Reviewerly/exHarmony huggingface dataset:
Formation stages of the Kholodninskoye pyrite-polymetallic deposit ore zone

The Kholodninskoe pyrite-polymetallic deposit (Baikal-Patom plateau, Russia) was discovered in 1968, but the questions of its genesis still remain controversial. It is assumed that the explosive activity of volcanic apparatuses of the southern part of the Baikal-Muya zone as well as underwater exhalations of the scattered spreading zone of the back-arc basin could have most likely influenced the geochemical features of amagmatic deposits of the Bodaibo and Patom zones. To investigate the influence of underwater hydrothermal activity on the geochemical features of Neoproterozoic carbonbearing sediments of the Baikal Mountain region, the Olokit zone, which is a fragment of the spreading zone of the backarc basin, was chosen. According to the geochemical parameters, the sediments of the Itykit and Ondokskaya formations hosting the Kholodninskoye deposit fall into the sedimentation area of DalnyaTaiga period paleobasins. Having compared the development of sulfide mineralization of ore objects of the Bodaibo and Olokit structural-formation zones, the authors suggest the unity of deposit formation processes within the Baikal Mountain area. The presence of framboidal pyrite indicates that the early stages of ore formation of the Kholodninskoe hydrothermal-stratiform polymetallic deposit were synchronous with sedimentation. The source of deposit ore zone enrichment with zinc lead, silver and other elements typical for low- and medium-temperature associations was a hydrothermal solution of the scattered spreading zone of back-arc basins. It is likely that the explosive and exhalation activities of the southern Baikal-Muya zone within the studied region also spread further northward, thus influencing the formation of siderochalcophilic geochemical specialization of amagmatic black shale strata of the Bodaibo and Patom zones.
Charlotte: Reformulating Blockchains into a Web of Composable Attested Data Structures for Cross-Domain Applications

Cross-domain applications are rapidly adopting blockchain techniques for immutability, availability, integrity, and interoperability. However, for most applications, global consensus is unnecessary and may not even provide sufficient guarantees. We propose a new distributed data structure: Attested Data Structures (ADS), which generalize not only blockchains but also many other structures used by distributed applications. As in blockchains, data in ADSs is immutable and self-authenticating. ADSs go further by supporting application-defined proofs ( attestations ). Attestations enable applications to plug in their own mechanisms to ensure availability and integrity. We present Charlotte , a framework for composable ADSs. Charlotte deconstructs conventional blockchains into more primitive mechanisms. Charlotte can be used to construct blockchains but does not impose the usual global-ordering overhead. Charlotte offers a flexible foundation for interacting applications that define their own policies for availability and integrity. Unlike traditional distributed systems, Charlotte supports heterogeneous trust: different observers have their own beliefs about who might fail, and how. Nevertheless, each observer has a consistent, available view of data. Charlotte’s data structures are interoperable and composable : applications and data structures can operate fully independently or can share data when desired. Charlotte defines a language-independent format for data blocks and a network API for servers. To demonstrate Charlotte’s flexibility, we implement several integrity mechanisms, including consensus and proof of work. We explore the power of disentangling availability and integrity mechanisms in prototype applications. The results suggest that Charlotte can be used to build flexible, fast, composable applications with strong guarantees.
Node injection for class-specific network poisoning

Graph Neural Networks (GNNs) are powerful in learning rich network representations that aid the performance of downstream tasks. However, recent studies showed that GNNs are vulnerable to adversarial attacks involving node injection and network perturbation. Among these, node injection attacks are more practical as they do not require manipulation in the existing network and can be performed more realistically. In this paper, we propose a novel problem statement — a class-specific poison attack on graphs in which the attacker aims to misclassify specific nodes in the target class into a different class using node injection. Additionally, nodes are injected in such a way that they camouflage as benign nodes. We propose NICKI, a novel attacking strategy that utilizes an optimization-based approach to sabotage the performance of GNN-based node classifiers. NICKI works in two phases — it first learns the node representation and then generates the features and edges of the injected nodes. Extensive experiments and ablation studies on four benchmark networks show that NICKI is consistently better than four baseline attacking strategies for misclassifying nodes in the target class. We also show that the injected nodes are properly camouflaged as benign, thus making the poisoned graph indistinguishable from its clean version w.r.t various topological properties.
The Effect of The Quantum Learning Model on Student Learning Outcomes

This research aims to analyze the effect of the Quantum Learning model on student learning outcomes in economics subjects. This research was motivated by the many students in class XI IPS at SMA Negeri 3 Purwokerto who obtained daily test scores below the Minimum Completeness Criteria (KKM) in economics subjects. The research method used is a Quasi-Experimental Design that uses an experimental and control class. This study's population was all class XI IPS students at SMA Negeri 3 Purwokerto. The number of samples in this study was 71 respondents who were students of class XI IPS 3 and 4. The sampling technique used was the purposive sampling technique. Based on the data analysis that has been done, the Quantum Learning Model has a positive effect on student learning outcomes in economics class XI IPS at SMA Negeri 3 Purwokerto. The implication of the conclusions above is that teachers and schools are advised to optimize learning by creating comfort, fun, and opportunities for students to be active in learning. The efforts that can be made are applying the Quantum Learning model because the Quantum Learning model has been proven to be able to improve student learning outcomes.
Blockchain-Based Cloud Storage Using Secure and Decentralised Solution

The fusion of blockchain with cloud computing is an exciting new area of research that could dramatically alter how businesses handle their data now and in the future. In this paper, we'll take a look at the upsides, downsides, and potential improvements of combining blockchain technology and cloud storage. Innovative solutions that are safe, clear, productive, and economical can be developed by combining the best features of both technologies. The combination of blockchain and cloud computing opens up hitherto impossible use cases, such as digital identity, decentralised banking, and supply chain management. Scalability, interoperability, security, regulatory compliance, and technological complexity are all issues that arise as a result of this merging of systems and must be addressed. The study stresses the need for forethought and funding of both infrastructure and talent for a successful rollout of blockchain-based cloud solutions. In sum, there is great potential for businesses to adapt to the rapid and unpredictable changes in the modern business landscape through the combination of blockchain and cloud computing.
Assessing the Effects of Influential Factors and Demographic Traits on the Digital Employee Experience in Oman's Telecommunications Industry

The purpose of this research is to investigate the impact of various influential elements and demographic characteristics on the digital employee experience, with a focus on the telecommunications sector in the Sultanate of Oman. The main objectives of the research are to assess the intensity of various elements such as digital tool usability, technology accessibility, training availability, leadership support, organisational culture, and working environment on the digital employee experience and to explore the relationship between digital employee experience and employee demographic characteristics such as gender, age, education level, work experience, digital literacy, and job tenure in the telecommunications sector in Oman. The data collection is made using an online survey with a sample size of 150 employees from top telecommunications companies in Oman using quota sampling. To accomplish the research goals, IBM SPSS Statistics was used to conduct quartile analysis and ANOVA. Based on the results, it was found that the intensity of digital tool usability on the digital employee experience is high when compared to the other elements, and all demographic characteristics except gender have an impact on digital experience. Several suggestions were made based on the information discovered from the findings.
Framework for Integrating the Synergies of Blockchain with AI and IoT for Secure Distributed Systems

The development of new, groundbreaking applications has been made possible by the merging of AI, IoT, and blockchain technology. Yet, as the number of people who rely on these technologies grows, so to do their security worries. There is a risk that Internet of things (IoT) devices will be attacked using artificial intelligence (AI), and blockchain technology is not immune to cyberattacks. New methods for securing AI, IoT, and blockchain infrastructure are the subject of active investigation as a means of resolving the security issues that have arisen. They include the creation of privacy-preserving strategies in blockchain networks, the use of machine learning to identify and prevent attacks, and the deployment of secure communication protocols in Internet of things devices. In addition, there may be further safety benefits from combining these technologies. For instance, blockchain can guarantee the honesty and openness of IoT data, whilst artificial intelligence can aid in the detection of irregularities in blockchain transactions. Whereas the combination of AI, IoT, and blockchain holds great promise, there are serious security concerns that must be solved before these technologies can be implemented without risk.
Calistung E-Module Innovation For Strengthening Basic Literacy and Numeracy Student

Calistung skills (reading, writing, arithmetic) must be mastered by students as the basis of literacy and numeracy. The lack of understanding of Calistung causes students to have difficulty understanding other subjects. This study aimed to develop and examine the feasibility, practicality, and effectiveness of the Calistung e-Module in strengthening students' basic literacy and numeracy. This research is a type of research and development (research and development) with the ADDIE model. Researchers collected data using questionnaires, document checklists, and documentation. The research subjects were class I students, totaling 30 students. Data analysis techniques are using descriptive qualitative analysis, quantitative, and inferential statistics. The results of the study, namely the validation test from media experts and learning design, obtained an average value of 3.6 (90%), which means it is very valid, while the material expert test and linguist test each was 3.75 (93.75%) with a very valid category. The product development practicality test obtained an average score of 3.89 (97.25%) in the very attractive category. The t-test results showed a significant increase in the average student learning outcomes after receiving a different treatment, namely using the Calistung e-Module. The listing e-module is a practical, interesting, and effective learning medium.
Fast and accurate computation of polar harmonic Fourier moments for image description

Continuous orthogonal moments are widely used in various image techniques due to their simplicity and good rotational invariance and stability. In recent years, numerous excellent continuous orthogonal moments have been developed, among which polar harmonic Fourier moments (PHFMs) exhibit strong image description capabilities. However, the numerical integration error is large in the calculation, which seriously affects the calculation accuracy, especially in higher-order calculation. In this paper, a continuous orthogonal moments-fast and accurate PHFM (FAPHFM) is proposed. It utilizes the polar pixel tiling technique to reduce numerical errors in the computation; this method particularly improves the accuracy of higher-order moments of traditional PHFMs. However, as accuracy increases, calculation complexity also increases. To address this issue, an eight-way symmetric/anti-symmetric calculation of the angular and radial functions was performed using the symmetry and anti-symmetry of traditional PHFMs, and clustering of pixels was performed as a way to improve the computational speed. The experimental results show that FAPHFMs perform better in image reconstruction (including noise), with higher computational accuracy, lower time complexity, and better image description ability.
From Stacks to Screens: Improving Access to Theses and Dissertations with Digitization and Automation

Institutional repositories are valuable platforms that facilitate access to the research output of an institution. Theses and dissertations are preserved at most higher education institutions and are valuable materials to prioritize for digitization when building an institutional repository. The purpose of this case study is to share reproducible workflows for digitization, enhanced with automation, that can be adopted by other institutions who aim to increase accessibility and discoverability of their unique institutional research. Few studies have shared their workflows in a detailed, transparent way that can be reproduced by others who are interested in taking on digitization projects to improve access and discoverability via an institutional repository.
The Effect of Parent Support and Self Regulated Learning on Learning Motivation on Students in SDIT Insan Utama

This study aims to determine how far the influence of parental support and self-regulated learning on students' learning motivation. This research is a research model of hypothesis testing (hypothesis testing study) with a quantitative approach. Data were collected using a questionnaire/questionnaire in the form of a Likert scale on parental support, self-regulated learning and learning motivation. The population was taken from all students of SDIT Insan Utama Yogyakarta with a simple random sampling technique with a sample of 152 students. Data analysis consisted of validity and instrument reliability tests, normality tests, linearity tests, hypothesis tests and correlation tests. The results of this study indicate that 1) Parental support has a significant effect on student learning motivation by 7.7% and has a moderate correlation with the Pearson Correlation value of 0.278; 2) Learning motivation has an effect on student achievement by 31.8% and is perfectly correlated with the Pearson Correlation score of 0.564; 3) Parental support and self-regulated learning together have an effect on student learning motivation by 32.9% with a significance value of 0.000. 4) The indicators of parental support variable are the provision of accommodation, motivation, appreciation, regulatory support, comfort, opportunities for activities, discussions and joint activities. Indicators of self-regulated learning variable are learning process control, setting learning goals, setting time and school assignments, motivation, planning, evaluating, implementing plans and asking for help if needed. The indicators of learning motivation variable include having an effort to learn, being able to maintain perseverance in learning and focusing on achieving learning goals.
Cute or creepy, that is the question of liveness: can artificial actors perform live?

This paper examines the liveness of the performative acts of technologically simulated or synthesized agents in interaction with humans. It questions the ability of artificial performers to construct a sense of live presence on stage and in other daily performative scenarios and evaluates our perception of liveness in those contexts. In this critical and comparative analysis, I study the live quality of Hatsune Miku’s live musical performances – a computer-generated cyber celebrity with a three-dimensional holographic body and an artificially synthesized singing voice – and the stimulating interactions of PARO, the seal – an adorable therapeutic cuddle-robot utilized as an emotional companion in nursing homes and hospitals. Using performance and media theories, I examine the audience/users’ affective responses and engagement quality with the holographic singer and the robotic pet, directly leading to the acceptance or rejection of the claim to the liveness of these techno-actors. This research also evaluates the impact of the application of cuteness (or kawaii) in designing non-human agents versus the emergence of creepiness in interactions with their incorporeal bodies on our emotive response. The objective is to study the difference between the liveness and aliveness of non-human, non-living performing agents and the impact of evoking the sense of “cute” versus “creepy” on warping the Uncanny Valley Curve.
Reservoir Computing-based Multi-Symbol Equalization for PAM 4 Short-reach Transmission

We propose spectrum-sliced reservoir computer-based (RC) multi-symbol equalization for 32-GBd PAM4 transmission. RC with 17 symbols at the output achieves an order of magnitude reduction in multiplications/symbol versus single output case while maintaining simple training.
Decentralised finance: the latest generation of crypto-assets

Rationale This article is intended as an introduction to the world of decentralised finance (DeFi). To this end, the focus is to explain, in a simplified manner, its key characteristics and most common use cases, along with the main risks. Takeaways •DeFi is one of the latest developments in the crypto-asset space and also the segment where most of the recent bouts of sector turmoil have taken place. It is a complex, but still largely endogenous, ecosystem and therefore its interaction with the real economy is limited. •DeFi is actually less decentralised than it purports to be. Its real benefits are limited, while the potential risks are considerable. Although the risks are not substantially different from those in the traditional financial sector, their drivers, speed of propagation and scale can differ entirely, compromising the authorities’ room to manoeuvre. •Although the policy and regulatory effort is, for now, focused on traditional crypto-assets, there is a growing debate as to what mix of measures would best contribute to mitigating the risks of DeFi. More specific action in this regard is expected in the years ahead.
CSMPQ: Class Separability Based Mixed-Precision Quantization

Network quantization has become increasingly popular due to its ability to reduce storage requirements and accelerate inference time. However, However, ultra low-bit quantization is still challenging due to significant performance degradation. Mixed-precision quantization has been introduced as a solution to achieve speedup while maintaining accuracy as much as possible by quantizing different bits for different layers. However, existing methods either focus on the sensitivity of different network layers, neglecting the intrinsic attribute of activations, or require a reinforcement learning and neural architecture search process to obtain the optimal bit-width configuration, which is time-consuming. To address these limitations, we propose a new mixed-precision quantization method based on the class separability of layer-wise feature maps. Specifically, we extend the widely-used term frequency-inverse document frequency (TF-IDF) to measure the class separability of layer-wise feature maps. We identify that the layers with lower class separability can be quantized to lower bits. Furthermore, we design a linear programming problem to derive the optimal bit configuration. Without any iterative process, our proposed method, CSMPQ, achieves better compression trade-offs than state-of-the-art quantization algorithms. Specifically, for Quantization-Aware Training, we achieve Top-1 accuracy of 73.03% on ResNet-18 with only 63GBOPs, and Top-1 accuracy of 71.30% with 1.5 Mb on MobileNetV2 for Post-Training Quantization.
Privately Automating Common, Uncommon, and Surprising Markup Tasks using AI Large Language Models

Generative AI is everywhere, from DALL-E for image generation to ChatGPT for language tasks. The power of these models boggles the mind even for people who’ve been involved in AI for ages. Can they help us? It’s been widely reported that they can be used to write rather sophisticated code in languages like Python and Javascript — but how well do they work with markup? Can they work with XML properly, or can they only treat it as tag soup? Even without any specialized XML training, large language models (LLMs) prove to have some very interesting, and in some cases impressive, capabilities. By using self-hosted LLMs rather than third-party services such as ChatGPT or Bard, we can exploit those capabilities even for private applications. There are certainly some clear limitations, but LLMs can also handle a surprising number of common markup tasks out of the box.
Turning a Battleship: Migrating ServiceNow Documentation to Use DITA Keys

Describes the analysis, design, planning, and data processing ServiceNow used to migrate all of its DITA-based ServiceNow Platform product documentation from the use of unmanageable direct-URL references for all reuse references and hyperlinks to the use of key-based indirect linking within and among publications. The technical solution uses DITA keys for all links, including use of DITA’s “cross deliverable” linking feature, which enables authoring links from one publication to another (similar to DocBook’s cross-book links but with different implementation challenges). Focuses on the XSLT and XQuery migration process used to perform the migration over a four-day period in the pause between semi-yearly product releases.
Retractions and Corrections at Scholars Portal Journals

Scholars Portal Journals, a service of the Ontario Council of University Libraries (OCUL), is an XML based repository that hosts e-journal content for universities in Ontario. As part of the OCUL mission to provide accurate and up-to-date access to scholarly research, Scholars Portal has recently taken on a project to improve how article corrections and retractions are handled in our workflow. This paper introduces the new process of utilizing the JATS metadata element &lt;related-article&gt; to link between articles and their corrections and retractions so that users are immediately aware that there have been changes to the article they are viewing. This paper will also discuss challenges in this process including the inability to handle articles that have not been registered with a Digital Object Identifier (DOI) and difficulties with inconsistent use of attribute values.
Artificial Intelligence with XForms

Classical AI techniques: Bayesian networks, fuzzy logic, forward and backward chaining inference rules, etc. have proved useful in providing clinical decision support. The Artificial Intelligence (AI) Workbench investigates how these might be used in an XRX (XForms, REST, XQuery) application with a view towards adding them to the cityEHR Electronic Health Records system.