Reviewerly/exHarmony huggingface dataset:
Research on Regulation Oriented Decentralized Oracle Machine

As a new computing mode, blockchain has been applied in various fields and urgently needs effective regulation. However, due to the lack of an effective information-sharing mechanism, regulating blockchain faces some challenges. Current related research mainly focused on asset circulation between independent blockchains or transferring data from single trusted data source to blockchain, but they cannot solve the problem of trusted information sharing in the blockchain regulation process. Therefore, this paper proposed a regulation oriented decentralized oracle machine method. The method mainly focuses on collecting data from the target blockchain for regulatory authorities. It uses multi-party joint verification and verifiable random selection mechanism to ensure the authenticity of data captured from blockchain and improve the efficiency and the scalability. The security, scalability, and efficiency of the method was analyzed, and the results showed that the method has lower overhead and good scalability while maintaining security.
Conversational Agent Development Through Large Language Models: Approach with GPT

This study investigates the potential of a Large Language Model (LLM) as a Chatbot Development Platform (CDP) for designing dialog systems in the context of conversational agents. While traditional systems often combine rule-based and machine learning approaches via third-party platforms for dialog design, this research explored the utilization of GPT for this purpose, especially for intent and entity detection integral to CDPs. Through a fine-tuning process, two GPT models were adapted to enhance their performance in these tasks, resulting in time and resource efficiency. The resultant system offers a flexible and adaptable framework for chatbot design, with fine-tuning showing significant benefits in maintaining output consistency and saving GPT tokens. The intent classifier demonstrated high precision (99.056%). However, the system faced challenges with dynamic entities, such as dates, due to GPT’s inability to access real-time data. Despite this, the study highlighted the immense potential of GPT and similar LLMs in developing conversational agents while drawing attention to the challenges of handling dynamic entities. These findings signal opportunities for innovative fine-tuning and system integration strategies, encouraging continued research in the dynamic field of conversational AI.
The Mind in Virtual Meetings: Comparing VR and Video Conferencing Environments Through Experiential Impact Assessment and EEG Analysis

The advent of digital communication technologies has notably fostered remote collaboration. While platforms like Zoom are prevalent, emerging Virtual Reality (VR) technologies like Meta Quest 2 introduce new dimensions for virtual collaboration. This study investigates whether VR-based group meetings are more conducive to participant engagement, motivation, and non-hindrance of collaboration than traditional video conferencing platforms such as Zoom. This study is novel in employing Electroencephalogram (EEG) technology and questionnaires to assess the human factors of engagement, valence, arousal, motivation, flow, system usability, emotional state, and social presence. In this study, participants were engaged in a collaborative turn-based drawing activity in two distinct environments - a traditional video conferencing setting using Zoom and a VR-based setting using Horizon Workrooms. Both environments were configured to maintain equivalent functionality and settings. EEG data was collected using an EMOTIV EPOC + 14 channel wireless EEG headset. Findings reveal that the activity in VR was more intrinsically motivating than the activity in Zoom, independent of the participants’ previous VR experience. Additionally, participants reported greater enjoyment (higher valence) and excitement (higher arousal) in the VR setting, with significant results from questionnaires. A strong correlation was found between EEG and questionnaire assessed arousal. Contrary to expectations, no significant differences were observed in usability, co-presence, focus, stress, and effort between the two platforms. These results provide insights into the potential of VR as a tool for fostering engagement and motivation in remote group activities and call for further investigation into the underlying mechanisms.
Towards Abnormal Behaviour Detection on Elderly People at Home Through Smart Plugs and Its Relationship with Activities of Daily Living

Elderly population is growing, and it contributes significantly to the saturation of public healthcare services due to multiple reasons. Nowadays, an increasing number of elderly people are living alone at home, and they expect to live independently for a longer period without help from caregivers or others, or at least with a minimum of it. The performance of activities of daily living can be measured through several tests and techniques according to the literature, providing us a useful tool to detect health problems related to independence levels, which could lead to major issues. The integration of new technologies at home could support caregivers and experts to detect abnormal behaviour when people perform certain activities, listed as basic and/or instrumental activities of daily living. Connected devices together with data analysis can be used to perform this task. This paper describes a proposal to monitor load measurements using smart-plugs connected to appliances distributed in a home environment, in order to detect specific activities of daily living. A recurrent neural network has been trained based on previously acquired time series, from such monitoring, framed at different time periods. The preliminary results of this work present how it would be possible to detect and associate some tasks at home with activities of daily living, generating activity patterns and, therefore being able to detect abnormal behaviours to act conveniently with the aim of increasing the life quality of elderly people at home.
Nubes

Serveless computing and the Function-as-a-Service (FaaS) model promise rapid development of cloud-based applications by abstracting away deployment and resource allocation. As the stateless nature of functions undermines the generality of the model, they are often paired with storage services to persist their state. However, this approach exposes state management to developers, who need to manually encode the interactions between functions and storage. The relations between functions and state are hidden within function implementations, negatively affecting modularity and reuse.
On Serving Image Classification Models

This paper aims to optimize model inference in interactive applications by reducing the infrastructure costs. It seeks to improve resource utilization, lower costs, and enhance the scalability and responsiveness of model serving systems. The focus is on achieving efficient inference in computer vision but has potential applications in other domains. The study involved experiments using a single GPU to analyze the impact of input image size and mini-batch size on request delivery time for image classification. Key findings include a model to estimate GPU warm-up time based on four parameters, the ratification of the existence of a linear relationship between mini-batch size and inference given one particular model, and the need to consider input size when selecting mini-batch size to avoid GPU crashes. Additionally, two mathematical models are proposed for further exploration using optimization algorithms. We also motivate the need to develop a more comprehensive mathematical model for soft and relaxed inference model serving.
Assumption-based argumentation for extended disjunctive logic programming and its relation to nonmonotonic reasoning

The motivation of this study is that Reiter’s default theory as well as assumption-based argumentation frameworks corresponding to default theories have difficulties in handling disjunctive information, while a disjunctive default theory (ddt) avoids them. This paper presents the semantic correspondence between generalized assumption-based argumentation (ABA) and extended disjunctive logic programming as well as the correspondence between ABA and nonmonotonic reasoning approaches such as disjunctive default logic and prioritized circumscription. To overcome the above-mentioned difficulties of ABA frameworks corresponding to default theories, we propose an assumption-based framework (ABF) translated from an extended disjunctive logic program (EDLP) since an EDLP can be translated into a ddt. Our ABF incorporates explicit negation and the connective of disjunction “|” to Heyninck and Arieli’s ABF induced by a disjunctive logic program. In this paper, first, we show how arguments are constructed from disjunctive rules in our proposed ABF. Then, we show the correspondence between answer sets of an EDLP P and stable extensions of the ABF translated from P with trivialization rules. After defining rationality postulates, we show answer sets of a consistent EDLP are captured by consistent stable extensions of the translated ABF with no trivialization rules. Finally, we show the correspondence between ABA and disjunctive default logic (resp. prioritized circumscription). The relation between ABA and possible model semantics of EDLPs is also discussed.
A Framework for Managing the Experimental Evaluation of Ambient Assisted Living Systems

Designing, setting up and running experiments is a key phases of studies, but it can be quite tedious due to the need to synchronise signals from data-collecting sensors, change variables between experiments, and manage volunteers. This article presents the architecture of a framework for managing and running experiments. It allows to easily manage the parameters of each of the tests assigned to the users and the information about them. It also enables the remote control of experiments via TCP communication, the integration and synchronisation of different devices via OpenViBE, and storage of data and signals in time series databases via InfluxDB. To illustrate the capabilities of the framework more clearly, we will also present a case study in an augmented reality environment using the HoloLens2 device. It describes the use of the framework in an experiment involving the display of different appearances of assistive drones to study participants to analyse their acceptance. After viewing the different drones in an augmented environment, participants are asked to provide subjective feedback through questionnaires and objective feedback through heart rate and eye-tracking sensors. This case study shows how the use of this framework streamlines testing by allowing test configuration, uniquely identifying the information provided by each participant and synchronising the information obtained from the different sensors.
A recent survey on perceived group sentiment analysis

Group sentiment analysis in today’s era is influencing many applications. Sentiment recognition is applicable for individuals, and group as well as crowd. This paper aims to highlight the ongoing present research scenario on perceived group sentiment analysis. The paper provides a brief overview of the strengths and weaknesses of well-known group sentiment analysis approaches. These approaches are broadly split into two basic categories: top-down and bottom-up approaches. A combination of top-down and bottom-up approaches is found superior in performance. This comprehensive review also discusses the available dataset with taxonomic representation and the importance of fusion techniques in group emotion recognition. This paper also narrates the novel attempt to demarcate the group emotion recognition approaches into the categories evolved by the influence of machine learning and deep learning at the feature extraction level and at the classification level. The multi-modality based group sentiment analysis has a great scope to overcome the challenges and improve the performance on real time applications.
Gestures recognition based on multimodal fusion by using 3D CNNs

Gestures have long been recognized as an interaction technique that can provide a more natural, creative, and intuitive way to communicate with computers. However, some existing difficulties include the high probability that the same type of movement done at different speeds will be recognized as a different category of movement; cluttered, occluded, and low-resolution backgrounds; and the near-impossibility of fusing different types of features. To this end, we propose a novel framework for integrating different scales of RGB and motion skeletons to obtain higher recognition accuracy using multiple features. Specifically, we provide a network architecture that combines a three-dimensional convolutional neural network (3DCNN) and post-fusion to better embed different features. Also, we combine RGB and motion skeleton information at different scales to mitigate speed and background issues. Experiments on several gesture recognition public datasets show desirable results, validating the superiority of the proposed gesture recognition method. Finally, we do a human-computer interaction experiment to prove its practicality.
Construction of real-time sharing model of energy big data based on end cloud collaboration technology

A real-time sharing model of energy big data based on end cloud collaboration technology is built to safely and efficiently share energy big data in all fields. Through the collaboration between the client layer and the cloud platform layer in the end cloud collaboration module, combined with the vertical federation learning algorithm and the homomorphic encryption algorithm, the energy big data knowledge in various fields is extracted and encrypted, and the encrypted knowledge is stored in the cloud platform as shared data. After the blockchain module combines the smart contract identification coding and parsing of such shared ciphertext, the ciphertext key is provided to the data user, and the shared energy big data plaintext is obtained after decryption, so as to realize the real-time security sharing of energy big data. According to the result analysis, the model performs well in data knowledge extraction and encryption, and has a good effect in ensuring the security and reliability of energy big data sharing. At the same time, the identification coding and analysis time of shared data knowledge is relatively short, making energy big data can be shared in real time. These results demonstrate the potential and feasibility of the model in facilitating big data sharing in the energy sector.
Index Matrix-Based Modeling and Simulation of Buck Converter

The approach described in this paper handles the parameters and characteristics (analog and discrete ones) of a Buck DC-DC converter (in its power and control parts) in a common manner. The usage of probably complicated differential equations for discrete dynamical systems is avoided by means of index matrix equations, which can be easily understood. Compared to classical matrix models, the proposed index matrix models are more descriptive and also smaller in size. Such functionality is widely applied by the authors, and a new operation is defined and used as well. The relevance of the proposed techniques in power electronics, because of switching topologies and a limited numbers of components, is argued. Respective examples of functioning modes of the considered converter, in which power circuits and controllers are modeled jointly, are given. Estimations of analog values are based on partly linear dependencies, which are shown to be adequate in first-order analyses. Specific expressions for a Buck DC-DC converter are presented. A model-solving technique and an exhaustive search on a parameter space are considered in detail and automated via well-formalized algorithms. Nested parameter intervals and verifications with normal probability distributions are used in an optimization procedure. The full agreement of implementation via MATLAB source code with results obtained via Simulink is demonstrated. The short simulation times of this software (compared to Simulink and a .NET desktop application developed by the authors) justify the search for optimal variants in a wide multi-dimensional space. A max–min procedure with 10,000 simulations in each verification step is presented.
Real-time Self-calibration Method of Vehicle Camera Attitude Based on ANN

Abstract The research of the monocular vision ranging method has great research value in the field of intelligent vehicles. Camera calibration is an important step in a monocular vision ranging model. The accuracy of camera calibration directly determines the accuracy of ranging. Because the camera attitude may change during vehicle driving, it will have a certain impact on the accuracy of the ranging algorithm. Therefore, based on a monocular ranging model and focal length calibration method based on geometric transformation, a real-time camera attitude self-calibration method based on the width measurement model and ANN model is proposed in this paper. Firstly, the method takes the wheel width of the known vehicle type as a fixed value, takes the contact points between the two wheels of the front vehicle and the ground as the feature points, and brings them into the width measurement model to form constraints. However, the single formula formed by a single pair of feature points cannot solve the camera inclination of multiple dimensions at the same time, and it is difficult to solve the analytical solution of the complex formula. In this paper, the ANN model is proposed to fit and solve the formula, and a large number of data sets are obtained through experimental point distribution to train and test the model. The results show that the ranging accuracy of using this method to calibrate the camera tilt angle is unchanged compared to the previous measurement accuracy using sensors, meeting the ranging requirements
An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time.
A Hard-Timeliness Blockchain-Based Contract Signing Protocol

In this article, we present the first proposal for contract signing based on blockchain that meets the requirements of fairness, hard-timeliness, and bc-optimism. The proposal, thanks to the use of blockchain, does not require the use of trusted third parties (TTPs), thus avoiding a point of failure and the problem of signatories having to agree on a TTP that is trusted by both. The presented protocol is fair because it is designed such that no honest signatory can be placed at a disadvantage. It meets the hard-timeliness requirement because both signatories can end the execution of the protocol at any time they wish. Finally, the proposal is bc-optimistic because blockchain functions are only executed in case of exception (and not in each execution of the protocol), with consequent savings when working with public blockchains. No previous proposal simultaneously met these three requirements. In addition to the above, this article clarifies the concept of timeliness, which previously has been defined in a confusing way (starting with the authors who used the term for the first time). We conducted a security review that allowed us to verify that our proposal meets the desired requirements. Furthermore, we provide the specifications of a smart contract designed for the Ethereum blockchain family and verified the economic feasibility of the proposal, ensuring it can be aligned with the financial requirements of different scenarios.
Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation

In the current framework of energy transition, renewable energy production has gained a renewed relevance. A set of 75 papers was selected from the existing literature and critically analyzed to understand the main inputs and tools used to calculate solar energy and derive theoretical photovoltaic production based on geographic information systems (GISs). A heterogeneous scenario for solar energy estimation emerged from the analysis, with a prevalence of 2.5D tools—mainly ArcGIS and QGIS—whose calculation is refined chiefly by inputting weather data from databases. On the other hand, despite some minor changes, the formula for calculating the photovoltaic potential is widely acknowledged and includes solar energy, exploitable surface, performance ratio, and panel efficiency. While sectorial studies—targeting a specific component of the calculation—are sound, the comprehensive ones are generally problematic due to excessive simplification of some parts. Moreover, validation is often lacking or, when present, only partial. The research on the topic is in constant evolution, increasingly moving towards purely 3D models and refining the estimation to include the time component—both in terms of life cycle and variations between days and seasons.
Timbre and the "Zone of Entanglement" in Electronic Dance Music

This article weaves together relational tendencies in recent scholarship spanning philosophy, mycology, psychopharmacology and timbre studies in order to argue that timbre’s unbounded ontology and cyclical re-use across electronic dance music (EDM) history affords listeners experiential access into radically distributed modes of being. Taking its cue from the biological structure of fungal mycelium, from whose flowers the psychoactive compounds of many psychedelic drugs are derived, this study builds on established models of EDM’s affectivity to propose that timbre in EDM manifests a psychedelic and specifically social form of consciousness. Highlighting the potential for entrainment that EDM’s synthesised timbres make possible, a conception of timbre as a mycelial “zone of entanglement” is put forward, in which the material and cultural, individual and social and spiritual and fleshly dimensions of the listening experience are folded into a reverberant unity, in turn encouraging a negotiation of the ethics that this entanglement entails.