Reviewerly/exHarmony huggingface dataset:
A New Regularization for Deep Learning-Based Segmentation of Images with Fine Structures and Low Contrast

Deep learning methods have achieved outstanding results in many image processing and computer vision tasks, such as image segmentation. However, they usually do not consider spatial dependencies among pixels/voxels in the image. To obtain better results, some methods have been proposed to apply classic spatial regularization, such as total variation, into deep learning models. However, for some challenging images, especially those with fine structures and low contrast, classical regularizations are not suitable. We derived a new regularization to improve the connectivity of segmentation results and make it applicable to deep learning. Our experimental results show that for both deep learning methods and unsupervised methods, the proposed method can improve performance by increasing connectivity and dealing with low contrast and, therefore, enhance segmentation results.
Application of artificial intelligence-based style transfer algorithm in animation special effects design

Abstract Today, the rapid development of computer technology changes with each passing day. In the computer field, computer animation has rapidly grown from a new thing to a leading industry, and animation has entered the era of three-dimensional animation and computer graphics. This article aims to study the application of artificial intelligence-based style transfer algorithm in animation special effects design. It proposes methods such as adaptive loss function, style transfer process, animation special effect design, etc., and conducts related experiments on the application of style transfer algorithm in animation special effect design in the article. The experimental results show that the style transfer algorithm based on AI can effectively improve the effect of animation special effects. In this survey, more than 80% of the people are satisfied with the animation special effects design based on the style transfer algorithm.
The use of annotations to explain labels: Comparing results from a human‐rater approach to a deep learning approach

Abstract Background Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about whether the salient words agree with those identified by humans as important. Objectives The current study examines in‐line annotations from human annotators and saliency map annotations from a deep learning model (ELECTRA transformer) to understand how well both humans and machines provide evidence for their assigned label. Methods Data were responses to test items across a mix of United States subjects, states, and grades. Humans were trained to annotate responses to justify a crisis alert label and two model interpretability methods (LIME, Integrated Gradients) were used to obtain engine annotations. Human inter‐annotator agreement and engine agreement with the human annotators were computed and compared. Results and Conclusions Human annotators agreed with one another at similar rates to those observed in the literature on similar tasks. The annotations derived using the integrated gradients (IG) agreed with human annotators at higher rates than LIME on most metrics; however, both methods underperformed relative to the human annotators. Implications Saliency map‐based engine annotations show promise as a form of explanation, but do not reach human annotation agreement levels. Future work should examine the appropriate unit for annotation (e.g., word, sentence), other gradient based methods, and approaches for mapping the continuous saliency values to Boolean annotations.
Data Layout from a Type-Theoretic Perspective

The specifics of data layout can be important for the efficiency of functional programs and interaction with external libraries. In this paper, we develop a type-theoretic approach to data layout that could be used as a typed intermediate language in a compiler or to give a programmer more control. Our starting point is a computational interpretation of the semi-axiomatic sequent calculus for intuitionistic logic that defines abstract notions of cells and addresses. We refine this semantics so addresses have more structure to reflect possible alternative layouts without fundamentally departing from intuitionistic logic. We then add recursive types and explore example programs and properties of the resulting language.
Ensuring confidentiality and privacy of cloud data using a non-deterministic cryptographic scheme

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C#, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution time of a cryptographic algorithm but rather the size of the security key.
The influence of communication language on purchase intention in consumer contexts: the mediating effects of presence and arousal

Abstract Panoramic visual, tactile, and auditory interactions have been widely used to create scene-based immersive experiences to enhance consumer experience and stimulate emotional responses, thereby increasing purchase intentions. Guided by scene marketing theory and the archetypal theory of mind-flow experience, this article introduces the concepts of presence and emotional arousal to examine how communication language causes emotional fluctuations in potential consumers, leading to purchase intentions. Therefore, from the interaction experience of “external perception - scene language” and “internal sensing - communication language”, the dual chain of mediation of presence perception and emotional arousal is selected to construct a consumer. The results reveal that the pathway of consumers’ purchase intentions is not always the same in the context of consumer language perception. In addition, the results demonstrate that the vividness and richness of the presentation of scene language in a consumer language perception context have positively influenced the relationship between communication language and presence perception. Compared to the direct effect on purchase intention, communication language tends to have an indirect positive effect on consumers’ purchase intention through the single mediation of presence perception and the chain mediation of emotional arousal. The increasing prevalence of interactive linguistic experiences, where emotional arousal has become an essential trigger for consumers’ purchase intentions, is an important representation of the specificity and complexity of consumer language elements. Thus, this research provides crucial theoretical guidance for the design of immersive experiential marketing and linguistic scenarios.
Improving transparency and representational generalizability through parallel continual learning

This paper takes a parallel learning approach in continual learning scenarios. We define parallel continual learning as learning a sequence of tasks where the data for the previous tasks, whose distribution may have shifted over time, are also available while learning new tasks. We propose a parallel continual learning method by assigning subnetworks to each task, and simultaneously training only the assigned subnetworks on their corresponding tasks. In doing so, some parts of the network will be shared across multiple tasks. This is unlike the existing literature in continual learning which aims at learning incoming tasks sequentially, with the assumption that the data for the previous tasks have a fixed distribution. Our proposed method offers promises in: (1) Transparency in the network and in the relationship across tasks by enabling examination of the learned representations by independent and shared subnetworks, (2) Representation generalizability through sharing and training subnetworks on multiple tasks simultaneously. Our analysis shows that compared to many competing approaches such as continual learning, neural architecture search, and multi-task learning, parallel continual learning is capable of learning more generalizable representations. Also, (3)Parallel continual learning overcomes the common issue of catastrophic forgetting in continual learning algorithms. This is the first effort to train a neural network on multiple tasks and input domains simultaneously in a continual learning scenario. Our code is available at https://github.com/yours-anonym/PaRT.
Optimized algorithms and architectures for fast non-cryptographic hash functions in hardware

The design and implementation of eight novel non-cryptographic hash functions is proposed in this paper. Those can suitably be employed in networking and security applications that require fast lookup and/or counting architectures. Our approach is inspired by the design of the existing non-cryptographic hash function Xoodoo-NC, which is constructed through the concatenation of several Xoodoo permutations. In a similar way, this work concatenates several rounds of symmetric-key ciphers to construct non-cryptographic hash functions. The goal is to achieve high performance while maintaining the avalanche properties. Meeting the avalanche criteria is necessary to yield a significant change in the output of hash functions even for the slightest change in the input. The number of rounds required to achieve satisfactory avalanche scores for each cipher is simulated. We implement the corresponding non-cryptographic hash functions on an FPGA and we synthesize them on ASIC to evaluate the occupied resources and the performance. This paper evaluates the performance and area of ASIC implementations optimized for high operating speed. This evaluation is done on the 65 nm TSMC technology. One of the proposed non-cryptographic hash functions, namely PHOTON-NC, outperforms all previously proposed non-cryptographic hash functions in terms of throughput and latency on FPGA, in exchange for an acceptable increase in FPGA resources, while Xoodoo-NC proves to have the highest performance on ASIC.
Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs.The WSC's main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold (limits), and also an Elitist Strategy (ES) is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria (average fitness values, best fitness values, and execution time) that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.The Wilcoxon signed-rank significant test is used where the proposed algorithm results significantly differ from other algorithms on 100% of datasets.
Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users' content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore, it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However, the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network (ANN) to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus, the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification (MLC) problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance (BR) Instance-Based Classifier, the BR Decision Tree, and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore, the accuracy of the proposed ANN achieves better results by 1.1% to 6.1% compared to other existing methods.
ViT2CMH: Vision Transformer Cross-Modal Hashing for Fine-Grained Vision-Text Retrieval

In recent years, the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process image and text information, respectively.This makes images or texts subject to local constraints, and inherent label matching cannot capture finegrained information, often leading to suboptimal results.Driven by the development of the transformer model, we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically, we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally, the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO (MS-COCO) and Flickr30K, comparing with baselines of some hashing methods and image-text matching methods, showing that our method has better performance.
Quantum Computing Based Neural Networks for Anomaly Classification in燫eal-Time Surveillance Videos

For intelligent surveillance videos, anomaly detection is extremely important.Deep learning algorithms have been popular for evaluating realtime surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts.Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power.Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics.As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning.This research develops a Quantum Computing-based Convolutional Neural Network (QC-CNN) to extract features and classify anomalies from surveillance footage.A Quantum-based Circuit, such as the real amplitude circuit, is utilized to improve the performance of the model.As far as my research, this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications.There are 13 anomalies classified from the UCF-crime dataset.Based on experimental results, the proposed model is capable of efficiently classifying data concerning confusion matrix, Receiver Operating Characteristic (ROC), accuracy, Area Under Curve (AUC), precision, recall as well as F1-score.The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37% greater when compared to other existing models.To measure the efficiency of the proposed work, QC-CNN is also evaluated with classical and quantum models.
Privacy-preserving identification of the influential nodes in networks

Identifying influential nodes in social networks has drawn significant attention in the field of network science. However, most of the existing works request to know the complete structural information about networks, indeed, this information is usually sensitive, private and hard to obtain. Therefore, how to identify the influential nodes in networks without disclosing privacy is especially important. In this paper, we propose a privacy-preserving (named as HE-ranking) framework to identify influential nodes in networks based on homomorphic encryption (HE) protocol. The HE-ranking method collaboratively computes the nodes’ importance and protects the sensitive information of each private network by using the HE protocol. Extensive experimental results indicate that the method can effectively identify the influential nodes in the original networks than the baseline methods which only use each private network to identify influential nodes. More importantly, the HE-ranking method can protect the privacy of each private network in different parts.
Implementation of The Certainty Factor Method in The Expert System For Early Diagnosis of Dyslexia in Childhood

Dyslexia is a condition in which a person has difficulty (especially) in areas related to learning abilities such as reading, writing, and arithmetic or matters relating to numbers. This condition is not the skills expected of people with chronological age and normal intelligence abilities or IQ (quality of intelligence). This condition is sometimes not realized by parents and only consider their child slightly delayed, even though it is under standard (minimum) abilities at his age. Therefore, a platform using an expert system with the Certainty Factor method was created to help parents detect early whether their child has dyslexia or not and find out what type of dyslexia the child is experiencing. The types of dyslexia that will be included in this study include surface dyslexia, phonological dyslexia, rapid naming deficit, dysgraphia, and dyscalculia. The white box results found that the system was in line with expectations because it had a low level of risk.
Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy

In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM) clustering unlike fuzzy c-means (FCM) clustering LFPFC represents the prototypes of clusters as linear functions, and this can lead to more reliable data analysis of complex regression problems. We propose two types of LFPFC such as an estimated output-based LFPFC and a distance-based LFPFC. The estimated output-based LFPFC uses the output estimated on a basis of the simple model instead of the target output to calculate the centroid of LFPFC. A centroid of distance-based LFPFC is computed through the Euclidean distance between input data and the centroid of the cluster. By using two kinds of LFPFC approaches, we propose three different types of fuzzy neural networks: i) the fuzzy neural networks through layer reconstruction-based network design strategy consists of two models. The first model serves as an estimate of the desired output and the estimated output is used in the LFPFC of the second model. ii) In the fuzzy neural networks applied to the basic architecture of distance-based LFPFC, the hidden layer using the membership function changes to basic distance-based LFPFC, and the partition matrix obtained from LFPFC is used as the output of the hidden layer. iii) in the fuzzy neural network with the advanced architecture of distance-based LFPFC, an additional auxiliary layer is considered between the hidden and output layers to estimate the membership function of output space through LFPFC. In the experiments, we evaluate the performance index of the proposed models using publicly available machine learning datasets. The superiority of the proposed fuzzy neural networks designed by using LFPFC is demonstrated through the comparative analysis with the diverse regression models offered in the Weka data mining software. By conducting the Friedman test we show that the proposed model exhibits visible competitiveness from the viewpoint of performance. In addition, a real-world Portland cement dataset is dealt with to demonstrate the superiority of the models designed with the aid of LFPFC and reinforced layer reconstruction-based network design strategy.
Image restoration with impulse noise based on fractional-order total variation and framelet transform

Restoring images corrupted by noise and blur is a burgeoning subject in image processing, and despite the large number of proposed restoration algorithms, the effort to bring about some improvement is always of great interest. The definition of fractional derivatives in recent years has created a powerful tool for this purpose. In the present paper, using fractional-order total variation and framelet transform, the nonconvex model for image restoration with impulse noise problem is improved. Then by alternating direction method of multipliers (ADMM) and primal-dual problem, the proposed model is solved. The convergence of the proposed algorithm is studied, and the proposed algorithm is evaluated using different types of tests.