Reviewerly/exHarmony huggingface dataset:
Optimizing Business Processes through Information System Integration A Case Study in Banda Aceh

This research aims to optimize business processes through information system integration using a case study in Banda Aceh. This research uses a qualitative approach with a single case study research method. Data was collected through in-depth interviews, direct observation and documentation. Research respondents consist of managers and employees involved in the business processes to be integrated. Data analysis was carried out using content analysis. The research results show that information system integration significantly increases the efficiency and effectiveness of business processes in Banda Aceh. Information system integration enables faster data processing, better monitoring of business processes, and more accurate decision making. Additionally, information system integration also enables better collaboration between different departments and stakeholders. Based on these findings, it can be concluded that information system integration plays an important role in optimizing business processes in Banda Aceh. By integrating information systems, companies can increase operational efficiency, reduce costs, and increase customer satisfaction. However, the implementation of information systems integration also faces challenges, such as technical difficulties and changes in organizational culture. Therefore, strong commitment and support from company management is needed to achieve optimal results.&#x0D;
Effects of blockchain technology (BT) on the university librarians and libraries: a systematic literature review (SLR)

Purpose The objectives of the study were to identify the effects of blockchain technology (BT) on the university librarians, the impact of BT on the university library services and to reveal the challenges to adopt BT in the university libraries. Design/methodology/approach A systematic literature review was applied to address the objectives of the study. Around 25 studies published in peer-reviewed journals were selected to conduct the study. Findings The findings of the study revealed that blockchain technology (BT) has positive effects on the university librarians as it assists them in digital resources management, provision of integrated library services, effective records management and continued professional development. The study also displayed that BT has a positive impact on the university libraries through effective information management, user privacy, collaboration, technological innovation and access control. Results also revealed that technical issues, financial constraints, security problems, skill issues and sociocultural issues created challenges to adopt BT in the university libraries. Originality/value The study has offered theoretical implications for future investigators through the provision of innovative literature on the prospectus and challenges associated with blockchain in the context of librarianship. The study has also provided practical implications for management bodies by offering recommendations for the successful adoption of blockchain in the university libraries. Additionally, a framework has been developed to adopt BT successfully in the university libraries for the delivery of smart library services to library patrons.
Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry

Abstract As the volume of telemetry data generated by satellites and other complex systems continues to grow, there is a pressing need for more efficient and accurate anomaly detection methods. Current techniques often rely on human analysis and preset criteria, presenting several challenges including the necessity for expert interpretation and continual updates to match the dynamic mission environment. This paper critically examines the use of deep anomaly detection (DAD) methods in addressing these challenges, evaluating their efficacy on real-world spacecraft telemetry data. It exposes limitations in current DAD research, highlighting the tendency for performance results to be overestimated and suggesting that simpler methods can sometimes outperform more complex DAD algorithms. By comparing established metrics for anomaly detection with newly proposed ones, this paper aims to improve the evaluation of DAD algorithms. It underscores the importance of using less accuracy-inflating metrics and offers a comprehensive comparison of DAD methods on popular benchmark datasets and real-life satellite telemetry data. Among the DAD methods examined, the LSTM algorithm demonstrates considerable promise. However, the paper also reveals the potential limitations of this approach, particularly in complex systems that lack a single, clear predictive failure channel. The paper concludes with a series of recommendations for future research, including the adoption of best practices, the need for high-quality, pre-split datasets, and the investigation of other prediction error methods. Through these insights, this paper contributes to the improved understanding and application of DAD methods, ultimately enhancing the reliability and effectiveness of anomaly detection in real-world scenarios.
The Future of Marketing: The Transformative Power of Artificial Intelligence

This research explores the profound impact of Artificial Intelligence (AI) on the marketing landscape, examining its evolution from early applications in data analytics to contemporary uses. AI’s role in transforming marketing strategies is pivotal, encompassing personalized customer experiences, predictive analytics, and efficiency improvements. The study highlights AI’s ability to process vast data sets rapidly, reshaping customer engagement and market analysis. Focusing on the multifaceted integration of AI into marketing, the research emphasizes its contribution to customer personalization, fostering brand loyalty, and boosting conversion rates. Predictive analytics, another cornerstone, enables businesses to craft strategies aligned with future market dynamics proactively. Despite its advantages, ethical considerations surrounding data privacy and consumer consent are pivotal, requiring responsible and transparent AI use. The study is a comprehensive resource for academic researchers and industry professionals, providing insights into historical development, current applications, and ethical implications. It offers organizations a clear roadmap for leveraging AI effectively in marketing operations amid the growing reliance on digital platforms and expanding data availability. Anticipating future developments, the research discusses the convergence of AI with augmented reality, virtual reality, predictive customer journeys, and the Internet of Things. Balancing technological advancements with ethical considerations remains crucial, emphasizing the need for ongoing adaptation in this dynamic landscape. The synergy between AI and marketing propels businesses into a new era of precision, personalization, and strategic insight, redefining customer connections. In this transformative journey, embracing change, innovation, and ethical practices becomes paramount for sustained success.
Zero Trust and Data Engineering

Abstract Pankaj Gupta, a data analytics engineer from Discover Financial Services, puts zero trust under the microscope and explores why it's necessary, how it works and what the concept might mean for the future of security.
Cybersecurity Challenges In The Era Of Digital Transformation A Comprehensive Analysis Of Information Systems

Increased digitalization in business transformation has provided significant benefits, but has also created new challenges for information security. This research aims to analyze the cyber security challenges faced in the era of digital transformation, with a focus on Information Systems. The research method used is a comprehensive analysis of literature and case studies to gain an in-depth understanding of these issues. In this research, we found that there are a number of significant challenges related to cyber security in implementing digital transformation, including malware attacks, phishing, insufficient network security, and privacy violations. Causal analysis concluded that insufficient training and security awareness, as well as weak design in information systems, were the main risk factors. The implication of this research is the need for proactive steps in implementing strong security practices in information systems, including adequate training for employees, careful security monitoring, and early warning of security threats. In addition, the importance of collaboration between organizations, governments and related entities in facing increasingly global threats was also highlighted.
Politicization of Hate and Weaponization of Twitter/X in a Polarized Digital Space in Nigeria

Hate speech on social media platforms during electioneering campaign has been institutionalized. The campaign hate-filled rhetoric continues unabated. Some have attributed this phenomenon to access to social media. This study therefore investigates the prevalence of hate speech on Twitter in Nigeria. Twitter API was used to generate data that was later content analyzed. Anchored on the technological determinism theory, the study revealed political periods saw the prevalence of hate speech and political, ethnic, religion and regional-based hate speech are the dominant themes on hate discourse on Twitter. Some regions in Nigeria weaponized Twitter to denigrate others while others used it to promote their agitation but in the process also resort to hate speech. The study argued that Twitter is just a technology in the hand of hate-filled people who used it to promote their bigotry and bile. This study recommends the development of a legal framework for the regulation of hate speech on social media. Also, the utterance of politicians must be guarded, and the electoral acts must be fully implemented.
Impact of age and comorbid heart failure on the utility of smart voice-assistant devices

Abstract Aims The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyze the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and Results Subgroup analysis (HF vs Caregivers and younger vs older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, median age was 51 (34-65) years and 21 (40%) were HF patients. The HF subgroup showed a significant lower percentage of agreement compared with caregivers (95% vs 99%, p=0.03), and both the HF and older subgroup tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use comparing with caregivers and younger individuals. Conclusions This subgroup analysis highlights important differences in the performance of a voice-assistant based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Trial registration number: NCT04508972.
Beyond Quantum Threats: Bridging Fully Homomorphic Encryption Schemes

In the advent of quantum computing, fully homomorphic encryption (FHE), whose security is based on the hardness of lattice problems, gains significant importance due to its quantum-resistant nature. This work provides a comprehensive survey of recent advancements in bridges between FHE schemes, with an emphasis on practical applications.
VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.
Comparison and improvement of some attention mechanisms in the video field

This paper compares the effects of SE-Net and the improved CBAM attention mechanism, and proposes a CTSA (Channel and Temporal and Spatial Attention) attention mechanism that adds temporal attention for temporal features, and further proposes a TSA (Time-domain and Spatial Attention) attention mechanism that focuses on time-domain on the basis of comparing the effects of multiple attention mechanisms. The experimental results show that in the field of behaviour recognition, the TSA attention mechanism designed in this paper can achieve better recognition results when using a lighter weight network structure.
The Use of Digital and Disruptive Technologies in the Artificial Intelligence Era

This study aims to analyze the emergent and disruptive digital technologies in an era of artificial intelligence (IA). It departs from the assumption that the emerging technologies create both opportunities and challenges with an impact on individuals, organizations, institutions, and society at large in terms of bias, surveillance, hacking, etc. The method employed is the critical analysis based on the recent developments reported in scientific literature. It is concluded that control over the ethical disruption of digital technologies and more specifically artificial strategy must require a digital conversation and leadership about acceptable ethical behaviors under the assumption that digital transformation cannot be interrupted and must be guided by humans.
PQC and QKD Are Both Required to Enable a Quantum-Safe Future

Quantum Key Distribution (QKD) and Post Quantum Cryptography (PQC) are often incorrectly assumed to be competitors of one another. In reality, they are complementary technologies aimed at solving different problems, with each having strengths and weaknesses that the other does not have. In this article, we will briefly summarise the current state of QKD and PQC, highlighting the advantages, disadvantages, and limitations of each technology stack. Finally, we explain our take on how both these technologies can be combined to provide better solutions that are better than any solution that uses solely PQC or QKD.
An Inductive Heterogeneous Graph Recommendation Model for High-Scoring Items Applied to Business Intelligence

Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution and direction of data and assists them in making correct decisions. The core of this feature is visual analysis charts, which need to be pre-created and integrated into the dashboard by the chart makers, so there are situations where user needs cannot be accurately grasped. At the same time, there may be omissions in the work of users, and a method is needed to remind them. Introducing recommendation models into data visualization is a good solution; therefore, this paper proposes a recommendation model suitable for this type of scenario, which recommends high-scoring items (charts) to users. This model consists of a inductive heterogeneous graph recommendation algorithm with user preferences and a slow-acting collaborative filtering method. The experimental results in two datasets showed an improvement of 0.020/0.045, 0.083/0.019 and 0.076/0.023 in Hit, F-score, and NDCG compared to baselines, which proves that it is more suitable for data visualization requirements and other similar scenarios that require inductive recommendations based on user preferences.
Adaptive variational ground state preparation for spin-1 models on qubit-based architectures

We apply the adaptive variational quantum imaginary time evolution (AVQITE) method to prepare ground states of one-dimensional spin $S=1$ models. We compare different spin-to-qubit encodings (standard binary, Gray, unary, and multiplet) with regard to the performance and quantum resource cost of the algorithm. Using state-vector simulations, we study two well-known spin-1 models: the Blume-Capel model of transverse-field Ising spins with single-ion anisotropy, and the XXZ model with single-ion anisotropy. We consider system sizes of up to 20 qubits, which corresponds to spin-1 chains up to length 10. We determine the dependence of the number of cnot gates in the AVQITE state preparation circuit on the encoding, the initial state, and the choice of operator pool in the adaptive method. Independent of the choice of encoding, we find that the cnot gate count scales cubically with the number of spins for the Blume-Capel model and quartically for the anisotropic XXZ model. However, the multiplet and Gray encodings present smaller prefactors in the scaling relations. These results provide useful insights for the implementation of AVQITE on quantum hardware.
Unlabeled learning algorithms and operations: overview and future trends in defense sector

Abstract In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field.
Genesis and Prospecting Potential of the Da’anhe Skarn Au Deposit in the Central of the Lesser Xing’an Range, NE China: Evidence from Skarn Mineralogy, Fluid Inclusions and H-O Isotopes

Skarn Au deposits exist in the circum-pacific metallogenic belt. Interestingly, the Da’anhe Au deposit is the only independent skarn gold deposit in the Lesser Xing’an Range. To determine the metallogenic mechanism and prospecting potential of the Da’anhe deposit, we performed skarn mineralogy, fluid inclusion (FI) and H-O isotope analyses. The results show the following: (1) The Da’anhe deposit is a calcareous reduced skarn Au deposit that formed between an Early Jurassic gabbroic diorite and the Permian Tumenling Formation marble. Its metallogenic process includes five stages: the early skarn stage (Stage I1), late skarn stage (Stage I2), early quartz-sulfide stage (Stage II1), late quartz-sulfide stage (Stage II2) and quartz-carbonate stage (Stage II3). Gold precipitated in Stage II1 and Stage II2. (2) The initial ore-forming fluid was derived from magmatic water and featured a high temperature and intermediate to high salinity. After boiling and mixing, the fluid eventually changed to a low-temperature and low-salinity reducing fluid dominated by meteoric water. (3) The formation depth of the Au orebodies was 2.27-3.11 km, and the orebodies were later lifted to the surface (&lt;500 m). The potential for finding skarn Au deposits in the study area is limited. (4) The distinctive nature of the ore-related magma (i.e., source, reducing conditions and high water content) was key to the formation of the Da’anhe skarn gold deposit.