Reviewerly/exHarmony huggingface dataset:
Based blockchain-lightweight cryptography techniques for security information: A verification secure system for user authentication

The Internet of Things (IoT) is a network of physical objects that can provide a series of new services for the next wave of technological innovation. The Internet of Things is developing rapidly, during which machines and devices are connected via wireless or wired networks. This technology brings many advantages. Facilities in many fields, such as urban smart building management, industrial medical, agricultural energy and other security conditions. However, there are many issues that need to be resolved, such as resource constraints, heterogeneity, mobility, and architecture. In the remainder of this article, we propose a new approach based on a merger algorithm that combines passwords and image functions to increase the pattern structure’s randomness and security, and we have developed a combination of encryption and blockchain techniques. This method ensures that the image validation system is protected when accepting default specifications for confidentiality and safety information by sending the model from a recorder to the Nodes database. So, our system secures confidentiality and availability. Thus, we use the blockchain to perform and access secure data. The experimental result shows that our model is more precise than the literature.
Monte Carlo Denoising with a Sparse Auxiliary Feature Encoder

Fast Denoising Monte Carlo path tracing is very desirable. Existing learning-based real-time methods concatenate auxiliary buffers (i.e., albedo, normal, and depth) with noisy colors as input. Such structures cannot effectively extract rich information from auxiliary buffers, however. In this work, we facilitate the U-shape kernel-prediction network with a sparse auxiliary feature encoder. Sparse convolutions can focus solely on regions whose inputs have changed and reuse the history features in other regions. With sparse convolutions, the computational complexity of the auxiliary feature encoder is reduced by 50-70% without apparent performance drops.
Thinging teachers: gleaning nearness in dis/embodied eLearning through poetic inquiry

This piece aims to give glimpses of how we slide and dis/connect in embodied-virtual nodes and nets during Covid-19 lock-down, where lecturers reach from the virtual to be near students whom they normally care for in a contact university. Similar to how the concept of the global South helps us to look at the world in more encompassing richness, poetic inquiry contributes to insight by exposing diversity and difference and by grounding research in body and emotion − because traditional research might miss the grains within which the stories sing. I conducted performative interviews to engage through Zoom with three women lecturers from the Durban University of Technology, where I support staff in eLearning. For close to thirty years here, I have been ever-perplexed by how we are affected and how selves morph as we criss-cross the thin membrane between corporeal and virtual. Here, encounters are often out-the-box performances that ring with grit and great heart, as networks of tech and teacher perform feats to be near their students. Bearing in mind teachers in relation to technology as things, I explore nearness philosophically − Heidegger’s (1971 Heidegger, M 1971, Poetry, Language, Thought, Harper & Row, New York. doi: https://doi.org/10.1017/CBO9781107415324.004 [Google Scholar]) preoccupation with “when the thing things”, and then, how Irigaray (1999 Irigaray, L 1999, The forgetting of air, The Athlone Press, London. [Google Scholar]) extends this western philosophical concept into embodied, intuitive knowing. To understand and analyse this line of argument, I wrote poems rooted in my personal, visceral-virtual, but also natural world – thus implying that the divide between us and nature is fluid too: similar to there being no “bright red line” between us and our technological tools, as Jackson (2014 Jackson, S 2014, ‘Rethinking repair’, in T Gillespie, P Boczkowski & K Foot (eds.) Media Technologies. Essays on Communication, Materiality, and Society, MIT Press, pp. 221–240. doi:https://doi.org/10.1017/CBO9781107415324.004[Crossref] , [Google Scholar], p.232) says in ‘Rethinking repair’. While rightfully many current debates concentrate on Fourth Industrial Revolution technologies such as implants, these poems invite you to spot and think about more subtle, ordinary, often overlooked, “artefacts and forms of knowledge associated with women”, to quote Judy Wajcman (2009 Wajcman, J 2009, ‘Feminist theories of technology’, Cambridge Journal of Economics, vol. 34, no. 1, pp. 143–152. doi:https://doi.org/10.1093/cje/ben057[Crossref], [Web of Science ®] , [Google Scholar], p.144).
Border Compensation Scheme for Image Deblurring

Border compensation is critical for many image processing problems, including image deblurring. Without a proper border compensation mechanism, the border is misidentified as a high-frequency component, and artifacts may be generated after image deblurring. There are several existing border compensation methods, including zero padding, border repetition, mirror reflection, and slope extension. However, all of these methods inevitably have artifact problems due to the discontinuity of higher-order differences. In this work, we propose an alternative border compensation model by using the Gaussian extension model. Since the derivative of the Gaussian function at the center is zero no matter what the order of the derivative is, using the proposed algorithm much reduces the artifact around the border. Experiment results show that, with the proposed border extension scheme, the image deblurring results have much less artifact. It is helpful for image quality improvement and computer vision.
Noisy multimodal brain image registration using markov random field model

In this paper, a new scheme is proposed to register brain Magnetic Resonance (MR) and Computed Tomography (CT) images under a noisy environment. The scheme is developed based on the notion of mutual information and this is extended to the feature space-based registration. The 2D joint histogram of the noisy MR and CT images has been computed and this is assumed to be the degraded version of the true 2D joint histogram that corresponds to the CT and MR images. The true 2D joint histogram is modeled as Markov Random Field (MRF) model and is estimated from the degraded one by formulating the problem in Maximum A Posterior (MAP) estimation framework. The MAP estimates are obtained by the combination of Simulated Annealing (SA) and Iterated Conditioned Mode (ICM) algorithms. The Mutual Information (MI) at different values of the parameter vector θ are computed from the estimated joint histogram and is maximized to determine the optimal registration parameter. The MI curve is modeled as multivariate Gaussian distribution and the registration parameter vector is obtained with a confidence interval. The scheme has been tested with the slices of patients from the Retrospective Image Registration Evaluation (RIRE) database. The efficacy of the scheme on MR and CT volumes is demonstrated by rendering the registered slices. The proposed algorithm is successfully tested with different degrees of Gaussian, Rician and Rayleigh noise conditions. Performance measures such as parameter estimation errors, error statistics and landmark based validation are used to demonstrate the efficacy of the proposed algorithm. The scheme is found to exhibit improved performance, especially at high noisy conditions as compared to the existing schemes.
Adding Interpretability to Neural Knowledge DNA

This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems.
Exploiting Dynamic Bit Sparsity in Activation for Deep Neural Network Acceleration

Data sparsity is important in accelerating deep neural networks (DNNs). However, besides the zeroed values, the bit sparsity especially in activations are oftentimes missing in conventional DNN accelerators. In this paper, we present a DNN accelerator to exploit the bit sparsity by dynamically skipping zeroed bits in activations. To this goal, we first substitute the multiply-and-accumulate (MAC) units with more serial shift-and-accumulate units to sustain the computing parallelism. To prevent the low efficiency caused by the random number and positions of the zeroed bits in different activations, we propose activation-grouping, so that the activations in the same group can be computed on non-zero bits in different channels freely, and synchronization is only needed between different groups. We implement the proposed accelerator with 16 process units (PU) and 16 processing elements (PE) in each PU on FPGA built upon VTA (Versatile Tensor Accelerator) which can integrate seamlessly with TVM compilation. We evaluate the efficiency of our design with convolutional layers in resnet18 respectively, which achieves over 3.2x speedup on average compared with VTA design. In terms of the whole network, it can achieve over 2.26x speedup and over 2.0x improvement on area efficiency.
Intra-Batch Features Separation for Indoor and Outdoor Pedestrian-View Intersection Classification

Pedestrian-view intersection classification is an important component to assist robots to navigate in the pedestrian path. To solve this problem, previous approaches simply fine-tune an ImageNet-pretrained network with intersection classification dataset using cross-entropy loss as classification loss in an end-to-end manner. In this work, we propose a novel additional loss to further improve the model's capability to discriminate intersection and non-intersection class. This loss is directly calculated on the features in a given mini-batch without requiring any additional inference. Furthermore, previous works cover only outdoor domain while we also propose indoor domain in addition to the outdoor intersection classification dataset. Extensive experiments show that the models trained using the proposed loss yields better performance compared to the models trained without it on both indoor and outdoor datasets. This demonstrates the potential of the proposed loss in improving the discrimination capability of our models.
On the Verification of Totally-Ordered HTN Plans

Verifying HTN plans is an intractable problem with two existing approaches to solve the problem. One technique is based on compilation to SAT. Another method is using parsing, and it is currently the fastest technique for verifying HTN plans and the only technique supporting state constraints. In this paper, we propose an extension of the parsing-based approach to verify totally-ordered HTN plans more efficiently. This problem is known to be tractable if no state constraints are included, and we show theoretically and empirically that the modified parsing approach achieves better performance than the currently fastest HTN plan verifier when applied to totally-ordered HTN plans.
Research on Industrial Blockchain: Concept, Architecture and Development

At present, with the rapid development of blockchain and digital transformation, the integration of blockchain technology and industrial fields has become a hot spot. This paper acts as pioneer in putting forward the concept of industrial blockchain, and summarizes its reference functional architecture. Then, it analyzes the advantages of industrial blockchain, reviews and lists the relevant application practices in China and the world. Finally, the suggestions to promote industrial blockchain, future propects and conclusion are proposed after analyzing the problems and challenges what are faced.
POI Recommend for Deep Neural Network Based on Explicit and Implicit Feature Joint

With the development of deep learning technology, a series of new recommendation algorithms are produced by using the idea of deep learning in the field of recommendation algorithm. These recommendation algorithms usually only mine user, item and context related features, and then combine the features, and use neural network for model training. However, it may be difficult for us to find a suitable feature expression for some features. At the same time, it is difficult for us to find all the feature expression forms for all kinds of features of a certain thing, which leads to the recommendation effect not reaching the expected level. To solve this problem, this paper proposes a deep neural network based on explicit and implicit feature joint (EIFJDNN) model, which is different from the features determined in Feature Engineering in the past. The features used in this model include explicit features and implicit features. In the recommendation of interest points, the access of interest points is affected by both time and space, we take on temporal and spatial features as explicit features, and other influencing factors as implicit features for recommendation. We designed FGMF algorithm, which is an improvement of GMF model. We use it to pretrain the model, then input the results into deep neural network for training, predict the score of each user on the points of interest, and then rank the scores to get the final recommendation list. Finally, we verified the recommendation performance of EIFJDNN model on Foursquare dataset, and the results show that the proposed model is superior to the existing work in hit rate and normalized discounted cumulative gain.
GPU-based centroidal voronoi tessellation using local search on thinnest digital surface

Voronoi tessellation is a classical geometric problem with different variants and nature of constraints. Centroidal Voronoi tessellation (CVT) is one such leading variant and used in many applications. Although there exists a multitude of CVT techniques for 3D objects considered as discrete volumes, e.g., as sets of voxels, there is no significant work in the literature on CVT computation for voxelized (a.k.a. 'digital') surfaces. In this paper we focus on this problem and propose a novel GPU-based algorithm for CVT computation on a digital surface. Its novelty rests on several fundamental ideas. Firstly, the digital surface is thinnest by construction; that is, while being voxelized from a triangulated surface, its every triangle is 2-minimal in topological sense. As a result, each voxel of the digital surface has the smallest possible neighborhood, which eventually limits the search space and aids in efficient computation. Secondly, as the optimization constraint, a novel formulation of Voronoi energy is used, which is easy to compute and hence leads to quick convergence of the algorithm. The GPU-based algorithm with related procedures and their complexity analysis have been discussed in detail, and related experimental results have also been furnished to adjudge the merit and usefulness of the proposed algorithm.
ANALYSIS OF THE EFFECTIVENESS OF APPLYING THE FREQUENCY-CONTEXT CLASSIFICATION ALGORITHM TO TEXTS OF DIFFERENT STYLES

This article discusses the application of the frequency-context classification algorithm to texts of various styles. The main features of different styles that affect the efficiency of the algo-rithm are highlighted. It is proved that the method of selecting the subject of the text using the fre-quency-context classification algorithm works best in relation to scientific and legal documents and, in its current form, is practically inapplicable for literary texts. This makes the task of modifying the algorithm to determine the subject of literary texts relevant.
Multistages mineralization and transformation of terrigenous rocks in the Vyun ore field, Yana-Kolyma metallogenic belt, Northeast Asia: insight from the sedimentary, diagenetic and hydrothermal sulfides and geochemistry of ore-hosting rocks

Abstract The article presents the results of studying the sulfidization zone of the Charky-Indigirka thrust fault within the Vyun ore field in the Upper Adycha sector of the Yana-Kolyma metallogenic belt. The purpose of the research is to study the composition and distribution of basic and trace elements in terrigenous rocks of the Upper Triassic and Middle Jurassic, as well as in distal metasomatites on the territory of the Vyun ore field. The petrochemical features of weakly altered terrigenous rocks, conditions of their formation and changes of composition during epigenetic processes were analyzed. Three generations of pyrite were identified: diagenetic Py1, metamorphogenic Py2 and metasomatic Py3. Typomorphic trace elements and variations of their distribution in pyrites were determined. Composition analyses of weakly altered sedimentary rocks of the Upper Triassic (V/(V+Ni)=0.5-0.8, V/Cr=0.1-2.9 and Ni/Co=2.5-10.3) and Middle Jurassic (V/(V+Ni)=0.7-0.9, V/Cr=0.2-2.0 and Ni/Co=1.3-8.8) yielded the conclusion that changes in oxygen conditions to disoxic and anoxic, as well as the enrichment of terrigenous material with ore elements, lead to the formation of authigenic sulfide mineralization at the early stages of the sedimentary strata formation. The subsequent multistage development of the territory was accompanied by an active migration of chemical elements, their input and redistribution.
An Optimum Decision Making Framework for Allocating Spot Instances to Execute Batch Workload in Cloud

Among all available cloud-based solutions, spot instances are ideal for batch job execution with flexible completion times and failure tolerance. The combination of spot instance sizes and prices provides flexibility to execute jobs satisfactorily with regards to their computational requirements. However, the demand for each type of batch instance fluctuates over time and it raises some challenges to provide adequate instances to match the workload demand. This makes it necessary to have a mechanism to dynamically adjust the number of instances and assign them to the incoming jobs.In this paper, we propose an optimization-based solution to allocate spot resources for executing arriving batch jobs in the cloud platforms over time. This method manages the trade-off between the cost of renting spot instances and the user-centric quality of service in terms of job waiting time. Given a set of jobs and spot instances of various types, our algorithm selects the best combination of instances in each analysis window. In addition, the method takes into account the job arrival prediction and characteristics of input jobs to adapt the algorithm.
High-Capacity Separable Reversible Data Hiding Algorithm for Encrypted Images

Reversible data hiding in the encrypted image domain (RDHEI) has received significant attention in order to protect the privacy of content owners when their images are to be used as a host for some data. This paper proposes a separable RDHEI algorithm that provides a compromise between the embedding capacity, quality of decrypted images and security. The algorithm encrypts the image blocks by either permutation or Advanced Encryption Standard (AES) depending on their complexity. Low complexity blocks are used for data hiding using the modification of prediction errors approach. Experimental results show the ability of the proposed algorithm in increasing the embedding capacity and security while maintaining acceptable quality for the directly-decrypted images.
Communication Latency Optimization for Mesos-based Cloud Computing Systems

Apache Mesos is a resource manager for clouding computing systems and it provides effective resource isolation and sharing across distributed computing frameworks such as Apache Hadoop, Spark, and Flink. Continuously increasing computing demands require a larger number of nodes in cloud computing nowadays, and it has even become normal for a master node to process tens of thousands of messages from slave nodes each second. This creates considerable communication pressure for the master node, which may dramatically affect the communication latency between master and slave nodes. To reduce the latency, we propose a novel method which consists of three steps: 1) we build an environment to simulate the scenario of 10,000 slave nodes that simultaneously send messages to the master node. 2) We systematically evaluate how the heartbeat interval and message size affect the communication latency through a series of experiments. 3) We adopt Zlib compression algorithm to effectively reduce the communication cost. Evaluation results indicate that compared to default Mesos-based cloud computing systems, our method can reduce the communication latency by up to 56.34%.
The Analysis of Fuzzy C-Means and Pearson Correlation Methods for Data Reduction in kNN Algorithm

The kNN algorithm, a reasonably popular method in data mining, is used for the data classification process. The traditional kNN method experiences several disadvantages, like slow computation in case the dimensions of the training data are significant and a strong dependence on the amount of training data. Therefore, before the data classification process was carried out, this study tested data reduction on the kNN algorithm using the FCM and Pearson correlation method on the training data. Simultaneously, the FCM algorithm is implemented in the data clustering process. It is operated to get the center point of the cluster and selects the training data with the lowest correlation level using the Pearson correlation equation for further reduction. The results of the two methods are in the form of reduced data, which will then be used as training data with more limited dimensions for the classification process in the kNN algorithm. Based on the research conducted, it can be concluded that the accuracy of data classification in the kNN algorithm by data reduction using the FCM algorithm and Person correlation achieved an average accuracy of 92.22% and an increase of 2.7%, compared to the classification results in kNN algorithm without data reduction with an average accuracy gain of 89.52%. In addition, the use of the data reduction method proposed in the kNN algorithm can reduce the training data into smaller dimensions with an average reduction percentage of 19.22%.
SimilarityGAN: Using Similarity to Loosen Structural Constraints in Generative Adversarial Models

Recently, generative adversarial networks have performed extremely well in image translation. When translating images current models adhere to a strict structural symmetry between the input and output images. This paper, presents a technique for image translation involving a pair of image domains that allows the output image to go beyond the structural symmetry constraints imposed by the input. By using a siamese model as the discriminator, we condition the generator to produce images that are only similar, rather than identical to the input. We show experimentally that using this modified loss a generator can generate realistic images for complex problems that only loosely adhere to the structure of the input.
Binary Trees

While the Collections framework provides a rich variety of real-world data structures, it unfortunately does not include trees for direct use. However, trees are helpful for various use cases and therefore desirable. Because the topic of trees is quite extensive, and not to go beyond the scope of this book, you will deal mainly with binary trees and binary search trees (BSTs) as special cases.