Reviewerly/exHarmony huggingface dataset:
Measuring the Performance of Ant Colony Optimization Algorithms for the Dynamic Traveling Salesman Problem

Ant colony optimization (ACO) has proven its adaptation capabilities on optimization problems with dynamic environments. In this work, the dynamic traveling salesman problem (DTSP) is used as the base problem to generate dynamic test cases. Two types of dynamic changes for the DTSP are considered: (1) node changes and (2) weight changes. In the experiments, ACO algorithms are systematically compared in different DTSP test cases. Statistical tests are performed using the arithmetic mean and standard deviation of ACO algorithms, which is the standard method of comparing ACO algorithms. To complement the comparisons, the quantiles of the distribution are also used to measure the peak-, average-, and bad-case performance of ACO algorithms. The experimental results demonstrate some advantages of using quantiles for evaluating the performance of ACO algorithms in some DTSP test cases.
A distributed cross-chain mechanism based on notary schemes and group signatures

Blockchain technology has attracted significant attention since its publication. However, most existing blockchain platforms are isolated systems, which makes it difficult to exchange data among different blockchains. The industry has proposed the notary schemes to enhance blockchain interoperability, which can exchange messages among different blockchains through a notary. However, conventional notary schemes are centralized, which suffers from single point of failures. To address these issues, this paper proposes a distributed cross-chain notary model with regulatory mechanisms, based on improved threshold group signatures. We improve the group signature scheme by setting up a group of tracing authorities to achieve distributed tracing. Furthermore, we incorporate this signature scheme into our notary scheme, where a group of notaries signs cross-chain transactions using group signatures. We also introduce a group of regulators to reveal the true identities of malicious notaries when disputes occur. Finally, we prove the security of our signature scheme based on the Random Oracle Model. In our experiments, when the number of notaries is 102 and the threshold is 2/3 of that, it takes 166 ms to set up and generate signature. Also, it takes 161 ms for regulators to reveal the true identities of malicious notaries.
SMAAMA: A named entity alignment method based on Siamese network character feature and multi-attribute importance feature for Chinese civil aviation

With the growth of demand for air-space integrated transportation industry, the civil aviation transportation industry plays an increasingly important role in the national economy and transportation. The diversity and diversified development of civil aviation emergencies have greatly affected the decision-making efficiency for emergencies in civil aviation emergency management systems. Constructing a knowledge graph of civil aviation and improving the reliability and richness of the knowledge graph has become an urgent problem to be solved. There are some problems during the construction of a civil aviation domain knowledge graph such as long entity length, existing hybrid and composite entities, the similarity of the font features of domain entity names, a difference of information between entities, separated coding between entities, and error-prone in the transmission process of coding. So we research the construction method of knowledge graph for civil aviation emergencies to resolve these problems. (1) We propose a font pre-training model and incorporate a character feature layer into the embedding layer. (2) We propose a multi-attribute attention alignment method based on the Siamese network; entities with similar structures in the civil aviation knowledge base are input into the font layer for pre-training; the complete semantic information of entities and the importance of different attributes to entities are learned through two sub-networks respectively to improve the integrity of civil aviation knowledge graph. Experiments were carried out on the self-built aviation emergency and public data sets, respectively. The experimental results show that compared with other models, the F1 value of the proposed model can reach 96.97%, which verifies the feasibility of the model.
N-IoU: better IoU-based bounding box regression loss for object detection

Abstract Object detection is one of the core tasks of computer vision, and bounding box (bbox) regression is one of the basic tasks of object detection. In recent years of related research, bbox regression is often used in the Intersection over Union (IoU) loss and its improved version. In this paper, for the first time, we introduce the Dice coefficient into the regression loss calculation and propose a new measure which is superior to and can replace the IoU. We define three properties of the new measure and prove the theory by mathematical reasoning and analysis of the existing work. This paper also proposes the N-IoU regression loss family. And the superiority of the N-IoU regression loss family is proved by designing simulation experiments and comparative experiments. The main results of this paper are: (1) The proposed new measure is better than IoU which can be used to evaluate bounding box regression, and the three properties of the new measure can be used as a broad criterion for the design of regression loss functions; and (2) we propose N-IoU loss. The parameter n of N-IOU can be debugged, which can be widely adapted to different application scenarios with higher flexibility, and the regression performance is better.
Biometric Authentication Methods on Mobile Platforms

In this work, the authors propose a new biometric authentication system on mobile devices, enhancing security at these terminals and preserving user privacy. The proposed system uses a method of extracting strong features from minutiae with refinement of the method with regard to the further elimination of false minutiae by the calculation of geometric information (orientations and distances between minutiae) to obtain true terminations and stronger bifurcations facilitating the recognition of individuals. A series of tests carried out using a recognition and authentication application allowed us to achieve a false rejection rate of 13.81% and a false acceptance rate of almost zero (0.021%). The authors also propose a security model using hash functions and a random number to make the recognition system revocable, more difficult to compromise and thus reducing the risk of usurpation.
Pre-Cutoff Value Calculation Method for Accelerating Metric Space Outlier Detection

Outlier detection is an important data mining technique. In this article, the triangle inequality of distances is leveraged to design a pre-cutoff value (PCV) algorithm that calculates the outlier degree pre-threshold without additional distance computations. This algorithm is suitable for accelerating various metric space outlier detection algorithms. Experimental results on multiple real datasets demonstrate that the PCV algorithm reduces the runtime and number of distance computations for the iORCA algorithm by 14.59% and 15.73%, respectively. Even compared to the new high-performance algorithm ADPOD, the PCV algorithm achieves 1.41% and 0.45% reductions. Notably, the non-outlier exclusion for the first data block in the dataset is significantly improved, with an exclusion rate of up to 36.5%, leading to a 23.54% reduction in detection time for that data block. While demonstrating excellent results, the PCV algorithm maintains the data type generality of metric space algorithms.
Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques

Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.
QALD-10 – The 10th challenge on question answering over linked data

Knowledge Graph Question Answering (KGQA) has gained attention from both industry and academia over the past decade. Researchers proposed a substantial amount of benchmarking datasets with different properties, pushing the development in this field forward. Many of these benchmarks depend on Freebase, DBpedia, or Wikidata. However, KGQA benchmarks that depend on Freebase and DBpedia are gradually less studied and used, because Freebase is defunct and DBpedia lacks the structural validity of Wikidata. Therefore, research is gravitating toward Wikidata-based benchmarks. That is, new KGQA benchmarks are created on the basis of Wikidata and existing ones are migrated. We present a new, multilingual, complex KGQA benchmarking dataset as the 10th part of the Question Answering over Linked Data (QALD) benchmark series. This corpus formerly depended on DBpedia. Since QALD serves as a base for many machine-generated benchmarks, we increased the size and adjusted the benchmark to Wikidata and its ranking mechanism of properties. These measures foster novel KGQA developments by more demanding benchmarks. Creating a benchmark from scratch or migrating it from DBpedia to Wikidata is non-trivial due to the complexity of the Wikidata knowledge graph, mapping issues between different languages, and the ranking mechanism of properties using qualifiers. We present our creation strategy and the challenges we faced that will assist other researchers in their future work. Our case study, in the form of a conference challenge, is accompanied by an in-depth analysis of the created benchmark.
Hydrogeological conditions in the Botevgrad Kettle

Groundwater in the Botеvgrad Kettle (West Bulgaria) is important for the local population. Despite the information available for this area, the catchment area of this kettle is poorly studied from a hydrogeological perspective. Therefore, the objective of this current study is to assess the hydrogeological conditions based on existing information for the purpose of better water resource utilization. Using GIS and data from the Geological Map 1:100000 and Hydrogeological Map 1:200000, water types in the Botеvgrad Kettle watershed and their spatial distribution in the area have been determined. The primary focus is on the region's most water-abundant hydrogeological unit, the Quaternary aquifer. Using various GIS layers that characterize spatial changes in hydrogeological characteristics, a conceptual model for this horizon has been developed, facilitating the initiation of activities related to numerical modeling.
Deep learning based sarcasm detection and classification model

Social media is becoming a crucial part of our everyday lives, whether it’s for product advertising, developing brand value, or reaching out to users. At the same time, sentiment analysis (SA) is a method for determining the emotions associated with online information. The main obstacle to SA’s success is the presence of sarcasm in the text. Previous studies on the identification of sarcasm use lexical and pragmatic signs such as interjection, punctuation, and sentimental change, amongst others. Deep learning (DL) models can be used to learn the lexical and contextual aspects of informal language because handcrafted features cannot be generalised. In addition, word embedding can be used to train the DL models and provide effective results on big datasets at the same time. Optimal Deep Learning based Sarcasm detection and classification using an ODL-SDC method is presented in this study. ODL-SDC analyses social media data to look for and classify any sarcasm that may have been used there. In addition, the Glove embedding approach is used to transform feature vectors. A approach known as the chaotic crow search optimization on deep belief network (CCSO-DBN) is also used to classify and detect satire. Many benchmark datasets were used to evaluate the ODL-SDC method, and the results show it to be more effective than existing approaches in a number of performance metrics.
Critical Control Points Course Detection Methodology at University Education

The research presents an innovative study based on adding value to the university educational process. Currently, there is no clear definition to detect at which stage of a career students lag behind after failing a specific subject or activity. An educational innovation proposal, based on the extrapolation of the Hazard Analysis and Critical Control Point
Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulness

Online product reviews (OPR) are a commonly used medium for consumers to communicate their experiences with products during online shopping. Previous studies have investigated the helpfulness of OPRs using frequency-based, linguistic, meta-data, readability, and reviewer attributes. In this study, we explored the impact of robust contextual word embeddings, topic, and language models in predicting the helpfulness of OPRs. In addition, the wrapper-based feature selection technique is employed to select effective subsets from each type of features. Five feature generation techniques including word2vec, FastText, Global Vectors for Word Representation (GloVe), Latent Dirichlet Allocation (LDA), and Embeddings from Language Models (ELMo), were employed. The proposed framework is evaluated on two Amazon datasets (Video games and Health &amp; personal care). The results showed that the ELMo model outperformed the six standard baselines, including the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. In addition, ELMo achieved Mean Square Error (MSE) of 0.0887 and 0.0786 respectively on two datasets and MSE of 0.0791 and 0.0708 with the wrapper method. This results in the reduction of 1.43% and 1.63% in MSE as compared to the fine-tuned BERT model on respective datasets. However, the LDA model has a comparable performance with the fine-tuned BERT model but outperforms the other five baselines. The proposed framework demonstrated good generalization abilities by uncovering important factors of product reviews and can be evaluated on other voting platforms.
Nonlocal phase modulation of multimode, continuous-variable twin beams

We investigate experimentally the nonlocal phase modulation of multiple-frequency-mode, continuous-variable entangled twin beams. We use a pair of electro-optical phase modulators to modulate the entangled probe and conjugate light beams produced by four-wave mixing in hot rubidium vapor. A single phase modulator in either one of the twin beams reduces the two-mode squeezing signal. The overall quantum entanglement is preserved, however, as the modulator nonlocally distributes the beam correlations among frequency modes of the multimode fields. The two-mode squeezing can be recovered by reversing the mixing with an additional out-of-phase electro-optical phase modulator (EOM) in the other beam.
An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification

Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the overall sentiment derived from the textual content is inadequate. Consequently, sentiment analysis was developed to extract nuanced expressions from textual information. One of the challenges in this field is effectively extracting emotional elements using multi-label data that covers various aspects. This article presents a novel approach called the Ensemble of DenseNet based on Aquila Optimizer (EDAO). EDAO is specifically designed to enhance the precision and diversity of multi-label learners. Unlike traditional multi-label methods, EDAO strongly emphasizes improving model diversity and accuracy in multi-label scenarios. To evaluate the effectiveness of our approach, we conducted experiments on seven distinct datasets, including emotions, hotels, movies, proteins, automobiles, medical, news, and birds. Our initial strategy involves establishing a preprocessing mechanism to obtain precise and refined data. Subsequently, we used the Vader tool with Bag of Words (BoW) for feature extraction. In the third stage, we created word associations using the word2vec method. The improved data were also used to train and test the DenseNet model, which was fine-tuned using the Aquila Optimizer (AO). On the news, emotion, auto, bird, movie, hotel, protein, and medical datasets, utilizing the aspect-based multi-labeling technique, we achieved accuracy rates of 95%, 97%, and 96%, respectively, with DenseNet-AO. Our proposed model demonstrates that EDAO outperforms other standard methods across various multi-label datasets with different dimensions. The implemented strategy has been rigorously validated through experimental results, showcasing its effectiveness compared to existing benchmark approaches.
DEFINING HUMAN EXPERIENCE IN VIRTUAL REPLICATED CULTURAL LANDSCAPES: A CASE OF KASHI VISHWANATH TEMPLE, VARANASI

'This paper aims to explore the relationship between physicality and the idea of virtuality with major concern of human experience in physical space by taking a case study of Kashi Vishwanath Temple, Varanasi. To examine the role of cognition when there is a virtual replication of existing cultural heritage. The following queries will be addressed in this paper:What will the future of human experience in virtual cultural landscapes be like?What are the factors that impact human experience in virtual cultural landscapes?How will design respond to the human experience in virtual landscapes keeping the cultural landscape in mind?What will be the components that need to be incorporated in the creation of virtual spaces of existing cultural landscapes.'
Augmented Intelligence in Service Design: Developing an AI-Powered Assistant for Enhanced Efficiency and Big Data Utilisation

As emerging technologies continue to shape our world, service designers must navigate the complexities and debates that arise by revising their methods and embracing a mindset of lifelong learning. This article proposes a practical roadmap for developing an AI-powered assistant that can help service designers create more customised solutions, minimise uncertainty, and view data as a catalyst for creativity rather than an obstacle. Augmented intelligence fosters human-machine collaboration and yields positive outcomes that are not achievable independently. However, it is crucial to be prepared before undergoing a paradigm shift.
Communication Tools for Reducing Language Barriers Between Primary School Teachers and Immigrant Parents: A Case Study in Turkiye from Teachers’ Perspectives

Previous studies found that Syrian immigrant parents and Turkish primary school teachers struggle to maintain effective communication due to language barriers. This situation has negatively affected Syrian students and the cohesion within the local community. This paper discusses primary school teachers’ viewpoints and examines existing solutions that might help reduce these barriers. Interviews and card-sorting activities designed based on Human-Centred Design principles were carried out with nine Turkish primary school teachers. The results suggested the current communication has been one-way (from teachers to parents). There is a need to make it two ways and help both parties develop better understanding of each other. Hence, the researcher would like proposes that school management team should bring parents to school more often through appropriate social events. Besides this, parent-teacher communication needs to be supported by an interpreter and WhatsApp. Further investigation will be carried out with parents to gain their points of view.
Needs of musicians during performance, without electronic amplification, in Concert Halls and Opera Houses

Drawing on experience of having listened to music in 60 concert halls and opera houses around the World, conducted experimental stage and orchestra pit modifications for live performance with audiences, analyzed the onset characteristics of musical instruments and their attack sequences; manipulated the distribution of acoustic energy using stage furniture with performer responses; and taught acoustics for 40 years, the author offers insights into the design of stages and orchestra pits for the performance of music without electronic amplification, in the context of his experience as a musician, architect, and engineer. In the orchestra pit of the Sydney Opera House: A second desk first violinist said he could not hear his instrument before the furniture was installed; and said after the furniture had been installed, “I am like thunder!” The tympani player asked, “What have you done? My instruments are alive!” In the Macquarie Auditorium, a violinist playing her Stradivarius violin said, “I used to dread playing in that auditorium now I am soaring.” The Australian Chamber orchestra refused to make a recording in the Gore Hill ABC TV Studio, without the stage furniture. What was done in several auditoria and responses from musicians is described in the paper.
A mathematical study about the sustaining phenomenon of overtone in Flageolet harmonics on bowed string instruments

Flageolet harmonics is a playing technique, in which a player lightly touches a nodal point on a string with their finger. Previous studies have reported that the harmonic sound sustains for a short time after the finger is removed from the string while flageolet harmonics is performed on bowed string instruments. However, the mechanism of this harmonics-sustaining phenomenon and the parameter dependency of its sustaining time remains unclear. The purpose of this study is to mathematically investigate the dependence of sustaining time on parameters related to violin playing and string characteristics, and thereby, to elucidate the mechanism of the harmonics-sustaining phenomenon. To this end, a mathematical model was devised by incorporating the effects of bowing and touching into a one-dimensional wave equation. Subsequently, numerical simulation were performed to analyze the behavior of the model. The devised model successfully reproduced the harmonics-sustaining phenomenon in which the parameter dependence of sustaining time was qualitatively consistent with the author’s empirical observations. It was found that the parameter dependence of sustaining time follows the power law. Furthermore, dimensional analysis was performed, yielding a formula that expresses the relationship between the sustaining time and the maximum and minimum bow force required to generate Helmholtz motion.
On the key generation in $\mathbf{SQISign}$

SQISign is an isogeny-based signature scheme that has short keys and signatures and is expected to be a post-quantum scheme. Its security depends on the hardness of the problem to find an isogeny between given two elliptic curves over $\mathbb{F}_{p^2}$,