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Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.

Pseudo-inverse learners (PILs) are a kind of feedforward neural network trained with the pseudoinverse learning algorithm, which can be traced back to 1995 originally. PIL is an approach for nongradient descent learning, and its main advantage is the lower computational cost and fast learning procedure, which is especially relevant in the edge computing research field. However, PIL is mostly applied to a deterministic learning problem, while in the real world, the greatest case that is of concern is the uncertainty learning problem. In this work, under the framework of the synergetic learning system (SLS), we introduce an approximated synergetic learning scheme, which can transform uncertainty learning into deterministic learning. We call this new learning framework the Bayesian PIL, and the advantages are also demonstrated in this work.

Fin clips (complete fin removal) or punch marks (stamping of a hole) in fins or operculum are quick and inexpensive methods to identify individuals or groups of fish. This study evaluated the retention of 6 mm diameter punch marks on the operculum, caudal (upper and lower lobes), dorsal, pelvic, and anal fins, and fin clips on adipose, pectoral, pelvic, dorsal and anal fins of rainbow trout Oncorhynchus mykiss for 279 days. Growth and survival were not significantly different among fish with the different mark types or locations. At the end of the experiment, 100% of adipose clips, 97% of pectoral and anal fin clips, and 93% of dorsal clips and pelvic fin clips were retained. Retention of punch marks was 57% and 50% for the lower and upper caudal fin lobe locations respectively, 23% for the dorsal fin, and 3% for the operculum. No pelvic or anal fin punch marks were identified at the end of the trial. Significant differences in retention among the treatment groups did not occur until 125 days post marking, after which all fin clip treatments had significantly greater retention than pelvic and dorsal punch marks. Beyond 244 days, fin clips outperformed all punch mark locations.

As an important and challenging problem, multidomain learning (MDL) typically seeks a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure design are handcrafted and fixed for all domains before model learning, resulting in learning inflexibility and computational intensiveness. With this motivation, we propose to learn a data-driven adapter plugging strategy with neural architecture search (NAS), which automatically determines where to plug for those adapter modules. Furthermore, we propose an NAS-adapter module for adapter structure design in an NAS-driven learning scheme, which automatically discovers effective adapter module structures for different domains. Experimental results demonstrate the effectiveness of our MDL model against existing approaches under the conditions of comparable performance.

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this paper, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption and linear encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy of mobile user. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

This article develops a dynamic version of event-triggered model predictive control (MPC) without utilizing any terminal constraint. Such a dynamic event-triggering mechanism takes the advantages of both event- and self-triggering approaches by dealing explicitly with conservatism in the triggering rate and measurement frequency. The prediction horizon shrinks as the system states converge; we prove that the proposed strategy is able to stabilize the system even without any stability-related terminal constraint. Recursive feasibility of the optimization control problem (OCP) is also guaranteed. The simulation results illustrate the effectiveness of the scheme.

Current study renders its focus on the investigation of functionally graded Yttria Stabilized Zirconia (YSZ) and alumina (Al2O3) thermal barrier coatings (TBC) deposited on EN steel substrates used in gun barrel through atmospheric plasma spray (APS) process. A bond coat and the top coats was made with two distinct compositions using YSZ-Al2O3 in 75:25 and 50:50 weight ratio through APS process with two carrier gas concentrations of 3 and 4 standard cubic feet per hour (scfh). Coatings were characterized with thermal testing, X-ray Diffraction (XRD) and Field Emission Scanning Electron Microscope examination (FESEM). Surface roughness test, wear and scratch test of the uncoated and coated steel specimens was also measured. YSZ-Al2O3 coated EN36C steel possessed greater insulation performance under a carrier gas flow rate of 3 scfh compared to all other specimens. EN36C steel coated with YSZ-Al2O3 in the ratio of 75:25 has better wear and scratch resistance indicating lesser deformation compared to other materials.

Software testing plays a critical role in the development and the assurance of the software quality. However, the quality of the code that is responsible for testing may, itself, be affected by poor design choices, known as test smells. In the literature, test smells may be interpreted differently by developers, which in turn can lead to different detection tools and results. In our work, we have selected the mostly commonly used detection tools and have investigated their overall agreement across different projects and different test smells. The found results were evaluated according to the average level of agreement, where we observed a definite disagreement between the tools. To overcome this gap of misinterpretation, we propose in this paper a multi-label classification approach to detect test smells based on a deep representation of the test code. We conducted our experiments using 4 problem-transformation techniques and 4 ensemble techniques. To evaluate our experimental results, we built a benchmark using a tool-based approach for labelling and made it publicly available. Binary Relevance and RAkEL are found to be the best multi-label techniques that achieve high performance results.

This study aims to analyse the robust exponential stability for neutral differential equations of the uncertain Takagi–Sugeno fuzzy system with distributed and discrete time interval delays by applying the Newton–Leibniz formula, the Lyapunov–Krasovskii functional, and the zero equations. By these methods, the less conservative exponential stability criteria are obtained for a special case of the generalized fuzzy of the neutral differential system. By applying Matlab LMI toolbox, the new conditions for exponential stability are obtained in the forms of linear matrix inequalities (LMIs), which can verify the numerical performance by the LMI toolbox of MATLAB. Finally, numerical examples are provided to manifest the efficiency and reduce the conservatism of the obtained results rather than the results which exist in the literature.

In this paper, the authors investigate the synchronization criteria of fuzzy impulsive sampled data control for Hindmarsh–Rose (H–R) neuronal system with time-delay. For the stability analysis, Lyapunov–Krasovskii functionals (LKF) are employed to deduce the conditions that guarantee the asymptotical stability of the proposed T–S fuzzy H–R neuron system. By utilizing free-matrix based inequality, some sufficient conditions are derived and expressed in terms of linear matrix inequalities. The obtained sufficient conditions can be checked easily by standard available software packages in MATLAB. Finally, some numerical simulations are given to validate the effectiveness of the proposed conditions.

This paper studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-Barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF (IMLBF) is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultanesouly achieve the obstacle avoidance and global tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties.

Radio-Frequency IDentification (RFID) technology is an essential enabler of a multitude of intelligent applications. The robust authentication of RFID system components is critical in providing trustworthy data delivery from/to tags. In this paper, we propose an authentication protocol based on monitoring the transmissions between readers and tags in the system. The proposed authentication scheme is based on injecting decoys within the exchanged communications (between RFID readers and tags) and is used in the authentication process. Furthermore, the proposed authentication scheme is mathematically modeled and validated using extensive simulation. The simulations results show that the proposed scheme provides a 100% confidence level in the authentication of tags and detection of compromised readers.

The Posit Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in Neural Network related tasks and produced some unit designs which are still far from being competitive with their floating-point counterparts. This paper proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, the most power-hungry units within Deep Neural Network architectures. When comparing with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively, without accuracy degradation.

Competitive opinion maximization (COM) in signed social networks targets at selecting a subset of influential individuals (i.e., seed nodes), spreading the desired opinions of the product to their neighbors against its opponents, and eventually achieving the maximum opinion propagation. Current studies mainly focus on competitive influence maximization and opinion maximization. However, COM in signed social networks has not been studied in depth. In this article, we study the COM in signed social networks and propose a novel reinforcement-learning-based opinion maximization framework (RLOM) to solve the COM problem. The proposed RLOM is composed of two phases: the activated dynamic opinion model and the reinforcement-learning-based seeding process. We theoretically prove the COM problem to be NP-hard. To model the opinion propagation process, we propose the activated dynamic opinion model based on a stateless Q-learning approach. Moreover, we propose the reinforcement-learning-based seeding scheme, which is leveraged in an unknown opponent strategy. Experiment results verify the effectiveness of our method in terms of effective opinions on three signed datasets.

A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L₂ norm-oriented one, which ignores target data's characteristics described by other metrics like an L₁ norm-oriented one. To investigate this issue, this article proposes an L₁-and-L₂-norm-oriented LF (L³F) model. It adopts twofold ideas: 1) aggregating L₁ norm's robustness and L₂ norm's stability to form its Loss and 2) adaptively adjusting weights of L₁ and L₂ norms in its Loss. By doing so, it achieves fine aggregation effects with L₁ norm-oriented Loss's robustness and L₂ norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L³F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

The modular multilevel matrix converter (M3C) is the high-power three-phase to three-phase AC/AC converter of the next generation. However, when the M3C is applied for power grid interconnection, with equal or close input and output frequency, the current distribution in its nine arms becomes significantly uneven. Furthermore, the current distribution pattern is directly influenced by the randomly changing input-output angle-difference, forcing each arm to be designed based on the highest-possible current, which significantly increases the required semiconductor rating and converter cost. To address this issue, a novel current reallocation is proposed to attenuate the highest-possible arm current of M3C. The equal-frequency operation of M3C is first modeled and analyzed, and the operation sweet-spot where each arm stays in natural energy balance is located. Then, the current reallocation is analytically derived for sweet-spot operation. By injecting proper circulating current, the highest-possible arm current is reduced by 13.4%, and the required circulating current component is analytically derived. The implementation method into the classic control structure of M3C is provided. Performance of the proposed strategy is validated by a simulation benchmark established in MATLAB/Simulink environment in the context of interconnection between two 220 kV/50 Hz power grids.

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In order to make the learned policies more transparent, we propose in this paper a policy iteration scheme that retains a complex function approximator for its internal value predictions but constrains the policy to have a concise, hierarchical, and human-readable structure, based on a mixture of interpretable experts. Each expert selects a primitive action according to a distance to a prototypical state. A key design decision to keep such experts interpretable is to select the prototypical states from trajectory data. The main technical contribution of the paper is to address the challenges introduced by this non-differentiable prototypical state selection procedure. Experimentally, we show that our proposed algorithm can learn compelling policies on continuous action deep RL benchmarks, matching the performance of neural network based policies, but returning policies that are more amenable to human inspection than neural network or linear-in-feature policies.

Mutation analysis assesses a test suites adequacy by measuring its ability to detect small artificial faults, systematically seeded into the tested program. Mutation analysis is considered one of the strongest test-adequacy criteria. Mutation testing builds on top of mutation analysis and is a testing technique that uses mutants as test goals to create or improve a test suite. Mutation testing has long been considered intractable because the sheer number of mutants that can be created represents an insurmountable problemboth in terms of human and computational effort. This has hindered the adoption of mutation testing as an industry standard. For example, Google has a codebase of two billion lines of code and more than 150,000,000 tests are executed on a daily basis. The traditional approach to mutation testing does not scale to such an environment; even existing solutions to speed up mutation analysis are insufficient to make it computationally feasible at such a scale. To address these challenges, this paper presents a scalable approach to mutation testing based on the following main ideas: (1) mutation testing is done incrementally, mutating only changed code during code review, rather than the entire code base; (2) mutants are filtered, removing mutants that are likely to be irrelevant to developers, and limiting the number of mutants per line and per code review process; (3) mutants are selected based on the historical performance of mutation operators, further eliminating irrelevant mutants and improving mutant quality. This paper empirically validates the proposed approach by analyzing its effectiveness in a code-review-based setting, used by more than 24,000 developers on more than 1,000 projects. The results show that the proposed approach produces orders of magnitude fewer mutants and that context-based mutant filtering and selection improve mutant quality and actionability. Overall, the proposed approach represents a mutation testing framework that seamlessly integrates into the software development workflow and is applicable to industrial settings of any size.

Dependency management in modern software development poses many challenges for developers who wish to stay up to date with the latest features and fixes whilst ensuring backwards compatibility. Project maintainers have opted for varied, and sometimes conflicting, approaches for maintaining their dependencies. Opting for unsuitable approaches can introduce bugs and vulnerabilities into the project, introduce breaking changes, cause extraneous installations, and reduce dependency understandability, making it harder for others to contribute effectively. In this paper, we empirically examine evidence of recurring dependency management issues (dependency smells). We look at the commit data for a dataset of 1,146 active JavaScript repositories to catalog, quantify and understand dependency smells. Through a series of surveys with practitioners, we identify and quantify seven dependency smells with varying degrees of popularity and investigate why they are introduced throughout project history. Our findings indicate that dependency smells are prevalent in JavaScript projects with two or more distinct smells appearing in 80% of the projects, but they generally infect a minority of a project's dependencies. Our observations show that the number of dependency smells tend to increase over time. Practitioners agree that dependency smells bring about many problems including security threats, bugs, dependency breakage, runtime errors, and other maintenance issues. These smells are generally introduced as developers react to dependency misbehaviour and the shortcomings of the npm ecosystem., Comment: Keywords: Dependency smells, Software ecosystems, Dependency management, npm

Deep reinforcement learning (DRL) has been adopted to provide a more powerful content caching policy. The current DRL-based scheme considers the requests for the same size and updates the caching for each request. However, the real-world data delivery systems usually refresh the content cache periodically, with different sizes of requests. To satisfy the real-world requirements, this study proposes a novel size adaptive content caching algorithm using DRL, termed SACC. SACC models the requests with random sizes and updates the cache after a batch of requests. Technically, SACC utilizes the Actor-Critic framework, which is able to process large discrete action space. SACC comprehensively considers the short-, medium- and long-term requests as the state to train the actor network. The reward is modeled as the cache hit rate. Once an action is selected from the policy network, it is expended to its k nearest neighbors. The critic network finds the action with the best reward from the k actions. The performance of the proposed SACC is evaluated through computer simulation. The experimental results showed that SACC could train the network much more efficiently and improve the cache hit rate by as much as 4% when comparing to the state-of-art DRL-based scheme

This work proposes an optimal pricing scheme for provisioning sensors-as-a-service (Se-aaS) for catering to applications with multi-tenancy requirements in a sensor-cloud platform. The scheme orchestrates a trade-off analysis between communication range and price in a sensor-cloud platform with range-reconfigurable nodes. The proposed scheme consists of two phases -- (a) selection of a neighbor node of a source node, and determination of optimal price for the selected neighbor node. In the first phase, a source node adjusts its communication range and selects its best possible neighbor node using selectivity factor of all the neighbor nodes. In the second phase, we design a utility function to determine the optimal price of the selected neighbor node. We use the Lagrangian function to model the proposed problem as a mixed-integer linear program (MILP) and obtain the optimal solution using the Karush-Kuhn-Tucker (KKT) conditions. The existing works on pricing in sensor-cloud are deficient in considering the presence of reconfigurable communication range of sensor nodes. Extensive experimental results show that the proposed scheme performs better compared to the existing pricing schemes for sensor-cloud. In particular, the proposed scheme is capable of reducing the charged price by 10.55%, as compared to the existing pricing scheme, DOPH.

Homomorphic Proxy Re-Encryption (HPRE) is an extension of Proxy Re-Encryption (PRE) which combines the advantages of both Homomorphic Encryption (HE) and PRE. A HPRE scheme allows arbitrary evaluations to be performed on ciphertexts under one (the delegator's) public key and, using a re-encryption key, it transforms the resulting ciphertext to a new ciphertext under another (the delegatee's) public key. Prior HPRE schemes are either CPA-secure or CCA-secure but only support partial homomorphic operations. We propose a generic construction of single-hop HPRE scheme which supports fully homomorphic operations. The proposed scheme is proven secure in our new index-based CCA-HPRE model. Our technique is to give a generic transformation that turns any multi-identity identity-based FHE (IBFHE) scheme with key switching into Fully Homomorphic Encryption (FHE) with key switching from which we can obtain the proposed single-hop HPRE scheme. We also present a concrete instantiation of multi-identity IBFHE with key switching from learning with errors (LWE) in the standard model.

Combinatorial Testing (CT) is an effective testing technique for detecting failures which are triggered by the interactions of various factors that influence the behaviour of a system. Although many studies in CT have designed elaborate test suites (called covering arrays) to systemically check each possible factor interaction, they provide weak support to locate the concrete failure-inducing interactions, i.e., the Minimal Failure-causing Schemas (MFS).To this end, a variety of MFS identification approaches have been proposed. However, as this study reveals, these approaches suffer from various issues such as cannot identify multiple overlapping MFSs, cannot handle MFSs with high degrees, cannot be applied to systems with large number of parameters, etc. These issues are essentially caused by the exponential computing complexity of checking every interaction in the test cases. Therefore, they can only focus on a subset of all the possible interactions, and thereby resulting in many interactions unnoticed. Ignoring these unnoticed interactions could potentially cause failures that have never been systematically checked. Hence, it is beneficial for MFS identification approaches to identify these interactions. In order to account for these unnoticed interactions in CT, this study introduces the notion of pending schema, based on which a theoretical framework of CT schemas is established. In particular, we formally define the determinability of a schema in CT with respect to given information; as such, the yet-to-be determined schemas are exactly the pending schemas. The relationships between the different schemas (faulty, healthy, and pending) and test cases are also theoretically analyzed. Based on which, we further propose three formulas, along with three corresponding algorithms, for the identification of the pending schemas in failing test cases, and formally prove their correctness. As a result, we reduce the complexity of obtaining pending schemas with respect to the number of factors that may have influences on the software.

In-flight alignment is the basis for projectile microstrap-down inertial navigation system (MSINS) to accurate navigation. Due to high dynamic and small space constraints of projectiles, MSINS with antihigh overload and low-precision is usually used, which makes it difficult to achieve initial alignment in flight. Hence, this article proposes a global navigation satellite system (GNSS) aided MSINS in-flight alignment method based on functional iteration. The Legendre polynomials integral recursive model is derived and functional iteration alignment scheme is constructed. The simulation results show that the alignment precision of the proposed method is improved by more than 18% compared with the traditional optimization-based alignment optimization-based alignment (OBA) method. The experiment results show that the alignment precision of the proposed method is improved by more than 10% compared with the traditional OBA method.

This article presents a planner for a master dual-arm robot to manipulate tethered tools with the help of an assistant dual-arm robot. The assistant robot assists the master robot by manipulating the tool cable and avoiding collisions. The provided assistance allows the master robot to perform tool placements on the robot workspace table and regrasp it, which would typically fail since cable tension may change the tool positions. It also allows the master robot to perform tool handovers, which would normally cause entanglements or collisions with the cable and the environment without assistance. Simulations and real-world experiments are performed to validate the proposed planner. Experimental results show that with the help of the assistant dual-arm robot and the proposed method, the master robot obtained improved flexibility in working with tethered tools.

Human behavior understanding is playing more and more important role in human-centered Industrial Internet-of-Video-Things (IIoVT) system with the deep combination of artificial intelligence and video based industrial IoT. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with numerous parameters, which limits its effectiveness and efficiency for IIoVT applications. In this paper, a Tensor-Train mechanism based deep model is presented for video human behavior understanding to meet the requirement of IIoVT applications. It can get competitive performance in accuracy and training efficiency with potentiality for combination of artificial intelligence and pre-front IIoVT system. On the one hand, to achieve desirable accuracy, we improved the conventional CNN and adopted the RNN mechanism to enhance the video representation over time, which takes the correlation between consecutive deep feature into consideration. On the other hand, to enhance the inference capacity between the spatial and temporal features, we carry out the self-critical reinforcement learning mechanism in parameters learning stage. Meanwhile, to further reduce parameter storage size to meet requirement for the deployment of deep neural network and edge device, the tensor-train mechanism is used, which transforms the parameter matrix to a tensor space and carry out tensor decomposition mechanism to decrease the number of parameter generated in parameter training. Finally, we conduct extensive experiments to evaluate our scheme, and the results demonstrate that our method can improve the training efficiency and save the memory space for the deep computation model with better accuracy.

In addition to 3D geometry, accurate representation of texture is important when digitizing real objects in virtual worlds. Based on a single consumer RGBD sensor, accurate texture representation for static objects can be realized by fusing multi-frame information; however, extending the process to dynamic objects, which typically have time-varying textures, is difficult. Thus, to address this problem, we propose a compact keyframe-based representation that decouples a dynamic texture into a basic static texture and a set of multiplicative changing maps. With this representation, the proposed method first aligns textures recorded from multiple keyframes with the reconstructed dynamic geometry of the object. Errors in the alignment and geometry are then compensated in an innovative iterative linear optimization framework. With the reconstructed texture, we then employ a scheme to synthesize the dynamic object from arbitrary viewpoints. By considering temporal and local pose similarities jointly, dynamic textures in all keyframes are fused to guarantee high-quality image generation. Experimental results demonstrate that the proposed method handles various dynamic objects, including faces, bodies, cloth, and toys. In addition, qualitative and quantitative comparisons demonstrate that the proposed method outperforms state-of-the-art solutions.

The lifetime of NAND flash cells significantly degrades with feature-size reductions and multi-level cell technology. On the other hand, we have more and more approximate data such as images and videos that are more error-tolerant than regular data like text. In this paper, we propose Rebirth-FTL which reuses faulty blocks that contain uncorrectable errors to store approximate data for lifetime optimization. Rebirth-FTL effectively manages two spaces, namely, the approximate space and the normal space, with an efficient address translator, a coordinated garbage collection, and a differential wear-leveler. In addition, we develop an MTR (Migration Times Restriction) policy to restrict the movement of the approximate data in the approximate space. We also develop a scheme to pass approximate information from userland to kernel space in Linux. Finally, a lifetime model is presented for lifetime analysis. Our experimental results show that Rebirth-FTL can extend the lifetime by 41.63% on average.

A 360-degree streaming system can provide immersive, interactive, and autonomous experiences surrounding the user by viewpoint changing to see different angles of the 360-degree video. Due to the limited and highly dynamic cellular network conditions, high-resolution 360-degree video playback over mobile devices often suffer from playback freezing, and inevitable bandwidth waste appears in delivering those out-of-viewpoint parts. In this paper, a hybrid control scheme is presented for segment-level continuous bitrate selection and tile-level bitrate allocation for 360-degree streaming over mobile devices to increase users' quality of experience. First, a deep reinforcement learning (RL) method is proposed to predict the segment bitrate and avoid playback freezing events. Second, a viewpoint-prediction-map-based cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for each tile to reduce unreasonable bandwidth waste. The proposed scheme is compared with state-of-the-art approaches over a wide variety of mobile network conditions with multiple viewpoint traces and 360-degree video contents. Experimental results indicate that the proposed method outperforms the state-of-the-art approaches in terms of different experimental objectives over mobile devices.

This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.

Nowadays, with the advancement of smart technologies, the Internet of Things (IoT) emerged as a booming technology that can provide better quality and facilities for the residents of smart cities. Smart cities can offer several services and have several applications in healthcare, transportation, education etc. Despite such a potential vision, the privacy of users on these IoT devices is a major concern. Most authentication schemes do not provide privacy and anonymity to legitimate users. to tackle this problem, we propose an efficient Zero Knowledge-based authentication scheme in the paper that authenticates devices on the network without knowing the information about user identity or revealing any other data entered by users. To explain our system framework at the micro-level, we apply our privacy-preserving scheme to IoT based healthcare applications, but it can easily be extended to the more general use cases where privacy-preserving authentication is required. This paper’s second major contribution is designing the data encryption algorithm ZKNimble that is mainly suitable for lightweight devices. Once the user is authenticated using Zero Knowledge Proof, the ZKNimble cipher can be used for legitimate users’ encryption and decryption processes.

Distributed deep learning has been widely used in training deep neural networks, especially for big models on massive datasets. Parameter Server (PS) architecture is the most popular distributed training framework, which can flexibly design the global parameter update manner. However, when scaling to complex heterogeneous clusters, stragglers make it difficult for existing distributed paradigms on PS framework to balance between synchronous waiting and staleness, which slows down the model training sharply. In this paper, we propose Grouping Stale Synchronous Parallel (GSSP) scheme, which groups workers with similar performance together. Group servers coordinate intra-group workers using Stale Synchronous Parallel while they communicate with each other asynchronously to eliminate stragglers and refine the model weights. We further propose Grouping Dynamic Tok-K Sparsification (GDTopK), which dynamically adjusts the upload ratio for each group so as to make communication volume differentiated and mitigate inter-group iteration speed gap. We have conducted experiments on LeNet-5 on MNIST, ResNet-18, VGG-19 on Cifar-10 and Seq2Seq on Multi30k. Results show that GSSP accelerates the training by 46%120%, with less than 1% accuracy drop. And GDTopK can make up for part of the lost accuracy.

We investigate the implementation of a sparsity promoting hands-off control scheme using the alternating direction method of multiplies (ADMM). In order to minimize the numerical control effort along with the actuation one, only a single ADMM iteration per time step is considered. We analyze the resulting closed-loop dynamics for linear systems with additive disturbances and show that the proposed scheme provides sparse controls as desired and, in addition, ensures input-to-state practical stability. We further point out a close relation between the proposed real-time scheme and (non-minimum time) dead-beat control. Finally, characteristics and effectiveness of the novel hands-off controller(s) are illustrated with numerical examples.

In Industry 5.0, the massive number of Internet of Things (IoT) devices have increasing demands for group communication with high communication efficiency and low energy consumption. However, group communication meets continuously increasing security risk challenges. Existing authentication and group key agreement schemes have encountered many problems, such as lack of anonymity and untraceability. In this investigation, we propose an anonymous authentication and dynamic group key agreement scheme based on the Blockchain and token mechanism, where each group member can apply for a time-sensitive token during the first authentication and only needs to check the validity of the token in the subsequent authentication, reducing the computational and transmission costs considerably. The verification on the security of the proposed scheme is tackled through mathematical analysis and validated using ProVerif, and comparisons with existing schemes demonstrate that the proposed scheme reduces the security risks and each group member's energy consumption.

Nurturing venture-capital-backed, high-growth entrepreneurship has been strongly promoted as an effective means to achieve local development in impoverished places. Yet growing evidence suggests th...

This article emphases an advanced method for controlling the pollution of vehicles through sensor integration process. All sensor nodes will be connected to a control centre and it will be monitored using Online Monitoring System (OMS) with the help of an Improved Smoke Detection Algorithm (ISDA). The purpose of using ISDA is to monitor the vehicles which are located in different areas under different temperatures where, the ISDA divides the regions into circles. The data obtained in OMS will be aggregated and it will be plotted in MATLAB for better understanding. To test the efficiency of the projected method three test cases which include control of energy, minimization of range and cot have been performed. It is observed that in all three cases ISDA proves to be more efficient in terms of energy consumption, cost and distance when compared with existing methods.

Massive physical devices are deployed in the industrial Internet of Things (IoT) to collect ambiance data while heavy storage and communication cost are imposed on these IoT devices. To overcome this constraint, cloud-assisted technologies are introduced to store and manage the collected data. In order to protect data quality and security, encryption is required before uploading data to remote clouds. Consequently, search function is added to cloud services to find the specific data. However, traditional data searching schemes are constructed in user-oriented systems, where the search function is mainly involved with the relationship between data and users rather than data and devices. As a result, traditional search schemes are not suitable to find special IoT devices. On the other hand, the status of these devices is described by many attributes, e.g., temperature, clean water storage, and machine speed in an early warning system for industrial sewage disposal equipment. Hence, multi-keyword conjunctive queries for partial attributes should be introduced so as to find the target device more accurately and more efficiently. To address these challenges, we propose a new universal device-oriented keyword searchable encryption scheme for cloud-assisted IoT in this paper. Furthermore, the functions of a single and conjunctive keyword search are maintained to handle the device search requirement of partial attributes. We conduct extensive experiments to evaluate the proposed scheme. Experimental results show that our scheme has excellent performance because of the lightweight index and query trapdoor.

PDF is one of the most commonly used office tools, and can be used by robots to share documents with people. However, it has become the target of hackers to attack a variety of devices since it is cross-platform and can be embed with JavaScript code or URIs. Therefore, how to protect the platform security while robots parsing PDF files becomes a serious problem. In this paper, we design and realize a novel malicious PDF file detector, MMPD, for mobile robots based on deep learning. Experiments show that the F1-Score of MMPD can achieve up to 99.478%. In the meantime, the hardware resource usage is low and no significant performance reduction of the whole system is observed.

This study aims at designing tools to test the coefficient of static timber friction on wood through friction force experiments. Crafting force experiments have been developed to determine the coefficient of static timber friction coefficient on wood. The data was collected 10 times each mass variation and obtained friction force data (fs). The data analysis used a weighted linear regression with REGLIN program written in Matlab and chisquere test. The results obtained by the form of coefficients of linear function are a0 = 0.309 and a1 = 1.937 with ralat (Sμ = 0.010). The amount of coefficient of static friction (μs) of wood on wood using Matlab is μs = (0.402 ± 0.010). This result is in accordance with the theory that μs = 0.40. The chisqure test (χ2) shows that there is a linear correlation between fs and m, with an opportunity value P () = 50.56%, so the data in this experiment is acceptable.Â

In this article, a novel distributed model predictive control architecture is proposed for the coordination and control of multivehicle formations moving within uncertain environments. As one of its main merits, multiagent swarm modeling and leader-follower configurations are jointly exploited within an ad hoc model predictive control framework to reduce as much as possible the use of onboard sensors that is essential in long-range missions. Moreover, the feasibility and asymptotic closed-loop stability of the resulting scheme are formally proved. Finally, a laboratory experiment is used to show the effectiveness of the proposed algorithm with particular attention to the follower capability of operating in a so-called blind fashion.

As a promising technology in the development of human healthcare services, the wireless body area networks (WBANs) technology has attracted widespread attention in recent years from both industry and academia. However, due to the sensitiveness of the medical system and the capability limitation of the wearable devices, security, privacy, and efficiency of the healthcare services in WBANs are remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. In this paper, we firstly provide a review and cryptanalysis on the state-of-theart authentication scheme. In order to meet the challenges and address the drawbacks in previous works, we then propose a new efficient and anonymous authentication scheme for cloud based WBANs. Through the security analysis, we show that our scheme could overcome the weaknesses in previous schemes and meet all the security requirements. Besides, we show the advantages of the proposed scheme through performance evaluation in terms of functionality features, computation overhead, communication overhead and storage overhead, which shows our scheme is more appropriate for practical applications on healthcare services.

Music Information Retrieval (MIR) is the task of extracting high-level information, such as genre, artist or instrumentation from music. Genre classification is an important and rapidly evolving research area of MIR. To date, only a small amount of research work has been done on the automatic genre classification of Nigerian songs. Hence, this study presents a new music dataset, namely the ORIN dataset, consisting of only Nigerian songs. The study dataset contains 478 Nigerian traditional songs from five genres: fuji, juju, highlife, waka and apala. The timbral texture and tempo features were mined from 30-second segments of each song using the Librosa Python library. For genre classification, the ORIN datasets was trained on 4 different classifiers- k-Nearest Neighbhour, Support Vector Machine, eXtreme Gradient Boosting (XGBoost) and Random Forest- with 85–15 train-test splits. The results obtained for the five different genres indicates that XGBoost classifier is a better model, having the highest accuracy of 81.94% and recall of 84.57%. This study uses the global mean (Tree SHAP) method to determine feature importance and impact on the classification model. Further analysis on the individual genres found some nearness in the timbral properties between some of the genres. This analysis was confirmed by human observation.

In this paper, we propose a novel successive cancellation flip (SCF) decoding to reduce the computational complexity compared to the conventional SCF decoding by using distributed CRC bits. The proposed decoding reduces the number of estimations for information bits by early termination of decodings for failed frames of the first SC decoding, while trying to minimize the additional sorting operations. Simulation results show that the proposed SCF decoding reduces the computational complexity of repeated SC decoding at least 27% compared to the conventional SCF decoding.

Increasing usage of Digital Manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g. g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufacturer. In this work we aim to quantify this hypothesis through a red-team/blue-team case study, whereby the red-team aims to introduce subtle defects that would impact the properties (strengths) of the 3D printed parts, and the blue-team aims to detect these modifications in the absence of the golden models. The case study had two sets of models, the first with 180 designs (with 2 compromised using 2 methods) and the second with 4320 designs (with 60 compromised using 6 methods). Using statistical modelling and machine learning (ML), the blue-team was able to detect all the compromises in the first set of data, and 50 of the compromises in the second., To appear in IEEE Embedded Systems Letters