Reviewerly/exHarmony huggingface dataset:
Random Matrix Transformation and Its Application in Image Hiding

Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality.
Integrating Project Management, Software Development, and Knowledge Management Models: A Case Study in a Public ICT Services Organization

In project-based organizations knowledge is a critical resource used to develop and deliver products and services with a high level of quality.Therefore, a systematic and sustainable process is necessary to coordinate knowledge management, project management and product lifecycle.This scenario predominates in companies focused on the creation and maintenance of information systems.This article presents an exploratory study based on a framework that integrates cognitive, managerial, and operational processes in a public Brazilian organization that provides services in the area of information and communications technology, focusing on the construction and maintenance of information systems.Those processes are operationalized by three management models considering knowledge, project, and software development processes.Our proposal aims to understand the relationships between those three management models and their influence on the software development process in the organization under study.Our premise is based on the principle that cognitive management, project management, and software development management must be integrated to fulfill the demands of product development and service provision.The research data was composed of registers of working hours spent on software development and maintenance projects involving 244 people allocated to 5064 projects in the period from 2007 to 2013.The study resulted in the identification of the relationships among the three management models adopted by the organization, with emphasis on knowledge management activities, which were not directly identified, making it difficult to account for and measure them.We established a set of activities connected to each one of the knowledge management model How to cite this paper:
Data-Based Approach to Educational Programs Quality Management

The purpose of this article is to describe and justify the approach to assessing the quality of educational programs that have been developed and tested at ITMO University since 2019. The article presents the main indicators of assessment and the results of testing this approach at the university. The main unit of quality is an educational program, as it has a long-life cycle and meets the needs of most stakeholders of the educational organization. Taking into account all the features of an educational program, the authors propose a comprehensive set of indicators to assess its quality, which makes it possible to objectively manage it data-based. In conclusion, for completing the task of analyzing the life cycle of an educational program, the authors present an internal rating of educational programs, concise examples of recommendations based on its results. The advantages of the approach to managing educational programs are described, and ways for further development are presented. The article may be of interest to the managers and management teams of institutions of higher education, as well as to the researchers in this field.
Improved PBFT Algorithm Based on Comprehensive Evaluation Model

Blockchain technology is well known due to the advent of Bitcoin. With the development of recent years, blockchain technology has been widely used in medicine, digital currency, energy, etc. The practical Byzantine fault-tolerant (PBFT) algorithm is a consensus algorithm widely used in consortium blockchains. Aiming to address the problems of the PBFT algorithm, low consensus efficiency due to high communication complexity, and malicious behavior of the primary node leading to consensus failure, an improved PBFT algorithm based on a comprehensive evaluation model (TB-PBFT) is proposed. First, nodes are divided into several groups based on the multi-formation control strategy of an unmanned aerial vehicle (UAV) cluster, which significantly reduces the communication complexity. Second, a comprehensive evaluation model combining the entropy method, TOPSIS method, and Borda count is proposed, which uses the behavior of nodes as an evaluation index, and the comprehensive score of nodes is obtained according to the preferences of other nodes. Finally, the highest ranking node is selected as the primary node through the comprehensive evaluation model to ensure the security and stability of the blockchain network. We analyze TB-PBFT algorithms and compare them with other Byzantine fault tolerance algorithms. Theoretical analysis and simulation results show that the TB-PBFT algorithm can improve node scalability and fault tolerance and reduce communication complexity and view switching probability. We also prove that the comprehensive evaluation model can improve the consensus success rate of the algorithm, and the feasibility and effectiveness of the improved consensus algorithm are verified. Hence, it can be applied to the consortium blockchain system effectively and efficiently.
The Graph Neural Network Detector Based on Neighbor Feature Alignment Mechanism in LIDAR Point Clouds

Three-dimensional (3D) object detection has a vital effect on the environmental awareness task of autonomous driving scenarios. At present, the accuracy of 3D object detection has significant improvement potential. In addition, a 3D point cloud is not uniformly distributed on a regular grid because of its disorder, dispersion, and sparseness. The strategy of the convolution neural networks (CNNs) for 3D point cloud feature extraction has the limitations of potential information loss and empty operation. Therefore, we propose a graph neural network (GNN) detector based on neighbor feature alignment mechanism for 3D object detection in LiDAR point clouds. This method exploits the structural information of graphs, and it aggregates the neighbor and edge features to update the state of vertices during the iteration process. This method enables the reduction of the offset error of the vertices, and ensures the invariance of the point cloud in the spatial domain. For experiments performed on the KITTI public benchmark, the results demonstrate that the proposed method achieves competitive experimental results.
Isokinetic Rehabilitation Trajectory Planning of an Upper Extremity Exoskeleton Rehabilitation Robot Based on a Multistrategy Improved Whale Optimization Algorithm

Upper extremity exoskeleton rehabilitation robots have become a significant piece of rehabilitation equipment, and planning their motion trajectories is essential in patient rehabilitation. In this paper, a multistrategy improved whale optimization algorithm (MWOA) is proposed for trajectory planning of upper extremity exoskeleton rehabilitation robots with emphasis on isokinetic rehabilitation. First, a piecewise polynomial was used to construct a rough trajectory. To make the trajectory conform to human-like movement, a whale optimization algorithm (WOA) was employed to generate a bounded jerk trajectory with the minimum running time as the objective. The search performance of the WOA under complex constraints, including the search capability of trajectory planning symmetry, was improved by the following strategies: a dual-population search, including a new communication mechanism to prevent falling into the local optimum; a mutation centroid opposition-based learning, to improve the diversity of the population; and an adaptive inertia weight, to balance exploration and exploitation. Simulation analysis showed that the MWOA generated a trajectory with a shorter run-time and better symmetry and robustness than the WOA. Finally, a pilot rehabilitation session on a healthy volunteer using an upper extremity exoskeleton rehabilitation robot was completed safely and smoothly along the trajectory planned by the MWOA. The proposed algorithm thus provides a feasible scheme for isokinetic rehabilitation trajectory planning of upper extremity exoskeleton rehabilitation robots.
SMPT: A Semi-Supervised Multi-Model Prediction Technique for Food Ingredient Named Entity Recognition (FINER) Dataset Construction

To pursue a healthy lifestyle, people are increasingly concerned about their food ingredients. Recently, it has become a common practice to use an online recipe to select the ingredients that match an individual’s meal plan and healthy diet preference. The information from online recipes can be extracted and used to develop various food-related applications. Named entity recognition (NER) is often used to extract such information. However, the problem in building an NER system lies in the massive amount of data needed to train the classifier, especially on a specific domain, such as food. There are food NER datasets available, but they are still quite limited. Thus, we proposed an iterative self-training approach called semi-supervised multi-model prediction technique (SMPT) to construct a food ingredient NER dataset. SMPT is a deep ensemble learning model that employs the concept of self-training and uses multiple pre-trained language models in the iterative data labeling process, with a voting mechanism used as the final decision to determine the entity’s label. Utilizing the SMPT, we have created a new annotated dataset of ingredient entities obtained from the Allrecipes website named FINER. Finally, this study aims to use the FINER dataset as an alternative resource to support food computing research and development.
Usage disambiguation of Turkish discourse connectives

This paper describes a rule-based approach and a machine learning approach to disambiguate the discourse usage of Turkish connectives, which not only has single and phrasal connectives as most languages do, but also suffixal connectives that largely correspond to subordinating conjunctions in English. Since these connectives have different linguistic characteristics, two sets of linguistic rules are devised to disambiguate their discourse usage. The linguistic rules are used in the rule-based approach and employed as feature sets in the machine learning approach to test whether they influenced the decision of our algorithms. The results of both approaches are evaluated over the Turkish section of TED-Multilingual Discourse Bank and Turkish Discourse Bank 1.1, two datasets annotated in the Penn Discourse TreeBank style. The paper attests to the predictive power of the linguistic rules in disambiguating the discourse usage of both types of connectives also offering new knowledge and insights for discourse processing from the view of a morphologically rich language.
Contextual Urdu Lemmatization Using Recurrent Neural Network Models

In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models.
Mitigating the Risk of Advanced Cyber Attacks: The Role of Quality, Covertness and Intensity of Use of Cyber Weapons

Modern countries employ computer networks that manage organizations in the private and public sectors. Cyber-attacks aim to disrupt, block, delete, manipulate or steal the data held in these networks, which challenge these countries’ national security. Consequently, cybersecurity programs must be developed to protect these networks from cyber-attacks in a manner that is similar to operations against terrorism. This study presents several models that analyze a contest between a network operator (defender) that deploys costly detectors to protect the network and a capable cyber attacker. Generally, when the deployed detectors become more potent or the defender exhibits higher vigilance, the attacker allocates more resources to R&D to ensure that the attack remains covert. We show that detectors may be substitutes, complements, or even degrade each other, implying that defenders must account for the cyber weapons’ characteristics and the attacker’s profile and strategic behavior. We derive the optimal number of detectors when the attacker’s R&D process features R&D spillovers and show that targeted detectors act as deterrents against high-quality weapons only if the attacker’s budget is not substantial. Finally, we demonstrate that common cybersecurity practices may be detrimental from a social-welfare perspective by enhancing an arms race with the attacker.
Automatic Modulation Recognition of Radiation Source Signals Based on Data Rearrangement and the 2D FFT

It is a challenge for automatic modulation recognition (AMR) methods for radiation source signals to work in environments with low signal-to-noise ratios (SNRs). This paper proposes a modulation feature extraction method based on data rearrangement and the 2D fast Fourier transform (FFT) (DR2D), and a DenseNet feature extraction network with early fusion is constructed to recognize the extracted modulation features. First, the input signal is preprocessed by DR2D to obtain three types of joint frequency feature bins with multiple time scales. Second, the feature fusion operation is performed on the inputs of the different layers of the proposed network. Finally, feature recognition is completed in the subsequent layers. The theoretical analysis and simulation results show that DR2D is a fast and robust preprocessing method for extracting the features of modulated radiation source signals with less computational complexity. The proposed DenseNet feature extraction network with early fusion can identify the extracted modulation features with less spatial complexity than other types of convolutional neural networks (CNNs) and performs well in low-SNR environments.
Robust sparse time‐frequency analysis for data missing scenarios

Sparse time-frequency analysis (STFA) can precisely achieve the spectrum of the local truncated signal. However, when the signal is disturbed by unexpected data loss, STFA cannot distinguish effective signals from missing data interferences. To address this issue and establish a robust STFA model for time-frequency analysis (TFA) in data loss scenarios, a stationary Framelet transform-based morphological component analysis is introduced in the STFA. In the proposed model, the processed signal is regarded as a sum of the cartoon, texture and data-missing parts. The cartoon and texture parts are reconstructed independently by taking advantage of the stationary Framelet transform. Then, the signal is reconstructed for STFA. The forward-backwards splitting method is employed to split the robust STFA model into the data recovery and robust time-frequency imaging stages. The two stages are then solved separately by using the alternating direction method of multipliers (ADMM). Finally, several experiments are conducted to show the performance of the proposed robust STFA method under different data loss levels, and it is compared with some existing state-of-the-art time-frequency methods. The results indicate that the proposed method outperforms the compared methods in obtaining the sparse spectrum of the effective signal when data are missing. The proposed method has a potential value in TFA in scenarios where data is easily lost.
INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping

Digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’ state and especially to identify ailments. To support supervised machine learning, digital phenotyping requires gathering data from study participants’ smartphones as they live their lives. Periodically, participants are then asked to provide ground truth labels about their health status. Analyzing such complex data is challenging due to limited contextual information and imperfect health/wellness labels. We propose INteractive PHOne-o-typing VISualization (INPHOVIS), an interactive visual framework for exploratory analysis of smartphone health data to study phone-o-types.Prior visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as smartwatches. However, unlike smartphones which are owned by over 85 percent of the US population, wearable devices are less prevalent thus reducing the number of people from whom such data can be collected. In contrast, the “low-level” sensor data (e.g., accelerometer or GPS data) supported by INPHOVIS can be easily collected using smartphones. Data visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health predictive phone-o-types. To guide the design of INPHOVIS, we performed a hierarchical task analysis of phone-o-typing requirements with health domain experts. We then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns, calendar views to visualize day level data along with bar charts, and correlation views to visualize important wellness predictive data. We demonstrate the usefulness of INPHOVIS with walk-throughs of use cases. We also evaluated INPHOVIS with expert feedback and received encouraging responses.
Identification lymph node metastasis in esophageal squamous cell carcinoma using whole slide images and a hybrid network of multiple instance and transfer learning

Difficulties associated with identifying lymph nodes metastasis in esophageal squamous cell carcinoma (ESCC LNM) can make it challenging to determine the clinical stage and devize precise treatment strategies for patients with esophageal cancer (EC). The lack of a large public dataset and expensive expert annotation are the factors responsible for the slow development of clinical computer-aided diagnosis for ESCC LNM. In this study, we collected 863 whole slide images from 198 patients at two hospitals, and developed a weakly supervised workflow based on a hybrid network of multiple instance and transfer learning (MITL) for automating identification of ESCC LNM. The results showed that MITL achieved a significant performance advantage over its competitors. The accuracy (ACC), F1-score, Matthews correlation coefficient, and areas under the curve were 0.976, 0.944, 0.929, and 0.991 for the internal testing data, and 0.969, 0.925, 0.905, and 0.988 for the external testing data, respectively. Compared to pre-train feature extractor, the improvement in ACC of pre-training aggregation network on the internal testing data was approximately twice. Furthermore, ACCs were determined using MITL from micrometastasis and macrometastasis (0.824 and 0.95, respectively). Visualization showed that the key features of ESCC LNM can be extracted by MITL for accurate detection and classification. In summary, our findings illustrated that MITL can achieve the high-efficiency identification and prediction of ESCC LNM with less investment of resources, and provide a new research strategy for the diagnosis of it.
Applications of the Metaverse as Disruptive Innovation

The virtual world was made possible by big changes in computer hardware, graphics software, and computer software. Considering the function of a metaverse, which is a 3D virtual world with computing infrastructure that is available to everyone. By making products and services that are much better, more affordable, and easier to use, disruptive technologies can grow the industries they are already in or start whole new ones. Disruptive technology offers a big change in how things are done or processes are run. The metaverse uses a number of useful technologies to give users 3D experiences that are both immersive and unique. Marketing, education, travel, and healthcare are among the industries that the metaverse might have an impact on. This chapter discusses how these technologies could be used in digital medicine and the future of the medical metaverse. The authors also talk about the research that is being done to speed up the metaverse's adoption in global education and make it a better place to learn and teach.