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In present paper, we put forward four types of hesitant fuzzy β covering rough sets (HFβCRSs) by uniting covering based rough sets (CBRSs) and hesitant fuzzy sets (HFSs). We firstly originate hesitant fuzzy β covering of the universe, which can induce two types of neighborhood to produce four types of HFβCRSs. We then make further efforts to probe into the properties of each type of HFβCRSs. Particularly, the relationships of each type of rough approximation operators w.r.t. two different hesitant fuzzy β coverings are groped. Moreover, the relationships between our proposed models and some other existing related models are established. Finally, we give an application model, an algorithm, and an illustrative example to elaborate the applications of HFβCRSs in multi-attribute decision making (MADM) problems. By making comparative analysis, the HFβCRSs models proposed by us are more general than the existing models of Ma and Yang and are more applicable than the existing models of Ma and Yang when handling hesitant fuzzy information.

To improve the application-level data efficiency, the scheduling of coflows, defined as a collection of parallel flows sharing the same objective, is prevailing in recent data centers. Meanwhile, optical circuit switches (OCS) are gradually applied to provide high data rate with low power consumption. However, so far few research outputs have covered the flow, let alone the coflow, scheduling in the context of OCS. In this work, we investigate coflow scheduling in OCS-based data centers. We first derive a novel operation called regularization processed respectively on the flow traffic demands and the flow start times, which can be efficiently implemented and reduce the circuit reconfiguration frequency dramatically. We then propose a 2-approximation algorithm, called Reco-Sin , for single coflow scheduling to minimize the coflow completion time (CCT). For multiple coflows, we derive Reco-Mul to minimize the total weighted CCT, which can transform any non-preemptive multi-coflow scheduling in packet switches to a scheduling scheme in OCS. Reco-Mul can achieve a constant approximation under the assumption that no tiny flows will be transmitted in OCS. To get rid of this assumption, we present another multiple coflow scheduling scheme, named Reco-Mul+ , which has an approximation ratio of $O(K)$ . Here, $K$ is the total number of coflows. Extensive simulations based on Facebook data traces show that our approaches outperform state-of-the-art schemes significantly, i.e., one single coflow can be finished up to $1.97\times $ faster with Reco-Sin , and multiple coflows can be completed up to more than $2\times $ faster with Reco-Mul and Reco-Mul+ .

Driven by a wide range of real-world applications, several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. Even if the repetitive motions are fairly well detectable through the wireless signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. To tackle these challenges, we propose a novel wireless sensing system, called Motion-Fi $\ ^+$ + , which marries battery-free wireless backscattering and device-free sensing in one clean sheet. Motion-Fi $\ ^+$ + is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-user performing certain motions simultaneously because of the relatively short transmission range of backscattered signals and dedicated signal separation method. We implement a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5 percent count error. With little efforts in learning the patterns, our method could achieve 95.2 percent motion-recognition accuracy for a variety of 7 typical motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100 percent with only three repetitions. Our experiments also show that the motions of multiple persons separating by around $ 2$ meters cause little accuracy reduction in the counting process.

The design of novel catalytic materials towards sustainable energy production and storage is important for tackling energy shortage and environmental issues. Here, we report the preparation of cobalt nanoparticles encapsulated by a nitrogen doped carbon nano-onion (Co-NCNO) and their applications in the oxygen reduction reaction (ORR) and lithium-ion batteries (LIBs). Co-NCNO was prepared by one-step carbonization using an imidazolium ionic liquid as the carbon and nitrogen source. An in situ transmission electron microscope (in situ TEM) was used for the real time investigation of the carbon nano-onion formation process during a heating process from 450 °C to 850 °C. When applied to the ORR, Co-NCNO-850 exhibited high activity with a half-wave potential of 767 mV in 0.1 M KOH solution and high structural stability. Density functional theory (DFT) simulation indicates that the cobalt enhances the adsorption of reaction intermediates on the nitrogen doped carbon surface. Furthermore, Co-NCNO-850 also exhibited excellent performance for LIBs with a specific capacity of 774 mA h g−1 at a current density of 0.5C after 500 cycles. The ionic liquid assisted synthesis of Co-NCNO opens a new avenue for the efficient fabrication of hybrid nano-catalysts for energy applications.

Privacy has become one of the major concerns in cloud video surveillance. Privacy protection of the surveillance videos strive to protect users’ privacy information without hampering regular security tasks of the surveillance, meanwhile retains the system’s high accuracy and efficiency. The current state of the art in protecting the video privacy is mainly realized through Privacy Region Protection, which only protects the privacy regions while keeps the non-privacy regions visually intact so that processing in the cloud is still feasible. However, the problem of determining the privacy regions has been ignored and not properly addressed. In this paper, we propose a novel notion - concept graph , and with the aid of that, we develop our system – PatronuS to determine the privacy regions subject to satisfying both privacy and security requirements. We further propose an event distilling model and a privacy inference model to assist in determining specific privacy regions. And we evaluate PatronuS in real-world settings and demonstrate its efficiency in privacy protection without degrading system’s surveillance functionality.

To prevent man-in-the-middle attack, Diffie-Hellman first proposed the concept of the session key exchange protocol, in which the author remove the long term keying material at the end of the session. In our scheme, we apply the session key concept of Diffie-Hellman for cloud computing in the presence of the Trusted Third Party (TTP). Our proposed scheme provides SEVEN security properties by using the session key exchange protocols as symmetric. In our scheme, TTP becomes free from a burden of activities, like to encrypt the requests (Ri) of the Cloud Service Users (CSUs) and to decrypt the services (Si) of the Cloud Service Providers (CSPs) again and again. By using S Performance (EP) for Elliptic Curve Point Multiplication (ECPM), Hash (H), Inversion (Inv), Exclusive oR (XoR), Encryption (Enc), and Decryption (Dec) is (87.5%, 62.5%, 57.14%), (87.5% and 75%), (75%), (97.82% and ≤ 1%), (92.85%) and (50%) respectively. The security requirements of our scheme are; data integrity, data confidentiality, authenticity, non-repudiation, forward secrecy, unforgeability, and untraceability. Our proposed scheme also outperformed performance in terms of flexibility, reliability, and efficiency as compared to existing schemes.

Photocatalysis for selective organic transformations has received tremendous attention recently. However, how to achieve high selectivity in the photocatalysis of organic reaction remains a challen...

The scale of the fractures can vary, making the seismic velocity and anisotropy substantially scale dependent. Two mechanisms of the scale-dependent phenomenon may be considered: scattering and wave-induced fluid flow. In this study, we measure the scale-dependent velocity and anisotropy effects through laboratory experiments on porous and non-porous artificial rocks containing aligned fractures. This allows us to isolate the effects of these two mechanisms for the first time, yielding some insights into the scale-dependent phenomenon. For short-wavelength waves, scattering dominates with less wave-induced fluid flow effects. For intermediate- and long-wavelength waves, the P-wave is strongly scale dependent mainly due to wave-induced fluid flow mechanism, and the slow shear-wave is also strongly scale dependent but due to both scattering and wave-induced fluid flow. However, the fast shear-wave is almost scale independent. Moreover, a multi-scale equivalent medium theory can model the P-wave propagation accurately.

Antimonene is an emerging semiconducting two-dimensional (2D) material with various attractive properties, but its applications in optoelectronics have rarely been explored. Herein, we report a highly efficient preparation of few-layered antimonene by a polymer ionic liquid (PIL) assisted liquid-phase exfoliation method, which gives above 20% yield of micrometer sized nanosheets. Stability studies suggested that the presence of water in the solvent significantly accelerated the decomposition of the produced antimonene, while the antimonene dispersed in dry DMF exhibited a very high stability under ambient conditions. For the first time, flexible photodetectors based on a hybrid structure of few layer antimonene nanosheets modified by PIL and CdS quantum dots (QDs) were fabricated, which showed a good responsivity of 10 μA W−1 and on/off ratio of 26.8 under the bias of 1 V. This work demonstrates that PIL assisted liquid-phase exfoliation is a very promising method to achieve highly efficient preparation, preservation, and surface modification of few-layer antimonene, and hence enable the application of antimonene-based 2D materials in optoelectronics.

Sb nanosheets, also known as antimonene, have received ever-growing consideration as a promising new type of two-dimensional (2D) material due to their many attractive properties. However, how their nonlinear optical (NLO) properties are affected by their nanosheet structure and measurement conditions remains unclear. Herein, we report a successful size-selective production method for Sb nanosheets, which is based on a combination of lithium ion intercalation, solvent exfoliation and size selection centrifugation. This high-yield and size-selective preparation method enables fundamental investigation on the relation of the intrinsic optical properties of Sb nanosheets. Nanosecond Z-scan measurements revealed a unique size-dependent broadband NLO response. When the average size is reduced from 3 micrometers to 50 nanometers, the Sb nanosheets exhibit a clear transition from saturable absorption to reversed saturable absorption. Ultrafast transient absorption spectroscopic investigation indicated that exciton cooling is significantly faster in a small nanosheet than in large ones, revealing that the different exciton relaxation dynamic plays key roles in the distinct size-tunable nonlinear optical response. This work paves new ways towards the mass production and practical application of antimonene.

In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors’ activation schedules according to their received energy. We formulate our problem as the maximum QoM CHA rging and S ch E duling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve $1/6$ 1 / 6 - and $1/(4 + \epsilon)$ 1 / ( 4 + e ) -approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

In edge-cloud computing, a set of servers (called edge servers) are deployed near the mobile devices to allow these devices to offload their jobs to and subsequently obtain their results from the edge servers with low latency. One fundamental problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of the job and the arrival of the computation result at the device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and at arbitrary times at the mobile devices and then offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time of all the jobs. The weight is set based on how latency-sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc , which is scalable in the speed augmentation model; that is, OnDisc is $(1 + \varepsilon )$ -speed $O(1/\varepsilon )$ -competitive for any small constant $\varepsilon >0$ . Moreover, OnDisc can be easily implemented in distributed systems. We also extend OnDisc with a fairness knob to incorporate the trade-off between the average job response time and the degree of fairness among jobs. Extensive simulations based on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms.

The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases people's concern on privacy infringement. Here, we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their intention. To make this work, we need to address three challenges: 1) how to enable users explicitly express their intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.

With the proliferation of mobile devices and various sensors ( e.g. , GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users’ privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e. , the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93% F-score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility.

A coflow is a collection of related parallel flows that occur typically between two stages of a multi-stage computing task in a network, such as shuffle flows in MapReduce. The coflow abstraction allows applications to convey their semantics to the network so that application-level requirements can be better satisfied. In this paper, we study the routing and scheduling of multiple coflows to minimize the total weighted coflow completion time (CCT). We first propose a rounding-based randomized approximation algorithm, called OneCoflow , for single coflow routing and scheduling. The multiple coflow problem is more challenging as coexisting coflows will compete for the same network resources, such as link bandwidth. To minimize the total weighted CCT, we derive an online multiple coflow routing and scheduling algorithm, called OMCoflow . We then derive a competitive ratio bound of our problem and prove that the competitive ratio of OMCoflow is nearly tight. To the best of our knowledge, this is the first online algorithm with theoretical performance guarantees which considers routing and scheduling simultaneously for multi-coflows. Compared with existing methods, OMCoflow runs more efficiently and avoids frequently rerouting the flows. Extensive simulations on a Facebook data trace show that OMCoflow outperforms the state-of-the-art heuristic schemes significantly (e.g., reducing the total weighted CCT by up to 41.8% and the execution time by up to 99.2% against RAPIER).

Real-time handwriting tracking is important for many emerging applications such as Artificial Intelligence assisted education and healthcare. Existing movement tracking systems, including those based on vision, ultrasound or wireless technologies, fail to offer high tracking accuracy, no learning/training/calibration process, low tracking latency, low cost and easy to deploy at the same time. In this work, we design and evaluate a wireless backscattering based handwriting tracking system, called MilliBack, that satisfies all these requirements. At the heart of MilliBack are two Phase Differential Iterative (PDI) schemes that can infer the position of the backscatter tag (which is attached to a writing tool) from the change in the signal phase. By adopting carefully-designed differential techniques in an iterative manner, we can take the diversity of devices out of the equation. The resulting position calculation has a linear complexity with the number of samples, ensuring fast and accurate tracking. We have put together a MilliBack prototype and conducted comprehensive experiments. We show that our system can track various handwriting traces accurately, in some testings it achieve a median error of 4.9 mm. We can accurately track and reconstruct arbitrary writing/drawing trajectories such as equations, Chinese characters or just random shapes. We also show that MilliBack can support relatively high writing speed and smoothly adapt to the changes of working environment.

The quality factor $Q$ is an indispensable parameter for studying wave propagation in viscoelastic media. $Q$ can not only be implemented for improving the quality of wave records but can also be used for directly indicating frequency-dependent anomalies induced by fluids, so it is widely used in seismic exploration and clinical medicine. $Q$ estimation here refers to the extraction of $Q$ information from seismic data; it has aroused lots of attention but is still somewhat controversial due to the limitations of existing methods. In this letter, combined with a logarithmic spectral ratio (LSR) algorithm, we have introduced a sparse-constrained inversion spectral decomposition (ISD) method for average- $Q$ estimation (LSR-ISD), and have used shaping regularization to solve for the spectrum ratio. Then, through regularized linear inversion, average- $Q$ was converted to an interval- $Q$ value. Finally, we have applied this method to synthetic data and field data. Numerical examples and field data application demonstrate that the proposed method produces a series of results with high resolution and good stability.

Simultaneous source acquisition has attracted more and more attention from geophysicists because of its cost savings, whereas it also brings some challenges that have never been addressed before. Deblending of simultaneous source data is usually considered as an underdetermined inverse problem, which can be effectively solved with a least-squares (LS) iterative procedure between data consistency ([Formula: see text]-norm) and regularization ([Formula: see text]-norm or [Formula: see text]-norm). However, when it comes to abnormal noise that follows non-Gaussian distribution and possesses high-amplitude features (e.g., erratic noise, swell noise, and power line noise), the [Formula: see text]-norm is a nonrobust statistic that can easily lead to suboptimal deblended results. Although abnormal noise can be attenuated in the common source domain at first, it is still challenging to apply a coherency-based filter due to the sparse receiver or crossline sampling, e.g., that commonly found in ocean bottom node (OBN) acquisition. To address this problem, we have developed a normalized shaping regularization to make the inversion-based deblending approach robust for the separation of blended data when abnormal noise exists. Its robustness comes from the normalized shaping operator defined by the confidence interval of normal distribution, which minimizes the abnormal risk to a normal level to satisfy the assumption of LS shaping regularization. In special cases, the proposed approach will revert to the classic LS shaping regularization once the normalized coefficient is large enough. Experimental results on synthetic and field data indicate that the proposed method can effectively restore the separated records from blended data at essentially the same convergence rate as the LS shaping regularization for the abnormal noise-free scenario, but it can obtain better deblending performance and less energy leakage when abnormal noise exists.

In order to study influence of tongue clearance on the hydraulic performance of double support vortex pump, three dimensional flow model with symmetric variable size tongue clearance was established by changing section parameter of tongue clearance, and applied to numerically simulate the steady inner flow in a vortex pump based on fixed working conditions and impeller through ANSYS FLUENT. The numerical results show the influence of tongue channel with two sections (non-uniform and uniform sections) on flow field characteristics in vortex pump. Firstly, the variation of tongue channel section changes the vortex structure distribution in circumferential flow channel. Specifically the spiral forward vortex structure in the circumferential direction shifts to large radius side with increasing tongue width. Secondly, the circumferential velocity gradient and axial pressure gradient both diminish with the increase of the tongue section, and the inlet/outlet pressure difference and velocity distribution also reduce with increasing tongue channel section. Finally, for vortex pump with non-uniform section of tongue channel, the head diminishes and the efficiency remains constant approximately with the increase of the tongue width, while the head and the efficiency both diminish for uniform section of tongue channel. Keywords: Vortex pump, Tongue clearance, Non-uniform flow passage, Numerical simulation

Cyber attacks and malfunctions challenge the wide applications of Internet of Things (IoT). Since they are generally designed as embedded systems, typical auto-sustainable IoT devices usually have a limited capacity and a low processing power. Because of the limited computation resources, it is difficult to apply the traditional techniques designed for personal computers or super computers, like traffic analyzers and antivirus software. In this paper, we propose to leverage statistical learning methods to characterize the device behavior and flag deviations as anomalies. Because the system statistics, such as CPU usage cycles, disk usage, etc., can be obtained by IoT application program interfaces, the proposed framework is platform and deviceindependent. Considering IoT applications, we train multiple machine learning models to evaluate their feasibility and suitability. For the target auto-sustainable IoT devices, which operate well-planned processes, the normal system performances can be modeled accurately. Based on time series analysis methods, such as local outlier factor, cumulative sum, and the proposed adaptive online thresholding, the anomalous behaviors can be effectively detected. Comparing their performances on detecting anomalies as well as the computation sources required, we conclude that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred.

Social network data is widely shared, transferred and published for research purposes and business interests, but it has raised much concern on users’ privacy. Even though users’ identity information is always removed, attackers can still de-anonymize users with the help of auxiliary information. To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. However, most existing approaches assume specific and restrict network structure as background knowledge and ignore semantic level prior belief of attackers, which are not always realistic in practice and do not apply to arbitrary privacy scenarios. Moreover, the privacy inference attack in the presence of semantic background knowledge is barely investigated. To address these shortcomings, in this work, we introduce knowledge graphs to explicitly express arbitrary prior belief of the attacker for any individual user. The processes of de-anonymization and privacy inference are accordingly formulated based on knowledge graphs. Our experiment on data of real social networks shows that knowledge graphs can power de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. This suggests the validity of knowledge graphs as a general effective model of attackers’ background knowledge for social network attack and privacy preservation.

China is accelerating automotive electrification to address the pressing oil shortage and environmental pollution issues. Automotive electrification can be achieved through four different major technology pathways: hybrid electric vehicles, plug-in hybrid electric vehicles, battery electric vehicles and fuel cell electric vehicles. These pathways all heavily rely on the use of critical mineral resources, such as rare earth elements (REEs). This study establishes different scenarios of the future technology mix and growth in automotive electrification in China by 2030 to predict the future demand of REEs associated with such scenarios. The widely applied Bass model is chosen to predict the future growth of these four technology pathways for electric vehicles under pessimistic, neutral and optimistic demand scenarios. Given the potential for technological advances, the effects of changes in the material intensity and component substitution are considered to effectively reflect future demand changes. Accordingly, the REE demand associated with the four technology pathways from 2018 to 2030 is estimated. The highest demand for REEs in automotive electrification will reach 315 thousand tons, accounting for 22% of global production during the prediction period. Specifically, the demands for Nd, Dy, Ce, Pr, and La will account for 51%, 20%, 12%, 9.5%, and 7.7% of the total demand, respectively. Moreover, the contrast between the supply and demand of Dy and Pr will be extremely large, and these elements will require more attention than others. For the successful development of automotive electrification in China, related policies and plans regarding the supplies of different types and quantities of REEs should be urgently established.

Following the trend of data trading and data publishing, many online social networks have enabled potentially sensitive data to be exchanged or shared on the web. As a result, users' privacy could be exposed to malicious third parties since they are extremely vulnerable to de-anonymization attacks, i.e., the attacker links the anonymous nodes in the social network to their real identities with the help of background knowledge. Previous work in social network de-anonymization mostly focuses on designing accurate and efficient de-anonymization methods. We study this topic from a different perspective and attempt to investigate the intrinsic relation between the attacker's knowledge and the expected de-anonymization gain. One common intuition is that the more auxiliary information the attacker has, the more accurate de-anonymization becomes. However, their relation is much more sophisticated than that. To simplify the problem, we attempt to quantify background knowledge and de-anonymization gain under several assumptions. Our theoretical analysis and simulations on synthetic and real network data show that more background knowledge may not necessarily lead to more de-anonymization gain in certain cases. Though our analysis is based on a few assumptions, the findings still leave intriguing implications for the attacker to make better use of the background knowledge when performing de-anonymization, and for the data owners to better measure the privacy risk when releasing their data to third parties.

In community emergency management, it is crucial to generate evacuation task plans (ETPs) to help reduce risks in complex disaster situations. Case-driven and model-driven approaches have their respective advantages in generating ETPs, which can complement each other. With case-driven approach, historical experience can be fully used to establish the relationship between typhoon scenarios and historical ETPs. Through model-driven approach, the continuity of ETPs can be guaranteed when required information to operate the plans is missing. This study aims at proposing an integrated approach that can combine the benefits of both case-driven approaches and model-driven approaches. Based on the structural modeling of evacuation tasks, this paper proposes an integrated approach to generate ETPs for community typhoon emergencies. Finally, a case that is based on actual problems is provided to verify the reasonability and effectiveness of the proposed method.

Fractures are widely distributed within the Earth’s crust and have a substantial influence on wave propagation. Although many studies have been conducted on fracture characterization based on seismic anisotropic attributes, natural horizontal fractures and hydraulic fracture networks have yet to be characterized. Here, we investigate the influence of the fracture orientation and saturation on the Vp/Vs ratio via laboratory observations and theoretical modeling. Then, we discuss the feasibility of using the Vp/Vs ratio to predict natural horizontal fractures and evaluate hydraulic fracture networks. Our study demonstrates that an abnormally low Vp/Vs ratio, together with a lack of shear wave splitting, could be used to predict horizontal fractures. Fracture network development that results from hydraulic fracturing stimulation is associated with a much higher Vp/Vs ratio and no shear wave splitting; these two characteristics could be used to estimate fracture development and evaluate hydraulic fracturing stimulation.

In this paper, a set of accountable protocols denoted as AccountTrade is proposed for big data trading among dishonest consumers. For achieving a secure big data trading environment, AccountTrade achieves book-keeping ability and accountability against dishonest consumers throughout the trading (i.e., buying and selling) of datasets. We investigate the consumers’ responsibilities in the dataset trading, then we design AccountTrade to achieve accountability against dishonest consumers that are likely to deviate from the responsibilities. Specifically, a uniqueness index is defined and proposed, which is a new rigorous measurement of the data uniqueness for this purpose. Furthermore, several accountable trading protocols are presented to enable data brokers to blame the misbehaving entities when misbehavior is detected. The accountability of AccountTrade is formally defined, proved, and evaluated by an automatic verification tool as well as extensive simulation with real-world datasets. Our evaluation shows that AccountTrade incurs at most 10-kB storage overhead per file, and it is capable of 8–1000 concurrent data upload requests per server.

The widely deployed WiFi APs and ZigBee devices make the coexistence for these two networks more pervasive than ever. Previous solutions focus on the contention resolution in time domain, but would lead to the underutilized frequency resources. However, for frequency domain schemes, to enable efficient coexistence for WiFi and ZigBee networks, the WiFi nodes or ZigBee nodes have to select the appropriate channel for transmission. Such scheme will need strict cooperations among these two independent systems, and could not work with the conventional legacy systems seamlessly. In this paper, we propose a frequency overlay approach named COFFEE (COexist wiFi For zigbEE networks), where subcarriers interfering with ZigBee transmissions are nullified by WiFi. According to our basic evaluations, due to the relatively robust feature in WiFi/OFDM design, nullifying small portion of subcarriers would affect the WiFi performance slightly, but could improve the capability for coexistence significantly. Thus by using COFFEE, WiFi and ZigBee nodes can transmit their packets concurrently without any coordination. Also, COFFEE and conventional legacy WiFi systems could work together seamlessly. Furthermore, we implement COFFEE with USRP software radio platform and evaluate the performance under real wireless network scenarios. The extensive evaluations show that compared to the coexistence schemes in time domain, COFFEE is able to increase the network throughput by more than 300%. Even when the ZigBee node never knows the transmission time of WiFi packet, it still can provide concurrent lossless transmission by using COFFEE with only 10% to 15% WiFi throughput reduction.

In this paper, an analytical design space of power amplifiers (PAs) including the Class-A/B/J continuum for dynamic load modulation (DLM) at the current generator plane (CGP) is proposed based on the theory of load modulated (LM) continuous Class-B/J PAs. By introducing a biasing operation factor ρ, the theory of DLM PAs provides an analytical design space for all the operation modes from Class-B/J mode to Class-A mode. The analytical design space of DLM PAs shows that high efficiency is maintained at a large dynamic range of output power back-off (OPBO) with a purely resistive load modulation at the CGP when Class-B/J mode is performed. For deep Class-AB mode, the analytical design space of the DLM PAs shows that a combination of resistive and reactive load modulation at the CGP is used to maintain the high efficiency at the OPBO. The effectiveness of the proposed analytical design space is validated by load-pull measurements of a bare-chip gallium nitride (GaN) device.

Seismic prospecting is one of the most important geophysical survey methods to understand the earth’s structure. Properly compensating the anelastic attenuation of seismic data help to clearly characterize the structure of subsurface. Inverse $Q$ -filtering processing is one of the most efficient ways to compensate for anelastic attenuation caused by the earth’s structure. Stability is the most challenging task in inverse $Q$ -filtering processing. In this letter, we introduce a shaping regularization method to stabilize the amplitude compensation operator in the inverse $Q$ -filtering. The stabilization operator can be used simultaneously with a phase correction operator. To further improve the robustness of our method to noise, we adopt an adaptive tapering window to control the compensation frequency components according to the frequency bandwidth of seismic data. These schemes can accentuate the attenuated amplitude through $Q$ -filtering and improve the stability of our method to noise. To demonstrate the effectiveness of our method, we first apply it to synthetic examples and to a real seismic data. Both results illustrate that the proposed shaping regularization inverse $Q$ -filtering is an efficient adaptive data-driven inverse $Q$ -filtering method.

Edge computing is a promising cloud computing paradigm that reduces computing latency by deploying edge servers near data sources and users, which is of great importance to implement delay-sensitive applications like AR, Cloud Gaming and Auto Driving. Due to the limited resources of edge servers, task dispatching and function configuration are the key to fully utilize edge servers. Moreover, a typical task request in edge computing (called a co-task) is consisted of a set of subtasks, where the task completion time is determined by the latest completed subtask. In this work, we propose a scheme named OnDisco, which combines reinforcement learning and heuristic methods to minimize the average completion time of co-tasks. Compared with heuristic algorithm, deep reinforcement learning can learn the inherent characteristics of the environment without any prior knowledge, and OnDisco is therefore well adapted to varying environments. Simulations on Alibaba traces shows that OnDisco reduces the average task completion time by \(58\%\) and \(76\%\) compared with the heuristic and random algorithm, respectively. Moreover, OnDisco outperforms the baselines consistently in various data environments and parameter settings.

Falls are the leading cause of fatal and non-fatal injuries for the elderly, and fall detection system is critical for reducing the aid response time. Wearable sensor-based system becomes popular for its convenience and non-invasion of privacy. In this paper, we focus on fall detection system to be integrated into low-cost COTS (Commercial Off-The-Shelf) smart band, which is expected to be worn by the elderly for a long time due to its advantages of low price and low power consumption. Aiming at the low sampling rate property inherent in such devices, we analyze the characteristic of fall signal and propose a system which achieve better accuracy by combining the features of time domain, time-frequency domain and instantaneous frequency. We further apply data augmentation mechanism to tackle the issue of fall data lack. Extensive experiments are conducted to evaluate the proposed system. The results demonstrate that our system can achieve over 98% accuracy on our dataset and 97% accuracy on open source dataset.

Acoustic motion tracking is an exciting new research area with promising progress in the last few years. Due to the inherent low propagation speed in the air, acoustic signals have the unique advantage of fine sensing granularity compared to RF signals. Speakers and microphones nowadays are pervasively available in devices surrounding us, such as smartphones and voice-controlled smart speakers. Though promising, one fundamental issue hindering the adoption of acoustic-based motion tracking is that the positions of microphones and speakers inside a device are fixed, which greatly limits the flexibility of acoustic motion tracking. In this work, we propose a new modality of acoustic motion tracking using earphones. Earphone-based tracking mitigates the constraints associated with traditional smartphone-based tracking. With novel designs and comprehensive experiments, we show earphone-based motion tracking can achieve a great flexibility and a high accuracy at the same time. We believe this is an important step towards "earable" sensing.

A cross-region transfer learning method is proposed to identify community (e.g. car parks, public spaces and shopping centers) fire hazards based on text input provided by community members. The key component of the method, which also accounts for data imbalance, is an improved transfer component analysis (TCA) that is embedded with a local discriminant analysis (LDA) to transfer non-local rich knowledge to the fire hazard identification of local communities with an insufficient number of samples. In addition, a fire hazard knowledge map is established and applied to supplement the missing key features for fire hazard identification, and ontology modeling is applied to standardize the text features and reduce the effect of semantic ambiguity brought by cross-region knowledge transfer. The proposed method is verified based on the text data of nine fire hazard classes from Lanzhou and Beidaihe in China. Machine learning experiments show that fire hazard identification performance of all nine classes were improved with the overall accuracy, precision, recall, F1 score and AUC increased by 12%, 15%, 16%, 15% and 15%, respectively. Under data imbalance scenarios, the proposed method outperforms the state of the art methods, such as sampling-based methods, FastText and ULMFiT. The results also show that the proposed method can achieve desired performance with only half of the training samples. These findings illustrate that the proposed method can assist regions by improving fire identification results significantly through knowledge transfer. The proposed approach can be followed to build smart systems for community fire risk management with reasonable performance and high efficiency.

Using temporal dimension in optical multiplexing is a promising method to increase the security of data encryption. However, adjusting the fluorescence lifetime of light-emitting material often results in inevitable changes in their fluorescence spectra, which is unfavorable for confidential information protection. Here, we report the preparation of various perovskite quantum dot/polymer nanospheres (PQD/polymer) with tunable and long fluorescence lifetimes but identical fluorescence spectra, which are ideal multidimensional data encryption materials. This new data encryption strategy utilizes the water sensitivity of perovskite and achieves spatial dimension encryption of information using different water stabilities between uncoated perovskite quantum dots and PQD/polymer. The fluorescence lifetime of PQD/polymer is used as the coding element to achieve temporal dimension data encryption, and the data are decrypted by fluorescence lifetime imaging microscopy and time-gated luminescence imaging techniques. This study shows the potential of PQD/polymer as a new class of materials for advanced data encryption.

Mechanical vibration sensing/monitoring plays a critical role in today’s industrial Internet of Things (IoT) applications. Existing solutions usually involve directly attaching sensors to the target objects, which is invasive and may affect the operations of delicate devices. Non-invasive approaches such as video and laser methods have drawbacks in that, the former incurs poor performance in low light conditions, while the latter has difficulties to monitor multiple objects simultaneously.In this work, we design RF-Ear, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID hardware. RF-Ear could accurately monitor the mechanical vibrations of multiple (up to 8) devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear can measure the vibration frequency up to 400Hz with a mean error rate of 0.2%. Our evaluation results show that RF-Ear can effectively detect 0.2cm screw loose with 90% accuracy. We further employ each device’s unique vibration fingerprint to identify and differentiate devices of exactly the same model. We also show that RF-ear can monitor not just the vibrations but also a large range of mechanical motions. Comprehensive experiments conducted in a real power plant demonstrate the effectiveness of our system with outstanding performance.

Magnetic resonant coupling wireless power transfer (MRC-WPT) enables convenient device-charging. When MIMO MRC-WPT system incorporated with multiple relay components, both relay On-Off state (i.e., power routing) and TX current (i.e., current scheduling) could be adjusted for improving charging efficiency and distance. Previous approaches need the collaboration and feedback from the energy receiver (RX), achieved using side-channels, e.g., Bluetooth, which is time/energy-consuming. In this work we propose, design, and implement a multi-relay MIMO MRC-WPT system, and design an almost optimum joint optimization of power routing and current scheduling method, without relying on any feedback from RX. We carefully decompose the joint optimization problem into two subproblems without affecting the overall optimality of the combined solution. For current scheduling subproblem, we propose an almost-optimum RX-feedback independent solution. For power routing subproblem, we first design a greedy algorithm with $\frac{1}{2}$ approximation ratio, and then design a DQN based method to further improve its effectiveness. We prototype our system and evaluate it with extensive experiments. Our results demonstrate the effectiveness of the proposed algorithms. The achieved power transfer efficiency (PTE) on average is 3.2X, 1.43X, 1.34X, and 7.3X over the other four strategies: without relay, with non-adjustable relays, greed based, and shortest-path based ones.

RFID technology has recently been exploited for not only identification but also for sensing including trajectory tracking and gesture recognition. While contact-based (an RFID tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and the situation gets even worse when the target is non-static, restricting its applicability in real world deployment. In this work, we present TagRay, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enabling mobile object tracking and even material identification. We design and implement our prototype on commodity RFID device. Comprehensive experiments show that TagRay achieves a high accuracy of 1.3 cm which is a 200% improvement over the-state-of-arts for trajectory tracking. For commonly seen four material types, the material identification accuracy is higher than 95% even with interference from people moving around.

To further improve the convenience and effectiveness of human computer interaction (HCI) with smart devices, human activity recognition (HAR) has been widely studied from various aspects. Unfortunately, deep learning based methods often suffer from either expensive labeling efforts or weak generalization ability. Inspired by recently developed domain adaptation strategies, we propose XHAR, a novel adversarial deep domain adaptation framework for HAR using smart devices, providing better device and user adaptation. XHAR first selects the most similar source dataset (with label), then extracts device and user independent spatial-temporal features through the combinations of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) feature extractors. Moreover, it removes the distribution discrepancy using multiple domain discriminators, and finally performs adaptation on the target dataset (without label) to obtain the predicted labels. We conduct extensive experiments on 50 users (i.e., of different ages, genders, and body shapes) and 4 smart devices with two kinds of datasets (i.e., gesture activities and sport activities). We compare our method with the source-only model and several state-of-theart domain adaptation models. The results show that XHAR increases the classification accuracy by at least 4.81% (to 74.50%) on the adaptation between different users, and accordingly by at least 9.25% (to 69.23%) between different devices.

With the rapid advance of smart home and office systems, it becomes possible to provide a fine-grained user activity tracking service accurately recognizing user activities and identities in a seamless and non-invasive manner. Such a system can find applications in various domains, such as elder safeguard, customized services, and simply personal activity diary. Recently, several radio frequency (RF) based sensing systems were proposed for human sensing, most of which focus on limited scenarios and suffer from interference caused by other users or wireless devices. To tackle this challenge, we propose Back-Guard, which achieves accurate and non-intrusive user activity recognition and then user identification through battery-free wireless backscattering. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains that can characterize the user behaviors. Leveraging the parallel attention based deep learning models, our system can discriminate different motions and users accurately and robustly in various situations. We implement a prototype system and collect data in actual scenarios from 25 users for over 2 months. Extensive experiments demonstrate the promising performance of our system. In particular, Back-Guard achieves 93.4% activity recognition accuracy and 91.5% user identification accuracy, respectively. Our experiments also demonstrate little accuracy reduction when multiple users are separated, e.g., by around 2 meters.

This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming power, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We prototype In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37~mW average charging power inside a 10~cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4$\times$--18.1$\times$ power gain when the implant is stationary, and 5.3$\times$--7.4$\times$ power gain when the implant is in motion., In MobiCom 2020: The 26th Annual International Conference on Mobile Computing and Networking, London, 15 pages

Labeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model's output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints respectively. The proposed framework doesn't require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53\% execution time without loss of any valuable labels.

The widespread creative application and smart devices call for convenient and secure interaction with human users. We propose, design, and implement a smartwatch-based two-factor real-time identification and authentication system named ThumbUp, where smartwatch users can identify and authenticate themselves by some simple hand and finger gestures, such as thumb-up. ThumbUp leverages the signal collected from the Inertial Measurement Unit (IMU) in Commercial Off-The-Shelf (COTS) smart devices and discovers the unique fingerprint pattern produced by each user’s simple hand gestures using a carefully crafted deep learning model. We implement our system and conduct extensive experiments to evaluate its efficacy and efficiency with 65 different users over a period of more than 3 months. It reaches an accuracy of 97% for identification, and EER 0.014 for authentication using only one simple gesture. We also survey the users’ acceptance of our system and discuss how the proficiency of gestures affects authentication accuracy.

Since most information on S-wave velocity and density exists in the middle to large angle range of seismic data, traditional inversion methods based on the Zoeppritz approximation have difficulty in obtaining satisfactory results. Therefore, as a high-accuracy amplitude varied with angle (AVA) inversion, exact Zoeppritz (EZ) equation inversion has aroused a lot of attention in recent years. As for any other nonlinear inversion, iterative convergence and error are the important problems. In this letter, based on a Bayesian framework, we introduce optimal transport into EZ equation AVA inversion. Then, the limited-memory Broyden–Fletcher–Goldfarb–Shanno method is adopted to solve the regularization-constrained least-square argument function to obtain the inversion results, including P-wave velocity, S-wave velocity, and density. We compare this method with a conventional method, which is based on an L2 norm or weighted L2 norm as a residual method in the model test. The results show that the proposed method not only reduces the error of the results to be smaller than L2 norm, but it also improves the convergence rate.

Data quality assessment is vital for many information services ranging from sensor networks to smart city systems. The current data quality assessments, however, are often derived from intrinsic data characteristics, disconnected from specific application contexts, or are not applicable or efficient for large datasets. In this work, we propose a novel task-oriented data quality assessment framework, which balances between the intrinsic and contextual quality. We carefully craft the assessment metrics, quantify them, and fuse them to rank candidate datasets by quality given specific tasks. To improve the system efficiency, two fast calculation algorithms are designed to quantify the relationship between datasets and the task, and the distribution of data items. We conduct extensive evaluations on six public image datasets (with 460, 247 images in total) and four text document datasets (with 37, 372 documents in total) to evaluate the efficacy and efficiency of our design. Experimental results show that our algorithms can save about 90% computing time with little accuracy loss which validates the feasibility and effectiveness of our framework for large datasets.

Isotactic polybutylene-1 (iPB) has lots of advantages and is best used as hot water pipe. However, to transform into stable crystal form I, the iPB needs as long as 7 days. In this process, the irreversible damage brings great difficulties to the use of the iPB. The method which convert it directly into crystal I has shortcomings such as being requiring complex operation and being expensive. In this study, an innovative idea was put forward, not paying attention to the crystal transformation of iPB but only focusing on reducing the time it can be applied. In this study, bamboo powder was modified by the silane coupling agent KH570 (KBP) to prepare iPB/KBP composite. The infiltration test and Fourier transform infrared (FTIR) analysis showed that the hydrophilicity of KBP is greatly reduced, which can greatly improve the compatibility of the iPB and KBP. The tensile strength, tensile modulus, flexural strength, and flexural modulus of the composites storage for 3 days is equal to the pure iPB with storage 7 days with the KBP additions of 3%, 3%, 7%, and 5%, respectively. The heat deformation temperature (HDT) of the composite with 3% KBP after 1-day storage reached the value of pure iPB storage for 7 days. This provides more space and possibilities for the industrialization of the iPB. The crystallization behavior of iPB/KBP composites proves that the addition of KBP accelerates the crystallization rate of iPB, but the crystallinity of the iPB/KBP composites is not changed. The SEM photograph of iPB/KBP composites showed that when the KBP addition was low the compatibility between KBP and iPB was good. When the KBP addition was increased the agglomeration of KBP in the iPB was very obvious, which leads to the poor mechanical properties of the composite.

In this paper, we propose a system called "RFRecorder" based on COTS RFID system which can inspect the string vibration and recognize the tone and tempo accurately. Our system can recover the music score played by some string instruments such as ukulele. Specifically, the string vibration influences the reflection of RF signal and causes a phase change. This change can be captured and analyzed to recover the frequency of the string vibration. In addition, the RFID tag is attached on the body of instrument but not on the string, which can not affect the music playing. Compared to the recorder, our system is immune to the environmental noise. Furthermore, it also can work on NLoS scenario. For single-string vibration, we use compressive sensing to recover the frequency duo to the low sampling rate of the COTS RFID. For multiple-string vibration, we recognize the music tone using machine learning method. We build a prototype and test the performance using guitar, ukulele and zither, which achieves 90%, 89%, 85% accuracy respectively.

Desktop is a reliable and amicable object carrier that accompanies us in our daily life, while working, eating and even entertaining. In this work, we devise a contactless but accurate object tracking system on desktop with commercial RFIDs. Comparing with conventional vision or acoustic based solutions, our system needs less computational resources and could be mucheasier for deployment. Moreover, ours could record the true positions for each device instead of the relative positions delivered in most of the previous studies. To this end, recording the user's access to the object on the desktop allows the user to interact with the smart device with simple actions. We present RFdesk, a contactless object location system that accurately locates every objects on the desktop. However, compare to tracking object withcontacted tag, several challenges are tackled before we make the system work. First of all, the signal employed for contactless tracking gets reflected twice which is thus much weaker, making the signal more susceptible to environmental noise and multipath. Another well-known challenge for contactless tracking is multi-target tracking as the signals reflected from multiple targets get mixed and interact. Extensive experiments show that RFdesk canflexibly deploy devices and tags, antennas, and localization itemscan be deployed on different planes. A median error of 3cm can be achieved for target tracking without attaching tags to the targets, even if there are 4 positioning objects on the desktop. Moreover, the average localization error can still be kept within 5.6cm outperforming the state-of-the-art systems by 100%.

Nowadays, there is a growing demand for sharing multimedia data among participants in the same activity. With existing social applications, users need to conduct friending and data sharing operations manually, which is troublesome due to changing attendees and highly diverse data content of different activities. To tackle this issue, in this work we propose a novel system SHAD to achieve privacy-friendly shared activity detection and multimedia data auto-sharing based on users' historical multimodal data. Facing noisy, incomplete and asynchronous data, as well as inaccurate recognition results of machine learning models, we design an algorithm to aggregate multimodal data relevant to the same activity and propose an activity-semantic graph to comprehensively characterize each activity by fusing knowledge of multimodal data. Based on the activity-semantic graph, the privacy-preserving shared activity detection and data sharing method is designed, which protects both raw data and semantic information of data. We implemented our system and conducted comprehensive evaluations with real-life multimodal data (including photos and motion sensor data). The results show the efficacy of our system. We can achieve 94.9% precision and 91.5% recall for shared activity detection.

In this paper, we propose an indoor positioning system, Lightitude, which utilizes the already existed, uneven indoor light intensity distribution established by densely deployed indoor lights as the medium. As common indoor lights cannot act as landmarks due to lack of unique features (e.g., unique intensity or flicker frequency), systems that exploiting the received light intensity (RLI) usually impose strong constraints on user's motion and make ideal assumptions about the indoor environment. Different from these approaches, we first propose a realistic light intensity model to reconstruct the RLI distribution given any motion (position, orientation) of the receiver, thus RLI collected with every motion of the receiver could be used for positioning. Then we design a particle-filter-based positioning module, which harnesses user's natural mobility to eliminate the ambiguity of a single RLI. Experiment results show that Lightitude achieves mean accuracy 1.93m and 1.98m in an office (720m2) and a library (960m2) respectively. Lightitude is still robust against interferences like sunlight, shading of human-body and several user behaviors.