Reviewerly/exHarmony huggingface dataset:
A searchable encryption scheme with hidden search pattern and access pattern on distributed cloud system

Dynamic searchable encryption methods allow a client to perform searches and updates over encrypted data stored in the cloud. Recent schemes enable secure searching over an encrypted database stored in a server, but these schemes only focus on hiding the access pattern using ORAM. Although the traditional Oblivious Random Access Machine (ORAM) can hide the access pattern, which refers to the set of documents that match the client’s queries, it also incurs significant communication overhead and cannot hide the search pattern. Existing researches show that the general dynamic searchable symmetric encryption (DSSE) scheme is vulnerable to statistical attacks due to the leakage of both search patterns and access patterns. Therefore hiding the access pattern is not enough, it is essential to hide both access patterns and search patterns with high efficiency. To overcome this limitation, a DSSE scheme called obliviously shuffled incidence matrix DSSE (OSM-DSSE) is proposed in this paper to access the encrypted data obliviously without using ORAM. The OSM-DSSE scheme realizes efficient search and update operations based on an incidence matrix. In particular, a shuffling algorithm using Paillier encryption combines the 1-out-of-n obliviously transfer (OT) protocol to hide access pattern and an optimized padding scheme to obfuscate the search pattern with low storage overhead. Besides, Simulation results and security analysis confirm that OSM-DSSE scheme achieves high security and efficient searches. Also, this scheme provides adaptive security against malicious attacks by adversaries. Furthermore, OSM-DSSE is capable of searching for a keyword out of $$9 \times 10^{10}$$ keyword-file pairs within 2.19 s which is 3–4 × better execution efficiency than the state-of-art solutions.
ChatGPT: Cross cultural tourism research imperative

Aim/purpose – Cultural differences play an influential role in driving tourism develop- ment and varying outcomes of technology acceptance. The impact of ChatGPT on tour- ism is yet to be explored. Therefore, this study discussed how ChatGPT may influence tourism development and management in different cultural contexts. Design/methodology/approach – Drawing specifically on individualism and collectiv- ism from Hofstede’s cultural dimensions theory, this study compared the differences in the perception of ChatGPT between Oriental and Western cultures. Findings – Individualism and collectivism explain the diversity of perception on ChatGPT between Oriental and Western cultures. Research implications/limitations – Implications and suggestions are provided to aca- demia and industry accordingly. Particularly, the difference between technological con- cepts and tools is highlighted to avoid having excessive expectations toward tools. Originality/value/contribution – This study extends the research on tourism and Artifi- cial Intelligence and advocates additional attention to the imperative of cross-cultural differences regarding ChatGPT and tourism. Keywords: ChatGPT, large language models, tourism, cross-cultural analysis. JEL Classification: L83, L86, M14, O33, Z32.
PENERAPAN PEMBELAJARAN PROBLEM BASED LEARNING (PBL) DENGAN ALAT PERAGA UNTUK MENINGKATKAN HASIL BELAJAR MATERI PEMBAGIAN

This study aims to describe the increase in student learning outcomes in dividing learning using teaching aids in class IV SDN Lowokwaru 2 Malang City. This study used the Problem Based Learning (PBL) learning model in the form of classroom action research and was carried out in two cycles. Data collection techniques were direct observation techniques and measurement techniques. Data collection tools used observation sheets and tests. The results of research on the teacher's ability to compose teaching modules on average cycle 1 reached 3.14 increased in cycle II reached 3.71, the ability of teachers to carry out learning in cycle 1 received an average of 2.92 increased in cycle II reached an average of 3, 75 while student learning outcomes in cycle I averaged 67.67 with the completeness of 15 people increased in cycle II with an average value of 87.50 with 27 students' completeness. The data above shows the teacher's ability to compile and carry out learning and student learning outcomes in learning mathematics about the distribution of using visual aids in class IV students has increased.
WEAPON DETECTION USING ARTIFICIAL INTELLIGENCE AND DEEP LEARNING FOR SECURITY APPLICATIONS

Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas.Abnormal detection and monitoring have major applications of computer vision to tackle various problems.Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring.This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms.Proposed implementation uses two types of datasets.One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually.Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy. I.
Reducing CNOT count in quantum Fourier transform for the linear nearest-neighbor architecture

Physical limitations of quantum hardware often necessitate nearest-neighbor (NN) architecture. When synthesizing quantum circuits using the basic gate library, which consists of CNOT and single-qubit gates, CNOT gates are required to convert a quantum circuit into one suitable for an NN architecture. In the basic gate library, CNOT gates are considered the primary cost of quantum circuits due to their higher error rates and longer execution times compared to single-qubit gates. In this paper, we propose a new linear NN (LNN) circuit design for quantum Fourier transform (QFT), one of the most versatile subroutines in quantum algorithms. Our LNN QFT circuit has only about 40% of the number of CNOT gates compared to previously known LNN QFT circuits. Subsequently, we input both our QFT circuits and conventional QFT circuits into the Qiskit transpiler to construct QFTs on IBM quantum computers, which necessitate NN architectures. Consequently, our QFT circuits demonstrate a substantial advantage over conventional QFT circuits in terms of the number of CNOT gates. This outcome implies that the proposed LNN QFT circuit design could serve as a novel foundation for developing QFT circuits implemented in quantum hardware that demands NN architecture.
Accelerating Convolutional Neural Network by Exploiting Sparsity on GPUs

The convolutional neural network (CNN) is an important deep learning method, which is widely used in many fields. However, it is very time consuming to implement the CNN where convolution usually takes most of the time. There are many zero values in feature maps and filters, which leads to redundant calculations and memory accesses if dense methods are used to compute convolution. Many works recently have made use of sparsity to skip the calculations for zero values to reduce the inference time of the CNN. On the graphics processing unit platform, current works cannot fully exploit the sparsity of the feature map and achieve satisfactory performance. Therefore, we design a new parallel strategy to transform the feature map into a new storage format to avoid the redundant computation of zero values on graphics processing units. Also considering the sparsity in the feature map, we propose a fused storage format to combine the convolution operation with the following pooling operation, to further improve the performance. We carry out experiments with mainstream CNN models and achieve better performance compared with cuDNN and cuSPARSE. For VGG-19, ResNet-50, DenseNet-121, and RegNetX-16GF, 1.97×, 2.23×, 2.74×, and 1.58× speedups respectively are obtained over cuDNN. The speedups over cuSPARSE respectively are 2.10×, 1.83×, 2.35×, and 1.35× when only using the first method.
Social Honeypot for Humans: Luring People Through Self-managed Instagram Pages

Social Honeypots are tools deployed in Online Social Networks (OSN) to attract malevolent activities performed by spammers and bots. To this end, their content is designed to be of maximum interest to malicious users. However, by choosing an appropriate content topic, this attractive mechanism could be extended to any OSN users, rather than only luring malicious actors. As a result, honeypots can be used to attract individuals interested in a wide range of topics, from sports and hobbies to more sensitive subjects like political views and conspiracies. With all these individuals gathered in one place, honeypot owners can conduct many analyses, from social to marketing studies. In this work, we introduce a novel concept of social honeypot for attracting OSN users interested in a generic target topic. We propose a framework based on fully-automated content generation strategies and engagement plans to mimic legit Instagram pages. To validate our framework, we created 21 self-managed social honeypots (i.e., pages) on Instagram, covering three topics, four content generation strategies, and three engaging plans. In nine weeks, our honeypots gathered a total of 753 followers, 5387 comments, and 15739 likes. These results demonstrate the validity of our approach, and through statistical analysis, we examine the characteristics of effective social honeypots.
Post-it: Augmented Reality Based Group Recommendation with Item Replacement

AR shopping has attracted significant attention with the emergence of popular AR applications, e.g., IKEA Place and Nike AR Fit. However, most AR stores merely provide auxiliary information for each user, instead of group recommendations that leverage new AR features. In this paper, we make the first attempt to explore AR shopping to simultaneously: i) leverage flexible display and item replacement to maximize preference, ii) construct an immersive environment with virtual item haptic feedback, and iii) stimulate social interactions with common user interests. We formulate the Tangible AR Group Shopping (TARGS) problem to partition users and recommend virtual substitutes. We then develop the Social-aware Tangible AR Replacement and Recommendation (STAR3) system, including a connected graph neural network for modeling graph features from different domains, a virtual-physical item mapping model to enable haptic experience by leveraging on-shelf items' passive haptic feedback, and a multi-goal recommender to dynamically split user groups and recommend substitutes for satisfaction maximization. Experimental results manifest that STAR3 outperforms baselines by $$23\%$$ to $$63\%$$ in three real-world datasets and a user study.
Impact of Corporate Social Responsibility on Financial Performance; Evidence from Banking Sector of Pakistan

The financial sector has grown significantly Corporate Social Responsibility (CSR) is becoming a priority in today's competitive climate major concern in addition to improving profitability.Companies are regarded as social units that must serve their stakeholders and generally carry out CSR with a priority on future disclosure.The main objective of this study is to investigate how stakeholders and agency theory influence how CSR affects the Financial Performance (FP) of Pakistan's banking sector from 2008 to 2020, a 13-year period, 32 banks that were listed on the Pakistan Stock Exchange (PSX), comprised our sample.As proxies for financial performance we employed accounting-based and market-based measures in our analysis.The Panel Least Square (PLS) model was used by the researcher to examine how CSR affects FP.Evidence supports the validity of the least squares model, which showed that CSR has a positive and substantial impact on Earnings Per Share (EPS) and Market Value Per Share (MVPS), but a negative and significant impact on Return on Equity (ROE), Return on Asset (ROA) and TOBIN's Q.The findings show that adequate CSR policy execution and disclosure the improvement of Pakistan's listed banks' financial performance and the reduction of resource wastage from CSR investment activities.The research's findings help investors and other shareholders to have peace of mind by providing a solid, practical, and dependable impact of CSR on the financial sector of developing nations.This leads in a better knowledge of CSR practices in the financial sector of an emerging country.
Image–text coherence and its implications for multimodal AI

Human communication often combines imagery and text into integrated presentations, especially online. In this paper, we show how image-text coherence relations can be used to model the pragmatics of image-text presentations in AI systems. In contrast to alternative frameworks that characterize image-text presentations in terms of the priority, relevance, or overlap of information across modalities, coherence theory postulates that each unit of a discourse stands in specific pragmatic relations to other parts of the discourse, with each relation involving its own information goals and inferential connections. Text accompanying an image may, for example, characterize what's visible in the image, explain how the image was obtained, offer the author's appraisal of or reaction to the depicted situation, and so forth. The advantage of coherence theory is that it provides a simple, robust, and effective abstraction of communicative goals for practical applications. To argue this, we review case studies describing coherence in image-text data sets, predicting coherence from few-shot annotations, and coherence models of image-text tasks such as caption generation and caption evaluation.
What Quality Aspects Influence the Adoption of Docker Images?

Docker is a containerization technology that allows developers to ship software applications along with their dependencies in Docker images. Developers can extend existing images using them as base images when writing Dockerfiles. However, a lot of alternative functionally equivalent base images are available. Although many studies define and evaluate quality features that can be extracted from Docker artifacts, the criteria on which developers choose a base image over another remain unclear. In this article, we aim to fill this gap. First, we conduct a literature review through which we define a taxonomy of quality features, identifying two main groups: configuration-related features (i.e., mainly related to the Dockerfile and image build process), and externally observable features (i.e., what the Docker image users can observe). Second, we ran an empirical study considering the developers’ preference for 2,441 Docker images in 1,911 open source software projects. We want to understand how the externally observable features influence the developers’ preferences, and how they are related to the configuration-related features. Our results pave the way to the definition of a reliable quality measure for Docker artifacts, along with tools that support developers for a quality-aware development of them.
Real-time Satellite Anomaly Data Tagging Based on DAE-LSTM

Abstract Spacecraft is the main carrier of human exploration of outer space, exploration and understanding of the Earth and the universe, and the development of spaceflight can promote human civilization andsocial development, and can meet the nee-ds of economic construction, scientific and technological development, security construction, social progress and other aspects. The current global number of satellites in orbit reaches 5,465, of which China has 541. The vigorous development of the space industry symbolizes the steady improvement of the country’s comprehensive national power and overall technology. During the operation, the satellite in orbit needs to transmit data to the ground, these data may be subject to interference from various aspects, or even equipment failure, we find these data in real time is very important to reduce losses. The data transmitted by satellite has obvious temporal characteristics, and Long Short-Term Memory (LSTM) network has obvious advantages for processing temporal data, so this paper proposes a BER marking model based on the combination of LSTM network and self-coding technology. By comparing the data before and after noise reduction, a threshold value can be determined, and the BERs can be accurately distinguished by this method. After testing with real satellite temperature data, the accuracy of the model detection reaches a high level.
Answering Why and When?

Immersive analytics (IA) is a fast-growing research field that concerns improving and facilitating human sense making and data understanding through an immersive experience. Understanding the suitable application scenario that will benefit from IA enables a shift towards developing effective and meaningful applications. This paper aims to explore tasks and scenarios that can benefit from IA by conducting a systematic review of existing studies and mapping them according to the multi-level typology for abstract visualization tasks, which is also known as the what-why-how framework. The study synthesizes several works to answer the why within the context of multiple levels of specificity. In addition, this study also explores the application domains and IA guiding scenarios to address when scenarios best integrate with IA. Then, the paper discusses the IA evaluation types and research methods to evaluate an IA application that can promote effective user engagement in IA. Finally, the limitations and potential future works are discussed.
Transfer Learning approach for Classification of Cervical Cancer based on Histopathological Images

Cervical Cancer is the fourth most prominent type of cancer in which occurrence and death rates are steadily rising, particularly in developing nations of the world. Cervical adenocarcinoma (CADC) and cervical squamous cell carcinoma (CSCC) are two distinct subtypes of cervical cancer (CC) with different genetic profiles, risk factors, and clinical behaviors. An essential benchmark for the diagnosis of cervical cancer subtypes (CADC and CSCC) is hematoxylin and eosin (H& E) histopathological whole-slide images (WSIs). Incorrect identification and misclassification frequently arise from the uniform appearance of pathological cervical images, the substantial number of interpretations, the lengthy and tedious process of reading, and the pathologist’s inadequate work experience. To overcome these limitations, computer-aided diagnosis (CAD) technologies, such as traditional machine learning (ML) and deep learning (DL), have recently have been employed to identify patterns that are helpful for medical diagnosis. In previous years, transfer learning (TL) achieved tremendous achievements in the field of DL, and the use of TL technology in cervical histopathology categorization became an emerging research area. The objective of this research is to create an automated, comprehensive system for identifying cervical cancer subtypes (CADC and CSCC) from histopathology images using TL. This study used 59 high-resolution WSIs, 45 WSIs were used to train the model, and 14 were used for testing. The proposed model obtained an accuracy of 85% and an AUC score of 86%.
Explainable Articial Intelligence for Cybersecurity in Smart Manufacturing

Industry 4.0 was first presented in 2011 and has revolutionized manufacturing in enormous applications by integrating artificial intelligence (AI), the Internet of Things (IoT), cloud computing, and other leading technologies. As technology continues to grow and expand, the concept of a new Industry 5.0 paradigm could be investigated. Industry 5.0 aims to transform the manufacturing sector into a more sustainable, human-centric, and resilient manufacturing industry. In this chapter, we demonstrate research for Cybersecurity in Smart Manufacturing in Industry 5.0 by leveraging AI and Explainable Artificial Intelligence (XAI) techniques. This chapter especially presents several essential perspectives for a potential approach of XAI to enable Smart manufacturing in the Industrial Revolution 5.0. There also is an illustrative example demonstrating the XAI approach for anomaly detection in the cyber network of an Industrial Control System in the Smart Manufacturing context.
Efficient and Trustworthy Federated Learning-Based Explainable Anomaly Detection: Challenges, Methods, and Future Directions

Artificial Intelligence (AI) and especially Machine Learning (ML) are the driving energy behind industrial and technological transformation. With the transition from industry 4.0 to 5.0, smart manufacturing proves the efficiency in industry, where systems become increasingly complex, producing massive data, necessitating more demand for transparency, privacy, and performance. Federated learning has demonstrated its effectiveness in various applications, however, there are still exist certain challenges that should be addressed. Thus, in this chapter, a comprehensive perspective on federated learning-based anomaly detection is provided. The problems have posed concerns and should be taken into account when researching and deploying. Then, our perspectives about efficient and trustworthy federated learning-based explainable anomaly detection systems are demonstrated as an end-to-end unified framework. Finally, to provide a complete picture of future research direction, the quantum aspect is introduced in the subject of machine learning.
Dependency Update Strategies and Package Characteristics

Managing project dependencies is a key maintenance issue in software development. Developers need to choose an update strategy that allows them to receive important updates and fixes while protecting them from breaking changes. Semantic Versioning was proposed to address this dilemma, but many have opted for more restrictive or permissive alternatives. This empirical study explores the association between package characteristics and the dependency update strategy selected by its dependents to understand how developers select and change their update strategies. We study over 112,000 Node Package Manager (npm) packages and use 19 characteristics to build a prediction model that identifies the common dependency update strategy for each package. Our model achieves a minimum improvement of 72% over the baselines and is much better aligned with community decisions than the npm default strategy. We investigate how different package characteristics can influence the predicted update strategy and find that dependent count, age, and release status to be the highest influencing features. We complement the work with qualitative analyses of 160 packages to investigate the evolution of update strategies. While the common update strategy remains consistent for many packages, certain events such as the release of the 1.0.0 version or breaking changes influence the selected update strategy over time.