Reviewerly/exHarmony huggingface dataset:
Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.
Toward Assured Data Deletion in Cloud Storage

Outsourcing data to remote cloud servers can significantly reduce the managing overhead and storage burden for individuals and enterprises. When the data stored in cloud are no longer needed, assured data deletion is a fundamental requirement to ensure that the sensitive data on the cloud can be safely deleted. Salient features of cloud storage such as virtualization, multi-tenancy, high availability and the platform complexity pose various challenges to the assured data deletion, which need to be addressed. In this article, we investigate the system model, desirable security properties as well as the potential solutions to the issues of assured deletion. We present the techniques used to achieve verifiable deletion and propose several methods which satisfy the timeliness, fine granularity and the verification of deletion at the same time. We consider various scenarios such as assured deletion with data transfer, fine-grained access control and data storage with multiple copies. Implementation results demonstrate the efficiency of the proposed protocols.
Weighted full binary tree-sliced binary pattern: An RGB-D image descriptor

We present an Algorithm to understand Inter-pixel similarity, which shall be observed in images with the help of a data structure Full Binary Tree. The Full Binary Tree has certain properties like every node must have 2 children or none. Based on this property of Binary Tree, the method of Sliced Binary Pattern is proposed. The inter-pixel similarity may be observed by converting any pixel information of an image within a block of size 3 × 3 to its binarized form, as the pixel information, whose similarity with neighboring pixel cannot be exploited, when it is in decimal form. Thus, we convert all pixel information within a block of size 3 × 3 to its binarized form then we compare the binary pattern of a central pixel with its 8-nearest neighbors. If there is a binary pattern match between central pixel and its 8-nearest neighbors of a block, we assign weights to it, where the weights are determined by the position of match that exist between central pixel and 8-other neighboring pixels of an image. This process helps in determining the inter-pixel similarity of 8-nearest neighbors with respect to central pixel of a block. Every block of 3 × 3 pixels is processed with this strategy to obtain the similarity between patterns in an image. The erected Weighted Full Binary Tree-Sliced Binary Pattern analyzes an image in RGB-Dimensions based on patterns of Inter-Pixel Similarity by tracing the similarity path. The proposed RGB-D texture based inter-pixel similarity addresses the verification of facial similarity. Further, the proposed WFBT-SBP has yielded a good classification accuracy of 77.4%, 77.3%, 77.98%, and 77.94% over a relations of F-S, F-D, M-S, M-D of KinfaceW-I and 76.89%, 76.72%, 77.01%, 76.99% over a relations of F-S, F-D, M-S, and M-D of KinfaceW-II respectively.
Explainability of SMOTE Based Oversampling for Imbalanced Dataset Problems

Since the advent of Artificial Intelligence (AI), the problem of imbalanced datasets and the lack of interpretability of complex AI models has been a matter of concern for the research community. These datasets contain a very low proportion of one class (minority class) and very large proportion of another class (majority class). Even though the quantitative representation is less for minority class they have high qualitative importance as the cost associated in case of misclassification in these domains is very high. The paper presents a novel solution to deal with the issue of imbalanced dataset by using the proven method of resampling Synthetic Minority Oversampling Technique (SMOTE). Further, the interpretability of such an approach is demonstrated by some powerful eXplainable AI (XAI) techniques such as LRP, SHAP and LIME. In this paper state-of-art models like Deep Learning and Boosting classifiers were trained to classify fraud instances with high accuracy and proved to be reliable by producing explanations for their predicted instances. The results of confusion matrices and explanations showcase excellent performance and reliability of the models.
NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

Depth-image-based rendering (DIBR), where arbitrary views are synthesized from a center image and depth image, has received much attention in the three-dimensional (3D) research field. With advances in depth-acquisition techniques and the proliferation of 3D glasses and 3D display devices, there is a growing demand for schemes to protect the copyrights of DIBR 3D images. Digital watermarking is a typical protection technology and designing a watermarking method for DIBR 3D images is a challenging task because the synchronization of watermarks can easily be broken in the process of generating synthetic images. To address this issue, we propose a non-subsampled contourlet transform (NSCT)-based blind watermarking for DIBR 3D images. To ensure the proposed method has properties of robustness against the DIBR process, we conduct an analysis of robustness for NSCT subbands against DIBR attacks. Based on the analysis results, we select subbands that are robust against DIBR attacks and embed watermark in the low coefficients of NSCT subbands using quantization-based embedding. While ensuring robustness, the proposed method also improves the imperceptibility of watermarks by adjusting their embedding strength according to computed perceptual masking values. Through experiments, we show that the proposed method is robust against both desynchronization attacks of the DIBR process and common attacks including signal processing operations and geometric distortions. The high imperceptibility of our method is also verified by several evaluation metrics in a subjective and objective manner.
Novel CNN-Based AP2D-Net Accelerator: An Area and Power Efficient Solution for Real-Time Applications on Mobile FPGA

Standard convolutional neural networks (CNNs) have large amounts of data redundancy, and the same accuracy can be obtained even in lower bit weights instead of floating-point representation. Most CNNs have to be developed and executed on high-end GPU-based workstations, for which it is hard to transplant the existing implementations onto portable edge FPGAs because of the limitation of on-chip block memory storage size and battery capacity. In this paper, we present adaptive pointwise convolution and 2D convolution joint network (AP2D-Net), an ultra-low power and relatively high throughput system combined with dynamic precision weights and activation. Our system has high performance, and we make a trade-off between accuracy and power efficiency by adopting unmanned aerial vehicle (UAV) object detection scenarios. We evaluate our system on the Zynq UltraScale+ MPSoC Ultra96 mobile FPGA platform. The target board can get the real-time speed of 30 fps under 5.6 W, and the FPGA on-chip power is only 0.6 W. The power efficiency of our system is 2.8× better than the best system design on a Jetson TX2 GPU and 1.9× better than the design on a PYNQ-Z1 SoC FPGA.
Probabilistic Embeddings for Speaker Diarization

Speaker embeddings (x-vectors) extracted from very short segments of speech have recently been shown to give competitive performance in speaker diarization. We generalize this recipe by extracting from each speech segment, in parallel with the x-vector, also a diagonal precision matrix, thus providing a path for the propagation of information about the quality of the speech segment into a PLDA scoring backend. These precisions quantify the uncertainty about what the values of the embeddings might have been if they had been extracted from high quality speech segments. The proposed probabilistic embeddings (x-vectors with precisions) are interfaced with the PLDA model by treating the x-vectors as hidden variables and marginalizing them out. We apply the proposed probabilistic embeddings as input to an agglomerative hierarchical clustering (AHC) algorithm to do diarization in the DIHARD'19 evaluation set. We compute the full PLDA likelihood 'by the book' for each clustering hypothesis that is considered by AHC. We do joint discriminative training of the PLDA parameters and of the probabilistic x-vector extractor. We demonstrate accuracy gains relative to a baseline AHC algorithm, applied to traditional xvectors (without uncertainty), and which uses averaging of binary log-likelihood-ratios, rather than by-the-book scoring.
Instance Genetic Selection for Fuzzy Rule-based Systems Optimization to Opinion Classification

Opinion Mining aims to identify people feelings about some item of interest based on content available on the Web, without the user having to find and read all the news about it. As opinions are related to feelings that are often described by imprecise terms, Fuzzy Systems appear as an alternative to treat the information subjectivity. An important task in developing Fuzzy Systems is define the rule base usually based on textual databases in case of opinion mining. However, these databases are extensive and the algorithms used to generate the rule base often result in many rules, making it difficult to achieve accuracy with a low computational cost. To deal with this problem, instance selection can be applied to reduce databases in order to save only relevant data. Thus, the aim of the present study is optimize a Fuzzy System, using instance selection to generate a reduced rule base. As the issue mentioned is a multiobjective problem, which seeks to increase accuracy and reduce the number of rules, we have chosen to apply a Multiobjective Genetic Algorithm, since it has been acknowledged as a promising approach in the literature. The results demonstrate that the Multiobjective Genetic Algorithms can be applied in instance selection for opinion classification problems, presenting a reduction in the number of instances and execution time, without significant changes in accuracy.
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensional. A way to tackle this problem is to introduce domain knowledge into the BO framework. We propose to exploit the geometry of non-Euclidean parameter spaces, which often arise in robotics (e.g. orientation, stiffness matrix). Our approach, built on Riemannian manifold theory, allows BO to properly measure similarities in the parameter space through geometry-aware kernel functions and to optimize the acquisition function on the manifold as an unconstrained problem. We test our approach in several benchmark artificial landscapes and using a 7-DOF simulated robot to learn orientation and impedance parameters for manipulation skills.
Buildings Reconstruction Within the New Educational Space Project in Ukraine

Since 2017, educational system has been reorganized in Ukraine, so the New Educational Space project was implemented within it. The aim of quality assurance in education has always been a topical issue, due to the fast world developing and changing. It is necessary to take into account all the aspects of technical, information, technological, architectural and artistic development of surrounding world and in accordance with these requirements to introduce new systems of educational space. The main components and recommendations for the development of educational space related to the educational environment modernization are the following: Reconstruction of buildings and offices, artistic and energy-efficient component should be considered in details.
Exploring gamified persuasive system design for energy saving

Purpose The purpose of this paper is to explore user-centered design possibilities at the intersection of gamification and persuasive technology to foster energy saving behavior. Design/methodology/approach We performed a large-scale empirical study of 480 participants and analyzed how different HEXAD gamification user types perceive selected persuasive strategies embedded in an energy saving prototype. Furthermore, we investigated the role of existing energy saving behaviors (pro-environmental behavior scale–e.g. regularly turning the lights off) and their effect on the perceived persuasiveness of the proposed persuasive strategies, which may has an impact on the overall design process. Furthermore, we applied partial least squares path modeling and conducted a one-way and repeated measure ANOVA. Findings Results show that user types play an important role in the design of persuasive systems for energy saving. For instance, people with a high tendency toward the Socializer user type were motivated by almost all of the employed persuasive strategies, whereas Philanthropists and Players only to a limited number of strategies. Furthermore, our study reveals that existing behaviors like the individual level of energy conservation influences the perceived persuasiveness of certain strategies and therefore should be considered in the design of such applications. Research limitations/implications Using storyboards to obtain feedback about the perceived persuasiveness of employed strategies has limitations compared to the actual use of a functional prototype. However, to offset that limitation the mock-ups used in the storyboard reflected the actual designs for a real-world prototype. Originality This is the first study that explores how HEXAD gamification user types can be used to inform the design of persuasive applications for energy saving (RQ1). Furthermore, and in particular, this study draws on the challenges when using user types within gamified persuasive systems by highlighting the impact and the importance of considering existing energy saving behaviors (RQ2), which has not been addressed so far.
Fully Test-time Adaptation by Entropy Minimization

A model must adapt itself to generalize to new and different data during testing. In this setting of fully test-time adaptation the model has only the test data and its own parameters. We propose to adapt by test entropy minimization (tent): we optimize the model for confidence as measured by the entropy of its predictions. Our method estimates normalization statistics and optimizes channel-wise affine transformations to update online on each batch. Tent reduces generalization error for image classification on corrupted ImageNet and CIFAR-10/100 and reaches a new state-of-the-art error on ImageNet-C. Tent handles source-free domain adaptation on digit recognition from SVHN to MNIST/MNIST-M/USPS, on semantic segmentation from GTA to Cityscapes, and on the VisDA-C benchmark. These results are achieved in one epoch of test-time optimization without altering training.
Medical image processing using python and open cv

Abstract This paper presents the implementation of the Python programming language and the Open CV library in medical image processing. Medical images play an important role in identifying diseases and detecting whether organs are functioning properly. Image processing related to medical images is an active research field where various techniques are used to facilitate diagnosis and various image processing techniques can be used. In this study, image processing was performed including image preprocessing, histogram equalization, smoothening, erosion, and dilation. The use of python was chosen because this programming language is open source with a lot of available resources. The open CV library is also available free of charge and provides tons of image processing functions. The software can also be installed in a small Raspberry Pi computer and low electrical power usage making it very suitable as an efficient resource of the medical image processing system.
The effect of online gamification on EFL learners' writing anxiety levels: a process-based approach

Writing is one of the most difficult and neglected skills for EFL learners. Gamification, the use of game elements to increase user experience and interest in non-game content, can be used to deal with writing anxiety which is considered as a hindering affective factor in successful language learning. Within this scope, this study aims to investigate the effect of the online gamification tool "˜Edmodo" on EFL learners' writing anxiety levels. The experimental design, one of the quantitative methods, was preferred. Data were collected through a background questionnaire and the Second Language Writing Anxiety Scale, involving 22 items scored on a five-point Likert response scale. Data were analysed with the Statistical Package for Social Sciences software. The results showed that the students who completed the activities and who were graded through Edmodo had significantly lower anxiety levels than the students using the traditional pen and paper. Keywords: Gamification, foreign language, writing, anxiety.
Ledger Combiners for Fast Settlement

Blockchain protocols based on variations of the longest-chain rule—whether following the proof-of-work paradigm or one of its alternatives—suffer from a fundamental latency barrier. This arises from the need to collect a sufficient number of blocks on top of a transaction-bearing block to guarantee the transaction’s stability while limiting the rate at which blocks can be created in order to prevent security-threatening forks. Our main result is a black-box security-amplifying combiner based on parallel composition of m blockchains that achieves $$\varTheta (m)$$ -fold security amplification for conflict-free transactions or, equivalently, $$\varTheta (m)$$ -fold reduction in latency. Our construction breaks the latency barrier to achieve, for the first time, a ledger based purely on Nakamoto longest-chain consensus guaranteeing worst-case constant-time settlement for conflict-free transactions: settlement can be accelerated to a constant multiple of block propagation time with negligible error. Operationally, our construction shows how to view any family of blockchains as a unified, virtual ledger without requiring any coordination among the chains or any new protocol metadata. Users of the system have the option to inject a transaction into a single constituent blockchain or—if they desire accelerated settlement—all of the constituent blockchains. Our presentation and proofs introduce a new formalism for reasoning about blockchains, the dynamic ledger, and articulate our constructions as transformations of dynamic ledgers that amplify security. We also illustrate the versatility of this formalism by presenting robust-combiner constructions for blockchains that can protect against complete adversarial control of a minority of a family of blockchains.
CacheOut: Leaking Data on Intel CPUs via Cache Evictions

Recent transient-execution attacks, such as RIDL, Fallout, and ZombieLoad, demonstrated that attackers can leak information while it transits through microarchitectural buffers. Named Microarchitectural Data Sampling (MDS) by Intel, these attacks are likened to drinking from the firehose, as the attacker has little control over what data is observed and from what origin. Unable to prevent the buffers from leaking, Intel issued countermeasures via microcode updates that overwrite the buffers when the CPU changes security domains. In this work we present CacheOut, a new microarchitectural attack that is capable of bypassing Intel's buffer overwrite countermeasures. We observe that as data is being evicted from the CPU's L1 cache, it is often transferred back to the leaky CPU buffers where it can be recovered by the attacker. CacheOut improves over previous MDS attacks by allowing the attacker to choose which data to leak from the CPU's L1 cache, as well as which part of a cache line to leak. We demonstrate that CacheOut can leak information across multiple security boundaries, including those between processes, virtual machines, user and kernel space, and from SGX enclaves.
FireLedger

Blockchains are distributed secure ledgers to which transactions are issued continuously and each block of transactions is tightly coupled to its predecessors. Permissioned blockchains place special emphasis on transactions throughput. In this paper we present FireLedger, which leverages the iterative nature of blockchains in order to improve their throughput in optimistic execution scenarios. FireLedger trades latency for throughput in the sense that in FireLedger the last f + 1 blocks of each node's blockchain are considered tentative, i.e., they may be rescinded in case one of the last f + 1 blocks proposers was Byzantine. Yet, when optimistic assumptions are met, a new block is decided in each communication step, which consists of a proposer that sends only its proposal and all other participants are sending a single bit each. In our performance study FireLedger obtained 20% -- 600% better throughput than state of the art protocols like HotStuff and BFT-SMaRt, depending on the configuration.