Reviewerly/exHarmony huggingface dataset:
Leveraging Active and Continual Learning for Improving Deep Face Recognition in-the-Wild

Face recognition systems play a vital role in various applications by providing identification and verification based on facial features. However, these systems face challenges in large-scale in-the-wild applications, where the current static pipelines are usually unable to cope with the high velocity, variety, and volume of the data, negatively affecting their accuracy and reliability. To overcome these challenges, in this paper, we propose departing from traditional static face recognition pipelines and moving towards dynamic and adaptable approaches. To this end, we propose a combination of an active and continual learning approach that can automatically augment the model with informative samples gathered during the inference, provided that the model is already confident enough, significantly improving its recognition accuracy. Furthermore, the proposed pipeline also natively incorporates active learning, allowing for using human feedback, when available, to further improve its performance. The effectiveness of the proposed method is validated on a challenging setup using two large-scale face recognition datasets.
PROBLEMS OF STUDYING CHOREOGRAPHIC DISCIPLINES IN THE CONDITIONS OF DISTANCE EDUCATION

The article analyzes the process of distance learning of graduates of the Department of Choreography of Kharkiv National Pedagogical University named after H.S. Frying pans Factors affecting the formation of professional abilities of students with traditional and distance forms of education are considered. Introduction of distant education in Ukraine has been stipulated by objective demands of modern Society. Cause-and-effect relationships between teaching methods and the formation of pedagogical competences of choreographers-apprentices have been established. On the basis of the studied facts, conclusions were drawn and recommendations were made regarding the improvement of the quality of the educational process of students of choreographic specialties during distance learning. Having considered the positive and negative aspects of distance education of students of the Department of Choreography of the Kharkіv National University, it should be noted that this form of education is effective, but only in a mixed format together with traditional education, along with the acquisition of professional practical knowledge, skills and abilities. There must be specially equipped rooms, but, unfortunately, most of the students do not have any. Moreover, there is a lack of motivation, self-discipline, and a lack of system as for gained knowledge. It is proved that the effectiveness of the distance format of professional training of future teachers of choreography during martial law depends on the level of studentsʼ motivation for learning and their self-organization. Self-organization ensures the studentʼs ability to plan and carry out their own learning activities in accordance with the priorities of professional training. It is motivation that is the driving force behind the studentʼs acquisition of the necessary professional knowledge and professional skills.&#x0D; It should be noted that this work does not cover all the issues related to the study of choreographic disciplines in the conditions of distance learning. This study requires further study and determination of the impact of distance education on the formation of competencies of students of choreographic specialties.
The Usage of Industry 4.0 Enabling Technologies in the Education

This paper analyzes the possibilities of applying technological trends of the Industry 4.0 concept in education within the framework of achieving Sustainable Development Goals. The task addressed in this work involves identifying the specifics and potential opportunities of digital technologies in the era of Industry 4.0 (both individually and through their integration) that are made available to education in the context of its digital transformation and transform it in the new concept «Education 4.0». The results show that not all digital technologies are equally applicable in the educational sphere.
Comparison Of K-Means, K-Medoids, and Fuzzy C-Means Algorithms for Clustering Drug User’s Addiction Levels

Narcotics, psychotropics, and addictive substances are drugs that can activate brain systems, affect dopamine levels, and cause addiction. In Indonesia, there is a law requiring drug addicts to receive treatment and care. To properly treat a drug addict, it is first necessary to determine the level of addiction. Data mining methods such as clustering can be used to assess a user's level of drug addiction. This study uses the clustering algorithms Fuzzy C-means, K-Medoids, and K-means. The performance of the three clustering algorithms will then be evaluated based on the average similarity of clusters. Data such as how many types of drugs that used, the length of time they were used, the psychiatric status, and the physical condition status, are used. Clustering was accomplished using the data mining software RStudio. The clustering algorithms were then evaluated with the Davies Bouldin Index (DBI). The K-Medoids algorithm was found to have the best average similarity value of cluster for determining drug users' addiction levels based on the results of the analysis.&#x0D; &#x0D;
Development of an augmented reality guidance system for head and neck cancer resection

Abstract The use of head‐mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying three‐dimensional (3D) holographic reconstructions of medical data. However, performing AR surgeries on complex areas such as the head and neck region poses challenges in terms of accuracy and speed. This study explores the feasibility of an AR guidance system for resections of positive tumour margins in a cadaveric specimen. The authors present an intraoperative solution that enables surgeons to upload and visualize holographic reconstructions of resected cadaver tissues. The solution involves using a 3D scanner to capture detailed scans of the resected tissue, which are subsequently uploaded into our software. The software converts the scans of resected tissues into specimen holograms that are viewable through a head‐mounted AR display. By re‐aligning these holograms with cadavers with gestures or voice commands, surgeons can navigate the head and neck tumour site. This workflow can run concurrently with frozen section analysis. On average, the authors achieve an uploading time of 2.98 min, visualization time of 1.05 min, and re‐alignment time of 4.39 min, compared to the 20 to 30 min typical for frozen section analysis. The authors achieve a mean re‐alignment error of 3.1 mm. The authors’ software provides a foundation for new research and product development for using AR to navigate complex 3D anatomy in surgery.
NCMPy: A Modelling Software for Neutrosophic Cognitive Maps based on Python Package

Cognitive maps are a vital tool that can be used for knowledge representation and reasoning. Fuzzy Cognitive Maps (FCMs) are popular soft computing techniques used to model large and complex systems, and they can aid in explainable artificial intelligence (AI). FCMs, however, cannot model the indeterminacy that arises in a system due to various uncertainties. Neutrosophic Cognitive Maps (NCMs), upgraded FCMs that could model indeterminacy, were introduced to address this issue. NCMs are a generalization of FCMs, a field of cognitive science firmly based on neural networks. NCMs have been used to solve a wide range of problems. NCMs were introduced in 2002, and even after 20 years, NCMs do not have any supportive software, package, toolbox, or visualization software like FCMs. The main reason for the absence of dedicated software is due to the indeterminacy concept 'I' and how it has to be handled. This paper presents the dedicated Python package created for handling the functioning of NCMs. The modelling software presented in this paper aids in visualizing the NCMs as a signed digraph with indeterminacy that is a directed signed neutrosophic graph. This package implements a sample case study using NCMs.
Verifiable Timed Accountable Subgroup Multi-signatures

Accountable subgroup multi-signature (ASM) is a form of multi-signature scheme that permits any subgroup of a group to sign a message on behalf of the group while ensuring that the signers are accountable for the multi-signature. Since the accountability principle cannot be provided in many multi-signature schemes, an ASM algorithm makes a difference by providing this feature. ASM has many application areas, especially due to its practical and efficient usage in blockchain or legacy systems. On the other hand, timed signatures allow one to send information to the future which is also necessary for many applications such as blockchain, secure communication, or proof of ownership. In this paper, we propose a timed version of a multi-signature scheme with the help of linearly homomorphic time-lock puzzles (LHTLP) using the method constructed by Thyagarajan et al. Also, we slightly modified the ASM scheme constructed by Boneh et al. to adapt it to the verifiable timed commitment schemes.
An edge cloud and Fibonacci-Diffie-Hellman encryption scheme for secure printer data transmission

&lt;abstract&gt; &lt;p&gt;Network printers face increasing security threats from network attacks that can lead to sensitive information leakage and data tampering. To address these risks, we propose a novel Fibonacci-Diffie-Hellman (FIB-DH) encryption scheme using edge cloud collaboration. Our approach utilizes properties of third-order Fibonacci matrices combined with the Diffie-Hellman key exchange to encrypt printer data transmissions. The encrypted data is transmitted via edge cloud servers and verified by the receiver using inverse Fibonacci transforms. Our experiments demonstrate that the FIB-DH scheme can effectively improve printer data transmission security against common attacks compared to conventional methods. The results show reduced vulnerabilities to leakage and tampering attacks in our approach. This work provides an innovative application of cryptographic techniques to strengthen security for network printer communications.&lt;/p&gt; &lt;/abstract&gt;
The Effect of Technology Utilization on Operations Performance with Process Innovation as a Mediator

This study aims to investigate the effect of technology utilization on operating performance, considering process innovation as a mediator in an organizational context, using the Smart Partial Least Squares (Smart PLS) analysis tool. The study involved 72 respondents from various industries and business sectors in Indonesia. The results showed that technology utilization has a positive and insignificant effect on operating performance. In addition, process innovation proved to be a strong mediator in the relationship between technology utilization and operating performance. This indicates that organizations that are able to utilize technology effectively are more likely to improve their operating performance, especially if they can generate innovation in their business processes. This research provides a deeper understanding of how technology can be a catalyst in improving an organization's operating performance, especially when supported by strong process innovation efforts. These results have important implications for organizations in their efforts to compete effectively in the ever-changing digital age. This research also contributes to the theoretical understanding of the relationship between technology utilization, process innovation, and operating performance in a business context. The practical implication of this study is that organizations should consider technology utilization and process innovation as an integral part of their strategy to improve operating performance
Word sense induction with agglomerative clustering and mutual information maximization

Word sense induction (WSI) is a challenging problem in natural language processing that involves the unsupervised automatic detection of a word’s senses (i.e., meanings). Recent work achieves significant results on the WSI task by pre-training a language model that can exclusively disambiguate word senses. In contrast, others employ off-the-shelf pre-trained language models with additional strategies to induce senses. This paper proposes a novel unsupervised method based on hierarchical clustering and invariant information clustering (IIC). The IIC loss is used to train a small model to optimize the mutual information between two vector representations of a target word occurring in a pair of synthetic paraphrases. This model is later used in inference mode to extract a higher-quality vector representation to be used in the hierarchical clustering. We evaluate our method on two WSI tasks and in two distinct clustering configurations (fixed and dynamic number of clusters). We empirically show that our approach is at least on par with the state-of-the-art baselines, outperforming them in several configurations. The code and data to reproduce this work are available to the public1.
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling

Recently slot filling has witnessed great development thanks to deep learning and the availability of large-scale annotated data. However, it poses a critical challenge to handle a novel domain whose samples are never seen during training. The recognition performance might be greatly degraded due to severe domain shifts. Most prior works deal with this problem in a two-pass pipeline manner based on metric learning. In practice, these dominant pipeline models may be limited in computational efficiency and generalization capacity because of non-parallel inference and context-free discrete label embeddings. To this end, we re-examine the typical metric-based methods, and propose a new adaptive end-to-end metric learning scheme for the challenging zero-shot slot filling. Considering simplicity, efficiency and generalizability, we present a cascade-style joint learning framework coupled with context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems effectively. Extensive experiments on public benchmarks demonstrate the superiority of the proposed approach over a series of competitive baselines.
Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning

Parameter-efficient fine-tuning (PEFT) has shown its effectiveness in adapting the pre-trained language models to downstream tasks while only updating a small number of parameters. Despite the success, most existing methods independently adapt to each task without considering knowledge transfer between tasks and are limited to low-data regimes. To overcome this issue, we propose Prototype-based HyperAdapter (PHA), a novel framework built on the adapter-tuning and hypernetwork. It introduces an instance-dense retriever and a prototypical hypernetwork to generate the conditional modules in a sample-efficient manner. This leads to comparable performance improvements against existing PEFT methods on multi-task learning and few-shot transfer learning. More importantly, when the available data size gets smaller, our method outperforms other strong baselines by a large margin. Based on our extensive empirical experiments across various datasets, we demonstrate that PHA strikes a better trade-off between trainable parameters, accuracy on stream tasks, and sample efficiency. Our code is publicly available at https://github.com/Bumble666/PHA
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning

We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates pre-trained network weights using contrastive learning so that the text fragments exhibiting similar emotions are encoded nearby in the representation space, and the fragments with different emotion content are pushed apart. While doing so, it also ensures that the linguistic knowledge already present in PLMs is not inadvertently perturbed. The language models retrofitted by our method, i.e., BERTEmo and RoBERTaEmo, produce emotion-aware text representations, as evaluated through different clustering and retrieval metrics. For the downstream tasks on sentiment analysis and sarcasm detection, they perform better than their pre-trained counterparts (about 1% improvement in F1-score) and other existing approaches. Additionally, a more significant boost in performance is observed for the retrofitted models over pre-trained ones in few-shot learning setting.
Frugal Prompting for Dialog Models

The use of large language models (LLMs) in natural language processing (NLP) tasks is rapidly increasing, leading to changes in how researchers approach problems in the field. To fully utilize these models’ abilities, a better understanding of their behavior for different input protocols is required. With LLMs, users can directly interact with the models through a text-based interface to define and solve various tasks. Hence, understanding the conversational abilities of these LLMs, which may not have been specifically trained for dialog modeling, is also important. This study examines different approaches for building dialog systems using LLMs by considering various aspects of the prompt. As part of prompt tuning, we experiment with various ways of providing instructions, exemplars, current query and additional context. The research also analyzes the representations of dialog history that have the optimal usable-information density. Based on the findings, the paper suggests more compact ways of providing dialog history information while ensuring good performance and reducing model’s inference-API costs. The research contributes to a better understanding of how LLMs can be effectively used for building interactive systems.
Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents

Automatically highlighting words that cause semantic differences between two documents could be useful for a wide range of applications. We formulate recognizing semantic differences (RSD) as a token-level regression task and study three unsupervised approaches that rely on a masked language model. To assess the approaches, we begin with basic English sentences and gradually move to more complex, cross-lingual document pairs. Our results show that an approach based on word alignment and sentence-level contrastive learning has a robust correlation to gold labels. However, all unsupervised approaches still leave a large margin of improvement.
Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation

Open-domain multi-turn dialogue generation encounters the significant challenge of lacking various types of knowledge from diverse sources. Existing models typically focus on identifying specific types of dialogue knowledge and utilize corresponding datasets for training. However, this approach often leads to limited generalization capabilities and increased computational resource requirements. Recently, large language models (LLMs) have shown impressive performance on natural language processing tasks. To harness the knowledge storage of LLMs, we propose a framework named KnowEE that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and then exploits the obtained knowledge for response generation. Our framework comprises two phases: First, we leverage five external datasets encompassing various types of knowledge to extract the most relevant samples to the dialogue context which are served as prompts to generate corresponding type of knowledge; Second, we inject the acquired knowledge into the ongoing dialogue context in fine-grained and coarse-grained manners, which is then fed into LLMs to generate the final dialogue response. Both automatic and manual evaluation results validate the effectiveness of our framework in exploring and exploiting multi-source multi-type knowledge to generate coherent, informative, and fluent responses.
Prompterator: Iterate Efficiently towards More Effective Prompts

With the advent of Large Language Models (LLMs) the process known as prompting, which entices the LLM to solve an arbitrary language processing task without the need for finetuning, has risen to prominence. Finding well-performing prompts, however, is a non-trivial task which requires experimentation in order to arrive at a prompt that solves a specific task. When a given task does not readily reduce to one that can be easily measured with well established metrics, human evaluation of the results obtained by prompting is often necessary. In this work we present prompterator, a tool that helps the user interactively iterate over various potential prompts and choose the best performing one based on human feedback. It is distributed as an open source package with out-of-the-box support for various LLM providers and was designed to be easily extensible.
An Adaptive Prompt Generation Framework for Task-oriented Dialogue System

The de facto way of utilizing black-box large language models (LLMs) to perform various downstream tasks is prompting. However, obtaining suitable prompts for specific tasks is still a challenging problem. While existing LLM-based methods demonstrate promising performance in task-oriented dialogue (TOD) task, they often require manual adjustment in prompt selection, or focus solely on dialogue understanding or generation. To address these issues, we propose an adaptive prompt generation framework to fully unleash the potential of LLMs for the comprehensive TOD system. Firstly, we design a trainable slot generator (TSG) that can generate domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation. Next, we propose an adaptive prompt generator (APG) that utilizes the prior knowledge to generate prompts for the LLM, deriving the belief state and system response of the dialogue for evaluation. Finally, we evaluate our framework on the MultiWOZ 2.0 dataset. Extensive experiments demonstrate that our method outperforms existing methods. Our code and data will be released.
EMO-KNOW: A Large Scale Dataset on Emotion-Cause

Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset’s reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event.