Reviewerly/exHarmony huggingface dataset:
Resource Provisioning in Cloud using ARIMA and LSTM Technique

Cloud computing is the distribution of computer services via the Internet ("the cloud"), including servers, storage, databases, networking, software, analytics, and intelligence that provide faster innovation, adaptable resources, and scale economies, Security, resource allocation, and resource provisioning are the three main difficulties in cloud computing. The paper is resource provisioning. Overprovisioning these resources raises costs for cloud users and providers; under provisioning these resources lengthen application response times and may also breach service level agreements (SLAs), which may eventually cost cloud providers clients and revenue. The various machine learning algorithms that are most suited for resource provisioning while taking CPU utilization statistics into consideration are thoroughly surveyed in this research.
Entity Recognition of Power Fault Disposal based on Attention Model

Power grid fault disposal preplan is an important reference for power grid fault disposal. Hence, extracting finegrained key entity information such as power equipments, name and number from the preplan is an important basis for the computer to understand the content and further support the intelligent disposal. A named entity recognition technology for power grid fault disposal preplan is proposed based on deep learning. Firstly, the character vector is used to represent the preplan text. Then the character features are extracted by combining the attention mechanism and the bidirectional long short-term memory network. Finally, the optimal serialization annotation is solved by the conditional random field. The example shows that the proposed entity recognition model can automatically and efficiently extract text features, thus accurately identifying entity words in the preplan. It proves that the model has better robustness.
Vehicle Tracking Method Based on Pyramid Layered Optical Flow Network

In order to solve the problem that the vehicles associated with the track cannot be uniquely identified when tracking a moving vehicle by MOT approaches in computer vision field, a moving vehicle tracking method based on pyramid hierarchical optical flow network was proposed. The Pyramid Optical Flow Net (POFN) was used to estimate the motion of moving vehicles, and the optical flow map containing the movement information of vehicles was obtained. The action refined net was designed to identify the optical flow map and estimate the more accurate position of vehicles. The motion information of the vehicle was correlated with the Intersection over Union (IOU), and the cosine similarity algorithm was used to verify the correlation of the appearance information of the vehicle boundary frame after the correlation of the motion information, so as to complete the vehicle tracking. The test results of UA-DETRAC data set show that the number of track ID switching of POFN model is less than that of current advanced tracking methods.
DynGCF: Augmenting Inactive Users and Items in Dynamic Graph-based Collaborative Filtering

Modeling user-item interactions in a dynamic manner bring new insight to the representation learning for recom-mender systems. Distinct from static graph-based approaches that model the whole user-item interaction graph, dynamic graph-based approaches model both the structural and temporal information from a sequence of snapshot graphs. Despite effectiveness, we argue that existing approaches do not explicitly address the temporal sparsity issue, which degrades the representation learning performance for inactive users and items. Therefore, we propose a new Dynamic Graph-based Collaborative Filtering(DynGCF) framework. In particular, it utilizes the vanilla interaction graph with the co-occurrence graph(co-graph) to jointly explores 1-hop collaborative and 2-hop implicit similarity for dynamic representation learning. Moreover, to further alleviate temporal sparsity, we explore representative(active) users and items via graph pooling and design an activity-guided gating(AGate) layer to augment inactive users and items. At last, we further stack a temporal aggregator layer to obtain the final representation. We conduct extensive experiments on four real-world benchmark datasets to demonstrate the significant performance gains for DynGCF over several state-of-the-art methods. Further analyses also show the necessity of alleviating temporal sparsity for improving recommendation performance.
Nested Multi-view Image Classification

There are classification tasks that take as inputs groups of images rather than single images. In order to address such situations, we introduce a nested multi-view deep network. The approach is generic in that it is applicable to general data instances, not just images. The network has several convolutional neural networks grouped together at different stages. This primarily differs from other previous works in that we organize instances into relevant groups that are treated differently. We also introduce a method to replace instances that are missing which successfully creates neutral input instances and consistently outperforms standard fill-in methods in real world use cases. In addition, we propose a method for manual dropout when a whole group of instances is missing that allows us to use sparser training data and obtain higher accuracy at the end of training. With specific pretraining, we find that the model works to great effect on our real world and public datasets compared to baseline methods, with our improvements ranging from 1% to 5%.
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a source dataset with dense pixel-level annotations to an unlabeled target dataset. However, the performance of UDA methods often suffers from the domain shift, which is the discrepancy between the feature distributions of the two domains. There have been several attempts to match these distributions at the image level marginally. However, due to the so-called category-level domain shift, such global alignments do not guarantee a good separability of deep features extracted from different categories in the target domain. As a result, the generated pseudo-labels can be noisy and thus poison the learning process on the target domain. Some recent methods focus on denoising the pseudo-labels online using category-wise information. This paper introduces a novel UDA method called Global-to-Local alignment (G2L) that leverages fine-grained adversarial training and a newly proposed chromatic Fourier transform to address the image-level domain shift from a global perspective. Next, our method deals with the category-level domain shift under a local view. Specifically, we propose a long-tail category rating strategy as well as apply dynamic confidence thresholds and category-wise priority weights when generating and denoising the pseudo-labels to favor rare categories. Finally, self-distillation is used to boost the final segmentation results. Experiments on popular benchmarks GTA5 → Cityscapes and SYNTHIA → Cityscapes show that our method yields superior accuracy performance than other state-of-the-art methods.
Enhanced Spatial Awareness for Deep Interactive Image Segmentation

Existing deep interactive segmentation approaches can extract the desired object for the user based on simple click interaction. However, the first click provided by the user on the full image space domain is generally too local to capture the global target object, which causes them to rely on a large number of subsequent click corrections for satisfactory results. This paper explores how to strengthen the spatial awareness of user interaction especially after the first click input and increase the stability during the continuous iterative correction process. We first design an interactive cascaded localization strategy to determine the spatial range of the potential target, and then integrate this space-aware prior into a dual-stream network structure as a soft constraint for the segmentation. The above operation can increase the network’s attention to the target of interest under very limited user interaction. A new training and inference strategy is also developed to completely adapt the benefit from the space-aware guidance. Furthermore, an object shape related loss is designed to better supervise the network based on user-provided prior guidance. Explicit subject, controllable correction and flexible interaction can help to significantly boost the interactive segmentation performance. The proposed method achieves state-of-the-art performance on several popular benchmarks.
Polarity Prediction on Product Reviews Based on Classification Algorithms

Sentiment analysis is analyzing customer’s opinion whether they are positive or negative on the product they purchased. It is opinion mining which is major task of Natural Language Processing. The main objective of sentiment analysis is to categorize the data based on product review data’s polarity, it majorly consists of three steps document level, sentence level, aspect level. Sentiment analysis is mostly grounded on supervised learning in machine learning. Online product reviews are collected and analyzed. Online platform of shopping is very much helpful to gather review or opinion of customers to analyze. A sentiment sentence is categorized as positive or negative review based on the type of words used in the sentence, where the sentence is tokenized into separate English words to get analyzed. Various demonstrations were performed on both sentence-level and review-level categorizations with the desired output.
Quantum Computing &amp;amp;Amp; Reinforcement Learning: Partners in Achieving Artificial General Intelligence

This paper seeks to consider the assertion by a group of scientists at DeepMind Technologies that the reward mechanism underlying reinforcement learning is sufficient for achieving artificial general intelligence (AGI). Reinforcement learning is a machine learning technique focused on reward maximisation that took off based on US government funding in the 1980s and '90s. It has been applied in self-driving cars, video gaming, game-playing computers, text-to-speech systems, and for training convolutional neural networks. The unique contribution of this paper is the proposal that quantum computing can support the application of reinforcement learning to achieve AGI. There have been attempts at achieving AGI, to no avail, which is why the DeepMind scientists view reinforcement learning as a viable path forward. Their position does not align with the mainstream thought about the potential of reinforcement learning, which is where quantum computing comes in. However, quantum computing has fewer real-world applications compared to reinforcement learning. Its underlying principles may notwithstanding accelerate the progress toward AGI.
A digital model of the educational process based on a cognitive approach

The model of the educational system transition to the next level focused on the acquisition by student's competencies is discussed. This work leads by the new Russian state educational standards of the 3rd generation. As is known, the educational process at the university is a complex semi-structured system that depends on many ill-defined factors. Therefore, when planning the educational process, it is useful to have a model for the growth of the student's competencies, which depends on the key factors affecting the student's learning. This work is devoted to the creation of such models. As the basic formalism, a model of the competence's growth dynamics obtained through cognitive simulation is considered.
NegDL: Privacy-preserving Deep Learning Based on Negative Database

In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(NDB)$</tex> is a type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$NDB$</tex> , Specifically, private data are first converted to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$NDB$</tex> as the input of deep learning models by a generation algorithm called QK-hidden algorithm, and then the sketches of NDB are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL is comparable to the original deep learning model in most cases, and it is also comparable to the method based on differential privacy.
Cross-Grade Curriculum Group Based Teaching Experiment System for Innovative Design of IoT Intelligent Dynamic Measurement and Control

One of the key hot spots and difficulties in the construction of “Engineering Education Certification” and “New Engineering” is how to strengthen the cultivation of the college students’ ability to solve complex engineering problems. In this paper, a software and hardware experimental platform has been designed, which organically integrates automatic judgement and sensing communications with electromechanical integrated IoT measurement and control. And a teaching system of cross-grade supporting experimental course group based on this platform has been proposed, which is driven by the application function topics from the shallower to the deeper. It is an engineering ability training mechanism that can continuously carry out experimental teaching such as design, installation and adjustment from the lower grade to the higher grade. More than five years of teaching practice shows that this system has significantly improved the students’ professional research interest and ability to solve complex engineering problems.
Analysis of Procedural Generated Textures for Video Games Using a CycleGAN

The creation of content for video games is a costly and time consuming task if done manually. Especially in modern times, the amount of content required to create a video game is expected to be much greater than in the past. Procedural Content Generation techniques can be used to alleviate this issue and allow the creation of more content with less input. The proposal in this work is to explore the use of Generative Adversarial Networks for video game textures to be developed by a computer, more specifically style-transfer with a CycleGAN. The generation experiments in this work use a variety of textures commonly used in video games such as sky-boxes and tiled images. In addition, the analysis performed has a usability perspective and an emotional one. The usability analysis detects any problems in the implementation. The affective/emotional analysis measures the players’ evoked feelings and then cross-references the traditionally-generated and the computer-generated emotional result vectors $$e = (V,A)$$ (composed of valence and arousal). The results obtained show an emotional transfer between the vectors to be at minimum 26.53% and at best 91.23%. These results demonstrate that the performed experiments always had a successful emotional transfer.
Decoupled Frequency Learning for Dynamic Scene Deblurring

Despite end-to-end deep learning methods have recently advanced state-of-the-art for dynamic scene deblurring, they are often biased towards learning low-frequency (LF) information, thus missing sufficient high-frequency (HF) details. In this paper, we experimentally verify that different image frequencies affect the final deblurring quality in different manners. Considering this, we point out that the LF learning bias problem arises from the existing training scheme with frequencies coupled, to some extent. Concretely, current training scheme fails to distinguish different frequencies but optimize them as a whole towards one common objective, thereby resulting in sub-optimal results. To ameliorate this problem, we propose an alternative training strategy, namely Decoupled Frequency Learning (DFL). Specifically, DFL treats deblurring task as two separate sub-tasks, which correspond to image LF and HF components, respectively. Different losses are tailored-designed for different frequencies to better guide their learning towards appropriate objectives. The proposed DFL scheme is simple yet effective, and compatible to any existing deep models. Extensive experiments on public benchmarks demonstrate its clear benefits to the state-of-the-art in terms of both quantitative measures and perceptual quality.
Atmospheric monitoring path optimization based on improved discrete cuckoo search algorithm

In this paper, we propose an improved discrete cuckoo search algorithm based on the spatial clustering for flight path optimization of atmospheric monitoring in industrial parks. This paper also optimizes the local operator and proposes the acceptance of the redesigned solution based on the Metropolis criterion to reduce the possibility of easily falling into local optimum. The standard TSPLIB library is selected for algorithm simulation and performance test in the analysis case, and the superiority of the algorithm is verified by comparing and analyzing with the standard discrete cuckoo search algorithm and particle swarm optimization algorithm. Finally, integration of improved trajectory optimization algorithms with the actual built UAV (Unmanned Aerial Vehicle) atmospheric monitoring platform, and the feasibility of the proposed flight path optimization algorithm is verified in an outdoor environment.
Ellipse Fitting Based on Neural Network Optimization

Ellipse fitting is to estimate the parameters of the ellipse from a set of coplanar points, but the geometric distance from the plane point to the elliptic curve is difficult to calculate directly. This paper proposes a neural network optimization-based algorithm to estimate the geometric distances from plane points to elliptic curves to solve the ellipse fitting problem. Specifically, the neural network is firstly applied to train the geometric distance between the plane point and the standard ellipse, and then obtain the geometric distance between the plane point and the general ellipse through the rotation and translation transformation. Moreover, use minimizing the sum of the Euclidean distances from the plane point to the elliptic curve as the objective function, and finally use the gradient descent method to iteratively solve the ellipse parameters. Consequently, the proposed method avoids the direct algebraic solution of the geometric distance from the plane point to the elliptic curve and the performance of the algorithm together with its robustness to noise are analysed via numerical simulation.
Privacy Preserving Security Integrating Method with Varying Key Encryption Model in Cloud

Cloud computing makes available the computer system resources like data storage and computing power without client management by the user. The security protocols U-IDMP, predicate encryption, Functional Re-encryption, SAPA, Dynamic auditing protocol, Fully Homomorphic encryption, PDP and PIR are analyzed with the vulnerabilities of cloud environment. The strength of these protocols against privacy, integrity and authorization were analyzed and the breeches were identified. The integrated approach of all the security parameters with a novel protocol is needed. In a customer service paradigm the personal details of customer are hided to the service providers by the block chain way in cloud environment. Instead of using real personal details the dummy details are only made known to the providers and inter mediators.
Machine Learning Tools to Improve the Quality of Imperfect Keywords

The availability of keywords that describe the content of a text or document certainly is essential for effective and efficient content-based retrieval. But their quality, the presence of spelling variants, synonyms, near-synonyms, and spelling errors make their use less effective. Here we present a set of tools we are developing for the management of tags. These tools are intended to be used to improve the quality of textual features and to enhance traditional ways of searching and browsing data on the web. This approach integrates different methods: word embedding models, able to capture the semantics of words and their context, clustering algorithms, able to identify/group semantically related terms, and methods able to calculate the syntactic similarity between strings. The work is still under development, and the paper will present some preliminary qualitative results that demonstrate the feasibility of our approach.
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks

Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.
Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious.