Reviewerly/exHarmony huggingface dataset:
ExPAD: An Explainable Distributed Automatic Anomaly Detection Framework over Large KGs

RDF data is playing an important role in publishing and integrating heterogeneous data in data lakes. However, since the data is generally created utilizing liberal curation approaches such as crowd-sourcing and automatic extraction tools with no cross-validation on input data, the data is prone to errors that can be hidden in several dimensions. The types of errors which can be considered as outliers may occur in any part of RDF statements, especially in literal objects. Although some scientific studies have revealed anomalies in knowledge graphs, none of the current approaches has the ability to explain the anomalies that have been discovered. In this paper, we present ExPAD, a scalable and distributed framework for explainable numeric anomaly detection on very large RDF knowledge graphs. Inspired by OutlierTree, ExPAD generates human-readable explanations for a given numeric result being an outlier by following and assessing supervised decision tree splits. The proposed framework ExPAD is open-source, well-documented, and fully integrated into the Semantic Analytics Stack (SANSA). Experiments on real-world use cases and synthetic datasets disclose that the framework can not only handle high volumes of RDF data but also efficiently generate explanations for hidden anomalies discovered in KGs.
What metadata is needed for semantic and data mappings?

A huge amount of data is produced each day through social networks like Facebook, Twitter, etc. Through semantic web technologies, data produced from these social networks can be shared, integrated, and used to gain knowledge. As social networks grow more heterogeneous, declarative data mapping can help address this issue. The use of declarative data mappings is a valuable method of describing the relationship between two different datasets. Theses mappings are created within the community, but without standard metadata such that quality mappings cannot be shared, discovered, and reused properly. The ultimate purpose of our research is to identify a set of metadata fields/model that can be used to annotate declarative semantic and data mappings to facilitate their discovery, reusability, and quality assessment. As an early step in our research, 18 participants completed an online questionnaire between June and September 2022. The questionnaire targeted participants with varying levels of experience with semantic and data mappings. The survey results indicate that the model we have proposed for mapping metadata has received a positive response from the expert and intermediate-level participants. Also, expert participants have suggested adding extra metadata fields which are being considered as the second version of the model that is being developed.
LFP: Layer Wise Feature Perturbation based Graph Neural Network for Link Prediction

Learning on graph-structured data is an area where graph neural networks (GNN) have gained widespread use. In several tasks, such as node classification and graph classification, they outperformed traditional heuristic techniques. When it comes to link prediction, where the edge features, particularly multi-dimensional edge data, are critical, GNNs generally perform poorly compared to simple heuristic approaches. In this research, we provide a novel method for graph neural networks family which can better explore edge characteristics. These features may include both directed and undirected edges, as well as edges with many dimensions. The suggested framework has the potential to unify existing models of graph neural networks like GCN and GAT. We build a new method for edge perturbation for every GNN layer which can process edge features with more than one dimension. We test our proposed model for graph link prediction on a wide range of publicly available graph datasets. Our proposed method surpass the existing stateof-the-art approaches, employed based on GCNs and GAT, demonstrating the significance of leveraging edge properties for graph neural networks.
Does Noise Really Matter? Investigation into the Influence of Noisy Labels on Bert-Based Question Answering System

Recent works with the BERT-based models demonstrate their generalization ability and high performance on the new domain tasks. However, this kind of model requires a large amount of data. Collecting this data can be error-prone, and it is important to know: how the errors in data affect the quality of the model. In this work, we study the impact of data with different errors - noisy data on the training of the question answering-over-text BERT-model. We use the concept of random, structural, and irrelevant question noises. We study the robustness of QAT models during the training process with different settings, datasets, and noise types and discuss possible reasons. We also propose a real-world domain dataset to probe our findings in a real-world scenario. The results of an experimental study showed that following developed recommendations allowed performance improvement up to 3.6% in a real-world setting.
Attention Masking for Improved Near Out-of-Distribution Image Detection

Detecting near out-of-distribution (OOD) data is important when deploying a deep learning model. The goal of near-OOD detection is to distinguish the OOD samples when distributions of inliers and outliers are similar. However, an input sample containing unexpected information may degrade the OOD detection performance in downstream tasks. To address this problem, we propose an algorithm called attention masking, which masks the less-attended parts of the given input to precisely calculate its Mahalanobis distance from the training distribution. In our experiments, we use a largescale pre-trained model to measure the performance of our approach on the vision OOD benchmark tasks. For instance, on CFAR-100vs. CIFAR-10 detection, we improve the AUROC of Mahalanobis distance-based OOD detection from 91.23% to 92.89% using ViT-Base model. In addition, we measured the performance of the challenging zero-shot OOD detection, which only utilizes the pre-trained weights without fine-tuning on CFAR-100 or CFAR-10, and achieved an average AUROC improvement of 7% on CFAR-100vs. CFAR-10 detection.
Enhancing Image Representation in Conditional Image Synthesis

Even though deep neural network-based conditional image synthesis has shown impressive advances in terms of image quality, they still fall short of dealing with domain-dependent global and local styles and distinct shape representations of synthesized images. To address this issue, we propose a novel GAN-based conditional image synthesis model that incorporates a conditional normalization layer called IAN for style and edge-weighted shape enhancing loss for shape. Comparative experiments and ablation studies on popular and different domain datasets show that the proposed model outperformed other popular image-to-image translation model for diverse image domains.
CLIP: Train Faster with Less Data

Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In datacentric approaches, the focus is to refine and improve the quality of the data to improve the learning performance of the models rather than redesigning model architectures. In this paper, we propose CLIP i.e., Curriculum Learning with Iterative data Pruning. CLIP combines two data-centric approaches i.e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed. The proposed scheme applies loss-aware dataset pruning to iteratively remove the least significant samples and progressively reduces the size of the effective dataset in the curriculum learning training. Extensive experiments performed on crowd density estimation models validate the notion behind combining the two approaches by reducing the convergence time and improving generalization. To our knowledge, the idea of data pruning as an embedded process in curriculum learning is novel.
Feed-forward Design vs. Mimic Learning for Interpretable Deep Models: A Comparative Study

Despite great success in deep neural networks (DNNs) due to high accuracy, there are still major limitations (esp. lack of interpretability). In some domains such as medicine, finance, and real-time embedded systems, the interpretability problem needs to be improved because every decision needs to be clearly explained due to huge failure costs. To do this, state-of the-art works from knowledge distillation (KD) of interpretable mimic models to feed-forward DNN models for interpretability (against back-propagation based black-box methods) are adopted and compared. In this comparative study, we 1) adopt two theoretically different interpretable approaches for DNN models: a) Mimic Learning from KD methods and b) feed-forward multilayer perception (FF-MLP) using two-class LDAs, 2) reproduce and compare in terms of accuracy and interpretability using quantitative metrics, and 3) summarize the pros and cons of the two approaches with the results that mimic learning exceeds FF-MLP in the accuracy and in the number of simulatability operation counts (# SOC).
Jobicon:A Job Icon Dataset for Concept Recognition by Understanding Compositionality

As an icon is a means of communication, understanding its concept is of utmost importance. However, in many cases, an icon’s appearance is not in line with its concept. To predict the abstract concept with appearance, we propose compositional learning for icons. The whole icon’s concept is predicted by combining its compositional components. We propose Jobicon, ajob symbolizing icon dataset suitable for compositional learning. Jobicon is classified by the components of the job’s icons. When applied in the image retrieval model, our dataset demonstrated the best performance and is suitable for compositional learning.
The Usage of Monte Carlo Simulation to Predict The Sales of Sustainable Batik Products in Small Medium Enterprise

As an intangible cultural heritage, Indonesian Batik continues to develop and is increasingly in demand by domestic and foreign people. In the current era, the concept of making batik that is sustainable or more environmentally friendly has emerged, one of the efforts is in terms of coloring that uses natural dyes. This innovation produces softer batik colors aka soil colors, besides that the manufacturing process is longer than using chemical dyes, so the price is more expensive. With these changes, analyzing the development of sustainable batik sales is important to identify an increase in sales. This study aims to determine the prediction of sustainable batik sales with Monte Carlo simulations precisely and accurately. The data processed is batik production using natural dyes, then converted into probability distributions in the form of compulsive frequencies and then generated random numbers to obtain random numbers. After that, group the interval limit of the random number that has been obtained and continue with the simulation process so that it gets the simulation results and the percentage of accuracy using the Monte Carlo method. The results of the study on data processing from 2020 to 2021 have an accuracy of 90%. So this research is very appropriate for predicting sales for the next year.
Coyote: A Compiler for Vectorizing Encrypted Arithmetic Circuits

Fully Homomorphic Encryption (FHE) is a scheme that allows a computational circuit to operate on encrypted data and produce a result that, when decrypted, yields the result of the unencrypted computation. While FHE enables privacy-preserving computation, it is extremely slow. However, the mathematical formulation of FHE supports a SIMD-like execution style, so recent work has turned to vectorization to recover some of the missing performance. Unfortunately, these approaches do not work well for arbitrary computations: they do not account for the high cost of rotating vector operands to allow data to be used in multiple operations. Hence, the cost of rotation can outweigh the benefits of vectorization.
Space-Efficient TREC for Enabling Deep Learning on Microcontrollers

Deploying deep neural networks (DNNs) for a resource-constrained environment and achieving satisfactory performance is challenging. It is especially so on microcontrollers for their stringent space and computing power. This paper focuses on new ways to make TREC, an optimization recently proposed to enable computation reuse in DNNs, space and time efficient on Microcontrollers. The solution maximizes the performance benefits while keeping the DNN accuracy stable. Experiments show that the solution eliminates over 96% computations in DNNs and makes them fit well into microcontrollers, producing 3.4-5× speedups with only marginal accuracy loss.
DefT: Boosting Scalability of Deformable Convolution Operations on GPUs

Deformable Convolutional Networks (DCN) have been proposed as a powerful tool to boost the representation power of Convolutional Neural Networks (CNN) in computer vision tasks via adaptive sampling of the input feature map. Much like vision transformers, DCNs utilize a more flexible inductive bias than standard CNNs and have also been shown to improve performance of particular models. For example, drop-in DCN layers were shown to increase the AP score of Mask RCNN by 10.6 points while introducing only 1% additional parameters and FLOPs, improving the state-of-the-art model at the time of publication. However, despite evidence that more DCN layers placed earlier in the network can further improve performance, we have not seen this trend continue with further scaling of deformations in CNNs, unlike for vision transformers. Benchmarking experiments show that a realistically sized DCN layer (64H×64W, 64 in-out channel) incurs a 4× slowdown on a GPU platform, discouraging the more ubiquitous use of deformations in CNNs. These slowdowns are caused by the irregular input-dependent access patterns of the bilinear interpolation operator, which has a disproportionately low arithmetic intensity (AI) compared to the rest of the DCN. To address the disproportionate slowdown of DCNs and enable their expanded use in CNNs, we propose DefT, a series of workload-aware optimizations for DCN kernels. DefT identifies performance bottlenecks in DCNs and fuses specific operators that are observed to limit DCN AI. Our approach also uses statistical information of DCN workloads to adapt the workload tiling to the DCN layer dimensions, minimizing costly out-of-boundary input accesses. Experimental results show that DefT mitigates up to half of DCN slowdown over the current-art PyTorch implementation. This translates to a layerwise speedup of up to 134% and a reduction of normalized training time of 46% on a fully DCN-enabled ResNet model.
Neural networks for calculation of complex fields from far-field intensities in mesoscopic photonic systems

Mesoscopic photonics is built on the foundations of theories from mesoscopic physics of electron propagation, although optical techniques have enabled major strides in experimental research on the topic. Theoretical techniques calculate relevant parameters using wave functions or electric fields inside a sample, while experiments mostly measure intensities in the far field. Ideally, the theoretically calculated and experimentally measured parameters must be on equal footing. Here, we employ deep neural networks that calculate near-field intensities and, subsequently, real and complex fields, from simulated far-field measurements. A fully connected deep neural network is built for one-dimensional systems, while a convolutional neural network is developed for two-dimensional systems. The accuracy of these networks is consistently above 95%. We reveal the improvement in estimation of transport parameters by the predicted near-field data from raw measurement data.
Digital Educational Resources in Teaching Professional English Communication

The article considers the issue of using the digital environment in teaching professional communication at a non-linguistic university and aims to specify the role of a digital educational resource in business English courses and to further upgrade higher education. The relevance of the issue under consideration is due to the intensified intro­duction of digital resources in higher education. In this research the authors apply theoret­ical and empirical methods of analysis, consider modern approaches to the classification of digital educational resources for learning English, classify digital resources in accord­ance with the degree of their accessibility, study the ways of applying the communicative approach in teaching students a foreign language for special purposes, aimed at building and developing productive and receptive skills that future specialists will practice in their professional activities. The authors also analyze the results of a survey conducted among students about the experience of using a multimedia program based on Moodle educa­tional software platform, as a part of a business English course. The researchers come to the conclusion that the use of computer technology with a well-planned training program not only makes the process of learning a foreign language more attractive and interesting to students, but also contributes to a more effective development of their receptive and productive skills.
The Influence of Online Format on Forming Soft Skills in Foreign Language Teaching

The purpose of this research is to identify opportunities to develop soft skills, which are essential for graduates’ future professional activities, in online foreign language learning. The study focuses on distance learning, which has become a reality in university education due to the pandemic and the increased availability of higher education to those who cannot attend in-person classes (because of the distance, health problems, etc). Specifically, the research aims to demonstrate which soft skills, prioritized by employers, can and must be developed in distance foreign language learning, the preferred typology of exercises for this task, the specifics of teachers’ work in this format, and how to mobilize active attention in distance education. The study uses survey results collected from teachers and students at Lomonosov Moscow State University, which are analyzed along with relevant theoretical models. The research prioritizes tasks and assignments targeting soft skills such as creativity, learnability, critical thinking, and collaboration, with independent student work gaining more importance. The study concludes that online lessons should emphasize autonomous work performance over primary educational elements, and the results can form the basis for developing foreign language methodology in distance instruction. The research suggests this could be an optional module in a hybrid format that shows promise for higher foreign language education.
A Preliminary Evaluation of Learning Sign Language Through Information and Communications Technology (ICT)

Sign language is a medium of communication used to teach students with hearing problems and is also used in their daily lives in lieu of spoken language. Although there are individuals with hearing problems who still have the ability to speak the language, their number is minimal, and their verbal ability is very limited. The educational application or aids which are tailored and designed for mobile devices towards sign language is scarce. And there is a lack of teaching and learning aids to assist the deaf community in the learning process and those who are interested in learning Malaysian Sign Language. So, this chapter investigates a preliminary evaluation and previous research regarding the use of information and communications technology (ICT) for sign language. Future studies for this research will help a researcher develop a teaching aid based on mobile devices and enable users to use it as a reference and improve their communication. This mobile application is not only suitable for the deaf community but also for the general public.
Cybersecurity Strategy, Formulation, Management, and Sustainability Issues in Digital Transformation and Sustainability

Digitalization of financial services has stimulated regulatory and regulatory concerns, technological progress, new capacities and client expectations, and has exposed the sector to competition. A dramatic, expensive, and risky turning moment challenged the conventional banking industry's viability. However, some safety and ethical difficulties still exist when it comes to the adaptation of business sustainability. Global transformation, harmonization, and convergence will be expanded, turning financial services into intelligent services in the services sector. The editorial argues that for digital transformation, we must first expand our understanding of the interactions and interrelationships that contribute to knowledge, creativity, and value creation among society's participants. Cybersecurity is a modern topic that is becoming a major concern owing to the epidemic in the industrial and service sectors. This research exposes cybersecurity strategy, formulation, management, sustainability issues, and organizational and corporate culture management in digital transformation.
Data Analysis Tools for Neural Data Cognitive Computing

The ability to draw conclusions and take action from data hasn't altered all that much despite significant technological developments in recent years. Applications are still typically created to carry out predefined tasks or automate business procedures; therefore, the logic must be coded to account for all possible usage scenarios. They do not grow from their mistakes or adjust to changes in the data. Although they are cheaper and faster, computers aren't substantially smarter. Of course, people now aren't all that much brighter than they were in the past. For both humans and machines, that is about to change. The old notion of computing as process automation is being replaced by a new generation of information systems that offer a collaborative platform for discovery. These systems' initial wave has already improved human cognition in a number of areas.
Knowledge Representation-Based Hemispheric Specialization of the Brain

Knowledge is an essential ingredient for the development of the majority of human cognitive skills. The subject of how to define knowledge is a challenging one. Knowledge is an organised collection of information that may be acquired by learning, perception, or the application of reasoning. This chapter focuses on human brains and computer knowledge models. Concepts and categories are offered as a paradigm for storing information, followed by semantic networks and a description of how individuals store and interpret information. The authors also explore artificial methods to store and retrieve information and make quick judgments, as well as biological features. After studying how information is stored and accessed in artificial and human systems, they analyse hemisphere specialisation. This chapter reviews trials that have advanced research in this area and examines if they interpret information differently.