Reviewerly/exHarmony huggingface dataset:
Welcome to SensoryX 2023

We are excited to welcome you to the third edition of the SensoryX workshop on multisensory experiences. Building on the success of the previous two workshops (also co-located with IMX) in New York and Aveiro, the current workshop examines different aspects of multisensory design - from authoring tools to the evaluation of multisensory experiences - with the aim of identifying the current challenges and opportunities of mulsemedia.
Problem-Based Learning Application Research in Logistics Simulation Software Course Teaching

Intelligent logistics is the current trend of industry development, and for the cultivation of logistics talents in universities, courses related to the Internet of Things need to be added. Therefore, the research on the application of problem-based learning (PBL) in the teaching of logistics practical simulation software courses is a topic worth exploring. This study utilizes commonly used software in logistics courses to implement problem-based learning teaching method in the classroom for students. Observing students' learning situation and obtaining feedback on problem-based learning teaching method is of substantial help to students' learning.
Intelligent auditing techniques for enterprise finance

Abstract With the need of social and economic development, the audit method is also continuously reformed and improved. Traditional audit methods have defects of comprehensively considering various risk factors, and cannot meet the needs of enterprise financial work. To improve the effectiveness of audit work and meet the financial needs of enterprises, a solution for intelligent auditing of enterprise finance is proposed, including intelligent analysis of accounting vouchers and of audit reports. Then, Bi-directional Long Short-Term Memory (BiLSTM) neural network is used to classify the audit problems under three text feature extraction methods. The test results show that the accuracy, recall rate, and F 1 value of the COWORDS-IOM algorithm in the aggregate clustering of accounting vouchers are 85.12, 83.28, and 84.85%, respectively, which are better than the self-organizing map algorithm before the improvement. The accuracy rate, recall rate, and F 1 value of Word2vec TF-IDF LDA-BiLSTM model for intelligent analysis of audit reports are 87.43, 87.88, and 87.66%, respectively. This shows that the proposed method has good performance in accounting voucher clustering and intelligent analysis of audit reports, which can provide guidance for the development of enterprise financial intelligence software to a certain extent.
XTR algorithm based encryption for Security in computer

The idea of XTR was first presented by Lenstra et al. in Crypto 2000. The efficient and compact subgroup trace representation is referred to as XTR Discrete logarithm (DLP) is considered as the basis for the security of the XTR. cipher system. It is regarded as a good substitute for the RSA and elliptic curve cryptosystems. We shall go into great detail on the mathematics underlying XTR and its uses in cryptography in this book chapter. Keywords \s• Cryptography\s• XTR
On the Localization of Ultrasound Image Slices Within Point Distribution Models

Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US). Longitudinal nodule tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. This task, however, imposes a substantial cognitive load on clinicians due to the inherent challenge of maintaining a mental 3D reconstruction of the organ. We thus present a framework for automated US image slice localization within a 3D shape representation to ease how such sonographic diagnoses are carried out. Our proposed method learns a common latent embedding space between US image patches and the 3D surface of an individual’s thyroid shape, or a statistical aggregation in the form of a statistical shape model (SSM), via contrastive metric learning. Using cross-modality registration and Procrustes analysis, we leverage features from our model to register US slices to a 3D mesh representation of the thyroid shape. We demonstrate that our multi-modal registration framework can localize images on the 3D surface topology of a patient-specific organ and the mean shape of an SSM. Experimental results indicate slice positions can be predicted within an average of 1.2 mm of the ground-truth slice location on the patient-specific 3D anatomy and 4.6 mm on the SSM, exemplifying its usefulness for slice localization during sonographic acquisitions. Code is publically available: https://github.com/vuenc/slice-to-shape .
The Pacific Innovations, Knowledge, and Opportunities (PIKO) Program: A Data Lifecycle Research Experience.

Pacific evidence-based clinical and translational research is greatly needed. However, there are research challenges that stem from the creation, accessibility, availability, usability, and compliance of data in the Pacific. As a result, there is a growing demand for a complementary approach to the traditional Western research process in clinical and translational research. The data lifecycle is one such approach with a history of use in various other disciplines. It was designed as a data management tool with a set of activities that guide researchers and organizations on the creation, management, usage, and distribution of data. This manuscript describes the data lifecycle and its use by the Biostatistics, Epidemiology, and Research Design core data science team in support of the Center for Pacific Innovations, Knowledge, and Opportunities program.
Role of Artificial Intelligence for Pedestrian Detection in IoV: A Systematic Review

Internet of Vehicles (IoV), is the internet on wheels having sensors, On Board Units (OBUs), and users to create a communication network. The IoV lessens road fatalities, pedestrian deaths, traffic jams, and road congestion. These applications significantly improve the development of Intelligent Transport Systems (ITS) and increase the transportation efficiency, safety, and global value of automobiles. Therefore, to assure the safety of pedestrians and decrease the risk of accidents, pedestrian detection in IoV plays a significant role. This paper provides detailed insights into existing research on pedestrian detection techniques and the role of pedestrian detection in IoV. Furthermore, a comparison between traditional, deep learning-based, and hybrid approaches has been discussed with its advantages and limitations.
Solar Energy Monitoring and Management System Using IoT

Solar Energy has the potential to significantly reduce our dependence on fossil fuels and mitigate the environmental impacts associated with conventional non-renewable energy resources. Building a solar power system for individual houses can be a great way to promote the use of renewable energy at the household level. However, managing the energy generated by solar panels may not always be sufficient to meet all the energy needs of a household. Integrating solar energy with conventional power supply and managing the electrical load effectively is a key challenge in maximizing the utilization of solar energy. This paper proposes an Internet of Things (IoT) based method for managing solar energy while supplying loads alongside conventional grid.This paper proposes a solar energy monitoring and management system that presents an innovative approach to optimize solar energy utilization, improve system performance, and contribute to sustainable energy management practices. The successful implementation of this work has the potential to bring significant environmental, social, and economic benefits, paving the way for a more sustainable and renewable energy landscape.
Beyond Hyperparameter Optimization: Using AI to address Digital Implementation Challenges

From a technology standpoint, machine learning has been extremely successful, improving the quality of digital implementation. In particular, Cadence’s product in this space (called Cerebrus Intelligent Chip Explorer) has demonstrated incredibly powerful results, enabling many production designs. This talk discusses how and why hyperparameter optimization can be so effective. However, there is clearly more work to do as we seek to use AI to solve explicit design problems and scale to multiple blocks and applications.
The Effectiveness of Using Tells Strategy on Students’ Reading Comprehension

This research aims to find out the significant effect of using TELLS strategy toward students’ reading comprehension. Based on the researcher’s preliminary study, it was found that the students could not comprehend the meaning of texts in their text book at the school. This problem was caused by some factors, some students could not understand about the content of reading text and identify the detailed information of the text. The design of this research was quasi- experimental design, non-equivalent control group design. The total number of populations was 62 students. The researcher used cluster sampling by taking two groups only as sample; group VII-J consisted of 31 students as experimental group and group VII-K consisted of 31 students as control group, totally 62 students. To analyze the data, the researcher adopted ANCOVA formula by using SPSS 21. From calculating the hypothesis test, it is known the result of significant value of group class is 0,023 which is lower than 0,05 (p&lt;.05), therefore there is significant different between experimental and control class. Point out the ANCOVA analysis, the “corrected model” value is reviewed. The result of corrected model is 0,000 which is lower than 0,05 (p&lt;.05). This is also supported by the mean value of the experimental class of 82.45 which is higher than the control class of 66.97. In conclusion, TELLS strategy is more effective on students reading comprehension for the first grade at MTsN 2 Kediri.
Veterinary Equipment

A full list of veterinary equipment is given in this chapter. In many developing countries second-hand human medicine equipment is available, but in essence the author has concentrated on equipment which is not going to cost a large amount.
Federated learning for generating synthetic data: a scoping review

IntroductionFederated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format. ObjectivesThe objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps. MethodsA scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk. ResultsA total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data. ConclusionsFederated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.
Research on Video Anomaly Detection Based on Cascaded Memory-augmented Autoencoder

&lt;p&gt;Video anomaly detection (VAD) is crucial in public safety and intelligent video surveillance systems and has been widely researched and applied in academia. This paper proposes a video anomaly detection method based on the Cascaded Memory-augmented Autoencoder (CMAAE). CMAAE stores feature prototypes of standard samples in a memory pool and embed multiple memory-enhancing modules in the encoder-decoder structure. SE attention is introduced into memory modules to improve their performance, and skip connections are used to share attention weights among memory modules, enabling the model to learn more comprehensive feature information and enhance the quality of reconstructed video frames. Multiple loss constraint models are used during training to improve anomaly detection accuracy. CMAAE achieves outstanding performance of 99.2% on the UCSD Ped2 dataset and 89.4% on the CUHK Avenue dataset, demonstrating the effectiveness of our approach.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Research on Curriculum Reform and Teaching Mode of Vocational Education Based on Learning Theory

At present, China is in a critical stage of rapid economic and social transformation and upgrading, and global scientific and technological innovation and industrial change present an unprecedented profound impact. In this context, the importance of vocational education is further emphasized. The traditional model of vocational education is faced with the rapid evolution of technology, knowledge, and market, and new occupational positions and skill requirements have emerged in many fields, posing brand new challenges to the education system and teaching mode. Guided by the learning theory, it has become an important issue in the field of vocational education nowadays to cultivate vocational talents with more comprehensive quality and innovative ability through curriculum reform and teaching mode innovation. The article explores the curriculum reform and teaching mode innovation of vocational education based on learning theory, to provide a reference for relevant staff.
Secure Federated Learning with a Homomorphic Encryption Model

Federated learning (FL) offers collaborative machine learning across decentralized devices while safeguarding data privacy. However, data security and privacy remain key concerns. This paper introduces "Secure Federated Learning with a Homomorphic Encryption Model," addressing these challenges by integrating homomorphic encryption into FL. The model starts by initializing a global machine learning model and generating a homomorphic encryption key pair, with the public key shared among FL participants. Using this public key, participants then collect, preprocess, and encrypt their local data. During FL Training Rounds, participants decrypt the global model, compute local updates on encrypted data, encrypt these updates, and securely send them to the aggregator. The aggregator homomorphic ally combines updates without revealing participant data, forwarding the encrypted aggregated update to the global model owner. The Global Model Update ensures the owner decrypts the aggregated update using the private key, updates the global model, encrypts it with the public key, and shares the encrypted global model with FL participants. With optional model evaluation, training can iterate for several rounds or until convergence. This model offers a robust solution to Florida data privacy and security issues, with versatile applications across domains. This paper presents core model components, advantages, and potential domain-specific implementations while making significant strides in addressing FL's data privacy concerns.
The Trends of Cybersecurity and Its Emerging Challenges in Africa

The cost of cybersecurity is steadily increasing, both in terms of the overall loss to the organisation and in terms of the number of cyberattacks. Every day, new cyber threat types and features appear in the worldwide environment. The hackers have progressed to the point where they come up with novel and creative ways to attack systems, networks, services, and data to steal valuable IT resources. It is impossible to be careless in the field of cybersecurity. Cybersecurity experts and administrators must constantly be on guard against possibly harmful events because new types of cyberattacks are constantly being developed. However, in the digital age, hacking has become a reality. The danger is extremely escalating. Cyber-intrusions are increasing in frequency, cost, and level of sophistication. African countries have recently suffered from an increase in cyber threats. The continent is experiencing an increase in cyberattacks, which can be ascribed to weak cybersecurity measures and vulnerable systems. In many African countries, cybersecurity is a luxury rather than a requirement. Its significance in the continent is still not adequately understood or acknowledged. According to reports, many organizations’ cybersecurity budgets are less than 1%, and many of them have no funding at all. In light of the foregoing, the chapter will look at the cybersecurity issue in Africa and the various cyber threats that have emerged recently. The chapter will also provide an overview of the African Union Convention on CyberSecurity and Personal Data Protection and the difficulties the continental organisation has faced in addressing the cybersecurity issue to reduce the threat level, which is on the rise. The chapter will conclude by identifying different approaches to tackling the problems of hacking in Africa.
Compact Encoding of Reified Triples Using HDTr

Contextual information about a statement is usually represented in RDF knowledge graphs via reification: creating a fresh ‘anchor’ term that represents the statement and using it in the triples that describe it. Current approaches make the connection between the reified statement and its anchor by either extending the RDF syntax, resulting in non-compliant RDF, or via additional triples to connect the anchor with the terms of the statement, at the cost of size and complexity. This work tackles this challenge and presents HDTr, a binary serialization format for reified triples that is model-agnostic, compact, and queryable. HDTr is based on, and compatible with, the counterpart HDT format, leveraging its underlying structure to connect the reified statements with the terms that represent them. Our evaluation shows that HDTr improves compression and retrieval time of reified statements w.r.t. several triplestores and HDT serialization of different reification approaches.
Discussing Job Satisfaction and Organizational Perceptions: Voice of Practitioners

To explore determinants of job satisfaction and factors influencing organizational perceptions using qualitative literature and focus group discussions (FGDs) with cross-departmental managers. From 55 articles on ScienceDirect and Google Scholar, 12 pivotal studies (2018-2023) were analysed. Topics ranged from cultural aspects in the Arab world to challenges of work-life balance in the digital age. The research unveils intertwined personal, organizational, and societal dynamics impacting job satisfaction and organizational perceptions. Highlighted is the need for strategies addressing these complexities to boost job satisfaction across sectors. This study uniquely merges academic perspectives with practitioners' voices, offering an enriched viewpoint bridging theory and real-world experiences, making it a valuable reference for both academic discourse and practical strategy development.
Analisis Dalam Pendukung Keputusan Seleksi Content Creator Mahasiswa Terbaik Menerapkan Metode EDAS dan ROC

One of the uses of technology is social media, which is an application that can convey information to many people. Around 49 percent of Indonesia's population are active users of social media because the use of social media as a means of promotion is considered easier and more efficient to get a wider target audience. In creating content on social media, a quality content creator is needed. The selection of student content creators at Budi Darma University in Medan is important to be able to get students who are creative and have the potential to become social media content creators. In selecting content creators, students at Budi Darma University have several predetermined criteria, namely the number of social media, the number of tiktok followers, the number of Instagram followers, content production per day, content creativity and content design updates. In the process of selecting student content creators, a system is needed that can speed up and simplify the process of selecting student content creators, namely the SPK (Decision Support System). In this study the EDAS (Evaluation Based on Distance from Average Solution) method and the Rank Oder Centroid (ROC) method used are expected to help the selection process for accepting student content creators. So the best alternative to become a student content creator lies in alternative A8 on behalf of Arsyillah with a score of 182001
Salvation into Nation: Topic Modeling Early Modern Economic Writings

Early modern economic thought is often left out of histories of economic thought, but it was an important inflection point in the development of the modern science of economics. Using network clustering and topic modeling, this research describes the subjects covered in the economic literature of the early modern era and how they change over time using a sample of 2,353 economics texts written from 1550 to 1700s. Topic modeling of the corpus produces sixty-six distinct topics. These topics are clustered using network science methods that reveal five main themes: trade, politics, travel, religion, and husbandry. Longitudinal analysis of the prevalence of topics shows increases in politics and decreases in travel, religion, and husbandry. The results also allow a more precise sense of when those shifts take place than has previously been established, implying that computational methods hold great promise for revealing new information and insights about the evolution and transformation of economic thought over time.
Projective Activities of Future Chemistry Teachers to Create Individual Educational Products

This article discusses the issues of increasing the professional readiness of future chemistry teachers on the basis of projective activities. One of the indicators of a teacher’s readiness for classes is the preparation of didactic materials. At the same time, the use of digital technologies opens up great prospects for the educational process. A methodology for creating educational products in the process of projective activity is proposed.