Reviewerly/exHarmony huggingface dataset:
IoT Edge Device Security: An Efficient Lightweight Authenticated Encryption Scheme Based on LED and PHOTON

IoT devices and embedded systems are deployed in critical environments, emphasizing attributes like power efficiency and computational capabilities. However, these constraints stress the paramount importance of device security, stimulating the exploration of lightweight cryptographic mechanisms. This study introduces a lightweight architecture for authenticated encryption tailored to these requirements. The architecture combines the lightweight encryption of the LED block cipher with the authentication of the PHOTON hash function. Leveraging shared internal operations, the integration of these bases optimizes area–performance tradeoffs, resulting in reduced power consumption and a reduced logic footprint. The architecture is synthesized and simulated using Verilog HDL, Quartus II, and ModelSim, and implemented on Cyclone FPGA devices. The results demonstrate a substantial 14% reduction in the logic area and up to a 46.04% decrease in power consumption in contrast to the individual designs of LED and PHOTON. This work highlights the potential for using efficient cryptographic solutions in resource-constrained environments.
Bounds on the Probability of Undetected Error for q-Ary Codes

We study the probability of an undetected error for general q-ary codes. We give upper and lower bounds on this quantity, by the Linear Programming and the Polynomial methods, as a function of the length, size, and minimum distance. Sharper bounds are obtained in the important special case of binary Hamming codes. Finally, several examples are given to illustrate the results of this paper.
A Novel Edge Cache-Based Private Set Intersection Protocol via Lightweight Oblivious PRF

With the rapid development of edge computing and the Internet of Things, the problem of information resource sharing can be effectively solved through multi-party collaboration, but the risk of data leakage is also increasing. To address the above issues, we propose an efficient multi-party private set intersection (MPSI) protocol via a multi-point oblivious pseudorandom function (OPRF). Then, we apply it to work on a specific commercial application: edge caching. The proposed MPSI uses oblivious transfer (OT) together with a probe-and-XOR of strings (PaXoS) as the main building blocks. It not only provides one-sided malicious security, but also achieves a better balance between communication and computational overhead. From the communication pattern perspective, the client only needs to perform OT with the leader and send a data structure PaXoS to the designated party, making the protocol extremely efficient. Moreover, in the setting of edge caching, many parties hold a set of items containing an identity and its associated value. All parties can identify a set of the most frequently accessed common items without revealing the underlying data.
Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy

The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system’s forecast data are under control for the future.
A BIM-Based XR Solution for Cooperative Infrastructure Design

In recent years, efforts have been made to improve productivity in Architecture, Engineering, and Construction (AEC) through Building Information Modeling (BIM), which involves sharing 3D models across the entire project. In civil engineering projects, which involve various stakeholders, remote meetings have been employed to reduce travel time and costs. However, conventional remote meetings using flat displays have faced challenges in terms of participant presence, understanding the three-dimensional shape of objects under discussion, tracking model changes, and grasping the evolution of the model during construction. Therefore, this research aimed to develop a remote meeting system for civil engineering design discussions using BIM and Extended Reality (XR) to support remote participants. The system consists of MR remote meetings, design change tracking, collaborative 3D model editing functionality, and 4D simulation for construction planning. A prototype system was developed, and a comparative evaluation was conducted between the proposed method and the conventional method.
A Novel Clustering Method Based on Adjacent Grids Searching

Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets.
Potential analysis of a Quantum RL controller in the context of autonomous driving

The potential of quantum enhanced Q-learning with a focus on its applicability to a lane change manoeuvre is investigated.In this context we solve multiple simple reinforcement learning environments using variational quantum circuits.The achieved results were similar to or even better than those of a simple constrained classical agent.We could observe promising behaviour on the more complex lane change manoeuvre task, which has an environment with an observation vector size twice larger than commonly used ones.For the Frozen Lake environment we found indications of possible quantum advantages in convergence rate.
On Feature Removal for Explainability in Dynamic Environments

Removal-based explanations are a general framework to provide feature importance scores, where feature removal, i.e. restricting a model on a subset of features, is a central component.While many machine learning applications require dynamic modeling environments, where distributions and models change over time, removal-based explanations and feature removal have mainly been considered in a static batch learning environment.Recently, an interventional and observational perturbation method was presented that allows to remove features efficiently in dynamic learning environments with concept drift.In this paper, we compare these two algorithms on two synthetic data streams.We showcase how both yield substantially different explanations when features are correlated and provide guidance on the choice based on the application.
Research on Multimodal Retrieval Method Based on Commodity Images

With the rapid development of science and technology, online shopping has gradually changed the way people buy goods because of its good quality and fast circulation. Therefore, online shopping has become one of the important modern shopping modes. With the development of the Internet, there are more and more channels to find product images and accompanying descriptive texts. How to retrieve the required goods accurately and efficiently is one of the important problems to be solved urgently. The existing traditional image retrieval methods mostly focus on the semantic information of the modality itself, and learn the binary code through quantization to find the similarity between the modalities. The heterogeneity of different types of modalities needs further discussion. Therefore, it is difficult to explain the low average accuracy of commodity image retrieval. Different from the traditional linear regression model retrieval method, this study uses a polynomial regression model method, which focuses on directly using polynomial regression to guide hash learning. The results show that the average accuracy of multimodal commodity retrieval using different methods to extract commodity image features is obviously better than that of traditional linear regression model, and polynomial regression model is the main factor to guide hash learning. When the code length is 128 bits, the average accuracy of LBP and TF_IDF is the highest. By analyzing the reasons why the average retrieval accuracy can be improved between different modes with the same semantics, the target commodity image can be searched to the greatest extent, saving more space cost and time cost.
Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis

Breast cancer is a prevalent and life-threatening disease affecting millions of women worldwide. Timely and accurate detection is crucial for improving patient outcomes. Deep learning techniques have shown remarkable success in image classification tasks, including breast cancer diagnosis. However, the integration of quantum computing into deep learning frameworks remains relatively unexplored. This paper investigates the potential of leveraging quantum computing to enhance image classification, particularly in breast cancer detection. The focus is on utilizing the "breakhis-400x" binary dataset to develop an advanced breast cancer image classifier. The proposed Quantum-Optimized AlexNet (QOA) approach, combines the feature extraction capabilities of the AlexNet model with a quantum layer acting as a linear layer. Experimental results on the BreakHis-400x dataset demonstrate the significant potential of the QOA model, achieving an overall accuracy of 93.67%. These findings highlight the utility of Quantum Computing in improving deep learning models for image classification, particularly in medical imaging analysis, and contribute to the advancement of precision medicine in breast cancer diagnosis.
Facial Expression Recognition Based on Fused Features and Support Vector Machine

Traditional facial expression recognition methods often suffer from low feature dimension and low recognition rates. These methods typically extract features limited to commonly used image features, while overlooking the semantic information of facial expressions. In this paper, we proposed an innovative facial expression recognition method that incorporates a novel calculation method for facial landmark features. These features are combined with Histogram of Oriented Gradients (HOG) and wavelet features using weighted concatenation to form the final feature vector. Support Vector Machines (SVM) are employed for classification. The proposed method achieves an impressive accuracy of 93.4% on the JAFFE dataset.
Soft Computing Framework for Digital Healthcare System

This Paper addresses the problem related to determination of dependency and relationship between diseases. Towards achieving the above objective, we propose Fuzzy Data Mining Approach. The contribution of this paper lies in the exploration of soft computing framework in addressing the vexing and often unresolved healthcare concerns. In today’s age and also in the foreseeable future, the resolution lies in healthcare based on digital systems. The differentiation of the paper lies in proposing a soft computing framework based digital system for healthcare. The paper not only considers fuzzy clustering mechanism, but also adopts a differentiated and hitherto seldom explored approach of data mining model based on fuzzy relational databases. We look at various perspectives and dimensions that would facilitate healthcare, such as the geographic penetration of diseases and similarity measures of the same, such that providers of healthcare systems may focus their efforts on incidence of certain diseases, based on dimensions such as geography, demography etc. Thus, this work provides a novel and hitherto seldom unexplored differentiated approach of digital healthcare system.
On the Interplay Between Self-adaptation and Energy Efficiency

Self-adaptation is an increasingly popular approach for enabling systems to make agile and data-driven decisions without human intervention. However, techniques for measuring the performance of a self-adaptive system and its effects on context are lacking. In this study, we present a mechanism that enables customizable and comprehensive evaluation of self-adaptive systems in real time. We apply our proposal to an example of resource management in a data center setting. We find that a self-adaptive approach to managing resources is multiple orders of magnitude better in terms of energy efficiency, especially as the size of the infrastructure grows, at the expense of slightly lower performance ( $${<}10\%$$ ).
Numerical Similarity Measures Versus Jaccard for Collaborative Filtering

Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some traditional “numerical” similarity measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures for CF tasks, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, this research focuses on presenting novel similarity measures by combining Jaccard with a multitude of numerical measures in our ongoing search for the most effective similarity measures for CF. Both existence and magnitude would benefit the combined measurements. Experimental results demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.
Evolutionary Algorithms and Their Applications in Intelligent Systems

The paper presents applications of evolutionary algorithms in intelligent systems. The research and literature review were performed based on the following scientific databases as Google Scholar, Springer, IEEE Xplore, Science Direct, Web of Science. The popularity of particular evolutionary methods in the aspect of intelligent systems was shown. The main application areas of these methods are also indicated and a brief review of the related literature is provided.
Attacks Against Machine Learning Systems: Analysis and GAN-based Approach to Protection

The rapid increase in the number of artificial intelligence applications has increased the importance of machine learning (ML) systems. The data used and the trained ML models is of great interest to competitors, while evasion and disabling of ML systems are of interest to intruders. This paper presents an analysis of existing attacks against ML systems, taking into account the stages of training, the use of ML algorithms and the operation of the information infrastructure. The classification of attacks against ML systems is proposed, the classes of attacks that are most relevant and dangerous for ML systems are identified. The analysis of possible measures to counter attacks against ML systems is carried out. It allows us to propose an approach to their protection based on the integrated usage of Neural-Clearance and Jpeg-Compression technologies, as well as a generative-adversarial network in the form of a hybrid neural network in which the autoencoder plays the role of a generator, and the classifier is a discriminator. An experimental evaluation of the proposed approach was carried out, which confirmed its high efficiency.
Automated Discovery of Direct Speech to Increase Quality and Battery Saving in Telephony

In a common talk over mobile phone there may be references to the speeches of the third persons or the indirect references to the previous words pronounced by the present partner. However, for the automatic control of telephone talks and their quality, that we started in one of our previous paper, it is extremely important to know whether some phrase was pronounced in the direct speech format, or as an indirect spoken phrase construction. The research provided in the present paper, applied an acoustic technique analysis scheme to study the role of pauses. It led us to the conclusion that the sufficient value of the timing pause in front of the phrase may be the key allowing to distinguish the direct oral speech from the indirect one. The examples, confirming this conclusion and its role in the power control, that we proposed half a century ago, are provided in this paper.
Method for Determining and Fixing the Information Need of Users in the Low-Code Development

Existing low-code platforms implement methods for developing business applications without considering the context of their use by business users and the information needs they satisfy. Existing low-code platforms implement methods for developing business applications without considering the context of their use by business users and information need they satisfy. The article describes a method for formally defining and fixing the information need that determines the context for using the developed business applications. The use of the proposed method in the implementation of new generation platforms - intelligent low-code platforms, will make it possible to use a relevant set of building blocks (library of IS components), which will expand the scope of the low-code approach to development, as well as reduce the number of changes in a business application. The method described in the paper makes it possible to determining an information need based on the reasoning recorded by the user about how he solves social business problems related to his job responsibilities.
Temporal Prediction Models for Technological Processes Based on Predictive Analytics

A new approach to assessing states and predicting the behavior of weakly formalized dynamic processes based on predictive analytics is proposed. Technological processes of transportation at railway freight stations are considered as an object of forecasting. The proposed approach is based on the idea of the predictive models having structural sensitivity to concept drift in the technological process under study. Based on the structural sensitivity, the developed model entitled eTSS is able to detect predictor states. eTSS belongs to the class of adaptive evolving models based on hybridization of fuzzy Takagi-Sugeno systems with online machine learning methods and a special mechanism for incremental updating of the knowledge base. Online learning mechanism provides the ability of eTSS to adjust incrementally to the changing data flow. The experimental results collected from imitation modeling of the operation of Chelyabinsk-Glavniy station showed the advantage of the developed eTSS over similar evolving models in solving the tasks.
Method of Dynamic Multi-Criteria Quality Assessment and Optimization for Proactive Complex Objects Functioning Schedules

The content of the problem under study lies in the fact that at present, in practice, in the transport and logistics sphere, a new class of topical multicriteria problems of high-dimensional scheduling theory has appeared, with a non-stationary heterogeneous input flow of requests, orderly works that determine possible technologies for their implementation under the influence of disturbing factors. However, to solve the considered class of tasks of scheduling the delivery of goods using passenger ships based on the use of a ground-based aviation transport and logistics system, neither methodology nor the corresponding methodological support has been developed.
Algebraic Bayesian Networks: The Generation of the Network Canonical Representation

Algebraic Bayesian networks belong to the class of probabilistic graphical models. They are represented as non-directional graphs with models of knowledge patterns in the nodes (KP). Each knowledge pattern contains closely related information about the subject domain, formalized in the form of an ideal of conjuncts or a set of quanta with truth probability estimates. In order to optimize the complexity of KPs through the use of scalar estimates, an approach to finding the canonical representation of KPs has previously been proposed. In this paper, the process of obtaining a canonical representation of the entire algebraic Bayesian network is proposed and studied for the first time. As a result, methods have been described that create a canonical representation based on the comprehensive KP and using chain generation. The results of this paper allow to reduce the time for calculating probability estimates in a priori inference by obtaining scalar estimates instead of interval estimates, which can be used to compute prior probability estimates or in systems where obtaining scalar probability estimates is preferable.