Reviewerly/exHarmony huggingface dataset:
Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network

Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse-autoencoder-based deep neural network (SAE-DNN) is proposed to deal with the problem of AMC for much neglected frequency selective fading scenarios with Doppler shift. The authors propose a set of low complexity spectral and cumulant based features for training SAE-DNN. The network is designed using forced dimensionality reduction and sparsity constraint to achieve a low complexity solution with improved ability to learn more refined and robust features from the input training data. A unique training method is presented in this study which incorporates a range of SNR values for the entire span of the training dataset, as compared to the conventional approach which only uses a single SNR value for all the training examples. A comprehensive performance analysis shows that the proposed method outperforms many conventional counterparts in the literature. Generalisation test verifies that network is feasible for all channel conditions. A robust classification behaviour is observed against phase-frequency impairments and Doppler shift for frequency selective fading scenarios.
Ultrasound image restoration based on a learned dictionary and a higher-order MRF

The restoration of images degraded by blur and multiplicative noise is a critical preprocessing step in medical ultrasound images which exhibit clinical diagnostic features of interest. This paper proposes a novel non-smooth non-convex variational model for ultrasound images denoising and deblurring motivated by the successes of sparse representation of images and FoE based approaches. Dictionaries are well adapted to textures and extended to arbitrary image sizes by defining a global image prior, while FoE image prior explicitly characterizes the statistics properties of natural image. Following these ideas, the new model is composed of the data-fidelity term, the sparse and redundant representations via learned dictionaries, and the FoE image prior model. The iPiano algorithm can efficiently deal with this optimization problem. The new proposed model is applied to several simulated images and real ultrasound images. The experimental results of denoising and deblurring show that proposed method gives a better visual effect by efficiently removing noise and preserving details well compared with two state-of-the-art methods.
Hard combinatorial problems and minor embeddings on lattice graphs

Today, hardware constraints are an important limitation on quantum adiabatic optimization algorithms: Computational problems are formulated as quadratic unconstrained binary optimization (QUBO) in the presence of noisy coupling constants, and the interaction graph of the QUBO needs an effective minor embedding into a two-dimensional non-planar lattice graph. We describe new strategies for constructing QUBOs for NP-complete/hard combinatorial problems that address both of these challenges for present-day hardware. Our results include asymptotically improved embeddings for number partitioning, filling knapsacks, graph coloring, and finding Hamiltonian cycles. These embeddings can also be found with reduced computational effort. Our new embedding for number partitioning may be more effective on future quantum annealing hardware. While we focus on embedding problems onto Chimera lattices, the techniques we use, along with their limitations, generalize to any non-planar lattice graph.
A Fine Granularity Object-Level Representation for Event Detection and Recounting

Multimedia events such as “birthday party” usually involve the complex interaction between humans and objects. Unlike actions and sports, these events rarely contain unique motion patterns to be vividly explored for recognition. To encode rich objects in the events, a common practice is to tag an individual video frame with object labels, represented as a vector signifying probabilities of object appearances. These vectors are then pooled across frames to obtain a video-level representation. The current practices suffer from two deficiencies due to the direct employment of deep convolutional neural network (DCNN) and standard feature pooling techniques. First, the use of max-pooling and softmax layers in DCNN overemphasize the primary object or scene in a frame, producing a sparse vector that overlooks the existence of secondary or small-size objects. Second, feature pooling by max or average operator over sparse vectors makes the video-level feature unpredictable in modeling the object composition of an event. To address these problems, this paper proposes a new video representation, named Object-VLAD, which treats each object equally and encodes them into a vector for multimedia event detection. Furthermore, the vector can be flexibly decoded to identify evidences such as key objects to recount the reason why a video is retrieved for an event of interest. Experiments conducted on MED13 and MED14 datasets verify the merit of Object-VLAD by consistently outperforming several state-of-the-arts in both event detection and recounting.
Contextual Location in the Home Using Bluetooth Beacons

Location sensing is a key enabling technology for Ubicomp to support contextual interaction.However, the laboratories where calibrated testing of location technologies is done are very different to the domestic situations where "context" is a problematic social construct.This study reports measurements of Bluetooth beacons, informed by laboratory studies, but done in diverse domestic settings.The design of these surveys has been motivated by the natural environment implied in the Bluetooth beacon standards relating the technical environment of the beacon to the function of spaces within the home.This research method can be considered as a situated, "ethnographic" technical response to the study of physical infrastructure that arises through social processes.The results offer insights for the future design of "seamful" approaches to indoor location sensing, and to the ways that context might be constructed and interpreted in a seamful manner.
Offering security diagnosis as a service for cloud SaaS applications

With the maturity of service-oriented architecture (SOA), microservices architecture, and Web technologies, web services have become critical components of Software as a Service (SaaS) applications in cloud ecosystem environments. Although these technologies promise impressive benefits, they put SaaS applications at risk against novel as well as prevalent attack vectors. This security risk is further magnified by the loss of control and lack of security enforcement over sensitive data manipulated by SaaS applications. We present our solution as Security Diagnosis as a Service (SDaaS) to analyze the security status of SaaS applications and detect potential information flow vulnerabilities. We evaluate the detection accuracy, performance, and scalability of our framework. The experiments are conducted over benchmark applications for assessing vulnerability detection tools and services. We contrast our solution with several tools comprising static code analyzers, penetration testers, and an anomaly detector. The experiments show that the framework is a viable solution to protect against data integrity and confidentiality violations. The evaluation results demonstrate that SDaaS reveals information flow vulnerabilities with not only high accuracy, performance and scalability, but also lightweight footprint on resource utilization.
Point-wise estimation for anisotropic densities

This paper considers point-wise estimation of density functions under the local anisotropic Hölder condition by the wavelet method. A linear wavelet estimate is first introduced and shown to be optimal. A data driven version is provided for adaptivity and the influence of the dimension is reduced under the independence structure of the estimated density.
Time-matching extended target probability hypothesis density filter for multi-target tracking of high resolution radar

Extended target probability hypothesis density (ET-PHD) filters have recently become popular owing to their relatively simple recursion processes, which makes them suitable for use in applications requiring real-time results, such as radar multi-target tracking. However, in the classic ET-PHD filters, the measurements of different targets are generally considered to be generated at the end of each scan. With this assumption, the measurement time diversity in radar applications cannot be modeled. To address this shortcoming, a novel time-matching ET-PHD filter in which a multi-prediction filtering framework is applied, and the true measurement times are used in the PHD propagation of each cell, i.e., prediction and correction, is proposed in this paper. In addition, a pre-partitioning strategy is employed to reduce the computational complexity of the proposed filter. The results of simulations conducted using the gamma Gaussian inverse Wishart PHD filter indicate that the proposed pre-partitioning-based time-matching ET-PHD filter is superior to standard filters in terms of both estimation accuracy and real-time performance.
Extensible persistence as a service for containers

Micro-services and containers are becoming the de-facto way of building software and deploying workloads in an agile manner. Following these paradigms leads eventually to replacing the old fashioned monolithic applications by compositions of single-purpose, independent services communicating with each other through light-weight mechanisms. These services are often deployed in containers which have proven to be a suitable runtime platform for micro-services. In the container landscape, there are different container frameworks enabling elastic horizontal scaling for the workloads that are designed as so-called 12-factor applications.1 These frameworks were built to accommodate stateless services and require that any state need to be maintained out-of-band of the platform. Support for stateful services in a scalable way is not well covered, particularly in scenarios where state is accessed from workloads in different deployment platforms. It is noteworthy that these workloads cover a wide spectrum of legacy software applications. In order to efficiently support these kinds of workloads, persistent storage such as filesystem or block storage is necessary to maintain the data and make it available for the containers. Managing different types of persistent storage across heterogeneous container orchestrators is a challenging task that people tried to resolve in a specific way for each storage backend or container orchestrator. In this paper, we present the Ubiquity framework, which provides seamless access to persistent storage across different container orchestrators (Cloud Foundry, OpenShift, Kubernetes, Docker and Mesos). Ubiquity is extensible to other container frameworks and different types of file and block storage systems, which can be managed independently of the container orchestrator. Ubiquity makes it easy to onboard stateful services in heterogeneous container environments and based on heterogeneous storage backends, extending the efficiency gains of containerization.
Electromagnetic radiation based continuous authentication in edge computing enabled internet of things

With the rapid proliferation of Internet of Things, the centralized cloud faces the performance problems caused by remote network access and real-time services. To ensure good performance, edge computing paradigm emerges, and requires a secure authentication between edge host and cloud server to safeguard resources against any illegitimate access. Securing edge host authentication is an effective method and cornerstone to protect the information security in edge computing. Existing methods most recognize an entity's identity by password only once at the time of initial stage, which may lead to security problem. Continuous authentication as a novel authentication modality has the ability to alleviate this issue. Unlike the authentication operating merely at point-of-entry, a continuous edge host authentication method is proposed on the basis of electromagnetic radiation which reflects the behavioral characteristics of edge host with heterogeneous hardware. We demonstrate the feasibility of the proposed method through analysis of success rate from ten thousand waterfall spectrograms, and experimental results show that the average success rate of 95% has been achieved by support vector machine combining multiple features.
SGX-LEGO: Fine-grained SGX controlled-channel attack and its countermeasure

The introduction of Intel Software Guard eXtension (SGX) prompted security researchers to verify its effectiveness. One of the frequently discussed attacks against SGX is the side-channel attack by gathering page-fault information (controlled-channel attack). Owing to SGX's hardware features, the faulting address of the enclave memory is page-masked. Because of this, both the controlled-channel attack and the defenses of SGX are built under the assumption that an attacker observes the memory access attempts of the enclave code with page-granularity. However, Van Bluck et al. recently demonstrated a controlled-channel attack technique which negates the prior assumption of page-granularity. In this paper, we introduce a new class of attack that stems from the reduced controlled-channel granularity, i.e., the Version IDentification attack (VID). The goal of VID attack is identifying the detailed code information inside SGX enclave by analyzing the fine-grained SGX controlled-channel. To protect enclave memory from such attack, we design and implement SGX-LEGO, an automated system that adopts execution polymorphism to the SGX enclave code. Previous defense approaches against controlled-channel attacks can be broadly categorized into two types: (i) disclosing the fault information and (ii) rendering the fault information useless. SGX-LEGO uses the latter approach by permuting the memory access sequence at the instruction level. In SGX-LEGO design, we leverage the concept of code-reuse-programming to overcome the implementation challenges regarding SGX page management. In the evaluation, we show how VID attacks the cryptographic functions, and demonstrate the efficacy of SGX-LEGO in security perspective and performance.
Reliable Architecture-Oblivious Error Detection Schemes for Secure Cryptographic GCM Structures

To augment the confidentiality property provided by block ciphers with authentication, the Galois Counter Mode (GCM) has been standardized by the National Institute of Standards and Technology. The GCM is used as an add-on to 128-bit block ciphers, such as the Advanced Encryption Standard (AES), SMS4, or Camellia, to verify the integrity of data. Prior works on the error detection of the GCM either use linear codes to protect the GCM architectures or are based on AES-GCM architectures, confining the mechanisms to the AES block cipher. Although such structures are efficient, they are not only confined to specific architectures of the GCM but might also not fully take advantage of the parallel architectures of the GCM. Moreover, linear codes have been shown to be potentially ineffective with respect to biased faults. In this paper, we propose algorithm-oblivious constructions through recomputing with swapped ciphertext and additional authenticated blocks, which can be applied to the GCM architectures using different finite field multipliers in GF (2128). Such obliviousness for the proposed constructions used in the GCM gives freedom to the designers. We present the results of error simulations and application-specific integrated circuit implementations to demonstrate the utility of the presented schemes. Based on the overhead/degradation tolerance for implementation/performance metrics, one can fine-tune the proposed method to achieve more reliable architectures for the GCM.
Modeling and Simulation of High-Frequency Solar Irradiance

As the use of solar power as a source of electricity is increasing, so is the interest in modeling radiation at high temporal resolutions. High-dimensional remote sensing data products depend on cloud cover variability, atmosphere aerosol levels, and other atmospheric parameters. Because of weather fronts and aerosols, it is difficult to quantify solar power variability based on distributed solar networks. The global horizontal irradiance (GHI) component in the National Solar Radiation Database (NSRDB) is available at a 30-min time resolution. The algorithm proposed in this paper produces 1-min-resolution GHI samples at locations where NSRDB data are available. The synthetic irradiance datasets are produced using log-additive non-Gaussian mixture models and resampling techniques. The model is trained over historical data, and predicted values are compared with in situ data. This approach allows for estimating the solar irradiance at subhourly temporal resolutions, while featuring variability for locations where measurements are otherwise not available.
Reversibility improved data hiding in 3D mesh models using prediction-error expansion and sorting

Reversible data hiding (RDH) recover the original media without any distortion after extraction of the hidden data. Since massive three-dimensional (3D) mesh models have been constructed from high-dimensional image data nowadays, existing deficiencies of prior studies on RDH of 3D mesh models need to be overcome for performance improvement. In this paper, we propose an improved RDH method for 3D mesh models based on prediction-error expansion and sorting. Instead of considering only several adjacent neighbors, the proposed method predict a vertex position with a ring pattern for better prediction accuracy. To make better capacity-distortion control, data bits are reversibly embedded into 3D mesh models with embedding operations including expansion, shifting and LSB replacement. Moreover, we propose smoothness sorting and twice-layered strategy to enhance the embedding performance further. Thus, the proposed method achieves improved embedding performance in terms of capacity enhancement and distortion reduction. Experimental results demonstrate that the proposed method produces relatively superior results compared with previous related state-of-the-art works.
Image hash authentication algorithm for orthogonal moments of fractional order chaotic scrambling coupling hyper-complex number

In this paper, the image Hash authentication algorithm for orthogonal moments of fractional order chaotic scrambling coupling hyper-complex number Tchebichef is proposed. The improved PDE function and Ring segmentation mechanism are used to process the initial image and output the secondary image to improve the robustness of Hash’s scaling and rotation; and by taking full account into the color information of the image, the image R, G and B position is regarded as its imaginary part. In addition, the invariant moment of the hyper-complex number Tchebichef is desgined, and the robust characteristics of the secondary image is fully extracted to greatly improve its ability to recognize the rotation tampering. At the same time, in order to improve the security of the Hash sequence, the fractional order Logistic mapping and chaotic scrambling technique is constructed to encrypt the eigenvector; and the image information is authenticated by the Hamming distance. Finally, the robustness and tampering recognition accuracy of the proposed Hash algorithm are tested.
Testing the public–private soil data and information sharing model for sustainable soil management outcomes

Abstract Soil data form the basis of soil information systems across the globe. Soil information needs, and the questions posed by users, are likely to evolve in response to advances in technology in this era of Big Data. This poses a challenge to the pedological community which is already experiencing a decline in soil knowledge and expertise. With a decrease in soil data collection by governments, it is timely to reconsider how and what soil information should be provided to future users. A public–private partnership is advocated to deliver timely and accessible soil information to users. Two public–private provisioning programs are presented, and advantages and considerations for sharing soil data and information amongst industry, government, research organizations, service providers and land managers for these are discussed. Interoperable, open‐source and agreed soil community standards are used to present soil data and information through spatial web portals with tools for interpretation of soil data for public and private beneficiaries.
High-Resolution Angular Displacement Technology Based on Varying Moiré Figure Phase Positions

Angular displacement measurement is a popular research subject. The grating of traditional angular measurement devices uses multi-circle code and is difficult to miniaturize. This paper proposes a novel angular displacement technology based on varying moiré figure phase positions which allows for reduced volume and high-resolution angle measurements in small-size gratings. First, a moiré figure phase model is established and the calculation arithmetic is written. Second, the proposed arithmetic based on differences in moiré figure phase positions is completed accordingly. At last, a test device was fabricated based on the proposed theory with a total diameter of 35 mm and grating diameter of 25 mm. These tests show that angular displacement method based on differences of moiré figure phase positions can achieve a high resolution measurement when the diameter of calibration grating is small. We hope the results of this paper provide a workable theoretical foundation for future photographic encoders.
Scalable complex event processing using adaptive load balancing

An essential requirement of large-scale event-driven systems is the real-time detection of complex patterns of events from a large number of basic events and derivation of higher-level events using complex event processing (CEP) mechanisms. Centralized CEP mechanisms are however not scalable and thus inappropriate for large-scale domains with many input events and complex patterns, rendering the horizontal scaling of CEP mechanisms a necessity. In this paper, we propose CCEP as a mechanism for clustering of heterogeneous CEP engines to provide horizontal scalability using adaptive load balancing. We experimentally compare the performance of CCEP with the performances of three CEP clustering mechanisms, namely VISIRI, SCTXPF, and RR. The results of experiments show that CCEP increases throughput by 40 percent and thus it is more scalable than the other three chosen mechanisms when the input event rate changes at runtime. Although CCEP increases the network utilization by about 40 percent, it keeps the load of the system two times more balanced and reduces the input event loss three times.