Reviewerly/exHarmony huggingface dataset:
Revolutionizing Staffing and Recruiting with Contextual Knowledge Graphs and QNLP: An End-to-End Quantum Training Paradigm

The staffing and recruiting industry is continuously evolving, and recent advancements in Knowledge Graphs (KG) and Quantum Natural Language Processing (QNLP) has garnered considerable attention. The integration of these state-of-the-art technologies is fueled by the necessity to improve language models' capacity to comprehend context and make precise decisions. This research paper presents a novel approach to revolutionize the staffing and recruiting industry by integrating Knowledge Graph (KG) and Quantum Natural Language Processing (QNLP) to formulate an end-to-end QNLP training pipeline. The proposed solution consists of three interdependent subsystems that work in unison to construct contextual KG and train language models. The Information Extraction subsystem extracts semantic relationships and connections between entities from large and complex recruitment data to construct domain specific contextual KG. The QNLP model training pipeline subsystem, which is fed with domain-rich KG data, runs on Quantum Circuits, accelerates the training process by effectively incorporating high-dimensional features to the deep layers of language models. Finally, the Information Retrieval subsystem is based on semantic data taxonomy, retrieving contextual data from the KG for the trained language models to be implemented on various distinctive use cases in the staffing and recruiting industry. This solution provides a faster and more contextual approach to analyze recruitment data, empowering recruiters to concentrate on strategic tasks such as candidate engagement and client relationship building, ultimately leading to better business decision-making capabilities.
Generation of New Bilingual Dictionaries Through Inference Techniques Supported by a Graph Oriented Database Management System

The popularization of the internet allowed the use of different technologies for storing and retrieving bilingual dictionaries. An example is the Resource Description Framework (RDF) datasets. The storage of bilingual dictionaries in this type of structure makes it possible to discover indirectly connected translations through inference. However, such datasets are not efficient in graph search operations, which are important actions for inference techniques in the discovery of translations. On the other hand, database management systems based on the graph model offer a collection of resources that facilitate the tasks that involve such operations. In this way, a group of bilingual dictionaries from an RDF dataset was adapted to a Graph Database Management System (GDMS) which made it possible to reduce and simplify the structures of these dictionaries. Subsequently, 2 translation inference techniques were implemented in which two new bilingual English-Portuguese dictionaries were automatically generated. Thus, such inference algorithms are efficient in generating new dictionaries through GDMS. The scientific contribution is the application of a new technology for data storage and retrieval in support of bilingual dictionaries, in which there are advantages together with inference techniques in the generation of bilingual dictionaries and not addressed by related papers in literature.
ASAGeR: Automated Short Answer Grading Regressor via Sentence Simplification

We propose an automated short answer grading system (ASAG) to estimate the student answer scores via text summarization from LLMs. The step of text summarization provides enough question answer normalization so that the summarized answers have the answer keys well organized and the grading based on that should be more accurate and easier than before, no matter the answers are graded by human or automatic graders. On the other hand, we also discuss the scenario when more than one grader are involved in the grading but providing inconsistent scores. We adopt a majority voting mechanism to overcome such difficulty and produce superior result in average. Overall the proposed methodology has its evaluation done to show the superiority to other state-of-the-art methods. The pre-trained transformer version 3.5 (GPT 3.5) is used to serve the text summarization tool given a well-designed prompt.
Incremental Integration of Fragmented Knowledge Via the Edition Protocol of a Shared Knowledge Base

The main approaches to knowledge representation and sharing (KS) focus on easing the exploitation and exchange of knowledge representations (KRs) between particular agents or for particular applications. These approaches lead to the proliferation of mainly independently developed KR bases (KBs) which are mutually partially redundant and contradictory, thus restricting a more general KS. This article shows how a shared KB edition protocol can support the incremental and cooperative integration of fragmented knowledge into a (networked or not) consistent well-organized KB, without manual or automatic selection between contradictory KRs. To that end, the protocol helps and enforces i) the setting of particular relations between “competing” KRs (i.e. those that are contradictory or at least partially redundant) to compare them and justify their joint existences in the KB, and ii) a representation of them that keeps the KB consistent. Besides providing more knowledge search and inference possibilities, these relations can be exploited by default or user-defined rules for KR filtering and choices between them. This article also shows that this approach can be implemented in various ways and be used with most inference engines.
Client-Edge-Cloud Hierarchical Federated Learning Based on Generative Adversarial Networks

Federated learning technology is a key approach to ensuring data privacy protection and promoting data sharing across different domains. Traditional federated learning, which relies on cloud servers, suffers from high computation latency and low reliability. Edge-based federated learning can reduce communication latency but has limitations in terms of the number of accessible clients, making it unsuitable for large-scale data computation. To address the integration of the cloud, edges, and end devices in real-world scenarios, previous research has proposed hierarchical federated learning architectures. However, these approaches have drawbacks such as limited model training diversity, high training costs for end devices, and high communication costs. To overcome these challenges, we propose a client-edge-cloud hierarchical federated learning protocol based on Generative Adversarial Networks (GANs). By combining GAN technology, it mitigates the issues of client and edge server data heterogeneity, thereby enhancing the stability of model training. The protocol allows edge servers to synthesize virtual datasets through GANs, enabling separate training of the client-edge federated learning module and the edge-cloud federated learning module. We conducted experiments on the MNIST dataset, and the results indicate that the protocol outperforms traditional hierarchical federated learning protocols in multiple scenarios. The protocol offers greater flexibility in model training and reduces overall system training costs, particularly in terms of lowering client communication and computation expenses.
Inductive Logic Programming for Explainable Graph Clustering

Graph clustering is a popular method to understand networks and make them accessible for downstream Machine Learning tasks. Especially for large networks, the results of graph clustering are difficult to explain, since visualizations can be chaotic and the metric used for clustering can be noninterpretable. We propose a method to use Inductive Logic Programming to provide a post-hoc, local explanation for the results of an arbitrary graph clustering algorithm. Our interactive approach is based on three steps: (1) Formalizing graph data as input for the Inductive Logic Programming system Popper, (2) Qualifying entities from the graph as instances for the variables in the resulting formal logic statements and (3) Re-integrating user feedback on the provided, comprehensible statements into Popper. Compared to other benchmark graph clustering explanation approaches, the proposed method shows superior behaviour in terms of interpretability of the rules and clauses as well as in terms of interactivity on six benchmark datasets.
Enhancing Network Alignment through Multi-Scale Information Fusion

The network alignment task aims to identify the correspondence of the same nodes across networks, which plays a crucial role in many fields. However, most existing methods focus solely on consistency assumptions at the local structure information of nodes, meaning that the alignment of neighboring node pairs tends to be consistent with each other, while ignoring information from other scales, such as higher-order information. This can easily lead to over-smoothing issues. In this paper, we propose a novel multi-scale embedding-based network alignment method in this paper. Specifically, we capture three types of node embedding information, including first-order embedding, multi-order embedding, and higher-order embedding, to obtain rich information. Then, we enhance the multi-order and higher-order embedding information of nodes by applying a stable-pair based fine-tuning method. Finally, we use a fusion mechanism based on importance weights to combine the alignment matrices obtained from these three types of node embedding information and obtain the optimal alignment matrix. We conduct extensive experiments on three real-world datasets to verify the effectiveness of our proposed model.
Context-Aware Session-Based Recommendation with Graph Neural Networks

Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the following limitations: 1) they fail to distinguish the item-item edge types when constructing the global graph for exploiting cross-session contexts; 2) they learn a fixed embedding vector for each item, which lacks the flexibility to reflect the variation of user interests across sessions; 3) they generally use the one-hot encoded vector of the target item as the hard label to predict, thus failing to capture the true user preference. To solve these issues, we propose CARES, a novel context-aware session-based recommendation model with graph neural networks, which utilizes different types of contexts in sessions to capture user interests. Specifically, we first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. Further, to encode the variation of user interests, we design personalized item representations. Finally, we employ a label collaboration strategy for generating soft user preference distribution as labels. Experiments on three benchmark datasets demonstrate that CARES consistently outperforms state-of-the-art models in terms of P@20 and MRR @20. Our data and codes are publicly available at https://github.com/brilliantZhang/CARES.
Online Multi-Label Classification with Scalable Margin Robust to Missing Labels

Multi-label classification with missing labels handles the problem that the label set contains unobserved missing labels due to the expensive human annotations. However, these works mainly concentrate on offline settings, and thus fail to deal with online streaming data in real time. In this paper, we focus on online multi-label learning with missing labels, and develop a novel maximum margin-based online algorithm with scalable margin (OML-SM). Specifically, we update the margin-based model incrementally based on the past model and the current incoming instance with its partially observed labels. To build robustness to missing labels, we first tune the classification margin of the negative labels via the label causality matrix, which is formed by the conditional probability of label pairs. Secondly, the label prototype matrix is proposed to adjust the margin by controlling the balancing parameter of the slack term. To adapt to the possible evolution of label distribution, the aforementioned two matrices are also incrementally updated based on the partially observed label set in an efficient way. Finally, the validity of the proposed method on multiple evaluation metrics is further supported by experiments conducted on a number of benchmark data sets.
Novel Design Algorithm for LCL Grid Filters Including Consideration of Arbitrary Grid Codes

For the connection of voltage source converters (VSC) to the low voltage grip, in addition to feasibility requirements, grid codes have to be met. Although the conditions differ depending on the area of application, the rated power or divergent regional standards, they typically specify limits regarding harmonics of the grid current. These limits are usually met by suitable passive filters. The most simple one would be a line choke, which is easy to dimension, but tend to be bulky, costly and inefficient. Higher order filters, e.g. of LCL-type, often perform better. Their design requires much more effort. Besides the harmonic attenuation, the placement of the inevitable resonances and their damping are a critical part of the process. Cost, volume, weight and losses also play an important role in optimization. Some of these properties lead to contradictions in the outcome of the design process. Although various methods for designing LCL filters have been published, filter design still is a laborious task, especially if the design is done by hand. This paper presents a design routine, which can entirely be implemented in software, generating a solution for an economic filter meeting an arbitrary grid code in a given environment. An exemplary filter is designed and analyzed regarding its performance. It is then compared to LCL filters designed with an established design method.
An Antropomorphic Robot with ChatGPT for Learning Activities: The Teachers' Perspective

With the advance in artificial intelligence, the use of robots during learning activities has attracted great interest. Since teachers are the most involved during the program of educational activities, is crucial to involve them during the design process of such activities involving new technologies. Our research seeks to examine the impact of incorporating ChatGPT on teachers' perception of social robotics in educational settings. The aim is to explore teachers' perspective on the use of social robots during learning activities. To achieve this, we enrolled 16 educators from Milan with over 5 years of teaching experience across three distinct school levels (4 from kindergarten, 6 from primary school, and 6 from middle school). We gathered insights from teachers of varying grade levels on the integration of social robots into their teaching practices. The study methodology involves two sessions with the teachers: the first session consists of a semi-structured interview, a demonstration of the NAO humanoid robot using the software Chroregraphe, and two role-playing activities with the robot to help teachers understand how it can be used in teaching activities; the second session includes a “635-brainwriting” activity and a focus group discussion about the use of educational robots in the classroom, with an additional focus group discussion after observing an experimenter interact with NAO using ChatGPT. The NVIVO program is used for transcription and labeling to identify units of analysis, which are be organized into macro categories. The purpose of the study is to explore the potential use of educational robots in teaching activities and to identify possible scenarios where these robots can be used to promote soft skills such as task orientation, problem solving, creativity, empathy, and conflict management. By using a combination of semi-structured interviews, role-playing activities, brainwriting, and focus group discussions, the study aims to generate rich data that can be analyzed qualitatively to provide insights into the potential use and effectiveness of educational robots in the classroom.
Virtual Experience Toolkit: Enhancing 3D Scene Virtualization From Real Environments Through Computer Vision and Deep Learning Techniques

Over the past few decades, Virtual Reality (VR) has emerged as a popular topic in a wide range of fields, such as information technology and psychology, among others. One reason for its importance was due to the ability to virtualize real-world scenes. This process typically involves capturing data from those scenes to generate accurate, detailed, and immersive 3D models. However, the creation of virtual content from real-world scenes has traditionally relied on manual techniques, as well as photogrammetry or Computer Vision (CV) algorithms. This frequently yields time-intensive, less accurate, intricate, and semi-automatic results. To tackle these limitations, a novel framework called Virtual Experience Toolkit (VET) has been proposed. It employs CV and Deep Learning (DL) techniques to swiftly and seamlessly virtualize any 3D scenario from real indoor environments. To demonstrate the effectiveness of VET, a diverse dataset of virtualized 3D scenes was generated, supplementing the information from the ScanNet dataset. VET has the potential to significantly enhance the virtualization of 3D indoor scenarios from real scenes, making the process easier, more precise, unified, consistent, automated, and effective for a broad spectrum of VR applications.
Securing Web Technology and Navigation Against Phishing Through CNN

Pervasive connectivity to the Internet paves the way to cyber threats, which are becoming more sophisticated and difficult to counteract. Phishing attack is one of the oldest, but still widespread threats, as it has evolved over the years. The impact of this attack compromises the security and privacy of information and knowledge transfer occurring on the Internet infrastructure. This paper aims at suggesting a new approach and technological solution to a phishing attack using CNN technologies to identify informative correlations between the images proposed by the browser and the URL of the website visited.
Electromyography Gestures Sensing with Deeply Quantized Neural Networks

Electromyography measures muscle electrical activity as the response to a nerve's stimulation. Recent developments in deep learning have also been applied to process bio-signals. The development of Deeply Quantized Neural Networks offers the opportunity to design low complex machine learning inferences. They target very low bit-depth representations for weights, biases, and activations. However, they could be highly affected by decreased performances if pushed to one-bit representation. This paper investigated hand gesture classification using electromyography signals. To accomplish this task at the lowest bit depth to save memory and complexity assets, was challenging from the accuracy perspective. One objective was to build a highly accurate model to classify the hand gestures while fulfilling the limited memory and computation latency constraints of a low-cost microcontroller, according to the model complexity. The classification accuracy achieved was 93.94% on the Italian Sign Language dataset and 83.70% on the Gestures Master dataset. The models were characterized on two STM32 microcontrollers in terms of execution time. On STM32H7, the 8-bits model ran around 8.306ms/inference. It was 11 ms faster than a fp32 based convolutional neural network. On STM32G4 instead, the 8-bits model was executed around 40 ms/inference while 102 ms for the fp32 model. Furthermore, these models have been profiled by ISPU.AI, an experimental tool to deploy pre-trained neural networks into super integrated inertial sensor processing unit, requiring less hardware resources than an off-the-shelf microcontroller. On the Italian Sign Language dataset and the Gestures Master dataset the maximum RAM was 29.17 KiB while the FLASH 21.13KiB with a computational complexity of 1,451,413 multiply and accumulates at 10 MHz
The Impact of the Metaverse on Health Professionals' Empathy

The evolution of digital technologies has opened up new possibilities in the field of health and wellness. One of the most revolutionary developments is the emergence of the concept of the “metaverse” as post-reality universe, a perpetual and persistent multiuser environment merging physical reality with digital virtuality. An increasing number of studies in service research are exploring the roles of metaverse technologies in health and care activities and their impact on human-avatar interactions. In addressing this interaction, in service research, there is a growing focus on the empathy experience of patients when interacting with avatars in health and care contexts. However, current service research tends to focus primarily on the technical aspects of these interactions, such as the design and functionality of avatars, rather than the emotional and empathetic aspects of the experience. As a result, there is a gap in knowledge regarding the impact of the metaverse on the empathy experience of physicians, cargivers and other actors in health and care settings. The purpose of this paper is to develop a framework on the role of the metaverse technologies in enhancing empathy of health providers in healthcare context. Data were collected for an ongoing study from January 2023 to May 2023. To augment our case selection, we utilized a “snowball” process by incorporating various identified evidence. The search for cases was concluded upon reaching a saturation point. Initially, we conducted Google searches using the keywords “metaverse” and “health” to acquire relevant data, articles, and other pertinent resources. Additionally, we analyzed blog posts and social media content, as these sources play a crucial role in the social construction and sense-making of various phenomena. Our research process has led us to define the metaverse as a collection of technologies that facilitate empathy among healthcare professionals. To address the diverse needs of patients, we have identified three primary activities that support healthcare providers' empathy: Immersive Embodiment, Relationship Weaving, and Skill Empowerment. Each activity is aimed to enhance patient experiences and outcomes in distinct ways within the context of the metaverse. By proposing a framework the paper aims to stimulate further research in understanding and enhancing empathy of health professionals by using these emerging technologies.
Time-Delay Tolerance of Visual and Olfactory Feedback in Multi-Sensory Extended Reality

In recent years, the continuous development of multi-sensory extended reality has led to significant advancements in visual, tactile, and gustatory feedback. However, re-search on the delay tolerance of visual and olfactory feedback has been limited. Delay tolerance refers to the user's satisfaction with the synchronization of visual and olfactory feedback in the extended real environment. To address this research gap, our team has developed a system that allows users to receive both visual and olfactory feedback simultaneously. Specifically, within the virtual environment, the system controls an odor diffuser to release a scent when the user reaches a specific location. This study aims to explore the delay tolerance interval of visual and olfactory feedback by adjusting the timing of the odor diffuser. Additionally, we will analyze the user experience feedback from different groups and use the collected data to establish an optimal delay feedback model. This research holds significant potential for improving user experiences and deepening our understanding of the complex interactions between visual and olfactory feedback in virtual environments.