Reviewerly/exHarmony huggingface dataset:
A Bayesian non‐parametric approach for automatic clustering with feature weighting

Despite being a well‐known problem, feature weighting and feature selection are a major predicament for clustering. Most of the algorithms, which provide weighting or selection of features, require the number of clusters to be known in advance. On the other hand, the existing automatic clustering procedures that can determine the number of clusters are computationally expensive and often do not make a room for feature weighting or selection. In this paper, we propose a Gibbs sampling‐based algorithm for the Dirichlet process mixture model, which can determine the number of clusters and can also incorporate a near‐optimal feature weighting. We show that in the limiting case, the algorithm approaches a hard clustering procedure, which resembles minimization of an underlying clustering objective similar to weighted k ‐means with an additional forfeit for the number of clusters and hence retains the simplicity of the Llyod's heuristics. To avoid the trivial solution of the resulting linear program, we include an additional entropic penalty on the feature weights. The proposed algorithm is tested on several synthetic and real‐life datasets. Through a detailed experimental analysis, we demonstrate the competitiveness of our proposal against the baseline as well as state‐of‐the‐art procedures for centre‐based high‐dimensional clustering.
Optimized color image watermarking through watermark strength optimization using particle swarm optimization technique

Digital Watermarking is a technique in which some presume content like image or data is embedded in any cover content in a way such that the quality of the digital content should not be degraded and it should not be visible to human eyes. In this paper a secure watermarking scheme has been proposed for color images and is optimized using one of the nature inspired algorithm called Particle swarm optimization. Here for embedding color watermark is used and is embedded in all three planes (R,G,B) of color host image using 2 D DCT and entropy encoding scheme. The value of embedding strength is optimized using PSO algorithm to provide balance between robustness and imperceptibility characteristics of watermarking. The proposed algorithm is tested by performing various types of attacks on watermarked image and good PSNR and NC values are received which proves the imperceptibility and robustness of the scheme.
Smart contracts: a remedial analysis

The perpetual script of a smart contract, that executes an agreement machine-to-machine without prejudice, guarantees performance of ‘contractual terms’ enabling the exchange or transaction of cryptoassets and other forms of property. Yet, smart contracts as recognisable or valid legal instruments within the boundaries of contract or property law remain uncertain and contentious. Contrary to perceptions of contractual streamlining and efficiency, understanding the uncertainty smart contracts produce lies in the technology's failure to meet many of the fundamental principles of contract law and theory concerning, for example, breach of promise and remedy for breach. Smart contracts appear to reduce contracting to a form and standard well below that developed by contract law and theory over many centuries in both civil and common law jurisdictions. Including elements of the law of restitution, this article's remedial analysis will examine smart contracts considering ‘traditional’ contract law to understand and, where possible, test the legal legitimacy of this post-human technology, and explore the potential of smart contracts to supplement or, in time, supersede traditional contract law.
Bottleneck-Aware Coflow Scheduling Without Prior Knowledge

Coflow scheduling is critical to the communication efficiency of data-parallel applications in data centers. While schemes like Varys can achieve optimal performance, they require a priori information about coflows which is hard to obtain in practice. Existing non-clairvoyant solutions like Aalo generalize least attained service (LAS) scheduling discipline to address this issue. However they fail to identify the bottleneck flows in a coflow and tend to allocate excessive bandwidth to the non-bottleneck flows within the coflow, leading to bandwidth wastage and inferior overall performance. To this end, we present Fai that strives to improve the overall coflow performance by accelerating the bottleneck flow without prior knowledge. Fai employs bottleneck-aware scheduling for coflows. Fai adopts loose coordination to update coflow priority and flow rates based on total bytes sent. In addition, Fai detects bottleneck flows based on a flow's rate and bytes sent, and de-allocates bandwidth for other flows to match the bottleneck rate without affecting the coflow completion time (CCT). The saved bandwidth is then distributed among coflows according to their priority to improve overall performance. Both testbed deployments and trace-driven simulations show that Fai outperforms Aalo substantially.
Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis

To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT).We retrospectively collected 441 patients who had undergone DBT in which the regions of interest (ROIs) covering the malignant/benign breast mass were extracted for model training and validation. In the proposed MSF framework, three multifaceted representations of the breast mass (gross mass, overview, and mass background) are extracted from the ROIs and independently processed by a multi-scale multi-level features enforced DenseNet (MMFED). The three MMFED sub-models are finally fused at the decision level to generate the final prediction. The advantages of the MMFED over the original DenseNet, as well as different fusion strategies embedded in MSF, were comprehensively compared.The MMFED was observed to be superior to the original DenseNet, and multiple channel fusions in the MSF outperformed the single-channel MMFED and double-channel fusion with the best classification scores of area under the receiver operating characteristic (ROC) curve (87.03%), Accuracy (81.29%), Sensitivity (74.57%), and Specificity (84.53%) via the weighted fusion method embedded in MSF. The decision level fusion-based MSF was significantly better (in terms of the ROC curve) than the feature concatenation-based fusion (p< 0.05), the single MMFED using a fused three-channel image (p< 0.04), and the multiple MMFED end-to-end training (p< 0.004).Integrating multifaceted representations of the breast mass tends to increase benign/malignant mass classification performance and the proposed methodology was verified to be a promising tool to assist in clinical breast cancer screening.
A Multi-property Method to Evaluate Trust of Edge Computing Based on Data Driven Capsule Network

As one of the computing paradigms in the process of traditional cloud computing turning to marginalization, edge computing is designed to meet the requirements of edge devices such as real-time response, security, privacy and so on. However, resources in edge computing are generally heterogeneous and dynamic, resulting in lack of trust among devices or a completely untrusted computing resources. Thus, novel and effective methods to evaluate the trust of edge devices in order to increasing the adoption of edge computing resources, have become an urgent issue. In this context the paper proposes a multi-property method to evaluate trust of edge computing, we establish an objective expression of trust property by considering which factors affect trust evaluation in resource request or service application process from the perspective of dynamic change, then the data-driven capsule network is utilized to analyze the correlation between trust properties and gives an objective prediction of trustworthy. The main contribution of this paper is the design of an effective trust evaluation method, which gives the objective reliability of edge devices also coordinates all levels to provide users with trustworthy application services.
<scp>CoGCN</scp>: Combining <scp>co‐attention</scp> with graph convolutional network for entity linking with knowledge graphs

Abstract Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.
Handwritten Meitei Mayek recognition using three‐channel convolution neural network of gradients and gray

Abstract The problem of searching a similar pattern is an exciting and challenging research field of pattern recognition. The intelligence of humans for vision to read is a crucial phenomenon for machine simulation and has been carried out for a few decades. Therefore, in this article, a recognition system of handwritten Meitei Mayek (Manipuri script) is introduced using a convolutional neural network. Generally, character recognition is performed using the gray scale of the image of characters. However, we have additionally considered the corresponding gradient direction and gradient magnitude images to create three‐channels image for every character so that supplementary information from gradient images can be obtained for efficient recognition. Experiments are conducted on 14 700 sample images collected from various individuals of different age groups and educational backgrounds. A recognition rate of 98.70% is obtained, which is compared with the existing methods, and it is found to be superior performance than other neural network methods on Meitei Mayek.
Tri-Blockchain Based Intelligent Vehicular Networks

Intelligent Vehicular Network (IVN) is a communication environment, where Intelligent vehicles communicate and share the information within the network. In an IVN, all vehicles are connected to the internet and vehicles ease to communicate information with other vehicles. IVN aims to improve safety and efficiency by sharing data with only nearby vehicles. The greatest challenge in IVN is trusted data circulation with reliable intelligent vehicles without affecting any personal information of Intelligent vehicles. In this article, we endeavor to resolve above mentioned issue by proposing an effective tri-Blockchain based communication network. We have introduced three blockchain servers, namely Public, Special and Supreme Blockchain server. The public blockchain server and special blockchain server show dynamic features whereas supreme blockchain server features are static for all the time. Our Tri-Blockchain servers provide a greater extent of reliable and quick response during emergency scenario. We have verified our proposal with an accidental USECASE scenario.
On improving the accuracy of self-calibrated stereo digital image correlation system

Abstract Stereo digital image correlation (stereo DIC), a full-field deformation measurement technique, is increasingly being used to resolve strains at µm-length scale by using microscope-like imaging systems. Self calibration of these imaging systems is more cost-effective and convenient than the conventional target-based calibration. Though the use of self-calibrated stereo DIC systems has already been reported, less attention has been paid to improving the accuracy of these systems. In the present work, we improve the accuracy of a self-calibrated stereo DIC system, which is composed of two full-frame DSLR cameras coupled to macro lenses and is used for testing ASTM E8M sub-sized flat dog-bone specimens. First, we collect the images of two of the speckled test specimens that subtend an angle of 12° between them using an f/25 aperture. Our image-collection strategy leads to a convergent imaging configuration with viewpoints that range from −45° to 45° across two perpendicular directions. Next, we process the collected images in a commercial photogrammetric calibration software by using more than nine image points for computing each object point. We validate our findings on a rigid-body motion test and a uniaxial tensile experiment, and we observe an excellent agreement between the stereo-DIC measurements and the ground truth. Using our findings, the reprojection error of self calibration is improved from 0.3 pixel to 0.1 pixel. The error in the stereo-DIC strain measurements is always less than 3.4% with the improvements made to self calibration, whereas it is as large as 7.6% without them.
Mutual Authentication and Authorized Data Access Between Fog and User Based on Blockchain Technology

One of the essential challenges to Fog computing security is authentication between service offering Fog servers and Fog users. This security confirmation is critical as a large number of users are offered services in Fog Computing. To address this challenge, in this paper, we first propose an efficient mutual authentication method between Clouds-Fogs-Edges. The solution enables authentication between edge users and the Fog servers that newly enter the system. The method makes the Fog servers to store one secret key per user and the user performs hash-based encryptions/decryptions. The proposed system is supported by Blockchain technique to achieve data integrity when confidential data are transferred between edge devices and nodes. The transactions between the nodes are recorded in the blocks which are hashed so that all edge and fog nodes are encircled by a secured system. Whenever any malicious edge server enters the network, it is identified based on ledger maintained at the nodes. The security and performance analysis shows the scheme is applicable for the Fog computing environment.
Differential privacy protection method based on published trajectory cross-correlation constraint

Aiming to solve the problem of low data utilization and privacy protection, a personalized differential privacy protection method based on cross-correlation constraints is proposed. By protecting sensitive location points on the trajectory and their affiliated sensitive points, this method combines the sensitivity of the user's trajectory location and user privacy protection requirements and privacy budget to propose a (R,Ɛ) -extended differential privacy protection model. Using autocorrelation Laplace transform, specific Gaussian white noise is transformed into noise that is related to the user's real trajectory sequence in both time and space. Then the noise is added to the user trajectory sequence to ensure spatio-temporal correlation between the noise sequence and the user trajectory sequence. This defines the cross-correlation constraint mechanism of the published trajectory sequence. By superimposing the real trajectory sequence on the user's noise sequence that satisfies the autocorrelation, a published trajectory sequence that satisfies the cross-correlation constraint condition is established to provide strong privacy guarantees against adversaries. Finally, the feasibility, effectiveness and rationality of the algorithm are verified by simulation experiments, and the proposed method is compared with recent studies in the same field on basis of merits and weakness and so on.
IMPROVING COMMITMENT TO AGREEMENTS: THE ROLE OF GROUP DECISION-MAKING METHODS IN THE PERCEPTION OF SENSE OF JUSTICE AND SATISFACTION AS COMMITMENT PREDICTORS

The objective of this study was to evaluate the chances of commitment in group decision making process, modeled by a multicriteria method based on game theory and its evaluation in relation to the satisfaction and sense of justice of the players. We hypothesized that mathematical methods may favor group decision-making, reflecting higher levels of sense of justice and satisfaction and consequently greater chances of commitment to the agreements made. By means of 75 simulations, with five volunteers each, the hypotheses of this study were confirmed, which include the affirmation that the commitment in group decision making can be increased by the use of a method to support group decision making and, consequently, that the sense of justice and satisfaction will be greater when agreements are supported by some sort of mathematical method.
High-Capacity Reversible Data Hiding in Encrypted Images Using Multi-Layer Embedding

Reversible Data Hiding in Encrypted Images (RDH-EI) has gained much popularity in the field of signal processing and cloud computing. In this study, we propose a high-capacity RDH-EI scheme using multi-layer embedding. It ensures that after extraction of the embedded additional data from the marked encrypted image, the original image is recovered completely. Broadly, the method comprises three parties: the content owner, the data hider/ cloud owner, and the receiver. In the beginning, the image is encrypted by the content owner using encryption based on the chaotic behaviour of PWL-memristor and sent to the data hider. At the data hider’s side, even-odd value embedding technique is applied in multiple layers of the encrypted image to increase the embedding capacity. Meanwhile, the auxiliary information consisting of location map (LM) for each layer and the number of layers, to be used in the recovery phase, is recorded during the embedding. After completing the embedding procedure, permutation operation is applied on the marked encrypted image to prevent the perceptual information leakage. Finally, at the receiver’s side the embedded additional data and the original image is recovered losslessly with the help of valid keys and shared auxiliary information. From the experimental results, it is observed that the quality of the directly decrypted image is 51.0 dB or above. The advantage of the proposed scheme is that the PSNR is almost same for each layer, i.e. even for the n number of layers used, the PSNR is 51.0 dB or above. Moreover, we applied chosen plaintext attack to test the encryption function and in case of any cropping attack on the marked encrypted image during the transfer, the cover image can be recovered with less distortion.
A SCA-Resistant AES Engine in 14nm CMOS with Time/Frequency-Domain Leakage Suppression using Non-Linear Digital LDO Cascaded with Arithmetic Countermeasures

An AES engine with uniform side-channel-attack (SCA) resistance across time/frequency domains using a high-bandwidth non-linear digital low-dropout (NL-DLDO) regulator in conjunction with AES arithmetic countermeasures is fabricated in 14nm CMOS. Randomized regulator loop parameters and cascading LDO and arithmetic transformations provide >250K× increase in frequency/time-domain MTD, with no CPA attack detected on current/electromagnetic (EM) traces measured from 1 billion encryptions.
Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning

This paper addresses learning with exploration driven by chaotic internal dynamics of a neural network. Hoerzer et al. showed that a chaotic reservoir network (RN) can learn with exploration driven by external random noise and a sequential reward. In this paper, we demonstrate that a chaotic RN can learn without external noise because the output fluctuation originated from its internal chaotic dynamics functions as exploration. As learning progresses, the chaoticity decreases and the network can automatically switch from exploration mode to exploitation mode. Furthermore, the network can resume exploration when presented with a new situation. In addition, we found that even when the two parameters that influence the chaoticity are varied, learning performance always improves around the edge of chaos. From these results, we think that exploration is generated from internal chaotic dynamics, and exploitation appears in the process of forming attractors on the chaotic dynamics through learning. Consequently, exploration and exploitation are well-balanced around the edge of chaos, which leads to good learning performance.
Probing the spectral dimension of quantum network geometries

Abstract We consider an environment for an open quantum system described by a ‘quantum network geometry with flavor’ (QNGF) in which the nodes are coupled quantum oscillators. The geometrical nature of QNGF is reflected in the spectral properties of the Laplacian matrix of the network which display a finite spectral dimension, determining also the frequencies of the normal modes of QNGFs. We show that an a priori unknown spectral dimension can be indirectly estimated by coupling an auxiliary open quantum system to the network and probing the normal mode frequencies in the low frequency regime. We find that the network parameters do not affect the estimate; in this sense it is a property of the network geometry, rather than the values of, e.g., oscillator bare frequencies or the constant coupling strength. Numerical evidence suggests that the estimate is also robust both to small changes in the high frequency cutoff and noisy or missing normal mode frequencies. We propose to couple the auxiliary system to a subset of network nodes with random coupling strengths to reveal and resolve a sufficiently large subset of normal mode frequencies.
Study of Detection Method on Real-time and High Precision Driver Seatbelt

The CPU-based deep learning seatbelt detection cannot satisfy the real-time requirements. The precision of seatbelt detection obviously decrease on the basis of deep learning due to the influence of some factors, like the light, rain and snow, window reflection etc. In order to improving detection precision under running CPU in real-time, the SSD MobileNet V2 network is simplified from network pruning and quantitative processing in this paper. The precision and speed of the detection have been improved according to the above optimization. The precision of seatbelt detection can be improved utilizing the regression constraint of seatbelt within the driver after integrating into one classifier. After that, the feature augmentation was added into the samples in order to improve the precision of seatbelt detection in deeply. Finally, the same driver was tracked by the particle filter algorithm. If the tracking failure, the obtained driver feature vector of deep learning will be used to instead of the histogram feature of particle filter and used for the following multi-frame image detection of seat belt. Finally, the detection results are 95.21% for detected precision, 97.19% for recall rate, and 21FPS for detected speed, respectively.