Reviewerly/exHarmony huggingface dataset:
Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Salt and pepper noise occurs randomly and causes image degradation. Numerous denoising methods have been proposed to suppress this noise. However, existing methods have two main limitations. First, noise characteristics, such as noise location information and sparsity, are often described inaccurately or even ignored. Second, many existing methods separate the contaminated image into a recovered image and a noise part, leading to the recovery of an image with unsatisfactory smooth and detailed parts. In this study, the authors introduce a noise detection strategy to determine the position of the noise and employ a non-convex sparsity regularization depicted by l p ${l_p}$ quasi-norm to describe the sparsity of the noise, thereby addressing the first limitation. We adopt the morphological component analysis framework with stationary Framelet transform to decompose the processed image into the cartoon, texture, and noise parts to resolve the second limitation. Then, the proposed model is applied by using the alternating direction method of multipliers (ADMM). Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image and outperforming some state-of-the-art methods.
An infrared pedestrian detection method based on segmentation and domain adaptation learning

Benefiting from the capability of night viewing, infrared images has been widely applied to surveillance systems as an effective complement to visible-light images. However, the development of infrared pedestrian detection is still impeded by weak features and limited diversity of infrared images. Aiming at these two problems, we designed a multi-task learning framework for pedestrian detection by incorporating a semantic segmentation branch and a domain adaptation branch. Composed of UNet network with Swin Transformer, the semantic segmentation could apply spatial constraints to pedestrian detection. The domain adaptation branch aligns the features between infrared and visible-light images to improve the scene diversity. In addition, three tasks shared a basic feature extraction network to reduce computation cost. The experiment results show that the average precision (AP) of our method is superior to the EfficientDet network by 2.0% on the XDU-NIR2020 dataset and 2.2% on the CVC-09 dataset respectively.
Sparse and low-dimensional representation with maximum entropy adaptive graph for feature selection

Traditional feature selection algorithms usually explore the relationship between data and cluster structure in a single space, so the internal relationship obtained is not very rich, and it is not enough to select more valuable features. To solve the above problems, in order to fully mine the intrinsic correlation information in different spaces, so that the relationship between data, features and clustering structure can be explored at the same time, this paper proposes a feature selection method based on sparse and low-dimensional representation with maximum entropy adaptive graph (SLMEA). Firstly, the SLMEA combines the sparse transform representation with pseudo-label matrix learning to optimize, and uses the pseudo-label matrix to guide the learning of sparse low-dimensional space. It can not only explore the relationship between data and pseudo-labels in data space, but also mine the association between features and pseudo-labels in feature space, so as to select more discriminative features. Secondly, based on the maximum entropy theory, the similarity matrix is constructed adaptively, so that the two different manifold structures corresponding to sparse transform representation and pseudo label matrix learning can be adaptively learned and retained in the iterative process. In addition, in order to ensure the sparsity of the transformation matrix, the constraint of ℓ2,1/2-norm is applied to the matrix, which can better deal with the redundant features and obtain more sparse solutions. Finally, the SLMEA uses an alternate iterative update method to optimize the objective function, and carries out extensive experiments on eight mainstream datasets. Compared with seven state-of-the-art algorithms, SLMEA can have higher clustering accuracy and normalized mutual information.
Moment is Important: Language-Based Video Moment Retrieval via Adversarial Learning

The newly emerging language-based video moment retrieval task aims at retrieving a target video moment from an untrimmed video given a natural language as the query. It is more applicable in reality since it is able to accurately localize a specific video moment, as compared to traditional whole video retrieval. In this work, we propose a novel solution to thoroughly investigate the language-based video moment retrieval issue under the adversarial learning. The key of our solution is to formulate the language-based video moment retrieval task as an adversarial learning problem with two tightly connected components. Specifically, a reinforcement learning is employed as a generator to produce a set of possible video moments. Meanwhile, a multi-task learning is utilized as a discriminator, which integrates inter-modal and intra-modal in a unified framework by employing a sequential update strategy. Finally, the generator and the discriminator are mutually reinforced in the adversarial learning, which is able to jointly optimize the performance of both video moment ranking and video moment localization. Extensive experimental results on two challenging benchmarks, i.e., Charades-STA and TACoS datasets, have well demonstrated the effectiveness and rationality of our proposed solution. Meanwhile, on the larger and unbiased datasets, i.e., ActivityNet Captions and ActivityNet-CD, our proposed framework exhibits excellent robustness.
Comparative Study of Classification of Histopathological Images

The majority of women who suffer from cancer are diagnosed with breast cancer. A type of breast cancer that accounts for about 80% of all other forms of breast cancer is Invasive Ductal Carcinoma (IDC). It is very difficult to diagnose the disease because of its invasiveness. Identification and classification of cancer are of great importance and automated approaches help make efficient usage of time and reduce errors. In this paper, the methods used for classifying histopathological images into Invasive Ductal Carcinoma or non-Invasive Ductal Carcinoma images include standard architectures of Convolutional Neural Networks and machine learning algorithms. The comparative study of the models is performed and is inferred that ResNet50 on the classification produces greater accuracy when compared to other models.
Sign Language Recognition System Using TensorFlow Object Detection API

Communication is defined as the act of sharing or exchanging information, ideas or feelings. To establish communication between two people, both of them are required to have knowledge and understanding of a common language. But in the case of deaf and dumb people, the means of communication are different. Deaf is the inability to hear and dumb is the inability to speak. They communicate using sign language among themselves and with normal people but normal people do not take seriously the importance of sign language. Not everyone possesses the knowledge and understanding of sign language which makes communication difficult between a normal person and a deaf and dumb person. To overcome this barrier, one can build a model based on machine learning. A model can be trained to recognize different gestures of sign language and translate them into English. This will help a lot of people in communicating and conversing with deaf and dumb people. The existing Indian Sing Language Recognition systems are designed using machine learning algorithms with single and double-handed gestures but they are not real-time. In this paper, we propose a method to create an Indian Sign Language dataset using a webcam and then using transfer learning, train a TensorFlow model to create a real-time Sign Language Recognition system. The system achieves a good level of accuracy even with a limited size dataset.
Performance optimization of annealing salp swarm algorithm: frameworks and applications for engineering design

Abstract Swarm salp algorithm is a swarm intelligence optimization algorithm enlightened by the movement and foraging behaviors of the salp population. The salp swarm algorithm (SSA) has a simple structure and fast processing speed and can gain significant results on objective functions with fewer local optima. However, it has poor exploration ability and is easy to suffer from the local optimal solutions, so it performs poorly on multimodal objective functions. Besides, its unfair balance of exploration and exploitation is another notable shortcoming. To ameliorate these shortcomings and enhance the algorithm’s performance on multimodal functions, this research proposes simulated annealing (SA) improved salp swarm algorithm (SASSA). SASSA embeds the SA strategy into the followers’ position updating method of SSA, performs a certain number of iterations of the SA strategy, and uses Lévy flight to realize the random walk in the SA strategy. SASSA and 23 original and improved competitive algorithms are compared on 30 IEEE CEC2017 benchmark functions. SASSA ranked first in the Friedman test. Compared with SSA, SASSA can obtain better solutions on 27 benchmark functions. The balance and diversity experiment and analysis of SSA and SASSA are carried out. SASSA’s practicability is verified by solving five engineering problems and the fertilizer effect function problem. Experimental and statistical results reveal that the proposed SASSA has strong competitiveness and outperforms all the competitors. SASSA has excellent exploration ability, suitable for solving composition functions with multiple peaks. Meanwhile, SASSA brings about a good balance of exploration and exploitation and dramatically improves the quality of the solutions.
QoS aware task consolidation approach for maintaining SLA violations in cloud computing

This paper aims to achieve a better Quality of Service (QoS) through Modified Task Consolidation (MTC) approach. The proposed MTC approach defines virtual machines (VMs) clusters based on the mean value of available resources and forms different clusters of tasks based on average resource requirement. These clusters of tasks and VMs help to make an appropriate mapping among tasks and VMs as per the suitability of CPU and memory resources. The approach has also considered tasks' bandwidth requirements and prioritizes them over the VMs with high bandwidth availability during cluster-wise mapping. Comprehensive simulations have been performed to analyze the performance of the proposed scheme, and results have been examined for different configurations over two real-time workloads. The result analysis has revealed that the proposed approach can reduce the number of migrations by up to 67.9%. Furthermore, it can reduce performance degradation due to migration up to 60%. Also, compared to the state-of-the-art approaches, the MTC approach can achieve up to 61.4% less SLA violation time per active host (SLATHAH) and up to 35.3% reduction Over SLA violations (OSLAV).
The Odyssey of Entropy: Cryptography

After being introduced by Shannon as a measure of disorder and unavailable information, the notion of entropy has found its applications in a broad range of scientific disciplines. In this paper, we present a systematic review on the applications of entropy and related information-theoretical concepts in the design, implementation and evaluation of cryptographic schemes, algorithms, devices and systems. Moreover, we study existing trends, and establish a roadmap for future research in these areas.
GabriellaV2: Towards better generalization in surveillance videos for Action Detection

Activity detection has wide-reaching applications in video surveillance, sports, and behavior analysis. The existing literature in activity detection has mainly focused on benchmarks like AVA, AVA-Kinetics, UCF101-24, and JHMDB-21. However, these datasets fail to address all issues of real-world surveillance camera videos like untrimmed nature, tiny actor bounding boxes, multi-label nature of the actions, etc. In this work, we propose a real-time, online, action detection system which can generalize robustly on any unknown facility surveillance videos. Our real-time system mainly consists of tracklet generation, tracklet activity classification, and prediction refinement using the proposed post-processing algorithm. We tackle the challenging nature of action classification problem in various aspects like handling the class-imbalance training using PLM method and learning multi-label action correlations using LSEP loss. In order to improve the computational efficiency of the system, we utilize knowledge distillation. Our approach gets state-of-the-art performance on ActEV-SDL UF-full dataset and second place in TRECVID 2021 ActEV challenge. Project Webpage: www.crcv.ucf.edu/research/projects/gabriellav2/
MultiEmo: Multi-task framework for emoji prediction

Emojis are becoming a new visual and linguistic tool that allows users to express their feelings and communicate with each other on social media. Driven by the importance of emoji interpretation, an emoji prediction task, which aims to predict the most likely emojis within a text, has gained significant attention. Thus, we propose a new deep neural network model for this task, MultiEmo (multi-task framework for emoji prediction), to predict the most relevant emoji by considering the emotion detection task. Our experiment shows that MultiEmo is superior to existing models using the Twitter dataset, implying that the models can learn richer representations from semantically related tasks. We systematically confirm that each emoji is associated with a particular emotion in a similar context. We also introduce new evaluation metrics to measure the comprehensive performance of the models.
A New Alignment Word-Space Approach for Measuring Semantic Similarity for Arabic Text

This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector space-based. The vector space-based method depends on a semantic net that represents the meaning of words as vectors. These vectors are lemmatized to enrich the search space. The alignment-based method generates an alignment word space matrix (AWSM) for the snipped texts according to the generated semantic word spaces. Finally, the degree of sentence semantic similarity is measured using some proposed alignment rules. Four experiments were carried out to evaluate the performance of the proposed approach, using two different datasets. The experimental results proved that applying the lemmatization process for the input text and the vector model has a better effect. The degree of correctness of the results reaches 0.7212 which is considered one of the best two results of the published Arabic semantic similarities.
Augmented Reality (AR) as a Tool for Engaging Museum Experience: A Case Study on Chinese Art Pieces

The research explores the efficiency of augmented reality (AR) technology as a tool for the positive triggering museum experience. This article shows the design, development, and evaluation of an AR prototype for information visualization based on a famous Chinese art piece named Along the River During the Qingming Festival. In total, 58 participants were invited to evaluate the prototype. Results suggest that AR technology can trigger users’ engagement, learning, meaningful experience, and emotional connection, and hence arouse their interest and learning process. These findings may interest researchers and designers to develop a broader range of innovative AR applications to prompt a digital learning experience.
Blockchain-Empowered Secure and Privacy-Preserving Health Data Sharing in Edge-Based IoMT

Health data sharing, as a booming demand, enables the patients with similar symptoms to connect with each other and doctors to obtain the medical history of patients. Health data are usually collected from edge-based Internet of medical things (IoMT) with devices such as smart wearable devices, smart watches, and smartphones. Since health data are highly private and have great financial value, adversaries ceaselessly launch diverse attacks to obtain private information. All these issues pose great challenges to health data sharing in edge-based IoMT scenarios. Existing research either lacks comprehensive consideration of privacy and security protection or fails to provide a proper incentive mechanism, which expels users from sharing data. In this study, we propose a novel blockchain-assisted data sharing scheme, which allows secure and privacy-preserving profile matching. A bloom filter with hash functions is designed to verify the authenticity of keyword ciphertext. Key-policy attribute-based encryption (KP-ABE) algorithm and smart contracts are employed to achieve secure profile matching. To incentivize users actively participating in profile matching, we devise an incentive mechanism and construct a two-phase Stackelberg game to address pricing problems for data owners and accessing problems of data requesters. The optimal pricing mechanism is specially designed for encouraging more users to participate in health data sharing and maximizing users’ profit. Moreover, security analysis illustrates that the proposed protocol is capable of satisfying various security goals, while performance evaluation shows high scalability and feasibility of the proposed scheme in edge-based IoMT scenarios.
Federated Multi-Target Domain Adaptation

Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Within this new Federated Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance of existing domain adaptation methods and propose an effective DualAdapt method to address the new challenges. Extensive experimental results on image classification and semantic segmentation tasks demonstrate that our method achieves high accuracy, incurs minimal communication cost, and requires low computational resources on client devices.
Blockchain based hierarchical semi-decentralized approach using IPFS for secure and efficient data sharing

Nowadays, cloud servers are gathering an increasing amount of data. Data is commonly stored on cloud servers in the form of ciphertext to protect security and concealment of data. When a consumer requests to access of encrypted data, a third party must provide an access key. The system's security, however, will be compromised if the third party or internal personnel are dishonest. To address this issue, a novel blockchain-based secure decentralized system using IPFS is proposed in this research for secure data transfer. Because all participant of system model are recorded the every action on the chain, and the continuously extending chain makes it conditionally difficult to modify any block without being detected, a blockchain based system is often regarded as a safe platform. In the proposed approach, the data owner uploads an encrypted file to IPFS, which is subsequently separated into n secret sections called hash codes for data security. The data owner must additionally write the access permissions in order to achieve access to this secure data. For security, the system uses two-level key management: first, the data owner encrypts the file, and then the IPFS server makes a hash code of that encrypted file. The proposed solution, which employs blockchain technology, enables consumers to be handled across several domains, erase the single-point failure in traditional centralized systems, and overhead related to communication and computation are decreased at the consumer level. According to the security analysis, the proposed system might effectively resist single and collaboratively malicious persons, as well as untrustworthy cloud servers.
Real-Time Gesture Recognition in the View of Repeating Characteristics of Sign Languages

Recognizing dynamic gestures from a continuous data stream has been treated as a difficult task. One challenge is the gesture spotting problem, or segmentation of a gesture pattern from a data stream containing consecutive gestures. Although various types of gesture spotting strategies have been introduced so far, all methods has its own limitations to be applied to real world situations such as sign language conversation. One of the conditions that must be satisfied for practical applications is to detect repetitive gestures for emphasizing a certain meaning.In the view of gesture spotting, repetitive gestures are hard to be detected because similar starting and ending moments are repeated without constraints of the number of repetitions. We introduce a data glove-based real-time dynamic gesture recognition system that covers both repetitive and nonrepetitive gestures. The proposed system consists of three steps: Gesture spotting, gesture sequence compression, and gesture recognition. For gesture spotting, we define a quantity termed as a gesture progress sequence, which quantifies a progress of a gesture using numbers from 0 to 1. Gesture sequence compression removes variations including the number of repetitions that seek to impart the same message. At the gesture recognition step, the compressed gesture patterns are classified. The proposed system was evaluated using 17 American sign language (ASL) gestures and some ASL sentences.
Deep learning applications for disease diagnosis

With the popularity of artificial intelligence (AI) and its integration into every field, it has yielded positive results that have increased productivity and aided us in solving complex problems. Deep learning (DL) is a subset of AI developed to mimic the human brain. It is a way for a computer to perform actions that come naturally to humans. It is a tool extensively used to organize unsupervised or unlabeled data and find patterns in them. DL has had a major impact on the field of medical science owing to its applications in medical drug discovery, medical imaging, genome synthesis, disease diagnosis, and much more. The growth of DL in this field has increased significantly owing to the processing and type of data used in the models. Focusing on the type of data, preexisting or curated, can drastically affect the success rate of a DL model. DL is used to diagnose diseases such as skin lesions, brain disease, and chronic disease. To understand the application of DL in disease diagnosis and how it has become one of the most efficient methods for disease diagnosis, this chapter will examine the different architectures of DL, such as convolutional neural networks, feed forward networks, and recurrent neural networks, and how their diagnosing methods compare with traditional disease diagnosis methods. In addition, it will explore advantages and disadvantages of DL compared with traditional methods by comparing various efficiency parameters such as accuracy, specificity, sensitivity, area under the curve, and receiver operating characteristics curve, showing how DL is more effective and produces better results. However, DL faces numerous obstacles. If these are overcome, it will lead to even better results and complete autonomous disease diagnosis.