Reviewerly/exHarmony huggingface dataset:
Single infrared image stripe removal via deep multi-scale dense connection convolutional neural network

Stripe noise removal is a crucial step for the infrared imaging system. Existing stripe removal methods are hard to balance stripe removal and image details preservation. In this paper, a deep multi-scale dense connection convolutional neural network (DMD-CNN) is proposed to address this problem. In DMD-CNN, a multi-scale feature representation unit (FR-Unit) is designed to decompose raw image into different scales which can extract diverse fine and coarse features. Dense connection is introduced into the network, which makes full use of the multi-scale information obtained by FR-Unit and avoids performance degradation. Moreover, the regularization term Lh is defined to depict the vertical direction smoothness property of stripe. Experiment results show that DMD-CNN performs more stable stripe removal effects in different scenes and diverse stripe intensity. Meanwhile, DMD-CNN outperforms seven state-of-the-art stripe removal methods on qualitative and quantitative evaluation.
A One-Stage Domain Adaptation Network With Image Alignment for Unsupervised Nighttime Semantic Segmentation

In this paper, we tackle the problem of semantic segmentation for nighttime images that plays an equally important role as that for daytime images in autonomous driving, but is also much more challenging due to very poor illuminations and scarce annotated datasets. It can be treated as an unsupervised domain adaptation (UDA) problem, i.e., applying other labeled dataset taken in the daytime to guide the network training meanwhile reducing the domain shift, so that the trained model can generalize well to the desired domain of nighttime images. However, current general-purpose UDA approaches are insufficient to address the significant appearance difference between the day and night domains. To overcome such a large domain gap, we propose a novel domain adaptation network "DANIA" for nighttime semantic image segmentation by leveraging a labeled daytime dataset (the source domain) and an unlabeled dataset that contains coarsely aligned day-night image pairs (the target daytime and nighttime domains). These three domains are used to perform a multi-target adaptation via adversarial training in the network. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We also include a step of image alignment to relieve the inaccuracy caused by the misalignment between day-night image pairs by estimating a flow to refine the pseudo supervision produced by daytime images. Finally, a re-weighting strategy is applied to further improve the predictions, especially boosting the prediction accuracy of small objects. The proposed DANIA is a one-stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our DANIA achieves state-of-the-art performance for nighttime semantic segmentation.
A fine‐grained and traceable multidomain secure data‐sharing model for intelligent terminals in edge‐cloud collaboration scenarios

Secure data-sharing technology is a bridge for various collaborative operations among intelligent terminals in the edge-cloud collaborative application scenario. For the shared data involves different levels of confidentiality, intelligent terminals for collaborative operations may be distributed in multiple management domains, and the private information of intelligent terminals is easy to be leaked in edge-cloud collaboration scenarios, the security of data sharing is severely threatened. To solve these problems, this paper proposed a fine-grained and traceable multidomain secure data-sharing model for intelligent terminals. In this model, a key self-certification algorithm is proposed, which avoids potential security threats of key leakage during the key distribution process. The model combines attribute encryption and threshold function to achieve more fine-grained and more flexible secure data sharing; it uses blockchain technology to achieve integrity verification of stored data and traceability of shared data, and it combines on-chain and off-chain databases to achieve rapid retrieval and positioning of shared data distributed among multiple domains, which improves the efficiency of data sharing among domains. The security of the model proposed by us is proved, and compared with the cited literature, it is shown that the proposed model has certain advantages in terms of computational complexity and time consumption.
Fair quantum secret sharing based on symmetric bivariate polynomial

In this paper, we propose a new fair (t,n) threshold quantum secret sharing scheme based on the d-dimensional Bell state and symmetric bivariate polynomial. In the distribution phase, the dealer uses the symmetric bivariate polynomial to encode the secret and produces the corresponding share for each participant. To achieve fairness, we construct a secret sequence, which can guarantee that each participant can recover the correct secret if all participants are legal and honest. In reconstruction phase, the dealer prepares the d-dimensional Bell state, and all participants perform the unitary operations produced by the share of their polynomials on the transmitted particles to reconstruct the secret. Through the sequential communications, the proposed scheme has a good scalability. Furthermore, we consider the situation that these participants cooperate to recover the secret when the number of participants is more than t. At last, we analyze the correctness, security and fairness of the proposed protocol.
A Secure Environment Using a New Lightweight AES Encryption Algorithm for E-Commerce Websites

Providing security for transmitted data through the e-commerce environment requires using a fast and high secure encryption algorithm. Balancing between the speed and the security degree is a problem that many of the encryption algorithms suffer from. Increasing the security degree requires increasing the level of complexity which results in increasing encryption time. On the other hand, increasing the algorithm speed may reduce the complexity degree which affects the security level. This paper aims to design an encryption algorithm that balances time and complexity (speed and security). This is done by suggesting a security environment that depends on creating and providing an agent software to be settled into each customer device that manages the purchase and security process without customer interference. The suggested encryption algorithm is applied within this environment. Several modifications are performed on the AES encryption algorithm. The AES was chosen due to its performance (security and speed), which makes it suitable for encrypting transmitted data over the Internet. These modifications involve adding preprocessing steps (padding and zigzag), eliminating Sub Byte step, and reducing the number of rounds. The experimental results showed that the suggested algorithm provides more security and speed in the encryption and decryption process. The randomness degree has increased by 29.5%. The efficiency is increased because the encryption and decryption times are reduced, as is the CPU usage. The throughput for the suggested algorithm is increased by 10% for the encryption process and is increased by 9.3% for the decryption process.
Sentiment Analysis on Twitter using Ordinal Regression

With around 186 million daily active users, Twitter is a popular social media platform with increasing user growth every year. The top three twitter markets are in the US, Japan, and India. Users tweet on various topics ranging from sports, politics, finance to entertainment. Twitter is also used by businesses to advertise their products and services. These tweets contain a vast amount of information that can be used in many different ways, from targeting advertisements, understanding customer sentiment, and analyzing users’ opinions about specific people or events. This research aims to apply sentiment analysis on Twitter data by Ordinal Regression. We use the twitter_samples from NLTK Corpus containing positive and negative tweets for our study. We use Random Forest (RF), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR) classifiers to classify the tweet sentiment into five categories. Results show that this approach outperformed the related work in terms of accuracy. This improvement was possible by applying TextBlob to find the polarity of the tweets and using lemmatization instead of stemming.
Recursive elimination current algorithms and a distributed computing scheme to accelerate wrapper feature selection

Feature selection (FS) for classification tasks in machine learning and data mining has attracted significant attention. Recently, increasing metaheuristic optimization algorithms have been applied to solve wrapper FS problems. Nevertheless, the algorithms that improve existing optimizers inevitably bring higher complexity and require higher computational cost in wrapper mode. In this work, we present recursive elimination current (REC) algorithms, a novel set of algorithms for wrapper FS, which consists of the simplest feature subset representation, an ingenious structure enlightened by the recursion technique in computer science and necessary components. To some extent, the proposed algorithms, recurrent REC (REC2) and distributed REC (DiREC), dispose of the issues, including but not limited to keeping diversity of population and scalability for high-dimensionality, which are often discussed in metaheuristics-based FS research. Thereinto, DiREC is a distributed computing scheme proposed to accelerate the FS process by distributing the tasks to different computing units in a cluster of computers. A series of experiments were carried out on several representative benchmark datasets, such as Madelon from UCI machine learning repository, using REC2 and DiREC with various numbers of logical processors. It is demonstrated that the proposed algorithms perform efficiently in wrapper mode, and the distributed computing scheme is effective and yields computational time savings.
To understand the relationship between Machine learning and Artificial intelligence in large and diversified business organisations

In the modern era, Machine learning and Artificial intelligence remain the most remarkable IT application, a technology that has seen unprecedented progress in recent decades. AI and machine learning are all closely linked with using a computer to simulate intelligent behaviour with little human interaction. It has been noticed that many large organisations are implementing technologies such as ML and AI to improve their performance and productivity in the business. Companies can create better, more customised and engaging campaigns as AI becomes increasingly adept at collecting massive data faster than ever before. As AI and ML technologies become more widely used, being a technology-powered company will undoubtedly be a must for survival. AI and ML are guiding on everything from production to delivering products and services to customers. Machine translation, chatbots, and self-learning algorithms are examples of AI technology that may help people better comprehend their surroundings and respond appropriately. Organisations have been embracing artificial intelligence technology advancements to create and maximising their strategic and competitive advantages. Hence, businesses rely heavily on AI to enhance their performance and develop new services to boost productivity and generate new offerings. In addition, they are fundamentally reshaping companies' business and operation processes to better meet customers' requirements and expectations, resulting in increased efficiency. Machine learning and artificial intelligence are broad concepts that will be examined further in this article. The report aims to understand the relationship between Machine Learning and Artificial Intelligence in large and diversified business organisations. The researcher used a study methodology oriented toward a more inclusive and comprehensive approach to better account for the intangible aids of ML and AI within organisations.
Conjunctive multi-key searchable encryption with attribute-based access control for EHR systems

With the growing demands of online access to health services, multiple medical institutions store their encrypted electronic health records (EHR) on the cloud and share them with the specified doctors and researchers. Multi-key searchable encryption (MKSE) is very suitable for this case for the sake of the privacy of patients and the system effeciency. In MKSE, multiple data owners can encrypt the data with their own secret keys and upload them to the remote cloud server. In order to search all the encrypted data on the cloud for a keyword, an authorized user only needs to generate a single trapdoor whose length is independent on the number of the data owners. MKSE allows multiple data owners to share their data with users efficiently. In this paper, we present an efficient MKSE scheme which supports fine-grained access control and conjunctive keyword searches. Both the control policy and keyword expressivity of our scheme are more flexible than the existing MKSE schemes. Meanwhile, our scheme can resist the keyword guessing attack. We will simulate our MKSE scheme and show that it is practical in the real world applications.
Recurrent Neural Networks With Column-Wise Matrix–Vector Multiplication on FPGAs

This article presents a reconfigurable accelerator for REcurrent Neural networks with fine-grained cOlumn-Wise matrix–vector multiplicatioN (RENOWN). We propose a novel latency-hiding architecture for recurrent neural network (RNN) acceleration using column-wise matrix–vector multiplication (MVM) instead of the state-of-the-art row-wise operation. This hardware (HW) architecture can eliminate data dependencies to improve the throughput of RNN inference systems. Besides, we introduce a configurable checkerboard tiling strategy which allows large weight matrices, while incorporating various configurations of element-based parallelism (EP) and vector-based parallelism (VP). These optimizations improve the exploitation of parallelism to increase HW utilization and enhance system throughput. Evaluation results show that our design can achieve over 29.6 tera operations per second (TOPS) which would be among the highest for field-programmable gate array (FPGA)-based RNN designs. Compared to state-of-the-art accelerators on FPGAs, our design achieves 3.7–14.8 times better performance and has the highest HW utilization.
A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.
Identifying the role of cloud computing technology in management of educational institutions

Cloud computing refers to a computer architecture that offers universal on-demand network access to a shared pool of configurable resources. Educational institutions are essential because they play a vital role in policy formulation and contribute to cultural, political, and social growth. Cloud computing is highly popular throughout the IT sector worldwide, and its importance and excitement is also expanding in educational institutions' IT demands. There is currently little information available on the use of cloud computing in educational institutions. Organizations can use cloud computing to decrease IT costs while also gaining more flexibility and scalability; nevertheless, the sustainability of many cloud computing services remains to be seen. Measures to address security, privacy, and legal concerns, as well as the standardization of cloud technology, are still in the works. This article discusses the importance of cloud computing technologies in educational institution management. This also presents a number of problems in terms of cloud computing adoption in educational institutions.
Internet of Things (IoT) Technology for the Development of Intelligent Decision Support Education Platform

Improving the intelligence of teaching environment and making the multimedia teaching equipment has become a major concern of the colleges and universities. To this end, the design of Internet of Things (IoT) technology based wisdom of higher education platform is of great interest. Designing the structure of online management platform for college education and realizing the functions of examination result inquiry, online teaching, and attendance management have gained more importance in the educational research. The wisdom classroom is the key structure of the wisdom education platform. A smart classroom architecture based on IoT technology is designed, which connects with traditional network facilities through the IoT gateway. Different layers of the architectures have been designed and implemented. The proposed platform tests results and shows that the intelligent education platform can effectively control classroom utilization and has high throughput, low application latency, and good practicability.
A fusion spatial attention approach for few-shot learning

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few images may include cluttered backgrounds or different scales of objects. Existing approaches mostly address this problem from either the original image space or the embedding space by using meta-learning. To the best of our knowledge, none of them tackle this problem from both spaces jointly. To this end, we propose a fusion spatial attention approach that performs spatial attention in both image and embedding spaces. In the image space, we employ a Saliency Object Detection (SOD) module to extract the saliency map of an image and provide it to the network as an additional channel. In the embedding space, we propose an Adaptive Pooling (Ada-P) module tailored to few-shot learning that introduces a meta-learner to adaptively fuse local features of the feature maps for each individual embedding. The fusion process assigns different pooling weights to the features at different spatial locations. Then, weighted pooling can be conducted over an embedding to fuse local information, which can avoid losing useful information by considering the spatial importance of the features. The SOD and Ada-P modules can be used within a plug-and-play module and incorporated into various existing few-shot learning approaches. We empirically demonstrate that designing spatial attention methods for few-shot learning is a nontrivial task and our method has proven effective for it. We evaluate our method using both shallow and deeper networks on three widely used few-shot learning benchmarks, miniImageNet, tieredImageNet and CUB, and demonstrate very competitive performance.
The Influence of Collaborative and Multi-Modal Mixed Reality: Cultural Learning in Virtual Heritage

Studies in the virtual heritage (VH) domain identify collaboration (social interaction), engagement, and a contextual relationship as key elements of interaction design that influence users’ experience and cultural learning in VH applications. The purpose of this study is to validate whether collaboration (social interaction), engaging experience, and a contextual relationship enhance cultural learning in a collaborative and multi-modal mixed reality (MR) heritage environment. To this end, we have designed and implemented a cloud-based collaborative and multi-modal MR application aiming at enhancing user experience and cultural learning in museums. A conceptual model was proposed based on collaboration, engagement, and relationship in the context of MR experience. The MR application was then evaluated at the Western Australian Shipwrecks Museum by experts, archaeologists, and curators from the gallery and the Western Australian Museum. Questionnaire, semi-structured interview, and observation were used to collect data. The results suggest that integrating collaborative and multi-modal interaction methods with MR technology facilitates enhanced cultural learning in VH.
An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Cloud Computing provides various services to the customer in a flexible and reliable manner.Virtual Machines (VM) are created from physical resources of the data center for handling huge number of requests as a task.These tasks are executed in the VM at the data center which needs excess hosts for satisfying the customer request.The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time.This process is carried out based on various algorithms which follow a predefined capacity of source VM leads to the capacity issue at the destination VM.The proposed VM migration technique performs the migration process based on the request of the requesting host machine.This technique can perform in three ways namely single VM migration, Multiple VM migration and Cluster VM migration.Common Deployment Manager (CDM) is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their customer.The VM migration requests are handled with an exposure of the source host capabilities.The proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high reliability.The objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM.