Reviewerly/exHarmony huggingface dataset:
Bi-directional quantum teleportation of GHZ-like states

In this paper, we propose a method through which [Formula: see text]-qubit states can simultaneously be bi-directionally transmitted between two users. We assume that Alice and Bob, the legitimate users, each have a [Formula: see text]-qubit GHZ-like state and want to teleport it to the other party. Also, a four-qubit cluster state plays the role of the quantum channel of this bi-directional quantum teleportation. The protocol is based on the method that at first, each user, through a series of [Formula: see text] gates, converts the [Formula: see text]-qubit state into a single qubit and some [Formula: see text] qubits. Then, by means of the Bell state measurement and proper operation, the single qubit state is transferred over the channel between the two sides. By re-applying the [Formula: see text] gates on the transmitted qubits and auxiliary [Formula: see text] states, each user reconstructs the initial GHZ-like state. Finally, we investigate the effects of some kind of noises on the density matrix of the channel due to its interaction with the environment and present a method to protect the channel against the bit-flip error.
Iterated AGM Revision Based on Probability Revision

Abstract Close connections between probability theory and the theory of belief change emerge if the codomain of probability functions is extended from the real-valued interval [0, 1] to a hyperreal interval with the same limits. Full beliefs are identified as propositions with a probability at most infinitesimally smaller than 1. Full beliefs can then be given up, and changes in the set of full beliefs follow a pattern very close to that of AGM revision. In this contribution, iterated revision is investigated. The iterated changes in the set of full beliefs generated by repeated revisions of a hyperreal probability function can, semantically, be modelled with the same basic structure as the sphere models of belief change theory. The changes on the set of full beliefs induced by probability revision satisfy the Darwiche–Pearl postulates for iterated belief change.
SGCN: A scalable graph convolutional network with graph-shaped kernels and multi-channels

Graph neural networks (GNNs) have demonstrated great success in graph processing. However, current message-passing-based GNNs have limitations in terms of feature aggregations and update mechanisms that rely on a fixed mode, resulting in inadequate representations of the neighborhood structure’s richness. Furthermore, the convolution layer in most GNNs lacks flexibility and multiple channels compared to convolutional neural networks (CNNs), which employ multiple channels, where different kernels capture shared patterns of images. To address these limitations, we propose a novel scalable graph convolution network (SGCN) to enhance structural information learning by introducing an expressive feature aggregation mechanism. Drawing inspiration from the design of CNNs, the SGCN includes graph-shaped kernels that perform multichannel convolutions on the subgraph structure. A sampler based on degree centrality is employed to simplify the computation costs, and extensive experiments demonstrate that the SGCN achieved state-of-the-art performances on graph datasets with various scales (with accuracy improvements of up to 5.38%).
*-chain: A framework for automating the modeling of blockchain based supply chain tracing systems

Nowadays, creating a blockchain-based system for supply chain tracing is a complex task. This paper defines a model, a graphical domain specific language, and a set of tools aimed at helping supply chain domain experts to create blockchain based tracing systems for their supply chains. Starting from a graphical representation of the supply chain, the solidity smart contracts implementing the related tracing system are automatically generated by our framework. Small interventions of programmers are required to customize and finalize such smart contracts. A set of web based interfaces to interact with such smart contracts are also automatically generated. We are confident that our results will increase blockchain usage for supply chain traceability thanks to the automatic process of smart contract generation.
Weaknesses of ICT integration in the initial teacher education curriculum

The article presents a Portuguese national study on integrating information and communication technologies (ICT) in initial teacher education courses (ITE), which sought to highlight to what extent ICT are presently considered in the training curriculum of future teachers. The study was developed on a documental corpus of 819-course syllabus sheets of 45 master's courses, which were analysed through a methodology supported by content analysis procedures. The results achieved were also put under analysis, considering the European Framework for Educators' Digital Competence (DigCompEdu), and a shy presence of technologies was identified in the analysed curricula. Regarding DigCompEdu, which functions as a common framework to be followed in the European Union, the results show that this is configured as another factor to be remedied in the context of integrating ICT in ITE in Portugal. Among the contributions that this study promotes is the finding of the reduced presence of ICT identified in the context under study, a reality familiar to several countries, which implies in curricular terms, initial teacher training is still ineffective in preparing teachers to meet the needs of today's digital society.
Bengali Movie Recommendation System using K Nearest Neighbor and Cosine Similarity

In this fast-paced world, it is important for each individual to have some form of entertainment that can help them rejuvenate and regain their energy. The reliance we acquire from entertainment allows us to work harder and enthusiastically. A movie can be one of the finest sources of this entertainment. But finding a good movie can be hectic sometimes. A movie recommendation system can provide a helpful solution to the problem of searching for preferred movies from a vast array of options. By utilizing such a system, one can easily discover movies that match their preferences, which saves time and reduces stress associated with the search process. As a result, it is essential that the system for suggesting movies to us is very trustworthy and gives us recommendations for the films that are either most similar to or identical to our tastes. This movie recommendation system is established using K-Nearest Neighbour and cosine similarity. The cosine similarity method is capable of bringing together documents that are similar, even if they have a large Euclidean distance between them because of their size. Moreover, the KNN algorithm, which is highly accurate in making predictions, can compete with other precise models. It is utilized to identify groups of individuals with similar movie rating preferences, and predictions are computed by taking an average of the highest k neighboring ratings.
Review on blockchain-based e-voting systems

A democratic society is built on the concept of voting. The majority of nations need voters to physically attend elections and cast paper ballots. That aforementioned constraint, among others, incurred rising administrative costs and declining voting turnout. Due to that, e-voting emerged as a mechanism that uses technology to let citizens cast votes for candidates or participate in group decision-making. The e-voting technology provides faster results, lessens human error, and more ease for persons with disabilities. Due to the process's lower cost and greater openness, e-voting has attracted a lot of interest lately. Some research has been done to create electronic voting systems that use the blockchain as a public polling system, enabling verifiability and self-tallying. The following study aims to provide using Systematic Literature Review (SLR) a thorough examination of several e-voting systems, to recognize, evaluate, and analyze prior research results in order to guide future work and to bear in mind all the needs, benefits, and downsides of the proposed solutions.
Comparative Analysis of Federated Learning and Centralized Approach for Detecting Different Lung Diseases

Access to a large dataset is necessary to improve disease detection with excellent accuracy. However, due to data confidentiality and privacy restrictions, collecting data from hospitals or other organizations is a significant challenge in the healthcare sector. Due to this, Federated Learning (FL), which adopts a decentralized approach, is developed to replace the conventional machine learning methodology in the development of improved screening methods. Since there is no requirement for data to be centralized in federated learning, patient data privacy is ensured. In this paper, we have compared the sequential model and the ensemble model for both federated learning and centralized approach, two different types of models. For each approach, these models were applied to separate X-ray images for the detection of two different lung diseases: lung cancer and tuberculosis. In this paper, we also have showed the analysis of their accuracy and demonstrated how FL can be the most effective strategy through comparison.
FPGA-Based Military Vehicles Detection and Classification from Drone Videos Using YOLOv5

Drones are prevailing in both military and civil applications. Tracking the rivalry from the surveillance videos recorded by the drone has become a necessity for the purpose of reconnaissance. Object detection is one of the most common tasks performed by drones in order to track and classify the objects in the drone video. The proposed model is built in two modules. In the first phase, the object detection model is developed using YOLOv5 which detects and classifies military vehicles such as Military Tanks and Armoured Personnel Carriers (APCs). In the second phase, the object detection model is deployed into the Field Programmable Gate Array (FPGA). FPGAs are frequently used as implementation platforms for real-time image processing applications since their structure can exploit both spatial and temporal parallelism. The proposed system can be implemented by using a complex detection algorithm in a real-time embedded system using Field Programmable Gate Array (FPGA). Along with providing real-time implementation of object detection, FPGA also improves the performance of the detection model by a high measure.
Fuzzy Fingerprinting Large Pre-trained Models

Large pre-trained models like BERT and RoBERTa have gained massive popularity as they have surpassed previous state-of-the-art models in various Natural Language Processing (NLP) tasks. Nevertheless, interpreting their behavior is still an ongoing challenge as these models are composed of millions of parameters. The introduction of the Fuzzy Fingerprint (FFP) framework provided a straightforward classification technique able to deliver result interpretations, however, this method was outperformed by these large pre-trained models. In this work, we introduce a novel method that combines the simplicity of the FFPs with the ability to detect complex patterns of large pre-trained models, in order to build a more interpretable classification framework. Furthermore, we show that it is feasible to obtain unique FFPs for each label that enable the examination of incorrect classifications. We evaluate our new method on four text classification benchmark datasets and show that it is possible to gain interpretability without any noticeable loss in performance.
Adaptive region aggregation for multi‐view stereo matching using deformable convolutional networks

Abstract Deep‐learning methods have demonstrated promising performance in multi‐view stereo (MVS) applications. However, it remains challenging to apply a geometrical prior on the adaptive matching windows to achieve efficient three‐dimensional reconstruction. To address this problem, this paper proposes a learnable adaptive region aggregation method based on deformable convolutional networks (DCNs), which is integrated into the feature extraction workflow for MVSNet method that uses coarse‐to‐fine structure. Following the conventional pipeline of MVSNet, a DCN is used to densely estimate and apply transformations in our feature extractor, which is composed of a deformable feature pyramid network (DFPN). Furthermore, we introduce a dedicated offset regulariser to promote the convergence of the learnable offsets of the DCN. The effectiveness of the proposed DFPN is validated through quantitative and qualitative evaluations on the BlendedMVS and Tanks and Temples benchmark datasets within a cross‐dataset evaluation setting.
Multi-exit DNN inference acceleration for intelligent terminal with heterogeneous processors

Recently, there has been a burgeoning popularity in the deployment of deep learning vision applications upon terminal devices. However, as the number of layers in deep neural networks (DNNs) and structural complexity increase, although the performance of DNN in handling computer vision tasks has become powerful, model inference tasks on computation resource constrained intelligent terminal devices are frequently incapable of meeting latency requirement. A commonly adopted solution to inference acceleration presents multi-exit DNNs to reduce latency via the provision of early exits. However, existing methods do not fully utilize the potential of heterogeneous processors (GPU/CPU) on intelligent terminal devices to cooperatively accelerate multi-exit DNN inference in parallel. Furthermore, the impact of complex image and video input on multi-exit DNNs, as well as the effects of different power consumption modes on processors within intelligent terminal devices, remain inadequately explored. To address these issues, we comprehensively considered the computing performance of heterogeneous processors in different power consumption modes, the structure and characteristic of multi-exit DNNs in inference acceleration, and proposed the Collaborative Inference Acceleration mechanism for intelligent terminal with Heterogeneous Processors (CIAHP). CIAHP includes a deep neural network computation time prediction model and a multi-exit DNN task allocation algorithm with heterogeneous processors. Our experiments demonstrate that CIAHP performs multi-exit DNN inference 2.31× faster than CPU alone, and is 1.23× faster than GPU alone when processing complex image samples.
Transformers are Short-Text Classifiers

Short text classification is a crucial and challenging aspect of Natural Language Processing. For this reason, there are numerous highly specialized short text classifiers. A variety of approaches have been employed in short text classifiers such as convolutional and recurrent networks. Also many short text classifier based on graph neural networks have emerged in the last years. However, in recent short text research, State of the Art (SOTA) methods for traditional text classification, particularly the pure use of Transformers, have been unexploited. In this work, we examine the performance of a variety of short text classifiers as well as the top performing traditional text classifier on benchmark datasets. We further investigate the effects on two new real-world short text datasets in an effort to address the issue of becoming overly dependent on benchmark datasets with a limited number of characteristics. The datasets are motivated from a real-world use case on classifying goods and services for tax auditing. NICE is a classification system for goods and services that divides them into 45 classes and is based on the Nice Classification of the World Intellectual Property Organization. The Short Texts Of Products and Services (STOPS) dataset is based on Amazon product descriptions and Yelp business entries. Our experiments unambiguously demonstrate that Transformers achieve SOTA accuracy on short text classification tasks, raising the question of whether specialized short text techniques are necessary. The NICE dataset showed to be particularly challenging and makes a good benchmark for future advancements. A preprint can be also found on arXiv [14]. Source code is available here: https://github.com/FKarl/short-text-classification .
Conducting Morality and Emotion Analysis on Blog Discourse

Social media has exploded in its usage, since the advancement of technology with individuals sharing their opinions and beliefs on its platform through text. This has led to the access of tons of text data that researchers can use to further understand human behaviour or user perspective. Emotion analysis is a common text analysis technique used to discover embedded emotion in content. For this research, we compare emotion analysis and morality assessment techniques in discovering embedded user opinions. Our results show that emotion analysis possesses limitations in its result. On the other hand, morality assessment provides a more comprehensive and accurate analysis of text data. For this research, we proposed that both methodologies are not mutually exclusive, and can complement each other to better understand the complexities of human communication and behavior.
Fine-Tuning Language Models for Scientific Writing Support

We support scientific writers in determining whether a written sentence is scientific, to which section it belongs, and suggest paraphrasings to improve the sentence. Firstly, we propose a regression model trained on a corpus of scientific sentences extracted from peer-reviewed scientific papers and non-scientific text to assign a score that indicates the scientificness of a sentence. We investigate the effect of equations and citations on this score to test the model for potential biases. Secondly, we create a mapping of section titles to a standard paper layout in AI and machine learning to classify a sentence to its most likely section. We study the impact of context, i. e., surrounding sentences, on the section classification performance. Finally, we propose a paraphraser, which suggests an alternative for a given sentence that includes word substitutions, additions to the sentence, and structural changes to improve the writing style. We train various large language models on sentences extracted from arXiv papers that were peer reviewed and published at A*, A, B, and C ranked conferences. On the scientificness task, all models achieve an MSE smaller than 2%. For the section classification, BERT outperforms WideMLP and SciBERT in most cases. We demonstrate that using context enhances the classification of a sentence, achieving up to a 90% F1-score. Although the paraphrasing models make comparatively few alterations, they produce output sentences close to the gold standard. Large fine-tuned models such as T5 Large perform best in experiments considering various measures of difference between input sentence and gold standard. Code is provided here: https://github.com/JustinMuecke/SciSen .
Dataset versus reality: Understanding model performance from the perspective of information need

Abstract Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real‐world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real‐world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question–answer pairs for the question answering (QA) task, and image‐caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real‐world problem or the research task they intend to tackle.
Medical applications of generative adversarial network: a visualization analysis

Deep learning (DL) is one of the latest approaches to artificial intelligence. As an unsupervised DL method, a generative adversarial network (GAN) can be used to synthesize new data.To explore GAN applications in medicine and point out the significance of its existence for clinical medical research, as well as to provide a visual bibliometric analysis of GAN applications in the medical field in combination with the scientometric software Citespace and statistical analysis methods.PubMed, MEDLINE, Web of Science, and Google Scholar were searched to identify studies of GAN in medical applications between 2017 and 2022. This study was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Citespace was used to analyze the number of publications, authors, institutions, and keywords of articles related to GAN in medical applications.The applications of GAN in medicine are not limited to medical image processing, but will also penetrate wider and more complex fields, or may be applied to clinical medicine. Eligibility criteria were the full texts of peer-reviewed journals reporting the application of GANs in medicine. Research selections included material published in English between 1 January 2017 and 1 December 2022.GAN has been fully applied to the medical field and will be more deeply and widely used in clinical medicine, especially in the field of privacy protection and medical diagnosis. However, clinical applications of GAN require consideration of ethical and legal issues. GAN-based applications should be well validated by expert radiologists.
Graph-Based Recommendation System Enhanced by Community Detection

Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article has considered the time of tag assignments in co-occurrence tags for determining the similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7%, respectively.