Reviewerly/exHarmony huggingface dataset:
A Synthetic Voice for an Assistive Conversational Agent: A Survey to Discover Italian Preferences regarding Synthetic Voice’s Gender and Quality Level

Based on a previous investigation, a quantitative study aimed to identify user’ preferences towards four synthetic voices of two different quality levels (classified through the sophistication of the synthesizer: low vs. high) is proposed. The voices administered to participants were developed considering two main aspects: the voice quality (high/low) and their gender (male/female). 182 unpaid participants were recruited for the study, divided in four groups according to their age, and therefore classified as adolescents, young adults, middle-aged, and seniors. To collect data regarding each voice, randomly audited by participants, the shortened version of the Virtual Agent Voice Acceptance Questionnaire (VAVAQ) was exploited. Outcomes of the previous study revealed that the voices of high quality, regardless of their gender, received a higher acclaim by all participants examined rather than the corresponding two voices assessed as lower quality. Conversely, findings of the current study suggest that the four new groups of participants involved agreed in showing their strong preference towards the high-quality voice gendered as female compared to all the other considered voices. Regarding the two voices gendered as male, the high-quality one was considered as more original and capable to arouse positive emotional states than the low-quality one. Moreover, the high-quality male voice was judged as more natural than the female low-quality one. Results provide some insights for future directions in the user experience and design field.
Application of Image Sharing and Encryption Based on Visual Cryptography in Networking Systems of AI

At present, it is very important to realize efficient and flexible data sharing and access while ensuring information security. As a new cryptography technology, visual cryptography provides a new solution for image sharing and encryption. This paper discusses the application of image sharing and encryption based on visual cryptography in NSAI (Networking Systems of AI), and gives the methods and steps of shared image preprocessing in detail. From the experimental simulation results, the MSE of the proposed algorithm is low, only 4.16. The low MSE value shows that the algorithm is effective in denoising, and it can effectively remove noise while maintaining the important features of the image. At the same time, the data transmission security, data storage security and key management security of this method are all high. This shows that the information security protection scheme using this method has high security. This study provides a new image sharing and encryption method to improve the level of information security and privacy protection; At the same time, it expands the application scope of visual cryptography in NSAI, promotes the development of NSAI, and provides reference for future research in related fields.
Design and Application of Chaos Algorithm based on the Sprott-B System

This article studies high-dimensional memristor chaotic systems and applies encryption algorithms to image encryption to improve the security of encryption. Based on the Sprott-B system, a magnetic flux-controlled secondary memristor and a memristor variable are added to construct a new four-dimensional memristor chaotic system. Under the condition of ensuring the initial values and parameters of the memristor chaotic system remain unchanged, four cryptographic sequences are iteratively generated. Then, the obtained initial cryptographic sequence is subjected to decimal point shift, modulus taking, integer taking, and other operations to form an encryption scheme's cryptographic stream. It is applied to image encryption including column scrambling, horizontal scrambling, and diffusion rotation. Then analyze the performance of image encryption algorithms from three aspects: key sensitivity analysis, histogram analysis, and information entropy analysis. The results show that the histogram features of the encrypted image are not obvious, and the information entropy reaches 7.9471. This encryption algorithm enhances the security of image encryption.
An Extensible and Traceable Data Operation Recording System Architecture and Method

Based on blockchain technology, this paper proposes an extensible and traceable data operation recording system architecture, which records the key operation information in the information system on the blockchain. When problems occur, it can be used for problem backtracking. If the system finds that the storage efficiency of the system is below the threshold or the disk space of the blockchain node is insufficient, it can automatically or notify the administrator to expand the blockchain network to meet the data storage requirements. Based on this architecture, a data encoding scheme and the corresponding query scheme is proposed. Compared with the existing technology of storing records off the blockchain and the hash values of the records on the blockchain, this method reduces the space occupied by data storage and can save all the content of operation records on the blockchain, which can better achieve tamper proof and destruction resistance than the existing technology. The query scheme for this coding scheme, can query the encoded records regardless of whether the query parameter is all coding fields or part of coding fields.
Data Privacy Protection and Sharing in Smart Grid Based on Blockchain Technology

With the rapid development of science and technology, the scale of power grid construction is gradually expanding, and the level of intelligence is also constantly improving, which also brings the problem of data leakage and data loss to the smart grid. In response to the data loss and leakage problems in the smart grid, this paper proposes a blockchain-based data privacy protection and data sharing protection system. This system integrates encryption algorithms and corresponding sharing mechanisms in distributed data storage, which not only effectively improves the storage capacity of data but also enhances the security of data storage to a certain extent. Test results show that the system can achieve fast encryption of datasets, and the encryption scheme has certain authenticity and non-forgeability, which can further enhance the security of data storage and sharing. It plays a very important role in improving the security level of smart grid operation.
A Method for Identifying Encrypted Webshell Traffic

Abstract: With the widespread application of web service technologies, implanting Webshells on target servers has become one of the most common attack methods. To evade detection, many WebShells are encrypted, increasing the difficulty of Webshell detection. This paper presents a method for recognizing traffic from encrypted webshells, extracting and filtering encrypted request and response packets in traffic files, and identifying the encryptions and keys used in the Webshell files. Based on this method, this paper has implemented an encrypted traffic identification system, decrypting the ciphertext, keys, and passwords in malicious traffic. Experimental results show that the system can effectively identify a large variety of encrypted WebShells, ensuring concurrent stability while being functionally comprehensive.
RevelioNN: Retrospective Extraction of Visual and Logical Insights for Ontology-Based Interpretation of Neural Networks

The need for AI explainability, which involves helping humans understand why an AI algorithm arrived at a particular decision, is crucial in numerous critical applications. Although deep neural networks play a significant role in modern AI, they inherently lack transparency. Consequently, various approaches have been suggested to clarify their decision-making processes to human users. One promising category of such approaches involves ontology-based methods. These methods have the potential to generate explanations using concepts from an ontology that are familiar to domain experts and the logical connections between these concepts. Specifically, post-hoc ontology-based explanations typically rely on concept extraction, which establishes a link between the internal representations formed by the neural network's inner layers and the domain concepts outlined in the ontology. This paper introduces the RevelioNN library, which comprises post-hoc algorithms designed to explain predictions made by deep convolutional neural networks in binary classification tasks, with a focus on leveraging ontologies. The library incorporates cutting-edge concept extraction techniques centered around constructing mapping networks. Furthermore, it provides the capability to form both logical and visual explanations for the predictions of convolutional neural networks by utilizing ontology concepts derived from their internal representations. An essential benefit of this library is its adaptability to interpret predictions from any pre-trained convolutional network implemented using the PyTorch framework. The code is licensed under the BSD-3-Clause license and is freely available at github.com/cais-lab/revelionn.
Promoting listening skills with an educational tool: VoScreen

In the modern digital age, educational technologies have become valuable resources that effectively support language learning and facilitate the development of various language skills. Among these skills, listening proficiency is of paramount importance as it is indispensable for effective communication and comprehension. With the increasing popularity of communicative approaches, the integration of educational technologies into language instruction has gained momentum due to their ability to provide learners with authentic listening experiences, exposure to diverse accents, and targeted practice opportunities. Considering the potential advantages of these technologies, the present study explores the efficacy of VoScreen in enhancing listening skills at the tertiary level. The study included 45 EFL learners at the tertiary level. Quantitative data were gathered through a pretest and a posttest while qualitative data were obtained through an evaluation and suggestion form. The findings revealed a statistically significant difference in learners' pretest and posttest listening scores. Moreover, learners' evaluations highlighted the contribution of VoScreen to their success in the target skill and other sub-skills. This study sheds light on the promising potential of educational technologies for enhancing learners' listening abilities.
DIRECTION OF DEVELOPMENT OF SYNERGY OF ECOLOGY AND INFORMATION SYSTEMS

The development of interaction of an effective system of environmental monitoring is the basis of environmental management, the creation of such systems thanks to information technologies is a powerful tool for preserving the environment.
INFORMATION AND COMMUNICATION TECHNOLOGIES AS A MEANS OF FORMING PROFESSIONAL KNOWLEDGE AND SKILLS IN TRAINING FUTURE SCIENCE TEACHERS

The article theoretically substantiates the application of information and communication technologies and tools in the training of future science teachers. Didactic conditions for the effective use of information and communication technologies are formula
A fusion deep learning framework based on breast cancer grade prediction

In breast cancer grading, the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency. With its rapid development, deep learning technology has been widely used for automatic breast cancer grading based on pathological images. In this paper, we propose an integrated breast cancer grading framework based on a fusion deep learning model, which uses three different convolutional neural networks as submodels to extract feature information at different levels from pathological images. Then, the output features of each submodel are learned by the fusion network based on stacking to generate the final decision results. To validate the effectiveness and reliability of our proposed model, we perform dichotomous and multiclassification experiments on the Invasive Ductal Carcinoma (IDC) pathological image dataset and a generated dataset and compare its performance with those of the state-of-the-art models. The classification accuracy of the proposed fusion network is 93.8%, the recall is 93.5%, and the F1 score is 93.8%, which outperforms the state-of-the-art methods.
Quantum discord and Fisher information for two-qubit system in the presence of thermal fields

We consider a double Jaynes–Cummings model for a system of two atoms that interact with a radiation field defined in a thermal spin state. We examine the impact of the parameters of the quantum model on the temporal evolution of quantum discord, quantum Fisher information, and fidelity. We explain how the quantifiers can be affected by the thermal noise and spin number with and without the effect of time-dependent coupling. We show that, despite the damaging effects of thermal noise, there is still some amount of discord and Fisher information present during the temporal evolution in accordance with the values of the spin number.
Analysis of DWT–DCT watermarking algorithm on digital medical imaging

Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images.In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping 8×8 blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR.The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76 dB.The proposed model works well for clinical medical images when compared with the existing techniques.
Comparative Analysis of Methodologies for the development of Computer Audits considering: Risk Analysis, Data Mining, Reference Frameworks and Standards and International Standards for Standardization

The computer audits that are carried out in organizations with or without profit, public or private, must use an adequate methodology and according to their objectives to solve the different problems that arise. For this reason, in this work comparisons are made between the methodologies for Risk Analysis, Data Mining, Reference Frameworks - Standards and International Standardization Norms, with the purpose of obtaining results and specific cases that allow the selection of an appropriate methodology against the problem to be solved in the organizations, or against what the organization wants to obtain as a result. All that, through the help of techniques or quantitative and qualitative comparison methods, established in the second part of the investigation; this will depend on which methodology can stand out against another. In the comparative tables, general and specific characteristics were analyzed, finding in the first part, the affinity that the methodologies have compared to their general phases and, when analyzing the specific characteristics, information was obtained that allowed each methodology to be distinguished and, in this way, find the distinctive results for each methodology.
Ontological analysis in the problems of container applications threat modelling

Objectives. The main purpose of the work is the experimental verification of the method of automatic threat modelling based on the ontological approach using the example of multicomponent container applications presented in the form of data flow diagrams. Methods. Methods of ontological modelling and knowledge management are used in the work. The Web Ontology Language is used to represent knowledge; automatic reasoning based on description logics is used for threat modelling. Results. A machine-readable set (dataset) of 200 data flow diagrams is developed; each diagram is obtained from the configuration of a real container application and is presented as an ontology and a knowledge graph. An ontological two-level domain-specific threat model of container applications is formed. An experiment is conducted to compare the coverage by threats using the common approach and using domain-specific threats for created dataset. For 95 % of the diagrams, the domain-specific threat model showed the coverage similar or greater than the common approach. Conclusion. The results of the experiment prove the suitability and effectiveness of the ontological approach for automatic threat modelling. The created dataset can be used for various research in the field of automation of threat modelling.
Fuzzy logic applied to mutation size in evolutionary strategies

Abstract Tuning of algorithm parameters is a complex but very important issue in the design of Evolutionary Algorithms. This paper discusses a new concept of mutation size tuning in Evolutionary Strategies. The proposed algorithm uses data on evolutionary history in earlier generations to tune the mutation size. A Fuzzy Logic Part examines this historical data and tunes the mutation size of individuals to improve the algorithm’s convergence and its resistance to getting stuck in a local optimum. The Fuzzy Logic Part tunes the mutation size and keeps an appropriate relation of algorithm’s exploration and exploitation. The proposed concept is discussed, and several tests on Function Optimization Problems are performed. In tests, we use a set of data and functions with different difficulties recommended in the commonly used benchmarks. The results of experiments suggest that the proposed method is more efficient and resistant to getting stuck in suboptimal solutions. The proposed algorithm has been used in recognizing the type of ultra-high energy cosmic ray particle that initiates the Extensive Air Showers when hit the Earth atmosphere. It could be used for a wide range of similar problems. It is possible that the proposed method could be adapted to other types of optimization methods, inspired by natural evolution, for example, Evolutionary Algorithms.
Technology, Pedagogy, and Content Knowledge: An Australian Case Study

Teacher Education students, at the bachelor’s and post-graduate level, complete programs that expose them to educational theories and best teaching practices. However, the extant literature has repeatedly demonstrated that many preservice teachers (PST) are unprepared to apply such knowledge to real-world educational settings. The problem may be particularly acute when it comes to the use of technology in classrooms. Given increasing government investment in technology and the burgeoning digital industries, teachers can play a critical role in demonstrating the effective use of technology in the course of teaching and learning. This study used a survey based on the Technological Pedagogical Content Knowledge (TPACK) model to evaluate PSTs self-perceived competencies in integrating technology into their teaching practices. Over a span of two years, PSTs enrolled in a unit offering six weeks’ professional experience were invited to respond to the survey and rate their cognizance of relevant teaching practices. Respondents indicated some familiarity with TPACK, but significant gaps were also evident. Moreover, despite the lack of significant differences among age groups in PSTs perceived ability to apply the TPACK model, noticeable differences were observed in their experiences regarding gender and prior employment.
Salient object detection based on hierarchical segmentation and objectness-guided

Unsupervised salient object detection aims to automatically detect important or attractive target objects in the scene without any user annotation. Compared to fully supervised, it can save a lot of manpower and resources invested in pixel-level annotation. However, the performance of existing unsupervised methods is still far from satisfactory because of the interference of complex backgrounds and the lack of knowledge in the semantic layer of scene objects. To solve these challenging problems, in this paper, we propose a new bottom-up method to better highlight the integral salient object region by exploiting the co-occurrence of saliency and objectness properties. Specifically, saliency estimation effectively utilizes low-level visual features based on high-quality hierarchical image segmentation (convolutional oriented boundaries). The objectness scoring is mainly obtained through the improved object proposals mapping map. Experiment results demonstrate that the proposed method can efficiently remove the interference of non-salient objects and complex background while preserving more salient object regions.
How Does Digital Economy Promote Agricultural Development? Evidence from Sub-Saharan Africa

Understanding the impact of the digital economy on agriculture in developing countries holds significant importance in enhancing agricultural production and addressing hunger-related challenges. This study uses panel data of 35 SSA countries from 2006 to 2021 and investigates the relationship between digital economy and agriculture using dynamic regression models. The impact mechanism is also analyzed using mediating models. The results show that the digital economy has a positive impact on agriculture through the increase of agricultural productivity, human capital accumulation and the improvement of government governance. The effects of the digital economy are larger in countries with higher and lower levels of agricultural development, significant in middle-income countries, and insignificant in low-income countries. This study may provide a better understanding of the nexus between digital economy and agricultural development, and offer valuable insights for governments in developing nations to formulate relevant and effective policies.