Reviewerly/exHarmony huggingface dataset:
Blockchain MEV minimisation solution with price guarantee reward

&lt;p&gt;Blockchain systems rely on miners to produce blocks, who solely build blocks for their financial benefit. Within the competitive market system, only correct blocks get accepted, which ensures security of the network. As financial applications migrate into the blockchain, miners in this privileged position have identified ways to extract additional revenues (known as MEV) that affect users and the overall security of the system. A possible direction for current research to resolve this exploitation is to separate the sequencing (building) and execution (mining/proposing) of transactions (PBS model). However, a weaker point of this model is the trust assumption between participants and the incentive model. This paper proposes a concept of separate incentive schemes for the work various participants conduct in their own interests.&lt;/p&gt;
Quantum Cryptography a Real Threat to Classical Blockchain: Requirements and Challenges

&lt;p&gt;Classical blockchain cryptographic primitives are susceptible to quantum computing technology due to its unprecedented growth because it has the potential to make current blockchain encryption techniques obsolete against quantum attacks. Shor algorithm can perform transactional hijacking and forge digital signatures to impersonate blockchain users. On the other hand, Grover algorithm can perform 51% attack and can accelerate brute force attack by detecting hash collisions to replace the blocks of a blockchain. With the advent of this threat, in attempt to mitigate the effects of quantum attacks on blockchain technology, the concept of post-quantum cryptography (PQC) or quantum resistant cryptography (QRC) has emerged. PQC involves cryptographic algorithms or cryptosystems that are robust against quantum attacks and are also applicable in blockchain for post quantum decentralization. In this article, we provide a brief overview of quantum threats to classical blockchain technology. We identify and discuss some important requirements for the implementation of PQC. Moreover, we also identify and explain notable unresolved challenges in implementing PQC algorithms. We also determine critical research questions for each identified challenge that are imperative to confer and requires discussion and interpretation by the researchers of this field. &lt;/p&gt;
UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection

Recent scene text detection methods are almost based on deep learning and data-driven. Synthetic data is commonly adopted for pre-training due to expensive annotation cost. However, there are obvious domain discrepancies between synthetic data and real-world data. It may lead to sub-optimal performance to directly adopt the model initialized by synthetic data in the fine-tuning stage. In this paper, we propose a new training paradigm for scene text detection, which introduces an \textbf{UN}supervised \textbf{I}ntermediate \textbf{T}raining \textbf{S}tage (UNITS) that builds a buffer path to real-world data and can alleviate the gap between the pre-training stage and fine-tuning stage. Three training strategies are further explored to perceive information from real-world data in an unsupervised way. With UNITS, scene text detectors are improved without introducing any parameters and computations during inference. Extensive experimental results show consistent performance improvements on three public datasets.
MirrorAlign: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning

Word alignment is essential for the downstream cross-lingual language understanding and generation tasks. Recently, the performance of the neural word alignment models has exceeded that of statistical models. However, they heavily rely on sophisticated translation models. In this study, we propose a super lightweight unsupervised word alignment model named MirrorAlign, in which bidirectional symmetric attention trained with a contrastive learning objective is introduced, and an agreement loss is employed to bind the attention maps, such that the alignments follow mirror-like symmetry hypothesis. Experimental results on several public benchmarks demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in word alignment while significantly reducing the training and decoding time on average. Further ablation analysis and case studies show the superiority of our proposed MirrorAlign. Notably, we recognize our model as a pioneer attempt to unify bilingual word embedding and word alignments. Encouragingly, our approach achieves {16.4X speedup} against GIZA++, and {50X parameter compression} compared with the Transformer-based alignment methods. We release our code to facilitate the community: https://github.com/moore3930/MirrorAlign.
Scheduled Multi-task Learning for Neural Chat Translation

Neural Chat Translation (NCT) aims to translate conversational text into different languages. Existing methods mainly focus on modeling the bilingual dialogue characteristics (e.g., coherence) to improve chat translation via multi-task learning on small-scale chat translation data. Although the NCT models have achieved impressive success, it is still far from satisfactory due to insufficient chat translation data and simple joint training manners. To address the above issues, we propose a scheduled multi-task learning framework for NCT. Specifically, we devise a three-stage training framework to incorporate the large-scale in-domain chat translation data into training by adding a second pre-training stage between the original pre-training and fine-tuning stages. Further, we investigate where and how to schedule the dialogue-related auxiliary tasks in multiple training stages to effectively enhance the main chat translation task. Extensive experiments in four language directions (English-Chinese and English-German) verify the effectiveness and superiority of the proposed approach. Additionally, we have made the large-scale in-domain paired bilingual dialogue dataset publicly available to the research community.
Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback

Large language models (LMs), while powerful, are not immune to mistakes, but can be difficult to retrain. Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user. Our approach pairs an LM with (i) a growing memory of cases where the user identified an output error and provided general feedback on how to correct it (ii) a corrector model, trained to translate this general feedback into specific edits to repair the model output. Given a new, unseen input, our model can then use feedback from similar, past cases to repair output errors that may occur. We instantiate our approach using an existing, fixed model for script generation, that takes a goal (e.g., "bake a cake") and generates a partially ordered sequence of actions to achieve that goal, sometimes containing errors. Our memory-enhanced system, FBNet, learns to apply user feedback to repair such errors (up to 30 points improvement), while making a start at avoiding similar past mistakes on new, unseen examples (up to 7 points improvement in a controlled setting). This is a first step towards strengthening deployed models, potentially broadening their utility. Our code and data is available at https://github.com/allenai/interscript/.
Automatic Noisy Label Correction for Fine-Grained Entity Typing

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weakly-supervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance of the FET task. Although previous studies have made great success in automatically identifying the noisy labels in FET, they usually rely on some auxiliary resources which may be unavailable in real-world applications (e.g. pre-defined hierarchical type structures, human-annotated subsets). In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel candidate noisy labels by training a robust model over the remaining clean labels. Experiments on two popular benchmarks prove the effectiveness of our method. Our source code can be obtained from https://github.com/CCIIPLab/DenoiseFET.
Increasing learning interest through android-based word square learning media

The use of media through the Android application is still limited, even though it is needed to foster interest in learning after the COVID-19 pandemic. The research focused on increasing learning interest through the application of the Android-based Word Square learning media. This experimental research used a pretest-posttest control group design. The population in this study were all students of class IX MTs Guppi Samata, which consisted of 2 classes. The saturated sampling method was used in the sampling procedure with six samples taken from the experimental class IX-A and six samples taken from the control class IX-B. The research instrument was a questionnaire consisting of 30 items. The data analysis technique used the Wilcoxon signed ranks test. Students' learning interests differed depending on whether or not they used the Android-based Word Square learning media (p = 0.046, p 0.05). The results showed the differences in students' interest in learning with and without using the media of Word Square learning media based on android human reproductive system material for class IX MTs Guppi Samata (p = 0.046, p &lt; 0.05). The average interest in learning among students through the media was 114.83. The value was higher than those without using the media (106.50). This proves that Word Square learning resources based on Android can increase students' learning interests. The findings of this study can be used to inform teachers' decisions about how to use engaging learning technologies and a variety of media to enhance the quality of their instruction.
From Diversity of Themes to Deepening of Contents: Actual trends of Japanese Pre-modern History Researches

This article examines recent research trends in Japanese pre-modern history from 2020 to 2021 and the following tendencies are noticed :BR Firstly, themes have become more diversified. Researchers have the ability to use more various primary sources and interpret them properly. As a result, their research were further intensified.BR Secondly, Researchers’ perspectives have been broadened. Research achievements that transcend time and region limits has increased.BR Thirdly, an attempt is made to analyze the relationship between Korea and Japan from the viewpoint of “ocean” or “ocean area”BR Fourth, traditional and core themes of Japanese history research in Korea have reached to a stage that can be named as a “Korean-style Japanese history research”.BR Finally, studies of ancient Japanese history and the Edo period by Japanese researchers are steadily increasing.BR Based on the research methods and achievements accumulated so far, it is expected that the study of Japanese pre-modern history will advance and mature.
Education and innovation policies

The link between innovation and education policies has strengthened over time, becoming very close in contemporary capitalism. Today, innovations depend on specialised knowledge, which is mostly owned by a university-trained working force, and increasingly emerge from university-industry collaboration in R&D activities. At the same time, the state’s commitment to widening the participation in higher education is one of the main levers for inequality reduction. Thus, education and innovation policies have become more and more strategic axes of the political economies of advanced economies. Using many indicators, the chapter highlights that each growth model has its style of intervention in education and innovation policies. In inclusive growth countries – especially those of the egalitarian variant – public funding is higher and universities represent a fundamental cornerstone for development policy, but also for inequality reduction. So far, the competitiveness of the economic systems mainly comes from private investments. In these cases, the state has mainly an ancillary role. It follows that the effects on the reduction of inequalities are lower. Finally, the non-inclusive low-growth model shows a modest investment in education and innovation policies.
MOM-VMP: Multi-Objective Mayfly Optimization algorithm for VM Placement supported by Principal Component Analysis(PCA) in Cloud Data Center

Abstract Virtual Machine Placement (VMP) is crucial in a cloud data cen-ter(CDC). It is a critical step carried out as part of the Virtual Machine (VM) placement to allocate the best Physical Machine (PM) to host the VMs. The efficacy of the virtual machine placement strategy has a considerable impact on cloud computing efficiency. The ineffec-tiveness of the VMP approach has a major negative impact on the CDC.Virtualization facilitated VM migration has met the ever-increasing demands of dynamic workload by transferring VMs inside CDC. Many resource management goals, including power efficiency, load balancing, fault tolerance, and system maintenance, are aided VM placement. As a result, VMP needs to assess characteristics that may impact placement performance and energy efficiency. Most past research has concentrated solely on reducing energy consumption while ignoring SLA (service level agreement) breaches, enhancing the resource usage of PMs, and ignoring the over-commitment issue. MOM-VMP To propose a multiobjective Mayfly VMP algorithm (MOM-VMP) meta-heuristic optimization algorithm with a massive CDC with different and multi-dimensional resources to handle these issues. A multi-objective dynamic VMP strategy is employed to reduce resource wastage, overcom-mitment ratio, migration time, SLA violation and energy consumption at the same time. This paper presents a dynamic multi-objective VMP in CDC based on overcommitment resource allocation to influence VM-PM mapping. We validated our method of conducting a performance evaluation study using the CloudSim tool. The experimental findings show that the suggested study decreases energy consumption, makespan, SLA violations, and PM overloading while enhancing resource utilization.
ComputerBank: A Community-based Computer Donation Platform using Machine Learning and NFT

Many people donate money to fund organizations, but very rarely do those donors have information about where those donations go. Donation platforms are both non-transparent and also leave a large portion of potential donors unnoticed: gamers [1]. This paper explores the concept of utilizing blockchain technology and its existence as a web3 token-based platform in order to provide transparency for donation routes, showing donors and other companies exactly where donations are coming from and where that money is going. Our application utilizes HTTP requests in order to greatly increase compatibility, and also uses multiple private key encryptions in order to ensure that any user data or information and monetary transactions are kept secure and private [2].
Handwritten Digit Recognition System based on CNN and SVM

The recognition of handwritten digits has aroused the interest of the scientific community and is the subject of a large number of research works thanks to its various applications. The objective of this paper is to develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN) combined with machine learning approaches to ensure diversity in automatic classification tools. In this work, we propose a classification method based on deep learning, in particular the convolutional neural network for feature extraction, it is a powerful tool that has had great success in image classification, followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and the results obtained showed that the combination of CNN with SVM improves the performance of the model as well as the classification accuracy with a rate of 99.12%.
Ensembles for Class Imbalance Problems in Various Domains

The paper is an analysis of class imbalance problems from various domains such as the medical field, sentiment analysis, software de-fects, water portability, and relationship status of students and summarizes the performance of data resampling techniques such as random undersampling and oversampling. Synthetic minority oversampling techniques combined with the power of ensemble methods such as bagging, boosting, and hybrid techniques are generally used to solve the class imbalance problem.
Blockchain in Insurance Industry: Turning Threat into Innovative Opportunities

Insurance has been around for more than centuries. This risk mitigation strategy has been utilized in maritime commerce as early thousand years ago, where Asian merchant seafarers were pooling together their wares in collective funds to pay for damages of individual’s capsized ship. In 2018, insurance industry made up 6% of global GDP while financial industry amounted to about 7-9% of the US GDP. In 2020, the industry net premiums written totaled $1.28 trillion, created 2.9 million jobs, and recorded $2.0 trillion investments. Despite of growing reform, the insurance market is dominated by intermediaries assisting people to match their insurance needs. While many predictions focused on artificial intelligence, cloud computing, blockchain stands out as the most disruptive technology that can change the driving forces underlying the global economy. We will focus on presenting blockchain use cases in insurance, demonstrating how the sector can turn blockchain threat into innovative opportunities.
A Data-Driven and Collaborative Mobile Application to Assist Sensors using Artificial Intelligence and Machine Learning

There are numerous arguments as to what the best GPS software is [1]. However, there are no definitive answers as to which is the best. In this paper, we use multiple GPS applications tracking user locations to determine what GPS is best in terms of tracking users through a mobile app [3]. The app utilizes a GPS as well as a Google Firebase Realtime Database to manage, pinpoint, and track users’ locations [2]. The application is explicitly applied to track locations of people that need care, such as the elderly. This will allow concerned caretakers to help keep track and take care of people in need.
Design of an Integrated Information System for Regional Property in Indonesia Probolinggo City Government

This study aims to analyze the integrated information system for a regional property in the Probolinggo City government using the concept of Business Process Management notation. The object of research is the government of Probolinggo City. This case study was analyzed to find problems in the research object and the design of procedures when conducting capital expenditures in each government agency. This study uses quantitative methods with resource based theory techniques with several stages ranging from data collection, case identification, and in-depth analysis to theory development and implications for its completion. The result of the research is a business process model based on Business Process Management Notation (BPMN) which can increase effectiveness and efficiency so that it has implications for improving the quality of business processes in Government agencies Related to Capital Expenditures so that they are understood and correct so that they are effective.
Learn to Live Longer: Counterfactual Inference using Balanced Representations for Parametric Deep Survival Analysis

&lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;We derive the fundamental frameworks and propose a deep neural network framework for analysing time-to-event under different treatment regimes.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;
Accurate Object Detection &amp; Instance Segmentation of Remote Sensing, Imagery Using Cascade Mask R-CNN With HRNet Backbone

&lt;p&gt;Experiments are conducted on a series of remote sensing images (NWPU VHR-10). Detection &amp; instance segmentation results demonstrate that our method provides better performance (significant improvement in accuracy) in terms of average precision (AP). The larger the value of AP is, the more accurate the prediction results and the better detection performance of the objects. Cascade Mask R-CNN framework with HRNet backbone for geospatial objects detection and instance segmentation from high-resolution remote sensing imagery.&lt;/p&gt;
Heterogeneous Graph Neural Network with Time Sequence Information Integration

&lt;p&gt;Recommendation systems, as a kind of information filtering system, can understand users' interests based on their personal data or historical behavior records, and are widely used in Web applications such as e-commerce, search, and streaming websites. Based on the fact that users with similar preferences may be interested in similar items, most existing recommendation methods mine this synergistic information through user-item interactions, called collaborative filtering. Recent interaction information can better reflect users' dynamic interests over time. Another group of work is called temporal-based recommendation, which takes into account temporal information to model the user's dynamic interests. A temporal recommendation system is based on the user's temporal interaction data as a context to predict which item the user is most likely to interact with next time. Markov chain-based recommendation models are a typical example of temporal sequential recommendation, which assume that the next interaction is only related to the previous one. Recently, much work has used recurrent neural networks to model previous behaviors and use hidden states to predict the next behavior. However, almost all temporal recommendation methods model user embeddings based only on their own temporal interaction history, ignoring the heterogeneous information widely available in recommender systems, such as product attributes, and encounter cold-start problems when data are sparse and users have fewer interactions. In this paper we proposed a model, the heterogeneous dynamic graph neural network. This network uses a static graph encoder to process the node representation of each heterogeneous graph at different time steps, which includes the node attribute information and edge information, to capture the changing node information in all time steps of the dynamic graph and obtain a valid node representation at each time step. The long and short-term memory model is then used to aggregate the temporal information between different time steps and to mine the connections and interdependencies of different types of nodes between different time steps. The goal of the heterogeneous dynamic graph neural network is to capture the changing node representations of the same nodes as well as different nodes at different time steps in the graph network, expecting to better perform the task of node classification in dynamic graphs. In this paper, we validate the model of heterogeneous dynamic graph neural networks from data, experimenting with node classification tasks on real graph structured data including social networks, e-commerce networks and business review networks, and show that heterogeneous dynamic graph neural networks outperform the latest representation learning methods for static and dynamic graphs.&lt;/p&gt;