Reviewerly/exHarmony huggingface dataset:
Innovative and secure decentralized approach to process real estate transactions by utilizing private blockchain

Abstract Purpose This research introduces a decentralized method for handling real estate transactions through the utilization of private blockchain technology. The authors pinpoint the primary challenges within the prevailing transaction procedures in India and advocate for the integration of blockchain technology as a solution. Ultimately, the study concludes that the proposed system has the potential to optimize transaction processes within Indian government offices, fostering heightened efficiency, transparency, and a reduction in corrupt practices. Methods/design/methodology The current transaction process and the centralized technology are investigated using a physical observation approach at the government office. Following that, numerous parties are questioned to identify the main pain areas in the process. The outcomes of the interviews are used to create a blockchain solution that addresses the identified pain points. Following the design, interviewees are requested to validate the suggested model. Findings Some of the primary pain areas found in the real estate transaction procedure include that it is impossible to avoid single-point failure due to the present centralized transaction process, the possibility of corruption at any point, and the lack of data available at each node. Using blockchain techniques, the suggested decentralized application enhances the way transactions are processed and ensures the quality of data availability, transparency, and the elimination of single points of failure. Practical implications and simulation process A private blockchain application is created to improve the real estate transaction procedure at the Indian government office. One complex front end is created to receive information about the seller, the buyer’s property, and the payment, and a suitable database is employed to hold the sensitive data. Data is moved to the private blockchain for final execution when the smart business logic has been applied to the necessary information. One artificial utility is created that places a heavy load on the proposed system and measures the load trashing to validate it. It generates an enormous amount of sample data to verify the suggested system. Originality/value According to recent research, blockchain technology has the potential to get better efficiency, transparency, security, data accessibility, and thus trust in the transaction process. As a result, the suggested application is beneficial to the future of the Indian real estate transaction process.
Emergency Floor Plan Digitization Using Machine Learning

An increasing number of special-use and high-rise buildings have presented challenges for efficient evacuations, particularly in fire emergencies. At the same time, however, the use of autonomous vehicles within indoor environments has received only limited attention for emergency scenarios. To address these issues, we developed a method that classifies emergency symbols and determines their location on emergency floor plans. The method incorporates color filtering, clustering and object detection techniques to extract walls, which were used in combination to generate clean, digitized plans. By integrating the geometric and semantic data digitized with our method, existing building information modeling (BIM) based evacuation tools can be enhanced, improving their capabilities for path planning and decision making. We collected a dataset of 403 German emergency floor plans and created a synthetic dataset comprising 5000 plans. Both datasets were used to train two distinct faster region-based convolutional neural networks (Faster R-CNNs). The models were evaluated and compared using 83 floor plan images. The results show that the synthetic model outperformed the standard model for rare symbols, correctly identifying symbol classes that were not detected by the standard model. The presented framework offers a valuable tool for digitizing emergency floor plans and enhancing digital evacuation applications.
Circuit of Quantum Fractional Fourier Transform

In this paper, we first use the quantum Fourier transform (QFT) and quantum phase estimation (QPE) to realize the quantum fractional Fourier transform (QFrFT). As diverse definitions of the discrete fractional Fourier transform (DFrFT) exist, the relationship between the QFrFT and a classical algorithm is then established; that is, we determine the classical algorithm corresponding to the QFrFT. Second, we observe that many definitions of the multi-fractional Fourier transform (mFrFT) are flawed: when we attempt to propose a design scheme for the quantum mFrFT, we find that there are many invalid weighting terms in the definition of the mFrFT. This flaw may have very significant impacts on relevant algorithms for signal processing and image encryption. Finally, we analyze the circuit of the QFrFT and the reasons for the observed defects.
Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances

In the last decade, many artificial intelligence (AI) techniques have been used to solve various problems in sustainable energy (SE). Consequently, an increasing volume of research has been devoted to this topic, making it difficult for researchers to keep abreast of its developments. This paper analyzes 18,715 articles—about AI techniques used for SE—indexed in Scopus and published from 2013 to 2022, which were retrieved and selected following a novel iterative methodology. Besides calculating basic bibliometric indicators, we used clustering techniques and a co-occurrence analysis of author keywords to discover and characterize dominant themes in the literature. As a result, we found eight dominant themes in SE (solar energy, smart grids and microgrids, fuel cells, hydrogen, electric vehicles, biofuels, wind energy, and energy planning) and nine dominant techniques in AI (genetic algorithms, support vector machines, particle swarm optimization, differential evolution, classical neural networks, fuzzy logic controllers, reinforcement learning, deep learning, and multi-objective optimization). Each dominant theme is discussed in detail, highlighting the most relevant work and contributions. Finally, we identified the AI techniques most widely used in each SE area to solve its specific problems.
Augmented Reality in the Science Classroom—Implementing Pre-Service Teacher Training in the Competency Area of Simulation and Modeling According to the DiKoLAN Framework

The digitalization of everyday school life has gained increasing importance for teachers in recent years. In Germany, this is especially true since the publication of the strategy on “Education in the Digital World” by the Standing Conference of the Ministers of Education and Cultural Affairs of the Länder in 2016, which calls for the acquisition of digital competencies by students. In this regard, it is of great importance that future teachers acquire important skills in the field of digitalization during their teacher training in order to effectively and pedagogically use digital media in instruction. In this paper, we present the concept of an intervention on the topic of “Simulation and Modeling” from the competency framework DiKoLAN, which provides possible guidance in relation to the question “which competencies in the field of digitalization should be taught during teacher training?” One focus of the presented concept is the technology of “Augmented Reality,” which has already been described as an effective teaching and learning tool. Furthermore, evaluation results of the seminar are presented, which examine both the effectiveness in terms of conveying the desired competencies through the measurement of self-efficacy expectations, and the attitudes of the pre-service teachers towards the use of AR in science education. The evaluation of the intervention measure shows a significant increase in pre-service teachers’ self-efficacy expectations across all areas of competencies to be taught, as well as a significantly more positive attitude towards the use of AR in science teaching.
Extent of adoption of mobile phone applications by smallholder dairy farmers in Tharaka Nithi County, Kenya

In Kenya, smartphones are readily available at lower cost than before, allowing farmers to access agricultural information through mobile applications. However, despite increased ownership of smartphones and the availability of mobile applications, the overall usage of mobile applications is still low. This research aimed to assess the factors that determine the adoption of mobile phone applications among smallholder dairy farmers in Tharaka Nithi County of Kenya. A multistage stratified sampling procedure was used to interview 427 farmers. A Double Hurdle approach was employed to analyze the drivers of decision and extent of adoption of mobile applications. The study revealed that 51% of the respondents used mobile applications to access agricultural information. The decision to use a mobile phone application was influenced by the sex of the household head, age and level of education, distance to the market center, farming (as the primary occupation), access to credit, channels of access to information, farmer’s perception on the cost and ease of use of the mobile application. Subsequently, farm size and the various channels through which farmers access information about the applications positively influenced the number of mobile applications used. This study recommends strengthening the existing extension pathways to disseminate more information regarding the use of mobile applications among smallholder farmers. Supporting public-private partnerships will be crucial in increasing the utilization of mobile phone applications.
Predictive Prospecting Using Remote Sensing in a Mountainous Terrestrial Volcanic Area, in Western Bangongco–Nujiang Mineralization Belt, Tibet

The Bangongco–Nujiang metallogenic belt of Tibet is a main suture zone in the Qinghai–Tibet Plateau, which is known as an important porphyry–epithermal–skarn Cu-polymetallic mineralization zone in China. The western part of the Bangongco–Nujiang metallogenic belt exposes several medium high-silica terrestrial alkaline volcanic rocks with strong alteration influenced by collision orogeny. Some research has shown that clues to mineralization such as malachite and gossan are found on the surface. However, volcanic rock areas with varied topography place a huge burden on geological investigation, and the existing research on predicting mineralization is relatively scarce. This paper describes the extraction of alteration mineral information based on medium spatial resolution and hyperspectral resolution images, establishing a spectral library of alteration minerals in this area. By analyzing radar data, digital elevation, and synthesis results of different spectral bands, we combine remote sensing with geographic information technology to establish crater markers. The extraction results from multisource and chemical exploration data are superimposed onto the analysis of mineralization characteristics and geological conditions so as to establish the mineralization signatures for terrestrial volcanic rock areas. Eighteen mineralization prospect areas were identified, which can provide technical support for future mineralization research in this belt.
Quantum Frequency Transducer and Its Applications

Quantum frequency transducer (QFT) is an important technology in quantum information processing. QFT can coherently convert quantum state from one wavelength to another wavelength, while the coherence, entanglement, and information encoded in the light beam can be well preserved. There are 2 main application directions based on the intrinsic properties of QFT: (a) quantum frequency interface, which transfer quantum state between different quantum systems work at different wavelengths; and (b) frequency conversion detection, which convert photon from the weak detection ability waveband to the waveband that has better detection ability. In this review article, after giving a brief introduction of the basic principle of QFT, some main progresses for applications of QFT in the aforementioned 2 aspects are described in detail. Finally, we indicate the research trend of the QFT and the problems that remain to be solved in this field.
Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

A map construction method based on a collision probability model and an improved A* algorithm is proposed to address the issues of insufficient security in mobile robot map construction and path planning in complex environments. The method is based on modeling the asymmetry of paths, which complicates problem solving. Firstly, this article constructs a collision probability function model, and based on this model it is fused with the obstacle grid map, which is based on the grid method, to draw a collision probability grid map (CPGM) containing collision probability information. Secondly, incorporating the collision probability values from the CPGM into the actual cost function of the traditional A* algorithm improves the security of path planning in complex environments. The experimental results show that the improved A* algorithm decreases the percentage of dangerous nodes in complex environments by 69.23%, shortens the path planning length by 19.52%, reduces the search time by 16.8%, and reduces the number of turns by 46.67%. Therefore, the method in this paper solves the problem of traditional grid maps lacking security information and can plan a path with higher security and which is smoother, improving the security and robustness of mobile robot autonomous navigation in complex environments.
Hyperchaotic image encryption using DNA coding and discrete cosine transform

Achieving privacy over sensitive data is challenging in digital communication. In an effort to realize this aim, we propose hyperchaotic image encryption through DNA (deoxyribonucleic acid) coding and the discrete cosine transform (DCT). This multi-layered security algorithm comprises advanced levels of confusion and multiple diffusion techniques. The diffusion among the pixel values is attained by employing DNA operations among pixel values and the random sequence generated from the hyperchaotic map. Thereafter, the data sequence is scrambled through the baker map in order to permute the data points. Later, the partially encrypted image is further diffused and compressed through the DCT for lightweight communication. The significant contribution of this algorithm is the excellent improved quality of encryption through a double chaotic DNA encoded scheme. The statistical results are competitive in support of the robustness of the scheme and have shown resistance against the underlying cryptanalytic attacks. The performance of the scheme is evaluated in terms of diverse metrics via entropy, correlation coefficient, PSNR (Peak Signal-to-Noise Ratio), Chi-squared values, etc., and are determined to be more efficient and outperforms some existing state of art techniques.
Performance analysis of aspect-level sentiment classification task based on different deep learning models

Aspect-level sentiment classification task (ASCT) is a natural language processing task that aims to correctly identify specific aspects and determine their sentiment polarity from a given target sentence. Deep learning models have been proven to be effective in aspect-based sentiment classification tasks, and the mainstream Aspect-level sentiment classification (ASC) models currently constructed generally assume that the training and test datasets are Gaussian distribution (e.g., the same language). Once the data distribution changes, the ASC model must be retrained on the new distribution data to achieve good performance. However, acquiring a large amount of labeled data again typically requires a lot of manpower and money, which seems unlikely, especially for the ASC task, as it requires aspect-level annotation. This article analyzes the performance of sequence-based models, graph-based convolutional neural networks, and pre-training language models on the aspect-level sentiment classification task using two sets of comment datasets in Chinese and English, from four perspectives: classification performance, performance with different aspect numbers, specific case performance, and computational cost. In this article, we design a state-of-the-art ASC-based classification method and conduct a systematic study on eight public standard English and Chinese datasets with various commonly used assessment measures that provide directions for cross-language migration. Finally, we discuss the limitations of the study as well as future research directions.
A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

The profusion of existing ontologies in different domains has made reusing ontologies a best practice when developing new ontologies. The ontology reuse process reduces the expensive cost of developing a new ontology, in terms of time and effort, and supports semantic interoperability. Existing ontology development tools do not assist in the recommendation of ontologies or their concepts to be reused. Also, existing ontology recommendation tools could suggest whole ontologies covering a set of input keywords without referring to which parts of them (e.g., concepts) can be reused. In this paper, we propose an effective ontology recommendation system that helps the user in the iterative development and reuse of ontologies. The system allows the user to provide explicit preferences about the new ontology, and iteratively guides the user to parts from existing ontologies which match his preferences for reuse. Finally, we developed a prototype of our ontology recommendation system and conducted a user-based evaluation to assess the effectiveness of our approach.
Real-Time Parametric Path Planning Algorithm for Agricultural Machinery Kinematics Model Based on Particle Swarm Optimization

In order to meet the obstacle avoidance requirements of unmanned agricultural machinery in operation, it is necessary to plan a path to avoid obstacles in real time after obstacles are detected. However, the traditional path planning algorithm does not consider kinematic constraints, which makes it difficult to realize the plan, thus affecting the performance of the path tracking controller. In this paper, a real-time path planning algorithm based on particle swarm optimization for an agricultural machinery parametric kinematic model is proposed. The algorithm considers the agricultural machinery kinematic model, defines the path satisfying the kinematic model through a parametric equation, and solves the initial path through the analytic method. Then, considering the constraints of obstacles, acceleration, and turning angle, two objective functions are proposed. The particle swarm optimization algorithm is used to search the path near the initial path which satisfies the obstacle avoidance condition and has a better objective function value. In addition, the influence of the algorithm parameters on the running time is analyzed, and the method of compensating the radius of the obstacle is proposed to compensate the influence of the discrete time on the obstacle collision detection. Finally, experimental results show that the algorithm can plan a path in real time that avoids any moving obstacles and has a better objective function value.
Explainable machine learning models for Medicare fraud detection

Abstract As a means of building explainable machine learning models for Big Data, we apply a novel ensemble supervised feature selection technique. The technique is applied to publicly available insurance claims data from the United States public health insurance program, Medicare. We approach Medicare insurance fraud detection as a supervised machine learning task of anomaly detection through the classification of highly imbalanced Big Data. Our objectives for feature selection are to increase efficiency in model training, and to develop more explainable machine learning models for fraud detection. Using two Big Data datasets derived from two different sources of insurance claims data, we demonstrate how our feature selection technique reduces the dimensionality of the datasets by approximately 87.5% without compromising performance. Moreover, the reduction in dimensionality results in machine learning models that are easier to explain, and less prone to overfitting. Therefore, our primary contribution of the exposition of our novel feature selection technique leads to a further contribution to the application domain of automated Medicare insurance fraud detection. We utilize our feature selection technique to provide an explanation of our fraud detection models in terms of the definitions of the selected features. The ensemble supervised feature selection technique we present is flexible in that any collection of machine learning algorithms that maintain a list of feature importance values may be used. Therefore, researchers may easily employ variations of the technique we present.
Stability of oscillator Ising machines: Not all solutions are created equal

Nonlinear dynamical systems such as coupled oscillators are being actively investigated as Ising machines for solving computationally hard problems in combinatorial optimization. Prior works have established the equivalence between the global minima of the cost function describing the coupled oscillator system and the ground state of the Ising Hamiltonian. However, the properties of the oscillator Ising machine (OIM) from a nonlinear control viewpoint, such as the stability of the OIM solutions, remain unexplored. Therefore, in this work, using nonlinear control-theoretic analysis, we (i) identify the conditions required to ensure the functionality of the coupled oscillators as an Ising machine, (ii) show that all globally optimal phase configurations may not always be stable, resulting in some configurations being more favored over others and, thus, creating a biased OIM, and (iii) elucidate the impact of the stability of locally optimal phase configurations on the quality of the solution computed by the system. Our work, fostered through the unique convergence between nonlinear control theory and analog systems for computing, provides a new toolbox for the design and implementation of dynamical system-based computing platforms.
Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning

In an increasingly technology-driven world, the security of Internet-of-Things systems has become a top priority. This article presents a study on the implementation of security solutions in an innovative manufacturing plant using IoT and machine learning. The research was based on collecting historical data from telemetry sensors, IoT cameras, and control devices in a smart manufacturing plant. The data provided the basis for training machine learning models, which were used for real-time anomaly detection. After training the machine learning models, we achieved a 13% improvement in the anomaly detection rate and a 3% decrease in the false positive rate. These results significantly impacted plant efficiency and safety, with faster and more effective responses seen to unusual events. The results showed that there was a significant impact on the efficiency and safety of the smart manufacturing plant. Improved anomaly detection enabled faster and more effective responses to unusual events, decreasing critical incidents and improving overall security. Additionally, algorithm optimization and IoT infrastructure improved operational efficiency by reducing unscheduled downtime and increasing resource utilization. This study highlights the effectiveness of machine learning-based security solutions by comparing the results with those of previous research on IoT security and anomaly detection in industrial environments. The adaptability of these solutions makes them applicable in various industrial and commercial environments.