Reviewerly/exHarmony huggingface dataset:
Integrating GPT-Technologies with Decision Models for Explainability

The ability to provide clear and transparent explanations for the outcome of a decision is critical for gaining user trust and acceptance, particularly in areas such as healthcare, finance, and law. While GPT-3 and ChatGPT are promising technologies for conversations, they cannot always be used to provide the correct outcome of operational decisions and to give explanations on questions about these decisions. The decision model logic can be employed to explain the decision-making process through various reasoning mechanisms. It is possible to provide automated reasoning and explanations for decisions with the use of chatbots powered by these decision models. However, these chatbots are not always user-friendly as users may struggle to determine the appropriate reasoning and explanation scenario for their questions. This paper explores the potential of GPT-3 technology to identify the appropriate reasoning and explanation scenario for decision-making in chatbots and compares its performance with directly asking the user questions. With GPT-3 technology’s ability to identify the appropriate scenario, it will facilitate the development of integrated GPT-3 and decision model powered chatbots that can provide correct and human-understandable explanations for operational decisions.
Scalable Concept Extraction in Industry 4.0

The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transform raw data into human understandable knowledge for reliable data-driven decision-making. Convolutional Neural Networks (CNNs) have been instrumental in processing image data, yet, their “black box” nature complicates the understanding of their prediction process. In this context, recent advances in the field of eXplainable Artificial Intelligence (XAI) have proposed the extraction and localization of concepts, or which visual cues intervene on the prediction process of CNNs. This paper tackles the application of concept extraction (CE) methods to industry 4.0 scenarios. To this end, we modify a recently developed technique, “Extracting Concepts with Local Aggregated Descriptors” (ECLAD), improving its scalability. Specifically, we propose a novel procedure for calculating concept importance, utilizing a wrapper function designed for CNNs. This process is aimed at decreasing the number of times each image needs to be evaluated. Subsequently, we demonstrate the potential of CE methods, by applying them in three industrial use cases. We selected three representative use cases in the context of quality control for material design (tailored textiles), manufacturing (carbon fiber reinforcement), and maintenance (photovoltaic module inspection). In these examples, CE was able to successfully extract and locate concepts directly related to each task. This is, the visual cues related to each concept, coincided with what human experts would use to perform the task themselves, even when the visual cues were entangled between multiple classes. Through empirical results, we show that CE can be applied for understanding CNNs in an industrial context, giving useful insights that can relate to domain knowledge.
Learning Depth Representation from RGB-D Videos by Time-Aware Contrastive Pre-training

Existing end-to-end depth representation in embodied AI is often task-specific and lacks the benefits of emerging pre-training paradigm due to limited datasets and training techniques for RGB-D videos. To address the challenge of obtaining robust and generalized depth representation for embodied AI, we introduce a unified RGB-D video dataset (UniRGBD) and a novel time-aware contrastive (TAC) pre-training approach. UniRGBD addresses the scarcity of large-scale depth pre-training datasets by providing a comprehensive collection of data from diverse sources in a unified format, enabling convenient data loading and accommodating various data domains. We also design an RGB-Depth alignment evaluation procedure and introduce a novel Near-K accuracy metric to assess the scene understanding capability of the depth encoder. Then, the TAC pre-training approach fills the gap in depth pre-training methods suitable for RGB-D videos by leveraging the intrinsic similarity between temporally proximate frames. TAC incorporates a soft label design that acts as valid label noise, enhancing the depth semantic extraction and promoting diverse and generalized knowledge acquisition. Furthermore, the adjustments in perspective between temporally proximate frames facilitate the extraction of invariant and comprehensive features, enhancing the robustness of the learned depth representation. Additionally, the inclusion of temporal information stabilizes training gradients and enables spatio-temporal depth perception. Comprehensive evaluation of RGB-Depth alignment demonstrates the superiority of our approach over state-of-the-art methods. We also conduct uncertainty analysis and a novel zero-shot experiment to validate the robustness and generalization of the TAC approach. Moreover, our TAC pre-training demonstrates significant performance improvements in various embodied AI tasks, providing compelling evidence of its efficacy across diverse domains.
Implementasi Algoritma RC4A Dalam Pengamanan Citra Digital

Digital images that are private and confidential are very vulnerable to wiretapping by irresponsible parties, especially if the images are distributed via the internet. The act of theft and abuse of secret images of course can harm the image owner. One of the techniques to reduce the above problems is by using cryptographic techniques. Cryptographic techniques can secure images by means of digital image encryption by implementing the RC4A algorithm then using the initial keyword as input in the process carried out to generate new, more random keywords, based on the Key Schedule Algorithm. (KSA) and pseudo random generation algorithm (PRGA). To determine the comparison between the original image (PlainImage) and the original image (CipherImage), processing was carried out with MeanSquareError (MSE) and PeakSiqnalToNoiseRation (PSNR). The results of this study were digital images encrypted with the RC4A algorithm in terms of time differences and image results after encryption where the time used to encrypt is shorter than the description and the image results before encryption are not the same after encryption.
Perancangan Aplikasi Penyandian Agenda Pribadi Menggunakan Algoritma Kunci Public Elgamal

In this final project, an encryption is created that is integrated with the existing agenda encoding application to add security to the security process on the mobile device. By using the ElGamal encryption method, it is hoped that the encryption process carried out through mobile devices will be more secure. Because, there is a public key and a private key that is only known by the user. The resulting output is ciphertext which when the user wants to read it, it is necessary to carry out the decryption process. In addition, in the encryption process for the same plaintext, different ciphertexts are obtained, but in the decryption process, the same plaintext is obtained. Thus, making the agenda safer than before.
Efektivitas Pembelajaran Daring Mapel PJOK Pada Masa Covid-19 di SD Negeri 2 Bumisari

Problems that arise with online learning are the limitations of an unstable internet network, especially in villages, limited mastery of technology, and the lack of attention and support from parents for their children. The purpose of this study is to determine the level of effectiveness of PJOK online learning according to the views of students and teachers' views. This study uses a quantitative research design with a survey research model. The population in this study were all students in grades 4.5 and 6 at SD N 2 Bumisari with the sampling technique used using total sampling. Data collection techniques using (1) Observation (2) Questionnaire (3) Documentation. The data analysis technique used is descriptive analysis. The results of the study on the Effectiveness of Online Learning in PJOK at SD N 2 Bumisari Purbalingga according to the views of students, the results were very effective at 0%, the effective category was 87.5%, the less effective category was 12.5%, the ineffective category was 0%. Meanwhile, according to the teacher's view, the results obtained in the very effective category of 0%, the effective category of 100%, the less effective category of 0%, and the ineffective category of 0%. So it can be concluded that the effectiveness of maple PJOK's online learning in the school is in the effective category
Minting DOIs for research modules

With the recent announcement that Hypergraph will evolve into a web publishing platform [https://libscie.org/blog/hypergraph-lessons-and-future] for research modules (with a new name forthcoming), Liberate Science also signed up to become a member of CrossRef [https://www.crossref.org/]. This means we'll start issuing ("minting") Digital Object Identifiers (DOIs) for research modules in the near future.
Hypergraph v0.10.6

We just released Hypergraph (Beta) v0.10.6 [https://libscie.org/hypergraph] 🎉 New releases of Hypergraph will occur on the 28th of every month — we only break that cycle if there are urgent fixes that we need to ship as soon as possible (e.g., for security). If you're interested in understanding how we version our releases, check out Semantic Versioning [https://semver.org/]. If you have Hypergraph (Beta) installed, it will auto-install next ti
Hypergraph v0.10.5

We just released Hypergraph (Beta) v0.10.5 [https://hypergraph.xyz] 🎉 New releases of Hypergraph occur on the 28th of every month — we only break that cycle if there are urgent fixes that we need to ship as soon as possible (e.g., for security). If you're interested in understanding how we version our releases, check out Semantic Versioning [https://semver.org/]. If you have Hypergraph (Beta) installed, it will auto-install next time you run it
SpeX: A Rewriting-Based Formal Specification Environment

This is a gentle introduction to SpeX, a rewriting-based logical environment and executable framework implemented in Maude that facilitates the experimental development of formal specification languages and tools. The environment is language agnostic, so it is not geared towards any particular syntax, semantics, or supporting technology; instead, it provides a rich collection of libraries that assist the continuous integration of parsers and information processors. We outline the general architecture of SpeX, discuss its operational semantics, and illustrate the steps necessary in order to integrate new languages.
Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques

Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.
Blockchain-based tripartite evolutionary game study of manufacturing capacity sharing

In the context of the new round of manufacturing innovation, the sharing economy drives the transformation of manufacturing industry to accelerate the integration and development. However, there are some problems in the process of manufacturing capacity sharing, such as information privacy and security, and difficulty in tracing the sharing process, etc. The application of blockchain technology can effectively solve these problems. To explore the capacity sharing behaviour of manufacturing enterprises from the perspective of blockchain, the article combines evolutionary game theory and constructs a tripartite game model of manufacturing capacity sharing. The replication dynamics and evolutionary stability of the model are analysed using evolutionary game theory, and numerical simulations are carried out using MATLAB software to analyse the impact of parameter changes on the evolutionary outcome. The research results show that the incentive and penalty coefficients under blockchain technology have a facilitating effect on enterprises to carry out sharing, and the enhancement of reputation gain coefficient and loss can promote positive services on the platform.
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration.
Segment Augmentation and Prediction Consistency Neural Network for Multi-label Unknown Intent Detection

Multi-label unknown intent detection is a challenging task where each utterance may contain not only multiple known but also unknown intents. To tackle this challenge, pioneers proposed to predict the intent number of the utterance first, then compare it with the results of known intent matching to decide whether the utterance contains unknown intent(s). Though they have made remarkable progress on this task, their method still suffers from two important issues: 1) It is inadequate to extract multiple intents using only utterance encoding; 2) Optimizing two sub-tasks (intent number prediction and known intent matching) independently leads to inconsistent predictions. In this paper, we propose to incorporate segment augmentation rather than only use utterance encoding to better detect multiple intents. We also design a prediction consistency module to bridge the gap between the two sub-tasks. Empirical results on MultiWOZ2.3 show that our method achieves state-of-the-art performance and improves the best baseline significantly.
SNAKE Challenge: Sanitization Algorithms under Attack

While there were already some privacy challenges organized in the domain of data sanitization, they have mainly focused on the defense side of the problem. To favor the organization of successful challenges focusing on attacks, we introduce the SNAKE framework that is designed to facilitate the organization of challenges dedicated to attacking existing data sanitization mechanisms. In particular, it enables to easily automate the redundant tasks that are inherent to any such challenge and exhibits the following salient features: genericity with respect to attacks, ease of use and extensibility. We propose to demonstrate the main features of the SNAKE framework through a specific instantiation focusing on membership inference attacks over differentially-private synthetic data generation schemes. This instance of the SNAKE framework is currently being used for supporting a challenge co-located with APVP 2023 (the French workshop on the protection of privacy).
Online Learning and Academic Satisfaction of University Students of Administrative Sciences in A Public University of ICA in Post-Pandemic Times

The importance of this research focused on the adaptation to online learning, since traditionally a face-to-face education was developed for university students, but the transition to an asynchronous education, with the needs and development of university students have changed, such as: learning resources, academic mentoring, and teaching didactics, the satisfaction of what is learned, the virtual platform used to study to identify their changes and others. The purpose of this study was to recognize the factors that influence asynchronous learning in the academic satisfaction of the students of the professional career of Administration of the San Luis Gonzaga National University, presenting the theoretical constructs as well as the results of the research that preceded us, both international and national, within the scheme of a curriculum based on professional competencies. The methodology of the study focused on basic quantitative research that allowed a correlational study in order to analyze the factors of distance learning of the 618 students that were part of the population, and to obtain the information from the students, a questionnaire of 23 questions was applied. Subsequently, current statistical tools were used, and the research findings were important in considering that the research objectives were validated, and the hypotheses were contrasted, where online learning is significantly related to the academic satisfaction of the students.
PS-SA: An Efficient Self-Attention via Progressive Sampling for User Behavior Sequence Modeling

As the self-attention mechanism offers powerful capabilities for capturing sequential relationships, it has become increasingly popular to use it for modeling user behavior sequences in recommender systems. However, the self-attention mechanism has a quadratic computational complexity of O(n^2), as it conducts interactions among all item pairs in the sequence. This can lead to expensive model training and slow inference speeds, which may hinder practical deployment. To this end, we pursue to develop alternative approaches to improve the efficiency of the self-attention mechanism. We observe that the attention scores calculated from each item interacting with other items (including itself) are sparse, indicating that there are limited valuable item pairs (with non-zero attention weight) that contribute to the final output. This motivates us to develop effective strategies for discerning valuable items and computing attention scores solely for these items, thereby minimizing the consumption of unnecessary computations. Herein, we present a novel Progressive Sampling-based Self-Attention (PS-SA) mechanism, which utilizes a learnable progressive sampling strategy to identify the most valuable items. Subsequently, we solely utilize these selected items to produce the final output. Experiments on academic and production datasets demonstrate PS-SA could still achieve promising results while reducing computational costs. It is notable that we have successfully deployed it on Alibaba display advertising system, resulting in a 2.6% CTR and 1.3% RPM increase.
Attribute-enhanced Dual Channel Representation Learning for Session-based Recommendation

Session-based recommendation (SBR) aims to predict the anonymous user's next-click items by modeling the short-term sequence pattern. As most existing SBR models generally generate item representations based only on information propagation over the short sequence while ignoring additional valuable knowledge, their expressive abilities are somewhat limited by data sparsity caused by short sequence. Though there have been some attempts on utilizing items' attributes, they basically embed attributes into items directly, ignoring the fact that 1) there is no contextual relationship among attributes; and 2) users have varying levels of attention to different attributes, which still leads to unsatisfactory performance. To tackle the issues, we propose a novel Attribute-enhanced Dual Channel Representation Learning (ADRL) model for SBR, in which we independently model session representations in attribute-related pattern and sequence-related pattern. Specifically, we learn session representations with sequence patterns from the session graph, and we further design an frequency-driven attribute aggregator to generate the attribute-related session representations within a session. The proposed attribute aggregator is plug-and-play, as it can be coupled with most existing SBR models. Extensive experiments on three real-world public datasets demonstrate the superiority of the proposed ADRL over several state-of-the-art baselines, as well as the effectiveness and efficiency of our attribute aggregator module.
GAN-IE: Generative Adversarial Network for Information Extraction with Limited Annotated Data

Extracting valuable information from a large corpus of unstructured data poses a formidable challenge in many applications. Transformer-based architectures e.g. BERT, employing transfer learning techniques, have exhibited promising results across diverse NLP tasks. Nonetheless, the practical implementation of these models presents a significant hurdle given the substantial demand for annotated data during the training phase. In this paper, we present GAN-IE, a novel GAN-based model architecture, designed specifically for information extraction from unstructured textual data while accounting for limited annotated resources. In a generative adversarial setting, GAN-IE leverages BERT’s rich semantic and contextual knowledge obtained from unlabelled data while fine-tuning. Experimental results show that GAN-IE achieves a level of accuracy that surpasses the current state-of-the-art models when trained using a fraction of labelled data ( $$\sim $$ 100–200 annotated samples).