Reviewerly/exHarmony huggingface dataset:
Machine learning &amp; Deep learning based Load Balancing Algorithms techniques in Cloud Computing

Cloud computing is a fledgling technology that provides computers in terms of efficient resource efficiency. The advancement of new applications in the cloud brings on an increase in load on servers. In addition to the many studies conducted in the province of Cloud computing, it still has to face some provocations, such as load balancing(LB) that require further upgrades and models for better performance and to achieve maximum user satisfaction. In this paper, we have discussed about the Machine Learning Techniques used to build VM teams according to their CPU. Deep Learning (DL) techniques are used to achieve QoS which implies improving of resource utilization and deployment; while reducing delays, response time and costs, balancing the load on all equipment, therefore, increases system reliability. This paper contains a comprehensive overview of a few machine learning as well as deep learning based algorithm on load balancing.
Quasi-Affine Transformation Evolutionary with Double Excellent Guidance

The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a swarm-based collaborative optimization algorithm, which has drawn attention from researchers due to its simple structure, easy implementation, and powerful performance. However, it needs to be improved regarding the exploration, especially in the late stage of evolution, and the problem of easy falling into a local optimal solution. This paper proposes an improved algorithm named Quasi-Affine Transformation Evolutionary with double excellent guidance (QUATRE-DEG). The algorithm uses not only the global optimal solution but also the global suboptimal solution to guide the individual evolution. We establish a model to determine the guiding force by the distance between the global optimal position and the suboptimal position and propose a new mutation strategy through the double population structure. The optimization of population structure and the improvement of operation mechanisms bring more exploration for the algorithm. To optimize the algorithm, the experiments on parameter settings were made to determine the size of the subpopulation and to achieve a balance between exploration and development. The performance of QUATRE-DEG algorithm is evaluated under CEC2013 and CEC2014 test suites. Through comparison and analysis with some ABC variants known for their strong exploration ability and advanced QUATRE variants, the competitiveness of the proposed QUATRE-DEG algorithm is validated.
Comparative analysis for Edge Detection Techniques

Edge detection is a vital feature in the field of Computer Vision for extracting meaningful information from digital images. The primary aim of our proposed technique is to obtain thin edges, so that the result derived is suitable more for further applications such as boundary detection, image segmentation, motion detection/estimation, texture analysis, object identification and so on. We have done comparative analysis for four edge detectors that use different methods for detecting edges and have tested their results under a variety of situations to determine which detector was preferable under different circumstances. For practical analysis it was used to detect the edges and results have been compared with the gradient based methods of edge detection like Sobel, Prewitt, Robert these are first order derivative methods. Testing and analysis on different satellite images by Laplacian and gradient based method have done by using the ideal thresholding parameter for good true edges.To evaluate the performance of Laplacian based algorithms and gradient based algorithms. There is a need of calculating the some parameter for calculating the image quality. These parameters are able to identify which algorithm is best suited in detecting edges for satellite images.
Specular highlight removal based on an iterative light field method

In this paper, a novel, to the best of our knowledge, iterative approach for highlight removal is proposed using lenselet-based plenoptic cameras without multiple exposures. An unsupervised k-means clustering approach that relates unsaturated pixels to chromatic dispersion based on the intrinsic decomposition and dichromatic reflection model is proposed to recover unsaturated highlights. Meanwhile, an adaptive direction method along with a Gaussian probability distribution model is designed to recover the saturated highlights. Finally, a method that combines the specular residual ratio with information entropy is built to quantitatively evaluate the quality of highlight removal. Generally, our method not only fully removes specular highlights, but also has low spatial complexity of image acquisition, more stability, and outstanding restoration for complex scenes.
A Comparative Study of Deep Transfer Learning Techniques for Cultural (Aeta) Dance Classification utilizing Skeleton-Based Choreographic Motion Capture Data

The advancement of motion-sensing technology and depth cameras has led to a vast opportunity in motion analysis and monitoring applications, kinesiology analysis, and safeguarding intangible cultural heritage (ICH). A technique that allows a computer to understand human behavior is necessary to analyze and identify the motion using the motion capture data. The integration of motion sensing technology such as markerless motion capture devices, inertial sensors, and deep learning techniques gives an innovative approach to recording, analyzing, and visually recognizing human choreographic motion. Convolutional Neural Network (CNN) is one of the best-known techniques in learning patterns from images and videos and is most widely used among deep learning architectures for vision applications. This study explored different CNN architecture to determine the best prediction classifier based on its performances, such as VGG19, InceptionV3, and MobileNetV2. This study aims to perform an image classification approach of one of the Philippines' cultural dances, Aeta dances, utilizing skeleton-based motion capture data using CNN. The test results were assessed based on the generated training accuracy and evaluation of the loss function to assess the models' overall efficiency. VGG19 produced the highest model cultural dance classification accuracy among the three architectures, which resulted in 98.68% compared to InceptionV3 and MobileNetV2. Thus, the VGG19 model illustrates the optimal transfer learning result implies the best fit model than InceptionV3 and MobileNetV2.
Post-Quantum Verifiable Random Function from Symmetric Primitives in PoS Blockchain

Verifiable Random Functions (VRFs) play a key role in Proof-of-Stake blockchains such as Algorand to achieve highly scalable consensus, but currently deployed VRFs lack post-quantum security, which is crucial for future-readiness of blockchain systems. This work presents the first quantum-safe VRF scheme based on symmetric primitives. Our main proposal is a practical many-time quantum-safe VRF construction, $$\mathsf {X\hbox {-}VRF}$$ , based on the $$\textsf{XMSS}$$ signature scheme. An innovation of our work is to use the state of the blockchain to counter the undesired stateful nature of $$\textsf{XMSS}$$ by constructing a blockchain-empowered VRF. While increasing the usability of $$\textsf{XMSS}$$ , our technique also enforces honest behavior when creating an $$\mathsf {X\hbox {-}VRF}$$ output so as to satisfy the fundamental uniqueness property of VRFs. We show how $$\mathsf {X\hbox {-}VRF}$$ can be used in the Algorand setting to extend it to a quantum-safe blockchain and provide four instances of $$\mathsf {X\hbox {-}VRF}$$ with different key life-time. Our extensive performance evaluation, analysis and implementation indicate the effectiveness of our proposed constructions in practice. Particularly, we demonstrate that $$\mathsf {X\hbox {-}VRF}$$ is the most efficient quantum-safe VRF with a maximum proof size of 3 KB and a possible TPS of 449 for a network of thousand nodes.
Topology-Hiding Communication from Minimal Assumptions

Topology-hiding broadcast (THB) enables parties communicating over an incomplete network to broadcast messages while hiding the topology from within a given class of graphs. THB is a central tool underlying general topology-hiding secure computation (THC) (Moran et al. TCC’15). Although broadcast is a privacy-free task, it was recently shown that THB for certain graph classes necessitates computational assumptions, even in the semi-honest setting, and even given a single corrupted party. In this work we investigate the minimal assumptions required for topology–hiding communication—both Broadcast or Anonymous Broadcast (where the broadcaster’s identity is hidden). We develop new techniques that yield a variety of necessary and sufficient conditions for the feasibility of THB/THAB in different cryptographic settings: information theoretic, given existence of key agreement, and given existence of oblivious transfer. Our results show that feasibility can depend on various properties of the graph class, such as connectivity, and highlight the role of different properties of topology when kept hidden, including direction, distance, and/or distance-of-neighbors to the broadcaster. An interesting corollary of our results is a dichotomy for THC with a public number of at least three parties, secure against one corruption: information-theoretic feasibility if all graphs are 2-connected; necessity and sufficiency of key agreement otherwise.
ARQGAN: An evaluation of generative adversarial network approaches for automatic virtual inpainting restoration of Greek temples

Image reconstruction has received much attention and has advanced in recent years with the rise of deep learning. Deep neural models have been able to perform image-to-image translation by transferring pictorial styles, coloring old photographs or filling in missing parts. This last technique is known as image inpainting and enables restoration of damaged or missing parts of an image or photograph to obtain the complete picture. However, it is not always possible to properly define which parts are missing or to identify where they are missing, as in the case of superimposing new information on an already complete image. In this paper, we propose the use of generative adversarial networks (GANs), a well-known deep learning model, for virtual inpainting restoration of artificial landscape images containing archaeological remains of Greek temples. The network identifies key features determined by the internal logic of the architectural style denoted by the ruins and adds the missing architectural elements to obtain an image of the restored building. Unlike other studies, it does not receive any information on which elements should be added or where. Virtual inpainting restoration is capable of representing a building’s envelope but also integrates particular aspects of the building related to the architectural language used for its design. The restoration of the fundamental parts of the classical Greek order was consistent, and the results were evaluated with objective metrics and through a subjective survey between academics and architects. They showed that adding segmented images to the training dataset gives better results.
Merkle Trees Optimized for Stateless Clients in Bitcoin

The ever-growing size of the Bitcoin UTXO state is a factor preventing nodes with limited storage capacity from validating transactions. Cryptographic accumulators, such as Merkle trees, offer a viable solution to the problem. Full nodes create a Merkle tree from the UTXO set, while stateless nodes merely store the root of the Merkle tree. When provided with a proof, stateless nodes can verify that a transaction’s inputs belong to the UTXO set. In this work, we present a systematic study of Merkle tree based accumulators, with a focus on factors that reduce the proof size. Based on the observation that UTXOs typically have a short lifetime, we propose that recent UTXOs be co-located in the tree. When proofs for different transactions are batched, such a design reduces the per-transaction proof size. We provide details of our implementation of this idea, describing certain optimizations that further reduce the proof size in practice. On Bitcoin data before August 2019, we show that our design achieves a 4.6x reduction in proof size vis-a-vis UTREEXO [], which is a different Merkle-tree based system designed to support stateless nodes.
A New PRNG Hardware Architecture Based on an Exponential Chaotic Map

Recent works have been claiming efficient hardware architectures, showing a considerable endeavor to implement chaotic maps in the digital domain. However, there is a critical issue with the chaotic degradation in the digital environment due to its finite numeric precision, that it is still an unsettled topic in the research community. Additionally, less attention has been given to synthesize a methodological approach to how to calculate the exponential function in hardware. In this paper, two novel hardware designs to represent the exponential chaotic map have been suggested. We have employed a perturbation method to avoid the chaotic degradation. 64-bit fixed-point and 32-bit floating-point formats were investigated. Moreover, an approximation of Euler's number by a finite series and the Horner's method have been undertaken to further minimize the proposed hardware. Results show that both proposed hardware architectures consume a fewer number of components. The designed systems present a positive Lyapunov exponent, which suggests a chaotic behavior. Ultimately, the NIST SP 800-22 test, the histogram, and the autocorrelation function show that the new hardware architectures present pseudo-random properties.
Coarse-to-fine Foreground Segmentation based on Co-occurrence Pixel-Block and Spatio-Temporal Attention Model

Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.
MRPC: An R Package for Inference of Causal Graphs

Understanding the causal relationships between variables is a central goal of many scientific inquiries. Causal relationships may be represented by directed edges in a graph (or equivalently, a network). In biology, for example, gene regulatory networks may be viewed as a type of causal networks, where X→Y represents gene X regulating (i.e., being causal to) gene Y. However, existing general-purpose graph inference methods often result in a high number of false edges, whereas current causal inference methods developed for observational data in genomics can handle only limited types of causal relationships. We present MRPC (a PC algorithm with the principle of Mendelian Randomization), an R package that learns causal graphs with improved accuracy over existing methods. Our algorithm builds on the powerful PC algorithm (named after its developers Peter Spirtes and Clark Glymour), a canonical algorithm in computer science for learning directed acyclic graphs. The improvements in MRPC result in increased accuracy in identifying v-structures (i.e., X→Y←Z), and robustness to how the nodes are arranged in the input data. In the special case of genomic data that contain genotypes and phenotypes (e.g., gene expression) at the individual level, MRPC incorporates the principle of Mendelian randomization as constraints on edge direction to help orient the edges. MRPC allows for inference of causal graphs not only for general purposes, but also for biomedical data where multiple types of data may be input to provide evidence for causality. The R package is available on CRAN and is a free open-source software package under a GPL (≥2) license.
Analysis on Two-stage Object Detection based on Convolutional Neural Networkorks

Nowadays, with the development and wide application of computer version technology, object detection becomes a popular study direction in image processing, which is widely applied in various fields, such as robot navigation, intelligent video monitoring, industrial inspection and aerospace. In recent years, with the development of deep learning algorithms, there are much more studies about object detection based on deep learning algorithms, which solves the disadvantages of traditional method on object detection, such as poor generalization ability, low detection accuracy, and slow operational rate. There are two mainly classes about object detection: One-stage object detection algorithm and two-stage object detection algorithms. This paper aims at introducing the reserch status of two-stage object detection based on convolutional neural networks. Besides, The paper analyzes the advantages and disadvantages of each algorithm and makes a comparation with these algorithms. In the end, there will be a prospect for the object detection in the future development.
A Robust Deep Affinity Network for Multiple Ship Tracking

Multiple ship tracking (MST) is an important task in marine surveillance and ship situational awareness systems. Considerable work has been conducted on multiple object tracking in recent years, but it has focused primarily on pedestrians and automobiles, leaving a gap in studies on MST due to the particularities of complex marine scenes, such as ship scale variations, the long-tailed distribution of ships, and long-term occlusions caused by ship movements. In this article, we present a robust deep affinity network (RoDAN) for MST. To overcome the above difficulties in MST, we start with the basic deep affinity network (DAN) and improve it in three aspects: scale, region, and motion. For the scale dimension, we integrate an atrous spatial pyramid pooling (ASPP) module to improve the modeling ability for multiscale ships. For the region dimension, we propose the joint global region modeling (JGRM) module, which further strengthens the modeling ability of DAN and exploit it to overcome the long-tailed distribution property of ships. For the motion dimension, we propose the motion-matching optimization (MMO) module to fine-tune the tracking results and make our tracker more robust, less reliant on the front-end detector, and ameliorate long-term occlusions. The experimental results demonstrate that our MST method outperforms the state-of-the-art methods. In particular, it reduces the number of ID switches (IDSs) and trajectory fragmentations (FMs), achieving holistically preferable performance. Meanwhile, our method achieves a comparable speed.
When low and high tech solutions converge: Adapting to teaching soils during the COVID‐19 pandemic

Abstract COVID‐19 restrictions required a transition of our Soil Science and Forest Soils courses to an online format. A pre‐transition survey found that ∼10% of students enrolled in our courses lacked high‐speed internet capable of streaming videos and/or computers compatible with the applications in our Learning Management System (LMS). To ensure that students with limited internet or technology were not left behind, we adopted a low‐tech/bandwidth delivery (slides + transcript with LMS‐delivered assessments) of all lectures and recitation activities. Students could also complete either a low‐tech/bandwidth (lab slides + transcripts) or high‐tech/bandwidth lab option (delivered via video). Upon completion, students were surveyed to assess preferences and perceptions which are vital in understanding how our approach impacted student motivation, engagement with the material, and overall course satisfaction. Despite 90% of the students having access to high‐speed internet, ∼45% of the students used the low‐tech solutions either exclusively or half the time, even when high‐tech options (such as video) were available. Overall, students felt the low‐tech/bandwidth delivery of the lecture (∼87%) and recitation (∼76%) material was effective. Students (∼74%) also reported that online delivery of the lab material effectively supported their learning and was an effective replacement for the in‐lab learning experience. Students preferred in‐person to online delivery (63 vs. 17%) with 20% undecided. Noting the flexibility and organization of the course, 69% of the students felt the online delivery of our courses was more effective than their other courses despite, or perhaps because of, the lack of high‐tech delivery. Our experience demonstrates one approach to adapting an in‐person course to a virtual environment that considers inequities in broadband and technology access. Despite being perceived as effective by our students, low‐tech options were less preferred than in‐person instruction suggesting that, while effective, it was not viewed as equivalent.
Automatic detection of prosodic boundaries in spontaneous speech

Automatic speech recognition (ASR) and natural language processing (NLP) are expected to benefit from an effective, simple, and reliable method to automatically parse conversational speech. The ability to parse conversational speech depends crucially on the ability to identify boundaries between prosodic phrases. This is done naturally by the human ear, yet has proved surprisingly difficult to achieve reliably and simply in an automatic manner. Efforts to date have focused on detecting phrase boundaries using a variety of linguistic and acoustic cues. We propose a method which does not require model training and utilizes two prosodic cues that are based on ASR output. Boundaries are identified using discontinuities in speech rate (pre-boundary lengthening and phrase-initial acceleration) and silent pauses. The resulting phrases preserve syntactic validity, exhibit pitch reset, and compare well with manual tagging of prosodic boundaries. Collectively, our findings support the notion of prosodic phrases that represent coherent patterns across textual and acoustic parameters.
Incorporating Hand-crafted Features in a Neural Network Model for Stance Detection on Microblog

Microblogs, especially twitter, has made unprecedented opportunities for users to assert their stance towards various entities, issues, and events. Analyzing user stances from tweets provide opportunities to various organizations for decision making. However, it is challenging to identify the stance of a tweet due to its short length characteristics and idiosyncratic nature. Most of the previous studies explore either neural network-based features or hand-crafted features for their learning models. In this paper, we introduce a stance detection method that incorporates both the deep semantic features and hand-crafted features in a unified neural model. We exploit several opinionated lexicons and other textual and twitter-specific characteristics to extract a rich set of hand-crafted features. We use a supervised feature selection method to devise effective features and pass them to train a multilayer perceptron (MLP) network. Besides, we employ a convolutional layer in conjunction with a BiLSTM network (Conv-BiLSTM) to extract the higher-level contextual features. Extensive experiments on the SemEval-2016 stance detection dataset demonstrate the efficiency of our method over several state-of-the-art methods.