Reviewerly/exHarmony huggingface dataset:
The Online Tutorials Evaluation in Education Statistics Course at Pokjar Kendal Indonesia

The implementation of online tutorials introduces difficulties in the learning process. Along with studying lecture material, students must also master technology. On this basis, an evaluation of the Education Statistics course is necessary. This evaluation focuses on implementing online tutorials and the factors that impede their implementation. This study aims to determine the quality of online tutorials available for the Education Statistics course. The research subjects are students from the Universitas Terbuka at Pokjar Kaliwungu and Kendal. This study is categorized as a qualitative descriptive study. Observation sheets, questionnaires, and interviews were used to collect data. The findings indicated that: 1) 47.1 percent of students use learning resources in the form of books , 47.1 % use learning videos , 70.6 % use websites , and 38.2 % use other sources; 2) m ost students (61.8 %) struggle with hypothesis testing material, while 64.7 % struggle with linear regression analysis material ; and 3) t he difficulty of the material is because the material is difficult to understand by 35.3%; high difficulty by 50%; students have never been 35.3%; high material complexity by 29.4% and other 5.9%. Based on the findings of these studies, it can be concluded that there are still a significant number of students who struggle with the material covered in Education Statistics. The analysis suggests that : 1) lecturers should focus on hypothesis testing and linear regression analysis material in the tutorial to help students overcome difficulties ; and 2) l ecturers emphasize the application of statistics in research.
Pengaruh kemajuan teknologi informasi terhadap perkembangan akuntansi

Technology that develops rapidly causes significant changes in the accounting field. These changes require accountants to continue to follow developments and improve their competence. Historically, the development of accounting has been divided into three eras, starting from the farming era and then the industrial era to the present, namely the information age. The more advanced information technology (IT), the more influence it will have on the accounting field. One of the effects of IT progress can be seen in the development of accounting information systems (AIS) in terms of processing data, internal control, and increasing the amount and quality of information in financial reporting. The development of computer-based AIS in producing financial reports also has an impact on the audit process. Although at first it gave the accounting profession a sense of worry, the development of IT in the end provided new opportunities for the accounting profession. The new opportunities that can be achieved include computer-based information system audits, computer-based information system consultants and web trust audits. Advances in IT brought significant changes to the development of accounting and also brought many benefits for both employees and companies.
A fully automatic method for segmentation of soccer playing fields

This paper proposes a strategy to segment the playing field in soccer images, suitable for integration in many soccer image analysis applications. The combination of a green chromaticity-based analysis and an analysis of the chromatic distortion using full-color information, both at the pixel-level, allows segmenting the green areas of the images. Then, a fully automatic post-processing block at the region-level discards the green areas that do not belong to the playing field. The strategy has been evaluated with hundreds of annotated images from matches in several stadiums with different grass shades and light conditions. The results obtained have been of great quality in all the images, even in those with the most complex lighting conditions (e.g., high contrast between sunlit and shadowed areas). In addition, these results have improved those obtained with leading state-of-the-art playing field segmentation strategies.
Applying NLP Techniques to Classify Businesses by their International Standard Industrial Classification (ISIC) Code

The application of machine learning has played an important role in several aspects of text classification across domains, and has brought with it great changes to the current state of the art. In this paper, we propose a novel application of NLP techniques to classify entities by their International Standard Industrial Classification (ISIC) code based on descriptions provided by the business owners themselves and the names of said businesses. Faced with the issues of irregularity and a small amount of noisy training data, we employ several different NLP models and data enhancement strategies. We identify DistilBERT, under normalized loss, as the best model for our task, with a 77.9% average accuracy on a 56 label multiclass classification task.
Manifold Curvature Estimation for Neural Networks

This paper introduces a novel method for creating a metric to measure curvature of a discretized manifold. For each data point x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> on a manifold, a subspace ${{\mathcal{S}}_i}$ is matched using a number of neighboring points of x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> . A local subspace is also matched to each neighboring point of x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> . Then, a set of weighted angles between ${{\mathcal{S}}_i}$ and each neighboring local subspace are computed and the minimum of those weighted angles is used as a measure of curvature of the manifold at x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> . The average curvature for all data points on the manifold is used as metric for the manifold’s curvature estimation. This research also uses the proposed metric to show that each layer of a neural network maps an input manifold to a flatter manifold during the training process. It is observed that each successive block in a neural network generates a manifold with less curvature than that of the previous layer. Another observation is that convolutional layers always generate flatter manifolds. Furthermore, it is shown that this metric can be used as a robustness measure for a neural network. The method has been tested successfully using two datasets MNIST and Extended YaleB datasets.
Anomaly Detection for Public Transport and Air Pollution Analysis

Anomaly detection is a machine learning task that has been investigated within diverse research areas and application domains. In this paper, we performed anomaly detection for air pollution and public transport traffic analysis for the city of Oslo, Norway. To this aim, the state-of-the-art method SparkGHSOM was considered to learn predictive models for normal (i.e. regular) scenarios of air quality and traffic jams in a distributed fashion. Furthermore, we extended the main algorithm to make the detected anomalies explainable through an instance-based feature ranking approach. The results showed that SparkGHSOM is able to detect anomalies for both the real applications considered in this study, despite the fact it was designed for different tasks.
FedProb: An Aggregation Method Based on Feature Probability Distribution for Federated Learning on Non-IID Data

Federated learning (FL) has been used to protect data contributors’ privacy by allowing training at clients and then feeding back machine learning models to servers for aggregation. Conventional methods of FL aggregation methods average model weights to produce a fused global model. However, in real-world applications in cyber-space systems, which often have heterogeneous Internet of Things data configuration and collection, FL encounters obstacles with non-independent and identically distributed (Non-IID) data. The main problem is the aggregated global models deviating from the optimal model trained on centralized servers. According to recent research, most Non-IID FL aggregation methods attempt to direct the movement of gradients to the optimal one using differentiation from trained models. In this paper, we propose a framework for using feature probability distribution in aggregation calculation. The proposed aggregation algorithm shows robustness on different Non-IID datasets and outperforms state-of-the-art methods in various FL experiments.
DATA-EX: Infrastructure for Cross-Domain Data Exchange Based on Federated Architecture

Data is the most important property for bringing innovation and digital transformation. In Japan, data-driven society is one of the most important concept for the future society referred in Society 5.0, DFFT (Data Free Flow with Trust), and National Data Strategy of Japan. Several public and/or industrial domains have already established data ecosystem including data sharing platforms. However, there are many scenarios and applications which cannot be supported by the domain-specific data platforms. For example, disaster response, tourism, GHG (Greenhouse Gas) management such as the Scope 3 protocol, supply chain management support, and smart city are among them. So, we need a cross-domain data-exchange platform to support these applications. The objective of this paper is to propose "DATA-EX (data exchange)", which is a platform which is under development and will be operated by Data Society Alliance (DSA), and works as an infrastructure for cross-domain data exchange consisting of open standard specifications and guidelines for data exchange and platform operation, software systems based on the standards, and governance rules for the operation of DATA-EX. This paper presents definition, objectives, basic strategies, and architecture of DATA-EX, and its current status and future plan.
Blockchain based Secure Group Data Collaboration in Cloud with Differentially Private Synthetic Data and Trusted Execution Environment

Data collaboration with cloud technologies is becoming more popular for personal use as well as business applications. Due to the increasing data protection regulations worldwide, different cryptographic techniques have been designed to enable secure data sharing for a user or a group of users. Although, these techniques have seen enterprise adoption, they fail to offer data visibility as data remains encrypted throughout the data sharing routine. This is why these techniques fail to offer a key features to data users, e.g., joining different datasets together and sharing it with all the users involved. This paper presents a blockchain based architecture for secure data collaboration in cloud using differentially private synthetic data and trusted execution environment (TEE). The proposed solution protects data confidentiality and integrity with TEEs, supports public-key infrastructure (PKI) with blockchain, and prevents privacy leakages with synthetic data. The results show that our synthetic data performs as good as real data and demonstrates how different users can securely aggregate their datasets and openly share among themselves.
Pipelining Graph Construction and Agent-based Computation over Distributed Memory

Graph streaming has received substantial attention for the past 10+ years to cope with large-scale graph computation. Two major approaches, one using conventional data-streaming tools and the other accessing graph databases, facilitate continuous analysis of endlessly flowing graphs and query-based incremental construction of huge graphs, respectively. However, some scientific graphs including biological networks need to stay in memory for repetitive but various analyses. Although a cluster system, thus distributed memory can entirely handle a big graph in memory, a challenge is substantial overhead incurred by loading graphs into memory. A solution is hiding such graph-loading and construction overheads with graph computation in a pipelined fashion. We adapted this pipelining approach for agent-based graph computing where thousands of agents traverse a graph for finding its attributes and shape. We used the multi-agent spatial simulation (MASS) library to implement the concept. A huge graph is incrementally constructed in batches, each spawning and walking agents over the corresponding subgraph, and thus all eventually completing a given computation. We coded and ran two MASS benchmark programs: triangle counting and connected components, with which we evaluated our pipelined graph processing. The best performance was obtained once the batch size shrunk enough to fit cache memory, regardless of the number of cluster nodes. For a single node execution of connected components over a 140MB graph, our graph-pipelining implementation performed 7.7 times faster than non-pipelining execution. Its parallel execution with 24 cluster nodes achieved 8.3 times speed-up as compared to the pipelined single-node execution.
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data

Data-centric AI encourages the need for cleaning, evaluating, and understanding data in order to achieve trustworthy AI. Existing technologies, such as AutoML, make it easier to design and train models automatically, but there is a lack of a similar level of capability to extract data-centric insights. Manual stratification of tabular data per a given feature of interest (e.g., gender) is limited to scaling up for higher feature dimension, which could be addressed using automatic discovery of divergent/anomalous subgroups. Nonetheless, these automatic discovery techniques often search across potentially exponential combinations of features which could be simplified using a preceding feature selection step. Existing feature selection techniques for tabular data often involve fitting a particular model (e.g., XGBoost) in order to select important features. However, such model-based selection is prone to model-bias and spurious correlations in addition to requiring extra resources to design, fine-tune and train a model. In this paper, we propose a model-free and sparsity-based automatic feature selection (SAFS) framework to facilitate automatic discovery of divergent subgroups. Different to filter-based selection techniques, we exploit the sparsity of objective measures among feature values to rank and select features. We validated SAFS across two publicly available datasets (MIMIC-III and Allstate Claims) and compared it with six existing feature selection methods. SAFS achieves a reduction of the feature selection time by a factor of 81× and 104×, averaged cross the existing methods in the MIMIC-III and Claims datasets, respectively. SAFS-selected features are also shown to achieve competitive detection performance, e.g., 18.3% of features selected by SAFS detected similar divergent group compared to using the whole features, in the Claims dataset, with a Jaccard similarity of 0.95 but with a 16× reduction in detection time.
Fine-Tuning BERT-based Language Models for Duplicate Trouble Report Retrieval

In large software-intensive organizations, trouble reports (TRs) are heavily involved in reporting, analyzing, and resolving faults. Due to the scale of modern organizations and products, multiple people independently often identify faults, leading to duplicate TRs. To mitigate the additional manual effort to identify and resolve these duplicate TRs, prior work at Ericsson focused on developing a 2-stage BERT-based retrieval system for identifying similar TRs when provided a new fault observation. This approach, although powerful, struggled to generalize to out-of-domain TRs. In this paper, we evaluate several fine-tuning strategies to integrate domain knowledge further, notably telecommunications knowledge, into the BERT-based TR retrieval models to (i) attain better performance on duplicate TR retrieval/identification and (ii) improve model generalizability to out-of-domain TR data. We find that adding domain-specific data into the fine-tuning models led to improved results on both overall model performance and model generalizability.
Identification of Encrypted Images Based on Image Statistics

Privacy is a fundamental human right and should be absolutely respected. It implies that in the area of monitoring systems, anonymization methods should be safe and robust. This study summarizes our experiments and results related to the IEEE BigData 2022 Cup: Privacy-preserving Matching of Encrypted Images data mining competition. Its goal was to verify three different obfuscation schemes. In the case of two subtasks, our predictive model was built on image statistics, such as average color, its standard deviation, or interquartile range. The algorithm achieved 99.88% and 95.45% accuracy, respectively. In addition, we proposed a novel, to our knowledge, data augmentation approach that improved the results very slightly but, in our opinion, has potential and is worth further analysis. The third obfuscation scheme, based on homomorphic encryption, turned out to be beyond our reach. In the end of paper, ideas for further research are provided.
Smardy: Zero-Trust FAIR Marketplace for Research Data

Over the past five years, different organisations have increasingly called for science to become more open and reproducible. They have endorsed a set of data-management principles known as the FAIR (Findable, Accessible, Interoperable, Reusable) principles. As such, there is a growing trend towards the open availability of research data, as researchers continue to enhance reproducibility by enabling sharing and opening of their findings and datasets. However, there is not yet a standardised way to openly enable access to datasets while keeping control of their final use, potentially obtaining benefits from their utilisation. This paper introduces Smardy, an EU-funded project which is deploying a traceable FAIR-compliant open innovation marketplace for data. Its innovative method for data exchange consists of the use of blockchain for controlling access rights to data, with data models able to grant access according to policies completely kept under the control of the data owner/producer. We also describe how Smardy employs dimensionality reduction techniques to automatically generate FAIR–compliant metadata, statistical fingerprinting to identify derivated datasets, and watermarking to help data owners trace the distribution of multiple copies of a dataset.
RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative methods.To expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images’ diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://github.com/yanzipei/CCN_and_ItsApp.
Task-Driven Privacy-Preserving Data-Sharing Framework for the Industrial Internet

Industrial Internet provides a collaborative computational platform for participating enterprises, allowing the collection of big data for machine learning tasks. Despite the promise of training and deployment acceleration, and the potential to optimize decision-making processes through data-sharing, the adoption of such technologies is impacted by the increasing concerns about information privacy. As enterprises prefer to keep data private, this limits interoperability. While prior work has largely explored privacy-preserving mechanisms, the proposed methods naively average or randomly sample data shared from all participants instead of selecting the most well-suited subsets for a particular downstream learning task. Motivated by the lack of effective data-sharing mechanisms for heterogeneous machine learning tasks in Industrial Internet, we propose PriED, a task-driven data-sharing framework that selectively fuses shared data and local data from participants to improve supervised learning performance. PriED utilizes privacy-preserving data distillation to facilitate data exchange, and dynamic data selection to optimize downstream machine learning tasks. We demonstrate performance improvements on a real semiconductor manufacturing case study.
Targeted Mining of Rare High-Utility Patterns

Pattern discovery has been widely studied and applied as a classical problem in data mining. As a subfield of itemset mining, identifying high-utility rare itemsets (HURI) can find abnormal but vital patterns in transaction databases. It plays a unique role in real-world scenarios such as anomaly detection and disease detection. However, with large-scale databases, the final results are often massive according to the user-specified threshold. In other words, the mining algorithm ignores the user’s subjective interests and lacks interaction during the mining process. A pattern discovery algorithm may output many useless or uninteresting patterns. To this end, in this paper, we define the problem of mining targeted HURIs and propose a list-based algorithm called TaRP for effectively solving this issue. In addition, based on preliminary research, we propose several effective pruning strategies for improving the algorithm’s performance. TaRP makes the results more interactive and specific by incorporating the user’s prior knowledge during mining. It also has a natural performance advantage with the help of effective strategies. We also evaluated the proposed algorithm on several real-life datasets. The extensive experimental results demonstrate that TaRP not only correctly solves the problem but also has advantages in runtime and memory consumption, especially on dense datasets.