Reviewerly/exHarmony huggingface dataset:
New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.
VGG16Unet: effective passive model for image splicing forgery localization

In this digital era, images are the essential medium of communication. They may be found on magazine covers, legal evidence, newspapers, and social media. With the advancement of numerous editing tools, image modification will pose a serious concern due to the spread of information. Image splicing is one of the leading strategies in image forgery. While there are several approaches to solving the problem, some have flaws, such as the accuracy of faked region localization. We present an effective image-splicing forgery localization model called VGG16Unet. The VGG16Unet is an encoder-decoder architecture for the localization of image-spliced forgery. The pre-trained VGG16 is used as an encoder part of the architecture, and our network’s decoder part is inspired by UNet architecture. The faked images and their respective masks are sent over the network in form of patches to train the model. This model is tested on the widely used dataset of image splicing forgery, i.e., NIST and CASIA v2. The experimental result illustrates that VGG16Unet’s performance of localizing the image-spliced forged regions is more effective than prior methods.
Modelling guidance in software engineering: a systematic literature review

Despite potential benefits in Software Engineering, adoption of software modelling in industry is low. Technical issues such as tool support have gained significant research before, but individual guidance and training have received little attention. As a first step towards providing the necessary guidance in modelling, we conduct a systematic literature review to explore the current state of the art. We searched academic literature for guidance on model creation and selected 35 papers for full-text screening through three rounds of selection. We find research on model creation guidance to be fragmented, with inconsistent usage of terminology, and a lack of empirical validation or supporting evidence. We outline the different dimensions commonly used to provide guidance on software and system model creation. Additionally, we provide definitions of the three terms modelling method, style, and guideline as current literature lacks a well-defined distinction between them. These definitions can help distinguishing between important concepts and provide precise modelling guidance.
Progressive residual feedback network with additional constraints for single image super-resolution

Recent studies have shown that the method combining the back-projection mechanism and deep convolutional neural network can well solve the problem of interdependence between high- and low-resolution images in single image super-resolution (SISR). However, this method still suffers from problems, such as large number of parameters and blurred detail textures. To address these problems, we propose a progressive residual feedback network with additional constraints for SISR. Specifically, we design a progressive residual structure that improves the densely connected network. This structure not only ensures feature reuse between each layer of the dense network through residual connections but also reduces the network complexity through progressive up-sampling reconstruction. In addition to enhance the detailed texture of the reconstructed image, we construct a spatial feature projection block based on the coordinate attention mechanism, so the extracted features contain spatial information that is relevant to the image reconstruction. Finally, we simulate the image degradation process with a degradation block to provide additional degradation constraints for the up-sampling network and optimize the convergence state-of-the-network. Numerous experimental results have proven the effectiveness of our method, which have advantages in both recovery quality and network complexity compared with other state-of-the-art SISR methods.
Advancing database security: a comprehensive systematic mapping study of potential challenges

Abstract The value of data to a company means that it must be protected. When it comes to safeguarding their local and worldwide databases, businesses face a number of challenges. To systematically review the literature to highlight the difficulties in establishing, implementing, and maintaining secure databases. In order to better understand database system problems, we did a systematic mapping study (SMS). We’ve analyzed 100 research publications from different digital libraries and found 20 issues after adopting inclusion and exclusion criteria. This SMS study aimed to identify the most up-to-date research in database security and the different challenges faced by users/clients using various databases from a software engineering perspective. In total, 20 challenges were identified related to database security. Our results show that “weak authorization system”, “weak access control”, “privacy issues/data leakage”, “lack of NOP security”, and “database attacks” as the most frequently cited critical challenges. Further analyses were performed to show different challenges with respect to different phases of the software development lifecycle, venue of publications, types of database attacks, and active research institutes/universities researching database security. The organizations should implement adequate mitigation strategies to address the identified database challenges. This research will also provide a direction for new research in this area.
Pengembangan Media Dokansi untuk Meningkatkan Motivasi Siswa pada Mata Pelajaran Bahasa Indonesia

This research was conducted to describe students’ responses in using Dokansi media on fable text material, and the Dokansi’s eligibility as a media on learning fable text material. Using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) methodology, this type of research is known as Development (Research and Development) research. However, the application and evaluation of this research are limited by the information gathered from experts in the media, literature, and academia. This research project was carried out at SMPN. 2 Gunungsitoli. A questionnaire is used to collect data. The subjects in this study were class VII-A. This method is carried out to collect the results of the study and assessment of students’ responses to the media used.
Flexemes in theory and in practice

Abstract This paper provides an in-depth investigation of the possibility of systematically using flexemes – i.e., lexical units characterized in terms of form, as opposed to lexemes, characterized in terms of meaning – to model overabundance – i.e., the availability of more than one form in the same paradigm cell. The starting point is a preliminary evaluation of the advantages and disadvantages of using flexemes to account for different overabundance phenomena, showing that flexemes are a good way to capture the systematicity of overabundance, either across lexemes or across cells. Consequently, it is suggested that flexemes can be an interesting technical solution for the creation of a lexicon of Latin verbs that not only documents all the competing wordforms available as principal parts, but also captures the systematic relationship that sometimes holds between variants filling different cells. A principled method to identify such systematicity is then described in detail. It is argued that a constructive approach based on the identity of stems and/or inflection class is not fully adequate for the data at hand. Therefore, the proposed procedure adopts an abstractive, word-based perspective that only relies on alternation patterns between unsegmented wordforms. Practical and theoretical implications of the work are finally discussed, particularly regarding the usefulness of a formal approach to the identification of lexical units and paradigm cells.
Estimating the degree of non-Markovianity using variational quantum circuits

Several applications of quantum machine learning (QML) rely on a quantum measurement followed by training algorithms using the measurement outcomes. However, recently developed QML models, such as variational quantum circuits (VQCs), can be implemented directly on the state of the quantum system (quantum data). Here, we propose to use a qubit as a probe to estimate the degree of non-Markovianity of the environment. Using VQCs, we find an optimal sequence of qubit-environment interactions that yield accurate estimations of the degree of non-Markovianity for the amplitude damping, phase damping, and the combination of both models. This work contributes to practical quantum applications of VQCs and delivers a feasible experimental procedure to estimate the degree of non-Markovianity.
SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios

In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.
Spatial system construction and design strategies for sustainable fire-resistant ancient villages

Ancient village fires have caused serious economic and cultural losses, and the construction of sustainable fire-resistant and disaster-resistant spatial systems and design strategies for ancient villages can effectively prevent and control ancient village fires. This paper uses the Dukezong Ancient City as the study area, chooses the hierarchical analysis method to analyze the factors of the 2014 Dukezong Ancient City fire, the risk assessment method to evaluate the riskiness of its post-disaster reconstruction, and proposes corresponding design strategies. The spatial system design strategy for sustainable fire-resistant ancient villages is designed to improve the risk assessment of disaster-causing factors in ancient villages, establish an evaluation mechanism for the regional characteristics of ancient villages, comprehensively improve the fire-fighting power of ancient villages, and establish a sustainable fire-resistant spatial system. It provides a theoretical basis and reference for the study of ancient village protection.
Research on Prediction of Breast Cancer Type using Machine Learning

The most typical cancer type among women worldwide is breast cancer. In 2020 alone, it afflicts about 0.68 million people and 6.9% of all cancer cases. How to categorize tumors as benign (non-cancerous) or malignant (cancerous) is one of the main obstacles to its diagnosis. This study helps to make an accurate and reliable diagnosis based on the initial data of the tumor, such as smoothness, texture, area using machine learning models. This study uses five machine learning models, Logistic Regression (RF), Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and three modelling systems, feature selection-ML and principal component analysis (PCA)-ML system to make predictions of the type of the tumor of Wisconsin Breast Cancer Dataset. Model performance are assessed by three performance evaluation which are accuracy, precision, recall. The results of full model show that random forest has the highest prediction accuracy of 98.25% out of the sample and 100% in the sample, and SVM's sigmoid-based kernel model has the lowest prediction accuracy of 83.33% outside and 85.27% inside the sample. The results of the feature selection model based on RF and LR shows that the RF with only 13 variables has the highest prediction accuracy 98.25% out-of-sample and 100% in-sample. Among all the PCA--ML models, PCA--NBC has the highest prediction accuracy of 97.33% out-of-sample. Nevertheless, PCA-RF has the highest prediction accuracy of 100% in-sample.
Analysis of Path Planning Algorithms for Service Robots Applied in Indoor Environments

Path planning technology is one of the key issues in solving mobile robot navigation technology. On the basis of traditional path planning algorithms, a large number of algorithms have been improved and applied to achieve better results, like fireworks algorithm, ant colony algorithm, A* algorithm, particle swarm algorithm, genetic algorithm, firefly algorithm, and artificial potential field method. The optimization and improvement of algorithms have made today's path planning technology more comprehensive and effectively improved the accuracy and efficiency of indoor service robot path planning. This article analyzes the development status of path planning technology for indoor environment service robot. Firstly, typical application scenarios are listed and the significance of path planning algorithms in them is introduced in combination with their application requirements. Then, some classic indoor scene path planning algorithms and improvement strategies are compared and analyzed. Finally, a summary and discussion of the above indoor environment intelligent robot path planning methods are presented.
Research on bridge detection and safety evaluation system based on BIM

With the improvement of social and economic level, the scale of different types of infrastructure is constantly widening, the social from all walks of life put forward more stringent requirements for the level and quality of construction in our country. At present, many construction accidents occur frequently, and bridge collapses occur from time to time. The development of bridge detection and safety assessment structure system is in the initial stage. The research of bridge structural health detection system started in 1990, and the capital construction project has been slowly developed, and bridge detection has been gradually taken seriously. From a practical point of view, this paper analyzes and expounds the construction of bridge detection and safety assessment system based on BIM in detail.
Domain Vocabulary Alignment using AML and LogMap

Introduction: In the context of the Semantic Web, interoperability among heterogeneous ontologies is a challenge due to several factors, among which semantic ambiguity and redundancy stand out. To overcome these challenges, systems and algorithms are adopted to align different ontologies. In this study, it is understood that controlled vocabularies are a particular form of ontology. Objective: to obtain a vocabulary resulting from the alignment and fusion of the Vocabularies Scientific Domains and Scientific Areas of the Foundation for Science and Technology, -FCT, European Science Vocabulary -EuroSciVoc and United Nations Educational, Scientific and Cultural Organization -UNESCO nomenclature for fields of Science and Technology, in the Computing Sciences domain, tobe used in the IViSSEM project. Methodology: literature review on systems/algorithms for ontology alignment, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses -PRISMA methodology; alignment of the three vocabularies; and validation of the resulting vocabulary by means of a Delphi study. Results: we proceeded to analyze the 25 ontology alignment systems and variants that participated in at least one track of the Ontology Alignment Evaluation Initiative competition between 2018 and 2019. From these systems, Agreement Maker Light and Log Map were selected to perform the alignment of the three vocabularies, making a cut to the area of Computer Science. Conclusion: The vocabulary was obtained from Agreement Maker Light for having presented a better performance. At the end, a vocabulary with 98 terms was obtained in the Computer Science domain to be adopted by the IViSSEM project. The alignment resulted from the vocabularies used by FCT (Portugal), with the one adopted by the European Union (EuroSciVoc) and another one from the domain of Science &amp; Technology (UNESCO). This result is beneficial to other universities and projects, as well as to FCT itself.
Future Applications for Data Storytelling Toolkits

When it comes to translating data as evidence into compelling and memorable arguments, many people need some guidance about how to approach crafting a story. There is great potential for toolkits that would provide such guidance, giving advocates and data analysts tools for communicating data in story form. Data Storytelling is an emerging area in the information sciences, and the term as used here means any form of communication of data that uses narrative strategies or story structures.
Partial visual-semantic embedding: Fine-grained outfit image representation with massive volumes of tags via angular-based contrastive learning

A novel technology named fashion intelligence system, which quantifies ambiguous expressions unique to fashion, such as “casual,” “adult-casual,” and “office-casual,” was previously proposed to support users in their understanding of fashion. However, the existing visual-semantic embedding (VSE) model, which forms the basis of the system, does not support images that are composed of multiple parts, such as those containing hair, tops, trousers, skirts, and shoes. Therefore, we propose a partial VSE (PVSE) model, which enables fine-grained learning of each part of the fashion outfit. The proposed model learns embedded representations via angular-based contrastive learning. This helps in retaining three existing practical functionalities and further enables image-retrieval tasks where changes are only made to specified parts and image-reordering tasks focusing on the specified parts. In other words, the proposed model enables five types of practical functionalities, even with a simple structure. Through qualitative and quantitative experiments, we demonstrate that the proposed model is superior to conventional models, without increasing computational complexity.
Extracting biomedical relation from cross-sentence text using syntactic dependency graph attention network

In biomedical literature, cross-sentence texts can usually express rich knowledge, and extracting the interaction relation between entities from cross-sentence texts is of great significance to biomedical research. However, compared with single sentence, cross-sentence text has a longer sequence length, so the research on cross-sentence text information extraction should focus more on learning the context dependency structural information. Nowadays, it is still a challenge to handle global dependencies and structural information of long sequences effectively, and graph-oriented modeling methods have received more and more attention recently. In this paper, we propose a new graph attention network guided by syntactic dependency relationship (SR-GAT) for extracting biomedical relation from the cross-sentence text. It allows each node to pay attention to other nodes in its neighborhood, regardless of the sequence length. The attention weight between nodes is given by a syntactic relation graph probability network (SR-GPR), which encodes the syntactic dependency between nodes and guides the graph attention mechanism to learn information about the dependency structure. The learned feature representation retains information about the node-to-node syntactic dependency, and can further discover global dependencies effectively. The experimental results demonstrate on a publicly available biomedical dataset that, our method achieves state-of-the-art performance while requiring significantly less computational resources. Specifically, in the "drug-mutation" relation extraction task, our method achieves an advanced accuracy of 93.78% for binary classification and 92.14% for multi-classification. In the "drug-gene-mutation" relation extraction task, our method achieves an advanced accuracy of 93.22% for binary classification and 92.28% for multi-classification. Across all relation extraction tasks, our method improves accuracy by an average of 0.49% compared to the existing best model. Furthermore, our method achieved an accuracy of 69.5% in text classification, surpassing most existing models, demonstrating its robustness in generalization across different domains without additional fine-tuning.
SCMD suspicious cryptographic message detection

The proposed framework aims to prevent global crimes, specifically terrorist attacks, bomb blasts, and drone attacks, by using the Suspicious Cryptographic Message Detection (SCMD) system to detect and decrypt suspicious messages. Terrorists often use encrypted messages to communicate with their teammates in different parts of the world, making it difficult for authorities to monitor their activities. The framework addresses this issue by detecting with gradient boosting algorithm and cryptanalyzing by using Simple Substitution Cipher techniques to decrypt suspicious messages and predict potential criminal activity. The framework utilizes various emerging trends in information technology, including machine learning techniques, pre-defined decision rules, wordnet, and semantic web ontology, to predict the type of crime, the criminal's name and location, and other criminal details. It comprises multiple components, including Semantic web Ontology, Suspicious Database, and machine learning techniques, to facilitate the detection and reporting of criminal activity. When the server receives a suspicious encrypted message, the framework detect and decrypts it and predicts the type of crime based on microblogs before it can be executed by the criminals. The details of the criminals are then alerted to the cybercrime department, reducing the burden on security departments and enhancing public safety. In summary, the framework provides a comprehensive solution to the challenges posed by encrypted messages in criminal activities. It uses advanced technologies to identify and predict potential criminal activities, enabling law enforcement agencies to take preemptive actions to prevent major and minor attacks, including terrorist attacks.