indoor light sources to broadcast archer locations through visible light communication (VLC). Benefited by the densely deployed light lamps, VLP holds the promise for more accurate positioning accuracy than RF based approaches, and thus enables interesting applications such as retail navigation and shelf-level advertising in supermarkets and shopping malls.

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden to receiving devices. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for resource-constrained wearable devices, e.g. smart glasses. Moreover, the polarization-based VLC can be applied to any illuminating light sources, thereby eliminating the dependency on LED.