Reviewerly/exHarmony huggingface dataset:
Federated Learning-Based Misbehaviour Detection for the 5G-Enabled Internet of Vehicles

The concept of federated learning (FL) is becoming increasingly popular as a method for training collaborative models without loss the sensitive information. The term has become ubiquitous due to the extensive development of autonomous vehicles. Vehicular Networks and the Internet of Vehicles (IoV) enable cooperative learning through federated learning. It is still necessary to address several technical challenges. In recent years, Federated Learning (FL) has attracted significant interest in various sectors, including smart cities and transportation systems. FL-enabled attack detection for IoVs are still in its infancy. However, to determine the main challenges of deployment in real-world scenarios, there needs to be research efforts from various areas. Performance metrics are used to evaluate the effectiveness of the proposed FL framework. According to experiments, the proposed FL approach detected attacks in IOV networks with a maximum accuracy of 99.72%. In addition to precision, recall, and F1 scores, 99.70%, 99.20%, and 99.26% were achieved. A comparison of the proposed model with the existing model shows that the proposed model is more accurate.
Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the traffic speed prediction as a case study, a vanilla LSTM model with the optimised LSTM cell achieves 17534 inferences per second while consuming only 3.8 $\mu$J per inference on the FPGA XC7S15 from Spartan-7 family. It achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy efficient than existing approaches.
The Effects of Cost Implications in the Adoption of Biometric Application Banks in Eldoret Town, Kenya

Biometric technology utilizes biological and behavioral characteristics to automatically identify individuals. The paper was guided by the question on how does cost implications affect for adoption of biometric application in authorization and identification in banks in Eldoret Town. Cost of implications does not influence for adoption of biometric application in authorization and identification in banks in Eldoret Town. Thus, this study will help government agencies, financial institutions and other corporate business in adopting biometric systems. The research indicated cost implications affects biometric application in authorization and identification in cyber security in banks where biometric applications in banks have insufficient technical infrastructure hence affecting inefficiency and ineffectiveness of bank operations. In conclusion, the research indicated that cost implications are a major indicator that influenced biometric application in authorization and identification in cyber security in commercial banks in Kenya.
Establishing scientific educational space within the university: factors contributing to successful Eurasian integration

Introduction. The problem of the article is to focus on a range of aspects in the field of integration of science and higher education, which play an important role in the process of establishing the Eurasian scientific and educational space. The purpose of the article is to identify factors contributing to successful Eurasian integration in the process of establishing scientific educational space within the university. Materials and Methods. The theoretical and methodological basis of the study is made up of general scientific (philosophical) methods and a systematic approach. The subject of the study required the use of a combination of different approaches, primarily systemic, which allowed to conduct the correct analysis of applying historically established and modern educational technologies and practices in Eurasian education, as well as to describe some aspects of the effective integration of science and education in the Eurasian space. The object of the study is the experience of the University under the IPA EurAsEC on developing a system of scientific, organizational, research, publishing and educational activities in the Eurasian space. The authors used the following research methods: comparative, historical, interdisciplinary, content analysis method, etc. Results. The article considers the experience of using methods of ethno pedagogy in preparing innovative staff for the Eurasian space as a means of developing scientific and educational cooperation, expanding Eurasian integration, and giving it a humanitarian dimension. The authors substantiate the idea that strengthening and expanding the capabilities and needs of a unified Eurasian scientific and educational space should be considered as conditions for achieving effective cooperation in the field of science and education, allowing to restore professional communication of researchers from different Eurasian countries, update the content of higher education and contribute to bringing new scientific knowledge to the scientific community. The authors emphasize that a significant contribution to the development of the Eurasian integration process is made by the University under the IPA EurAsEC, which initiated a number of international projects contributing to the inclusion of new scientific knowledge and methods in the educational process within the framework of Eurasian economic integration. Conclusions. The article concludes about the values of higher education and about a need for integrating science and education as a basis for further development of Eurasian integration process. The authors draw the conclusion that today integration must be based on the historical experience of cooperation between Eurasian states in the field of science and education. Such interaction is considered to be the basis for further successful integration.
Large language models for aspect-based sentiment analysis

Large language models (LLMs) offer unprecedented text completion capabilities. As general models, they can fulfill a wide range of roles, including those of more specialized models. We assess the performance of GPT-4 and GPT-3.5 in zero shot, few shot and fine-tuned settings on the aspect-based sentiment analysis (ABSA) task. Fine-tuned GPT-3.5 achieves a state-of-the-art F1 score of 83.8 on the joint aspect term extraction and polarity classification task of the SemEval-2014 Task 4, improving upon InstructABSA [@scaria_instructabsa_2023] by 5.7%. However, this comes at the price of 1000 times more model parameters and thus increased inference cost. We discuss the the cost-performance trade-offs of different models, and analyze the typical errors that they make. Our results also indicate that detailed prompts improve performance in zero-shot and few-shot settings but are not necessary for fine-tuned models. This evidence is relevant for practioners that are faced with the choice of prompt engineering versus fine-tuning when using LLMs for ABSA.
An Application of Deep Learning Technology in The Recognition of Forged Documents with Color Laser Printing

&lt;p&gt;Advanced laser printing technology is now widely used in daily life and at work. Because laser printers are so easy to obtain and use, they have also become a popular method for criminals to forge important documents like currency, securities, and certificates. To obtain evidence for document forgery, traditional forensics use chemical analysis of ink, but the cost is high. Laser color printing uses halftone technology. This technology combines the CMYK printing colors&amp;rsquo; halftone screens at their respective angles. When correctly superimposed, they will form a moir&amp;eacute; pattern. And the moir&amp;eacute; pattern of a color block differs depending on the printer brand or model. This paper will use deep learning to create an image recognition model that can quickly classify forged documents by combining image processing, data augmentation, and transfer learning. The experimental results are examined using indicators of accuracy, precision, recall, F1-score, and PR curve. The results show that ResNet-50 has the best performance and can be used to detect document forgery. The study&amp;rsquo;s findings will be useful for future criminal investigators and forensic personnel in their work.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Initial state preparation for quantum chemistry on quantum computers

Quantum algorithms for ground-state energy estimation of chemical systems require a high-quality initial state. However, initial state preparation is commonly either neglected entirely, or assumed to be solved by a simple product state like Hartree-Fock. Even if a nontrivial state is prepared, strong correlations render ground state overlap inadequate for quality assessment. In this work, we address the initial state preparation problem with an end-to-end algorithm that prepares and quantifies the quality of initial states, accomplishing the latter with a new metric -- the energy distribution. To be able to prepare more complicated initial states, we introduce an implementation technique for states in the form of a sum of Slater determinants that exhibits significantly better scaling than all prior approaches. We also propose low-precision quantum phase estimation (QPE) for further state quality refinement. The complete algorithm is capable of generating high-quality states for energy estimation, and is shown in select cases to lower the overall estimation cost by several orders of magnitude when compared with the best single product state ansatz. More broadly, the energy distribution picture suggests that the goal of QPE should be reinterpreted as generating improvements compared to the energy of the initial state and other classical estimates, which can still be achieved even if QPE does not project directly onto the ground state. Finally, we show how the energy distribution can help in identifying potential quantum advantage.
STV+Reductions: Towards Practical Verification of Strategic Ability Using Model Reductions

We present a substantially expanded version of our tool STV for strategy synthesis and verification of strategic abilities. The new version adds user-definable models and support for model reduction through partial order reduction and checking for bisimulation.
Classifying Participant Standing and Sitting Postures Using Channel State Information

Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand.
Generator of Fuzzy Implications

In this research paper an algorithm for the derivation of methods based on theorems and axioms of fuzzy logic is presented, analyzed and applied. This new proposed procedure generates fuzzy methods and evaluates the value of fuzzy propositions through fuzzy implication. The new implications of the family should satisfy a number of axioms. Also, we denote the conditions in order to satisfy the maximum number of axioms. Moreover, authors state and prove theorems focusing on fuzzy implication, observing the rule: the fuzzy function of fuzzy implication is strong and that lead to fuzzy negation. In addition, the optimal number of repetitions is calculated according to the expected truth value. The formulas produced are verified through the use of temperature and humidity values over a certain period of time and in a certain geographical area in Greece. The basic steps of the methodology are the fuzzification of the data given ​​using four membership degree functions and the application of the membership degrees of temperature and humidity values on a new type of fuzzy implication. Finally, we observed that the isosceles trapezium gave the best results and the type of fuzzy implication can be effectively applied.
Dissipation entanglement control of two coupled qubits via strong driving fields

An analytical theory to calculate the dissipatively stable concurrence in the system of two coupled flux superconducting qubits in the strong driving field is developed. The conditions for the entanglement state generation and destruction during the formation of the multiphoton transitions regions due to the interference of Landau--Zener--St\"uckelberg--Majorana are found. Based on the solution of the Floquet--Markov equation, the technique is proposed to adjust the amplitudes of dc- and ac-fields for effective control of the entanglement between qubit states while taking into account the effects of the decoherence.
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks

We present a highly compact run-time monitoring approach for deep computer vision networks that extracts selected knowledge from only a few (down to merely two) hidden layers, yet can efficiently detect silent data corruption originating from both hardware memory and input faults. Building on the insight that critical faults typically manifest as peak or bulk shifts in the activation distribution of the affected network layers, we use strategically placed quantile markers to make accurate estimates about the anomaly of the current inference as a whole. Importantly, the detector component itself is kept algorithmically transparent to render the categorization of regular and abnormal behavior interpretable to a human. Our technique achieves up to ~96% precision and ~98% recall of detection. Compared to state-of-the-art anomaly detection techniques, this approach requires minimal compute overhead (as little as 0.3% with respect to non-supervised inference time) and contributes to the explainability of the model.
Anomaly Detection of Service Function Chain Based on Distributed Knowledge Distillation Framework in Cloud-Edge Industrial Internet of Things Scenarios

Due to the increasingly complex and dynamic network topology, as well as multiple layers in the Cloud-Edge-End Collaboration Scenarios in the Industrial Internet of Things (IIoT), Service Function Chains (SFC) generated from user requests are more prone to anomalies compared to traditional hardware solutions. In order to timely detect anomalies in the SFC and ensure service quality, we propose a time series anomaly detection model based on a distributed knowledge distillation framework (DTS-KD) in this paper. Firstly, to detect each status of Virtual Network Function (VNF) in the SFC, we propose a distributed teacher-student knowledge distillation architecture to perform anomaly detection on each link containing different Virtual Network Functions (VNFs). Secondly, to address the problem of neglecting spatial topology information of feature nodes in traditional SFC anomaly detection schemes, we propose a feature fusion-based spatial-temporal dilated convolution module encoding scheme, which utilizes spatial convolution with dilated convolution to jointly encode and capture spatial-temporal dependencies. Lastly, during the knowledge transfer process between the teacher and student networks, we propose a progressive knowledge distillation algorithm, which automatically adjusts the student network learning stages by adjusting task attention weights. After training, the student network measures the presence of anomalies in the links at each moment through the reconstruction data anomaly scores, thereby completing the SFC anomaly detection at that moment. The effectiveness of this proposed method under model compression conditions is validated on the ITU AI/ML in 5G dataset using four performance metrics: F1 score, accuracy, precision, and recall.
Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning

Abstract We introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning (ML) algorithms designed to detect, locate, and estimate the magnitude of an earthquake starting from 3 s of P ‐waves recorded by a single station. The system is made of six Ensemble ML algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral, and cepstral domains. The training set comprises data sets from Peru, Chile, Japan, and the STEAD global data set. E3WS consists of three sequential stages: detection, P‐phase picking, and source characterization. The latter involves magnitude, epicentral distance, depth, and back azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis‐classified as earthquakes) and very few false negatives (earthquakes mis‐classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P‐phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single‐station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time‐dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.
Class-Wise Classifier Design Capable of Continual Learning Using Adaptive Resonance Theory-Based Topological Clustering

This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.
Victimization by Deepfake in the Metaverse: Building a Practical Management Framework

Deepfake is digitally altered media aimed to deceive online users for political favor, monetary gain, extortion, and more.Deepfakes are the prevalent issues of impersonation, privacy, and fake news that cause substantial damage to individuals, groups, and organizations.The metaverse is an emerging 3-dimensional virtual platform led by AI and blockchain technology where users freely interact with each other.The purpose of this study is to identify the use of illicit deep fakes which can potentially contribute to cybercrime victimization in the metaverse.The data will be derived from expert interviews ( n=8) and online open sources to design a framework and provide solutions to mitigate deepfake-related victimization in the metaverse.This study identifies and further suggests a framework for advocacy of deepfake crime victimization in the metaverse through the application of the routine activities theory, as well as offender motivation and potential explanation of criminal behavior through Eysenck's theory of criminality.
Understanding the Use of Artificial Intelligence in Cybercrime

Artificial intelligence is one of the newest innovations which offenders exploit to satisfy their criminal desires.Although understanding cybercrime that is associated with this relatively new technology is essential in developing proper preventive measures, little has been done to examine this area.Therefore, this paper provides an overview of the two articles featured in the special issue of the International Journal of Cybersecurity Intelligence and Cybercrime, one about deepfakes in the metaverse and the other about social engineering attacks.
Cybersecurity Risk in U.S. Critical Infrastructure: An Analysis of Publicly Available U.S. Government Alerts and Advisories

As threat actor operations become increasingly sophisticated and emphasize the targeting of critical infrastructure and services, the need for cybersecurity information sharing will continue to grow.Escalating demand for cyber threat intelligence and information sharing across the cybersecurity community has resulted in the need to better understand the information produced by reputable sources such as U.S. CISA Alerts and ICS-CERT advisories.The text analysis program, Profiler Plus, is used to extract information from 1,574 U.S. government alerts and advisories to develop visualizations and generate enhanced insights into different cyber threat actor types, the tactics which can be used for cyber operations, and sectors of critical infrastructure at risk of an attack.The findings of this study enhance cyber threat intelligence activities by enabling an understanding of the trends in public information sharing as well as identifying gaps in open-source reporting on cyber-threat information.
Exploring Pre-Trained Model and Language Model for Translating Image to Bahasa

In the last decade, there have been significant developments in Image Caption Generation research to translate images into English descriptions. This task has also been conducted to produce texts in non-English, including Bahasa. However, the references in this study are still limited, so exploration opportunities are open widely. This paper presents comparative research by examining several state-of-the-art Deep Learning algorithms to extract images and generate their descriptions in Bahasa. We extracted images using three pre-trained models, namely InceptionV3, Xception, and EfficientNetV2S. In the language model, we examined four architectures: LSTM, GRU, Bidirectional LSTM, and Bidirectional GRU. The database used was Flickr8k which was translated into Bahasa. Model evaluation was conducted using BLEU and Meteor. The performance results based on the pre-trained model showed that EfficientNetV3S significantly gave the highest score among other models. On the other hand, in the language model, there was only a slight difference in model performance. However, in general, the Bidirectional GRU scored higher. We also found that step size in training affected overfitting. Larger step sizes tended to provide better generalizations. The best model was generated using EfficientNetV3S and Bidirectional GRU with step size=4096, which resulted in an average score of BLEU-1=0,5828 and Meteor=0,4520.