Reviewerly/exHarmony huggingface dataset:
Integrating Ontologies and Cognitive Conversational Agents in On2Conv

Multiagent systems have been successfully used in many domains. Being social, they are expected to communicate with human users in natural language. Nevertheless, the natural interaction between agents and humans is still challenging. Chatbot technologies are a key enabler to boost the communication between humans and software agents, but few technical solutions exist that make the agents' reasoning capabilities easily accessible by a human user via a chatbot and, on the other hand, the chatbot's answers more controllable and explainable. Dial4JaCa is one of such tools. It creates a bridge between Dialogflow and the JaCaMo cognitive-oriented and symbolic AI-based framework: the user's interface is a Dialogflow chatbot allowing the user to interact in natural language, and the backend implementing the reasoning and performing required actions is a JaCaMo agent. However, in Dial4JaCa the consistency between data that feed the JaCaMo agent and those that feed the Dialogflow chatbot must be guaranteed by the developer via an error-prone and tedious manual process. By taking an ontology describing the domain of interest in input and generating both the skeleton for the JaCaMo agent's behaviour and the intents for the Dialogflow chatbot, On2Conv improves Dial4JaCa robustness and reliability, and moves one step towards an explainable integration of agents and chatbots.
Synthesising Reward Machines for Cooperative Multi-Agent Reinforcement Learning

Reward machines have recently been proposed as a means of encoding team tasks in cooperative multi-agent reinforcement learning. The resulting multi-agent reward machine is then decomposed into individual reward machines, one for each member of the team, allowing agents to learn in a decentralised manner while still achieving the team task. However, current work assumes the multi-agent reward machine to be given. In this paper, we show how reward machines for team tasks can be synthesised automatically from an Alternating-Time Temporal Logic specification of the desired team behaviour and a high-level abstraction of the agents' environment. We present results suggesting that our automated approach has comparable, if not better, sample efficiency than reward machines generated by hand for multi-agent tasks.
On Admissible Behaviours for Goal-Oriented Decision-Making of Value-Aware Agents

The emerging field of value awareness engineering claims that software agents and systems should be value-aware, i.e. they should be able to explicitly reason about the value-alignment of their actions. Values are often modelled as preferences over states or actions which are then extended to plans. In this paper, we examine the effect of different groundings of values depending on context and claim that they can be used to prune the space of courses of actions that are aligned with them. We put forward several notions of such value-admissible behaviours and illustrate them in the domain of water distribution.
A Comprehensive Presentation of the Jadescript Agent-Oriented Programming Language

Jadescript is an agent-oriented programming language based on JADE that aims to become a dependable tool for the construction of industrial-strength multi-agent systems. This paper contributes to this objective by providing researchers and practitioners with a comprehensive description of Jadescript that discusses the most relevant features attained in several years of continuous development. In particular, this paper focuses on how Jadescript promotes the adoption of some ideas taken from agent-oriented programming by providing direct support for agent-oriented abstractions, like messages and ontologies, by encouraging the use of event-driven programming to govern interactions, and by allowing fine-grained task management using behaviours. Finally, to illustrate the practical applicability of Jadescript, this paper presents in detail the implementation of a well-known election algorithm traditionally used to coordinate distributed systems.
Naval Cybersecurity in the Age of AI: deceptive ISAR Images Generation with GANs

Navigational systems, the heart of maritime operations, face escalating cybersecurity risks due to their system of systems nature and reliance on diverse suppliers. Amid concerns about supply chain attacks and insider threats, the potential for malicious radar image injections, including Inverse Synthetic Aperture Radar (ISAR) images, is emerging. Such manipulations can critically undermine navigational integrity through the generation of decoy targets, the strategic relocation of existing ones, and the effective concealment of additional targets. We have identified where an Advanced Persistent Threat (APT) could be concealed within the processing chain of a modern radar system. This study demonstrates the potential of APTs to exploit Generative Adversarial Networks (GANs) for the creation of deceptive ISAR images, thereby spotlighting this previously unexplored threat vector. Our findings offer novel insights into bolstering maritime cybersecurity in an increasingly AI-dominated landscape.
Beyond the Field of View: Neglected Audio Part in VR Video Transmission Frameworks

As VR video resolutions approach bandwidth limits, spatial audio becomes crucial for immersion but requires higher bandwidth. This paper highlights the lack of attention to audio optimization in current VR video streaming frameworks. We provide evidence that certain tile-based caching frameworks, which were initially efficient, become inefficient in specific scenarios due to the disregard for audio caching details. To overcome this, we propose an audio transmission and caching framework with a mechanism for transmitting audio files and a fast, multi-mobile edge computing (MEC) collaborative caching algorithm called GlobCache. The goal is to reduce data transmission in the network. We evaluate the algorithm’s effectiveness in different scenarios through experiments.
Trusted Sharing of Data Under Cloud-Edge-End Collaboration and Its Formal Verification

With the development of cloud computing and edge computing, data sharing and collaboration have become increasing between cloud edge and end. Under the assistance of edge cloud, end users can access the data stored in the cloud by data owners. However, in an unprotected cloud-edge-end network environment, data sharing is vulnerable to security threats from malicious users, and data confidentiality cannot be guaranteed. Most of the existing data sharing approaches use the identity authentication mechanism to resist unauthorized accessed by illegal end users, but the mechanism cannot guarantee the credibility of the end user’s network environment. Therefore, this article proposes an approach for trusted sharing of data under cloud-edge-end collaboration (TSDCEE), in which we verify the trustworthiness of the data requester’s network environment based on the mechanism of attribute remote attestation. Finally, this article uses model checking Spin method to formally analyze TSDCEE, and verifies the security properties of TSDCEE.
Secure Plaintext Acquisition of Homomorphically Encrypted Results for Remote Processing

For secure remote processing, homomorphic encryption can be used. It allows operations to be performed on encrypted data. If the processor needs the result’s plaintext, it relies on the data owner to perform the decryption. This poses a vulnerability since a malicious data owner could inject a self–selected value. Moreover, it sees the plaintext result, breaching its confidentiality. In this paper, we propose a solving approach for the CKKS homomorphic encryption scheme. Using a commutative property, the data owner removes its encryption, while a processor’s encryption reinforces integrity and confidentiality. In an evaluation, we demonstrate feasibility regarding computational effort, transmitted data size, and introduced error.
QRadar: A Deployable Quantum Euclidean Similarity Large-scale Localization System

Quantum computing is a fast-developing field that has the ability to tackle complex problems that conventional computers struggle with. Recently, quantum fingerprinting localization has been introduced, which allows for the implementation of large-scale location determination systems across the globe. In this paper, we introduce QRadar, a localization system that uses quantum similarity fingerprints based on the Euclidean similarity metric, which is an early and widely used measure. The computational complexity of QRadar is exponentially superior to classical systems. We explain how to generate the quantum fingerprint, how to encode the received signal strength (RSS) measurements as quantum particles, and we describe the quantum algorithm for computing the Euclidean similarity distance between the online RSS measurements and the fingerprint ones. Additionally, we investigate various sources of errors in quantum machines and how they affect the accuracy of QRadar. We then discuss how to choose an appropriate quantum machine to minimize localization errors.We installed QRadar on a real IBM Quantum Experience machine. The results we obtained from both the installation and simulations conducted on two real test sites confirm that QRadar can accurately determine the estimated location with a significant improvement in processing time when compared to conventional classical methods.
Humidity Estimation Using WiFi Channel State Information

As infrastructure becomes smarter and filled with sensors, the need to combine device functionality is more apparent. Reducing the number of devices by introducing multifunctional sensors will reduce cost and overall complexity. There has been a rapid increase in the development of wireless sensing using WiFi Channel State Information (CSI). WiFi sensing using COTS devices has been a valuable tool in supporting multiple applications such as object movement tracking. One unconventional utility of CSI is to sense meteorological conditions. Temperature, humidity and air pressure are important factors for many applications from air traffic safety to personal health. Given the broad deployment of WiFi, the use of the involved devices in assessing meteorological conditions would enable better coverage and mitigate the cost of employing specialized sensors. This paper empirically demonstrates WiFi sensing, where variations in the CSI are used to estimate humidity using machine learning.
Antifreeze: High-Quality Adaptive Live Streaming with Real-time Transcoder

The demand for real-time video streaming is increasing due to emerging live and interactive applications like virtual conferencing/collaboration and augmented/mixed reality. Real-time video transcoding and streaming face challenges, as inefficient transcoding can cause delays and hinder viewer QoE. In this paper, we propose Antifreeze, a complete end-to-end solution for real-time transcoding and streaming. Antifreeze includes a transcoding-aware adaptation algorithm that considers visual quality, bandwidth, buffer dynamics, and transcoding time to maximize client QoE. By dynamically adapting through on-the-fly transcoding, Antifreeze provides a personalized streaming experience. Results demonstrate that Antifreeze reduces playback stalls and improves visual quality in live video streaming sessions across different bandwidth profiles.
Trade-Off Between Compression and FEC in Image Transmission Over Wifibroadcast

Wireless transmission of images in real time over large distances is a challenge for applications such as agricultural UAV-based remote sensing. The Wifibroadcast project uses standard WiFi hardware to provide a purely unidirectional transmission link for such situations. This paper examines the trade-off between image compression and FEC for image transmission via Wifibroadcast. The goal is to achieve highest robustness against transmission errors with regards to visual quality. For this, we conducted two experiments: First, we implemented a complete simulation of the transmission process to assess different degrees of packet and byte corruption. Afterward, we incorporated real network traces into the simulation in order to model realistic corruption patterns. The results show that for uniformly distributed byte corruption patterns, compression in combination with redundancy does not yield benefits to visual quality. However, if packet loss is the dominating corruption source, FEC is able to make image transmission more robust.
Demo: A Reproducible Link Emulation Environment for the Evaluation of Network-Coded Video Streaming

This demo presents link ’em version 2, an open source modification of the Linux Network Emulator (netem), to enable trace-based loss and delay emulation of pre-recorded real-world network conditions. It is used in an experimental setup to demonstrate and evaluate the impact of Network Coding-based Forward Erasure Correction for TCP (TCPyNC) on the performance of Dynamic Adaptive Streaming over HTTP (DASH). Thus, the proposed demonstration showcases the novel features of link ’em v2 and highlights the capability of network coding-based FEC to improve the overall video quality and reduce stalling of DASH video streams.
Context based diversification on keyword search by conceptualization of typed terms of the query

The human era is evolved and dominated through the ultimate intention to know about the Universe and its assets more and more. This in turn persuaded him to gather the immense information of need in the form of theory, tools, intuitions, visuals, and ultimately as the form of abstract objects. In modern Information practice, the art of Conceptualization and the induction of the data driven paradigm identifies the user query intention over a Conceptual network to mold a confident search segment of user choice. The proposed Conceptualization with Typed Terms of Query (CTTQ) approach derives a confident segment of the user query which is contextually measured by means of novel randomized Machine learning algorithms and its surrogates. The Normalized Discounted Cumulative Gain (nDCG) measures of the CTTQ show the usefulness of the query suggestions. It's efficiency improvements are represented with varying impact lexical feature indicator (θ) values.
PENERAPAN ALGORITMA NAIVE BAYES DAN SVM UNTUK ANALISIS SENTIMEN BOY BAND BTS PADA MEDIA SOSIAL TWITTER

BTS or Bangtan Sonyeondan is one of the vocal groups originating from South Korea, which is currently popular among Indonesian teenagers, resulting in many fans providing positive and negative comments through Twitter social media. The method used to determine whether these comments are positive or negative is by conducting sentiment analysis. The stages to perform data analysis are Preprocessing to clean the data, word weighting, labeling data into positive and negative classes, classification, and data visualization using pie charts. In this study, Naive Bayes and Support Vector Machine, were used for comparison, result of an accuracy score is 79% for Naive Bayes and 81% for Support Vector Machine. Among these two methods, Support Vector Machine achieved a higher accuracy score, and the sentiment analysis revealed that the comments obtained from Twitter users are predominantly positive.
A DCNN-LSTM based human activity recognition by mobile and wearable sensor networks

Systems for recognizing human activities are being developed as part of a larger framework that will allow for continuous monitoring of human behavior in intelligent home environments for elderly care, ambient assisted living, rehabilitation, sports injury detection, surveillance, and entertainment. The most challenging component of the sensor-based human activity detection pipeline for mobile and wearable devices is extracting relevant features. Using mobile and wearable sensors, the most challenging step in the pipeline for recognizing the behaviors of humans is extracting relevant features. The algorithm's performance is affected by feature extraction, which also reduces computation complexity and time. However, existing methods of recognizing human activity rely on hand-crafted characteristics that cannot handle complicated behaviors. There are several known systems for recognizing individual activities, including deep learning, but more must be developed to recognize transitions between actions. To solve these challenges, a new deep learning-based system is proposed for recognizing human actions in this research. Initially, a fuzzy logic-based genetic algorithm (FGA) is used for extracting features from sensor data. The introduced feature selection approach, called PSOACO, aims to enhance the particle swarm optimizer (PSO) algorithm's performance through the ant colony optimizer (ACO) operators. Then, the deep learning approach, named Deep Convolutional Neural Network and Long Short Term Memory (DCNN-LSTM), is used to classify the activities. F1 Score, Recall, Precision, and Accuracy are used to analyze the implementation's effectiveness. The overall recognition accuracy of the proposed model for UCI-HAR, PAMAP2, and WISDM datasets is 99.92%, 99.86%, and 99.94 %. According to the implementation, the HAR with privacy performs better than existing methods done on the Python platform.
A national-scale mineral potential assessment for carbonatite-related rare earth element mineral systems in Australia

The production of rare earth elements is critical for the transition to a low carbon economy. Carbonatites (>50% carbonate minerals) are one of the most significant sources of rare earth elements (REEs), both domestically within Australia, as well as globally. Given the strategic importance of critical minerals, including REEs, for the Australian national economy, a mineral potential assessment has been undertaken to evaluate the prospectivity for carbonatite-related REE (CREE) mineralisation in Australia. CREE deposits form as the result of lithospheric- to deposit-scale processes that are spatially and temporally coincident. Building on previous research into the formation of carbonatites and their related REE mineralisation, a mineral system model has been developed that incorporates four components: (1) source of metals, fluids, and ligands, (2) energy sources and fluid flow drivers, (3) fluid flow pathways and lithospheric architecture, and (4) ore deposition. This study demonstrates how national-scale datasets and a mineral systems-based approach can be used to map the mineral potential for CREE mineral systems in Australia. Using statistical analysis to guide the feature engineering and map weightings, a weighted index overlay method has been used to generate national-scale mineral potential maps that reduce the exploration search space for CREE mineral systems by up to ∼90%. In addition to highlighting regions with known carbonatites and CREE mineralisation, the mineral potential assessment also indicates high potential in parts of Australia that have no previously identified carbonatites or CREE deposits.
Time and phase features network model for automatic modulation classification

Automatic Modulation Classification (AMC) constitutes a fundamental technology for enabling automatic demodulation in Cognitive Communication Systems (CCS). Due to the size, weight, and power (SWaP) constraints of embedded computers employed in CCS, there are limited computational and memory resources. While deep neural networks possess strong feature representation and high accuracy recognition capabilities, they usually come with a high number of network parameters and high computational complexity, thereby reducing the real-time processing ability of CCS. Therefore, neural network structures intended for CCS must be lightweight and computationally efficient. In this paper, we propose a high-performance and resource-friendly network model based on an analysis of the modulation mechanism of communication signals. The network extracts phase features and short-time features sequentially using directional convolutional filters. Long short-term memory (LSTM) units are then used to extract long-term features, and only one fully connected layer is used for classification. Experiments with a standard dataset consisting of 11 communication modulation types demonstrate that our proposed model achieves an accuracy greater than 84.5%, even when the signal-to-noise ratio (SNR) is 0 dB, and the model has only 29187 parameters. On a Jetson Nano embedded platform, the model achieves a processing speed of up to 375366 in-phase and quadrature samples/s. Overall, the results suggest that our proposed approach is both lightweight and highly efficient, making it more suitable for CCS applications.
Concurrent encryption and lossless compression using inversion ranks

For secure and efficient transmission or storage, data files are commonly compressed and encrypted. In this work, we introduce a cost-effective encryption method of files as a built-in component of a lossless compression algorithm, thus avoiding the added cost of employing two separate processes. We have shown in earlier studies that preprocessing data with Burrows–Wheeler Transformation followed by Inversion Ranking transformation in advance of the utilization of an entropy coder resulted in an extremely effective general-purpose lossless compression technique. In our proposed algorithm, we isolate the inversion frequency vector obtained from the Inversion Ranking transformation and transmit the compressed file. Since the inversion frequency vector is necessary for decompression, we use it as our secret key and transmit it over a secure channel. Compared to the size of a data file, its frequency vector is rather small. Thus, encrypting only the inversion frequency vector, instead of the entire compressed file, results in substantial savings in computational cost. We demonstrate that the proposed technique is effective and resistant to common attacks on image and audio data sets.
Assessing Uncertainty in Space Weather Forecasting Using Quantile Regression and Complex Nonlinear Systems Identification Techniques

Space weather forecasting is of global interest, and its importance is well established in research community and recognized by government, industries and stockholders.Over the past years, many types of predictive models have been developed in the literature.There is a general agreement that forecasting models should not only provide point prediction but also inform the uncertainty associated with the prediction.This study presents a novel method bases on quantile regression and complex dynamic modelling for measuring uncertainties in space weather forecasting.The approach is implemented using Quantile regression and Nonlinear AutoRegressive Moving Average with Exogenous inputs (NARMAX) methods (for short the approach is called Q-NARMAX).The method is applied to Disturbance storm index (Dst) observations to examine its interpretability and capability for uncertainty analysis.Results show that the proposed Q-NARX model can produce excellent predictions of the Dst index, and meanwhile provides a measure for assessing the uncertainty in the forecast.The innovative integration of quantile regression, complex dynamic modelling and nonlinear system identification techniques enables the proposed work to have following attractive advantages and properties: 1) it can produce excellent prediction accuracy for space weather forecasting, 2) it uses transparent models to approximate (represent) black-box systems, enabling to interpret the dependent relationship between space weather indices (system outputs) and their drivers (system inputs), and 3) more importantly, it allows for uncertainty assessment and analysis of models and forecasts.