Reviewerly/exHarmony huggingface dataset:
Trust-Based Smart Contract for Automated Agent to Agent Communication

Blockchain technology is now regarded as one of the most interesting and possibly innovative technologies. It enables information to be stored and exchanged securely and transparently without the need for a centralized authority to regulate it. Some of the primary benefits of this technology are the atomicity of the stored data. Given its features, this technology has the potential to provide answers to challenges encountered in a very sensitive sector, namely, Internet of Vehicles (IoV). In IoV, vehicles and service providers autonomously capture and produce data without human intervention. This exchanged data must meet certain criteria such as decentralization, automation, security, and stakeholder trust management. To overcome these challenges, the integration of blockchain technology and multi-agent systems is a key solution. Based on smart contracts, the proposed solution consists of exploiting role-based access control (RBAC) and attribute-based access control (ABAC) techniques. This solution removes the central authority (CA) to reduce maintenance costs and eliminate legacy threats from centralized systems. The results, obtained from consumption costs, show that the developed platform is characterized by security, availability, and privacy.
Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

Simulating facial appearance change following bony movement is a critical step in orthognathic surgical planning for patients with jaw deformities. Conventional biomechanics-based methods such as the finite-element method (FEM) are labor intensive and computationally inefficient. Deep learning-based approaches can be promising alternatives due to their high computational efficiency and strong modeling capability. However, the existing deep learning-based method ignores the physical correspondence between facial soft tissue and bony segments and thus is significantly less accurate compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix. Experimental results on patients with jaw deformity show that our proposed method can achieve comparable facial change prediction accuracy compared with the state-of-the-art FEM-based approach with significantly improved computational efficiency.
Analisis Metode Apriori Untuk Memprediksi Persediaan Barang Pada Warung

The data processing system in the shop is still manual. Previously, the shop had not been able to predict the stock or inventory of goods. Therefore, this system predicts the inventory of goods in the shop. so that the data processing system at the shop can be handled by problems that occur and can form information quickly, precisely, and well. The use of the a priori method which will be used to calculate the prediction of the supply of goods in stock that is still there. And this a priori method will later be used to calculate odds. Data storage using SQL Server database. With the development of technology, the ability to collect and process data is also growing. Data processing has become the advantage of computers, computers have also penetrated various aspects, both in the field of education and in the business world. Competition in the global business has built tight competition between one stall and another. This algorithm is used to analyze when purchasing goods that have run out of stock by classifying which goods have been added to stock or not, as a result, the availability of goods remains stable and maintained. The author is currently making inventory predictions at the stalls to develop an existing system with the a priori method.
Detection on Cell Cancer Using the Deep Transfer Learning and Histogram Based Image Focus Quality Assessment

In recent years, the number of studies using whole-slide imaging (WSIs) of histopathology slides has expanded significantly. For the development and validation of artificial intelligence (AI) systems, glass slides from retrospective cohorts including patient follow-up data have been digitized. It has become crucial to determine that the quality of such resources meets the minimum requirements for the development of AI in the future. The need for automated quality control is one of the obstacles preventing the clinical implementation of digital pathology work processes. As a consequence of the inaccuracy of scanners in determining the focus of the image, the resulting visual blur can render the scanned slide useless. Moreover, when scanned at a resolution of 20× or higher, the resulting picture size of a scanned slide is often enormous. Therefore, for digital pathology to be clinically relevant, computational algorithms must be used to rapidly and reliably measure the picture's focus quality and decide if an image requires re-scanning. We propose a metric for evaluating the quality of digital pathology images that uses a sum of even-derivative filter bases to generate a human visual-system-like kernel, which is described as the inverse of the lens' point spread function. This kernel is then used for a digital pathology image to change high-frequency image data degraded by the scanner's optics and assess the patch-level focus quality. Through several studies, we demonstrate that our technique correlates with ground-truth z-level data better than previous methods, and is computationally efficient. Using deep learning techniques, our suggested system is able to identify positive and negative cancer cells in images. We further expand our technique to create a local slide-level focus quality heatmap, which can be utilized for automated slide quality control, and we illustrate our method's value in clinical scan quality control by comparing it to subjective slide quality ratings. The proposed method, GoogleNet, VGGNet, and ResNet had accuracy values of 98.5%, 94.5%, 94.00%, and 95.00% respectively.
Multi-Level Cross-Modal Semantic Alignment Network for Video–Text Retrieval

This paper strives to improve the performance of video–text retrieval. To date, many algorithms have been proposed to facilitate the similarity measure of video–text retrieval from the single global semantic to multi-level semantics. However, these methods may suffer from the following limitations: (1) largely ignore the relationship semantic which results in semantic levels are insufficient; (2) it is incomplete to constrain the real-valued features of different modalities to be in the same space only through the feature distance measurement; (3) fail to handle the problem that the distributions of attribute labels in different semantic levels are heavily imbalanced. To overcome the above limitations, this paper proposes a novel multi-level cross-modal semantic alignment network (MCSAN) for video–text retrieval by jointly modeling video–text similarity on global, entity, action and relationship semantic levels in a unified deep model. Specifically, both video and text are first decomposed into global, entity, action and relationship semantic levels by carefully designing spatial–temporal semantic learning structures. Then, we utilize KLDivLoss and a cross-modal parameter-share attribute projection layer as statistical constraints to ensure that representations from different modalities in different semantic levels are projected into a common semantic space. In addition, a novel focal binary cross-entropy (FBCE) loss function is presented, which is the first effort to model the unbalanced attribute distribution problem for video–text retrieval. MCSAN is practically effective to take the advantage of the complementary information among four semantic levels. Extensive experiments on two challenging video–text retrieval datasets, namely, MSR-VTT and VATEX, show the viability of our method.
Blockchain-based Federated Learning Framework Applied in Face Recognition

The significant improvement of face recognition technology has mainly resulted from the rapid enhancement of Deep Neural Network performance and the use of large face datasets. As the use of face datasets concerns data privacy, it is difficult and unwilling for organizations and individuals to share their data for model training. Although existing federated learning methods can train data locally, the problems of the single point of failure and insufficient user incentives still exist. In response to the above issues, we propose an innovative BFLFace framework. BFLFace is a framework that uses blockchain instead the central server of federated learning to implement the decentralized training process of the face recognition model. At the same time, the combination of committee nodes that can evaluate the quality of the updated model and incentive mechanism based on blockchain that is developed to encourage more reliable users to participate in the training process can improve model performance. On the premise of ensuring the accuracy of the face recognition model, BFLFace can significantly solve the problem of data silos and further enhance the data security of users.
On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference

Applied to the problem of automatic program generation, Genetic Programming often produces code bloat, or unexpected solutions that are, according to common belief, difficult to comprehend. To study the comprehensibility of the code produced by Genetic Programming, attribute grammars obtained by Genetic Programming-based semantic inference were compared to manually written ones. According to the established procedure, the research was carried out as a controlled classroom experiment that involved two groups of students from two universities, and consisted of a background questionnaire, two tests and a feedback questionnaire after each test. The tasks included in the tests required the identification of various properties of attributes and grammars, the identification of the correct attribute grammar from a list of choices, or correcting a semantic rule in an attribute grammar. It was established that solutions automatically generated by Genetic Programming in the field of semantic inference, in this study attribute grammars, are indeed significantly harder to comprehend than manually written ones. This finding holds, regardless of whether comprehension correctness, i.e., how many attribute grammars were correctly comprehended, or comprehension efficiency is considered, i.e., how quickly attribute grammars were correctly comprehended.
Visual Cryptography: A Study And Its Application To Biometric Authentication

Because of advancements in information and computer technology, we now live in the information age (ICT).Data security is becoming more and more important as concerns about personal information in computer systems grow.In this study, visual cryptography is reviewed and used to biometric authentication.The idea behind visual cryptography is to randomly distribute the private images.The k-n secret sharing schema, which divides the image into several pieces, is employed in this instance of encryption.The shares are converted into the secret image via decryption.For the goal of user identification, I have developed a new k-n secret sharing schema for black and white images and applied it to biometric information like finger print images.
Extending OpenMP to Support Automated Function Specialization Across Translation Units

OpenMP’s variant directives support specialization at compilation time using OpenMP context for portability or performance. This specialization is confined either to variants explicitly written by the code author, which can cross translation units, or to implicit context passed by the compiler. The implicit context allows a metadirective directive to choose a directive variant based on context or the compiler to optimize out runtime interactions. However, that implicit context only exists in a single translation unit (TU), either in a single compilation or with link-time optimization linking the set of TUs. In order to enable more optimization opportunities, we propose the metavariant directive, a new variant directive to define possible function specializations over a set of specified OpenMP contexts that are available across different translation units. The compiler lowers the definition of a metavariant-annotated function to different instances for each specified context. Calls of the function in different translation units use local OpenMP context for specialization, relying on the fact that the compiler will have generated appropriately mangled symbol names for those instances. Using a prototype source-to-source tool and a set of use cases, we evaluate our approach to observe a speedup of up to 30 $$\times $$ with inter-procedural specialization versus no specialization, while simplifying and enhancing modular adaptation with modest user effort.
Object Detection of Tobacco-related Information Based on Visual Features

The forms of tobacco-related crimes are changeable and highly concealed, resulting in a waste of public security police resources. Tobacco-related images are unstructured, high-dimensional, and diverse. Therefore, this paper establishes a database of tobacco-related cases, and implements a target detection system for tobacco-related information. The system is divided into two parts: software and algorithm. The software part realizes the transmission of unformatted data, and detects the massive tobacco-related data on the cloud server to complete end-to-end case tracking. In the algorithm part, the detection and recognition of tobacco-related images, license plates, and text are improved. We propose Yolox-Cr, LPRNet-St, DBNet-lite corresponding tobacco-related target detection and recognition algorithms. In the three self-built datasets, the recognition accuracy of each algorithm for the corresponding tobacco-related targets exceeds 85%. The intelligent object detection system for smoking-related cases has been successfully applied to the analysis and judgment of smoking-related cases by a provincial public security department, improving the efficiency of law enforcement.
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation

Axie infinity is a complicated card game with a huge-scale action space. This makes it difficult to solve this challenge using generic Reinforcement Learning (RL) algorithms. We propose a hybrid RL framework to learn action representations and game strategies. To avoid evaluating every action in the large feasible action set, our method evaluates actions in a fixed-size set which is determined using action representations. We compare the performance of our method with two baseline methods in terms of their sample efficiency and the winning rates of the trained models. We empirically show that our method achieves an overall best winning rate and the best sample efficiency among the three methods.
Machine Learning for Change-Prone Class Prediction: A History-Based Approach

Classes have a very dynamic life cycle in object-oriented software projects. They can be created, modified or removed due to different reasons. The prediction of prone-change classes in the early stages of the project positively impact the team’s productivity, the allocation of resources, and the quality of the software developed. Existing work uses Machine Learning (ML) and different kind of class metrics. But a limitation of existing work that they do not consider the temporal dependency between instances in the datasets. To fulfill such gap, this work introduces an approach based on the change history of the class in different releases from public repositories. The approach uses the Sliding Window method, and adopts as predictors structural and evolutionary metrics, as well as frequency and diversity of smells. Five projects and four ML algorithms are used in the evaluation. In the great majority of the cases our approach overcomes a traditional approach considering all the indicators. Random Forest presents the best performance and the use of smell-related information does not impact the results.
CARS: Workshop on Context-Aware Recommender Systems 2022

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2022 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.
What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition

Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
Swarm Based Optimization for Image Dehazing from Noise Filtering Perspective

Haze may readily corrupt digital photos acquired in an outside situation, degrading the information communicated. To address this issue, several studies on picture haze reduction have been done, with the technique based on the dark channel prior assumption being regarded the state-of-the-art in recent years. Consolidation of observations is a key component of this strategy. However, the suggested technique takes a theoretical approach to picture degradation, treating the degraded image as a tainted product. To mark the noise severity and ambient light, two maps are created. In terms of color change, the parameters are adjusted using Bat algorithm with a penalty function. The outcomes of the experiments were compared to seven existing methodologies, as well as an examination of algorithm complexity. These studies back up the suggested approach's efficacy, efficiency, flexibility, and theoretical soundness.
Optimal strategy for multiple-phase estimation under practical measurement with multimode NOON states

Quantum multiple parameter estimation can achieve an enhanced sensitivity beyond the classical limit. Although a theoretical ultimate sensitivity bound for multiple phase estimation is given by the quantum Cramer-Rao bound (QCRB), experimental implementations to saturate the QCRB typically require an impractical setup including entangled measurements. Since it is experimentally challenging to implement an entangled measurement, the practical sensitivity is given by the Cramer-Rao bound (CRB) relevant to the measurement probabilities. Here, we consider the problem of practical sensitivity bound for multiple phase estimation with quantum probe states and a measurement setup without entanglement, which consists of a beam splitter followed by the photon-number-resolving measurement. In this practical measurement scheme, we show that lower CRB can be achieved with a quantum probe state even with higher QCRB.
BaNeP: An End-to-End Neural Network Based Model for Bangla Parts-of-Speech Tagging

In Natural Language Processing, Parts-of-Speech tagging is a vital component that significantly impacts applications like machine translation, spell-checker, information retrieval, and speech processing. In languages such as English and Dutch, POS tagging is considered a solved problem (accuracy: 97%). However, for low-resource languages like Bangla, challenges are still there. In this article, we have proposed a novel RNN-based network named BaNeP to determine parts of speech for Bangla words. The proposed network extracts structural features through a bidirectional LSTM-based sub-network, and intricate contextual relations among words of a sentence are identified through an elaborate weighted context extraction procedure. These features are then combinedly utilized to generate the final Parts-of-Speech prediction. Training the model requires only an annotated dataset vanishing the need for any hand-crafted features. Experimental results on the LDC2010T16 dataset show significant accuracy improvement compared to existing Bangla POS taggers.
An Automated Data Desensitisation System Based on the Middle Platform

Built on top of a big data platform, the Middle Platform develops data through abstraction, sharing, and reuse capabilities to provide data products and data services for upper-level business development. While fully analysing and mining the intrinsic value of data, privacy and sensitive information in the data must also be protected, so the Middle Platform needs a data desensitisation system to ensure the safe and open use of data. In order to solve the problems of high usage costs, low efficiency, and lack of standardised results of desensitisation that exist in conventional data desensitisation systems, an automated desensitisation system with data assets, access control, and desensitisation strategies as the main modules is established using an adaptive method of generating dynamic desensitisation rules, combined with a security monitoring mechanism of sensitivity classification and two-level permissions. The system optimises the configuration structure to obtain stable and reliable desensitisation results and efficiently respond to diverse business needs. Users are able to get rid of complex rule management and focus on the data usage itself.