Reviewerly/exHarmony huggingface dataset:
Priority-Aware Resource Allocation Algorithm for Cloud Computing

Cloud is an innovative model of the computing paradigm, where servers, networks, storage, development tools, and applications are enabled to the users through the Internet in the form of services. These services are provided by the cloud service providers (CSPs) by deploying virtual machines (VMs) as per the resource request received from the users. These VM requests are of various types and priorities, and therefore have varying impacts in terms of importance, criticality, accountability, and domains of applications. On the other hand, the resources/hosts are of varying sizes and assigned to the requests based on their capability, availability, and feasibility. Therefore, it is an exigent issue to map the VM requests with the hosts to maximize the allocation rate with respect to their priority. One of the recent solutions is to assign the high-priority VM requests in close proximity, and other requests can be assigned using first fit decreasing (FFD). However, this solution does not partition the hosts to discriminate the type of requests. This paper introduces a priority-aware resource allocation (PARA) algorithm to consider three types of VM requests: highly critical, critical, and normal, and partition the hosts into three levels. PARA enables the high-priority VM requests to a relatively large pool of hosts compared to low-priority requests. The proposed algorithm is simulated by considering 10000 to 100000 VM requests and 20 to 30 hosts and compared with a non-partitioned pool of hosts with FFD to show effectiveness in allocation rate and weighted score concerning the serving requests.
Black-box models for non-functional properties of AI software systems

Non-functional properties (NFPs) such as latency, memory requirements, or hardware cost are an important characteristic of AI software systems, especially in the domain of resource-constrained embedded devices. Embedded AI products require sufficient resources for satisfactory latency and accuracy, but should also be cost-efficient and therefore not use more powerful hardware than strictly necessary. Traditionally, modeling and optimization efforts focus on the AI architecture, utilizing methods such as neural architecture search (NAS). However, before developers can start optimizing, they need to know which architectures are suitable candidates for their use case. To this end, architectures must be viewed in context: model post-processing (e.g. quantization), hardware platform, and run-time configuration such as batching all have significant effects on NFPs and therefore on AI architecture performance. Moreover, scalar parameters such as batch size cannot be benchmarked exhaustively. We argue that it is worthwhile to address this issue by means of black-box models before deciding on AI architectures for optimization and hardware/software platforms for inference. To support our claim, we present an AI product line with variable hardware and software components, perform benchmarks, and present notable results. Additionally, we evaluate both compactness and generalization capabilities of regression tree-based modeling approaches from the machine learning and product line engineering communities. We find that linear model trees perform best: they can capture NFPs of known AI configurations with a mean error of up to 13 %, and can predict unseen configurations with a mean error of 10 to 26 %. We find linear model trees to be more compact and interpretable than other tree-based approaches.
MPPT Control Strategy of Photovoltaic Based on Improved Particle Swarm Optimization

Since 21th century, the global economy has developed rapidly, coupled with the improvement of people's awareness of environmental protection, renewable energy technology has attracted more and more attentions around the world. Solar energy has the characteristics of cleanness and abundant reserves, so it is more in line with the current requirements for energy in terms of quality and quantity. According to the basic principle of maximum power point tracking technology of photovoltaic, this paper comprehensively compares and analyzes the current maximum power point tracking technology of photovoltaic cells. In order to solve the shortcomings of the traditional maximum power point tracking control strategy, an improved particle swarm optimization (PSO) control strategy based on parameter coordination for maximum power point tracking of photovoltaic cells proposed in the paper. So it can solve the problem that the traditional PSO algorithm falls into local extreme value in the tracking process. Finally, by building a simulation model of three-phase photovoltaic grid-connected power generation system using Matlab/Simulink, the improved PSO algorithm is used to track the maximum power point. The simulation results show that the improved PSO algorithm can be better applied to the three-phase photovoltaic grid-connected power generation system, and the MPPT can be realized faster. Meanwhile, the output power quality is better.
Fast matrix multiplication for binary and ternary CNNs on ARM CPU

Low-bit quantized neural networks (QNNs) are of great interest in practical applications because they significantly reduce the consumption of both memory and computational resources. Binary neural networks (BNNs) are memory and computationally efficient as they require only one bit per weight and activation and can be computed using Boolean logic and bit count operations. QNNs with ternary weights and activations (TNNs) and binary weights and ternary activations (TBNs) aim to improve recognition quality compared to BNNs while preserving low bit-width. However, their efficient implementation is usually considered on ASICs and FPGAs, limiting their applicability in real-life tasks. At the same time, one of the areas where efficient recognition is most in demand is recognition on mobile devices using their CPUs. However, there are no known fast implementations of TBNs and TNN, only the daBNN library for BNNs inference. In this paper, we propose novel fast algorithms of ternary, ternary-binary, and binary matrix multiplication for mobile devices with ARM architecture. In our algorithms, ternary weights are represented using 2-bit encoding and binary - using one bit. It allows us to replace matrix multiplication with Boolean logic operations that can be computed on 128-bits simultaneously, using ARM NEON SIMD extension. The matrix multiplication results are accumulated in 16-bit integer registers. We also use special reordering of values in left and right matrices. All that allows us to efficiently compute a matrix product while minimizing the number of loads and stores compared to the algorithm from daBNN. Our algorithms can be used to implement inference of convolutional and fully connected layers of TNNs, TBNs, and BNNs. We evaluate them experimentally on ARM Cortex-A73 CPU and compare their inference speed to efficient implementations of full-precision, 8-bit, and 4-bit quantized matrix multiplications. Our experiment shows our implementations of ternary and ternary-binary matrix multiplications to have almost the same inference time, and they are 3.6 times faster than full-precision, 2.5 times faster than 8-bit quantized, and 1.4 times faster than 4-bit quantized matrix multiplication but 2.9 slower than binary matrix multiplication.
Scalable and Revocable Attribute-Based Data Sharing With Short Revocation List for IIoT

The cooperative works between connected smart devices in the Industrial Internet of Things (IIoT) have greatly made the growth in productivity and economics for the conventional industry. However, due to the introduction of the communication network, the budding IIoT also confronts the unprecedented cyber threats. To prevent the data from being intercepted by malicious intruders, we propose an efficient and fully secure data sharing work with a short revocation list (DS-SRL) for IIoT. The DS-SRL not only enables flexible access control to the massive data in IIoT but also provides a direct revocation approach for handling the potential issues of key disclosure and membership expiring in application scenarios. Particularly, compared with existing directly revocable ABE works, the revocation list in the DS-SRL scheme will keep constant size even with the increasing number of users. Thus, the consumption for computing and disseminating the revoke-related part of ciphertext are low. This resource-saving merit makes our DS-SRL scheme suitable for IIoT where the smart devices are weak in the ability of both processing and storage. The DS-SRL works without boundary such that the public parameters involved in the system require no predefinitions and can be dynamically adjusted after deployment. Furthermore, the proposed DS-SRL work is demonstrated to be fully secure under the decisional linear assumption. Hence, it owns high flexibility, scalability, and security, which are essential and desirable in real-life applications. Finally, the superior feasibility, efficiency, as well as effectiveness of our DS-SRL work are fairly confirmed by the detailed performance evaluation.
HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS

The healthcare industry is one of the many out there that could majorly benefit from advancement in the technology it utilizes. Artificial intelligence (AI) technologies are especially integral and specifically deep learning (DL); a highly useful data-driven technology. It is applied in a variety of different methods but it mainly depends on the structure of the available data. However, with varying applications, this technology produces data in different contexts with particular connotations. Reports which are the images of scans play a great role in identifying the existence of the disease in a patient. Further, the automation in processing these images using technology like CNN-based models makes it highly efficient in reducing human errors otherwise resulting in large data. Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient. Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) architectures. This study uses the transfer learning technique during training and early stopping criteria are used to avoid overfitting during the training phase. these models use LSTM parallel layer imposed in the model to experiment with four considered architectures such as MobileNet, VGG16, and ResNet with 101 and 152 layers. The experimental results produced by these hybrid models show that the capability of Hybrid ResNet101 and Hybrid ResNet152 architectures are highly suitable with an accuracy of 90% and 92%. Finally, this study concludes that the proposed Hybrid ResNet-152 architecture is highly efficient in classifying the histopathology images. The proposed study has conducted a well-focused and detailed experimental study which will further help researchers to understand the deep CNN architectures to be applied in application development.
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery

Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we present a fundamen-tal theory for rule-based knowledge graph reasoning, based on which the connectivity dependencies in the graph are captured via multiple rule types. It is the first time for some of these rule types in a knowledge graph to be considered. Based on these rule types, our theory can provide precise interpretations to unknown triples. Then, we implement our theory by what we call the RuleDict model. Results show that our RuleDict model not only provides precise rules to interpret new triples, but also achieves state-of-the-art performances on one benchmark knowledge graph completion task, and is competitive on other tasks.
Extended Reality Training for Business and Education: The New Generation of Learning Experiences

The use of Extended Reality (XR) technology in educational and corporate settings is becoming increasingly important. Although much research has been done on the use of XR for corporate and educational training, little has been published on the integration of XR into real-world applications and scenarios. In this paper, we will discuss the lessons learned from the integration of XR training into a real business environment and educational settings. Furthermore, we will use research that has been further developed into commercial products. The lessons learned are discussed, as well as the real-world challenges and problem solving approaches necessary for success.
FFL: Fine-grained Fault Localization for Student Programs via Syntactic and Semantic Reasoning

Fault localization has been used to provide feedback for incorrect student programs since locations of faults can be a valuable hint for students about what caused their programs to crash. Unfortunately, existing fault localization techniques for student programs are limited because they usually consider either the program's syntax or semantics alone. This motivates the new design of fault localization techniques that use both semantic and syntactical information of the program.In this paper, we introduce FFL (Fine grained Fault Localization), a novel technique using syntactic and semantic reasoning for localizing bugs in student programs. The novelty in FFL that allows it to capture both syntactic and semantic of a program is three-fold: (1) A fine-grained graph-based representation of a program that is adaptive for statement-level fault localization; (2) an effective and efficient model to leverage the designed representation for fault-localization task and (3) a node-level training objective that allows deep learning model to learn from fine-grained syntactic patterns. We compare FFL's effectiveness with state-of-the-art fault localization techniques for student programs (NBL, Tarantula, Ochiai and DStar) on two real-world datasets: Prutor and Codeflaws. Experimental results show that FFL successfully localizes bug for 84.6% out of 2136 programs on Prutor and 83.1% out of 780 programs on Codeflaws concerning the top-10 suspicious statements. FFL also remarkably outperforms the best baselines by 197%, 104%, 70%, 22% on Codeflaws dataset and 10%, 17%, 15% and 8% on Prutor dataset, in term of top-1, top-3, top-5, top-10, respectively.
Overloading the Nonce: Rugged PRPs, Nonce-Set AEAD, and Order-Resilient Channels

We introduce a new security notion that lies right in between pseudorandom permutations (PRPs) and strong pseudorandom permutations (SPRPs). We call this new security notion and any (tweakable) cipher that satisfies it a rugged pseudorandom permutation (RPRP). Rugged pseudorandom permutations lend themselves to some interesting applications, have practical benefits, and lead to novel cryptographic constructions. Our focus is on variable-length tweakable RPRPs, and analogous to the encode-then-encipher paradigm of Bellare and Rogaway, we can generically transform any such cipher into different AEAD schemes with varying security properties. However, the benefit of RPRPs is that they can be constructed more efficiently as they are weaker primitives than SPRPs (the notion traditionally required by the encode-then-encipher paradigm). We can construct RPRPs using only two layers of processing, whereas SPRPs typically require three layers of processing over the input data. We also identify a new transformation that yields RUP-secure AEAD schemes with more compact ciphertexts than previously known. Further extending this approach, we arrive at a new generalized notion of authenticated encryption and a matching construction, which we refer to as nonce-set AEAD. Nonce-set AEAD is particularly well-suited in the context of secure channels, like QUIC and DTLS, that operate over unreliable transports and employ a window mechanism at the receiver's end of the channel. We conclude by presenting a generic construction for transforming a nonce-set AEAD scheme into an order-resilient secure channel. Our channel construction sheds new light on order-resilient channels and additionally leads to more compact ciphertexts when instantiated from RPRPs.
Adversarial Domain Generalization with MixStyle

The performance of deep neural networks deteriorates when the domain representing the underlying data distribution changes during training and testing. Domain generalization expects learning from multiple source domains to improve generalization to never-before-seen target domains. We propose hybrid domain generalization using source domain and multiple latent domains as a new research scenario, and we attempt to train a generalization model that self-generates latent domain labels. In order to solve this scenario, we use the MixStyle to generate latent domain samples and assume that the styles of the samples are closely related to their domains. Therefore, we propose that GMM cluster latent domains according to style features and iteratively assign pseudo domain labels before introducing them into adversarial training. By using image style features, Our proposed method successfully synthesizes latent domains and achieves adversarial domain generalization without latent domain labels. Meanwhile, considering that the original domain labels are underutilized, this method introduces an auxiliary feature extractor to improve the performance of the model. Experiments demonstrate that our method has excellent generalization performance and outperforms classical domain generalization methods.
Development of Mobile Learning English Web Application

E-learning has become an important part of distance education to comprehend students' skills and knowledge during the COVID-19 pandemic. The adoption of e-learning for kindergarten children is a challenging key for design and development that must be involved in parental care during the use of e-learning. This research aims to design and evaluate a digital learning English system based on a web application for kindergarten students who require additional attention from instructors and parents. The study investigates students' learning achievement and end-user perceptions based on the extended technology acceptance model. The results contribute and confirm a significant positive to technology adoption of the digital teaching and learning framework by offering real-time learning, assessments, achievement records, and learning session activities using web applications on mobile. Perceived ease of use, perceived usefulness, and attitude positively influence behavioral intention to use the proposed learning web application.
Synthetic Robust Model Predictive Control With Input Mapping for Constrained Visual Servoing

This article proposes a synthetic robust model predictive control method (RMPC) with input mapping for the image-based visual servoing (IBVS) problem with constraints, where the novel control law is constructed by the robust control law designed offline and the online linear compensation of the past data. This proposed method can overcome the conservatism of RMPC and reduce the online computational burden. The input mapping method is suitable for the IBVS with no requirement of the slow time-varying model or time-invariant model as most adaptive control methods need. Its linear combination coefficients can be online optimized by solving a quadratic programming problem. The stability of the visual servoing system under our proposed method is proven, and its convergence speed is demonstrated to be faster than the traditional RMPC. A real-time experiment on a six-degree-of-freedom manipulator with eye-in-hand construction is designed to evaluate the proposed method. The results indicate that besides the ability to handle the constraint and the singularity problem, our proposed method provides a faster convergence rate than several classic robust control methods, and improves the computational efficiency by an order of magnitude compared with the online RMPC.
Implementation of Deep Learning Based Compression Technique and Comparative Analysis With Conventional Methodologies

Recently, the movement in information science and advancement completely impacts the human ordinary everyday practice. The size of mechanized data is filling rapidly to achieve images of unrivalled grade. The image compression works with to send large size images with insignificant bytes and to restore the image with incredible quality on social event. The focuses are to make an extra little data size; better nature of data during entertainment and connect transmission of data over confined information move limit with security. In this paper, conventional compression techniques i.e. Luminous DCT, Biorthogonal DWT, Quadtree Fractal and Huffman compression, are implemented and analyzed based on compression efficiency parameters like compression ratio, PSNR, MSE and SSIM. Here a deep learning based compression technique using stack autoencoder model is also proposed and implemented to compress the image data and result are compared with conventional compression techniques based on compression efficiency parameters. Simulation results show that proposed deep learning based compression scheme provides high quality reconstructed images having satisfactory compression ratio.
Open Set Few-Shot Remote Sensing Scene Classification Based on a Multiorder Graph Convolutional Network and Domain Adaptation

Few-shot scene classification aims to recognize task data, which consists of unlabeled data and a few annotated samples, given some labeled auxiliary data. The task and the auxiliary data often come from the same source with non-overlapping categories, such tasks are defined as closed set few-shot scene classification. However, most remote sensing applications involve open set few-shot scene classification, where the categories of the task and auxiliary data are often partly overlapped and sources of data are usually different (i.e., different domains). We propose an open set few-shot scene classification method called the multi-order graph convolutional network (MGCN) specifically for such tasks. MGCN solves the open set few-shot scene classification task in two ways: reducing the inter-domain discrepancy by a feature dispersion degree weighting domain adaptation method, and reducing the dispersion degree of node features by a proposed multi-order graph convolutional network. Based on a shallow backbone and an improved graph convolutional network, MGCN improves the classification performance of the open set few-shot classification task by utilizing information from common categories and reducing domain discrepancies. The experimental results, based on three public remote sensing image datasets and a Tibetan Plateau remote sensing scene dataset we collected, demonstrate the effectiveness of the proposed method on the open set few-shot scene classification task.
Neural Knowledge Transfer for Sentiment Analysis in Texts with Figurative Language

Sentiment analysis in texts, also known as opinion mining, is a significant Natural Language Processing (NLP) task, with many applications in automated social media monitoring, customer feedback processing, e-mail scanning, etc. Despite recent progress due to advances in Deep Neural Networks (DNNs), texts containing figurative language (e.g., sarcasm, irony, metaphors) still pose a challenge to existing methods due to the semantic ambiguities they entail. In this paper, a novel setup of neural knowledge transfer is proposed for DNN-based sentiment analysis of figurative texts. It is employed for distilling knowledge from a pretrained binary recognizer of figurative language into a multiclass sentiment classifier, while the latter is being trained under a multitask setting. Thus, hints about figurativeness implicitly help resolve semantic ambiguities. Evaluation on a relevant public dataset indicates that the proposed method leads to state-of-the-art accuracy.