Reviewerly/exHarmony huggingface dataset:
STRUCTURAL ELEMENTS OF THE THESIS&#x0D; (content, sequence of presentation, connections and relationships)

The requirements for the dissertation and the conditions for its successful defense and approval are given. The structural elements of the dissertation are listed, their connections and relationships with each other are established, their content is clarified. Recommendations are given on the sequence of actions of the applicant in their description. A descriptive structure of the main structural element of the dissertation is proposed – the position submitted for defense. Wishes were expressed to correct regulatory documents in terms of including a description of scientific novelty only in the provisions submitted for defense, as well as to include a description of a scientific problem in the general description of the work. The presented material contributes to self-testing in the form of an independent examination of the dissertation work by the authors themselves.
A new zero–inflated discrete Lindley regression model

Recently, providing a new count regression model is very popular for many researchers. These count regression models are constructed by using a new discrete distribution or one of the existing distributions in the literature. In this paper, we consider a new zero-inflated regression model as an alternative to the zero-inflated regression models. We present two real data applications to illustrate the usefulness of the suggested regression model in modeling data, and compare the competitor models such as Poisson, discrete Lindley, and zero-inflated regression models. We provide a new count regression model which is useful in modeling overdispersed data.
Evidence for lexical and phonetic determinatives in Mayan writing: The case of T713

Abstract This article utilizes the Maya Hieroglyphic Database (Looper and Macri 1991–2022) to study the spellings of three glyphic values based on T713 (Thompson 1962), namely, the logogram K′AB′ ‘hand, arm’ ( n = 88), the logogram K′AL ‘to close, wrap, adorn’ ( n = 484), and the syllabogram mi ( n = 68), cataloged as MZ1, MR1, and MR2, respectively, by Looper et al. (2022). The main goal is to study the functions of certain graphemes typically placed atop T713 (T713's “holding site”) when it has the value K′AL , and to determine to what extent such signs can be described as lexical determinatives . The article concludes that MZ1 K′AB′ constitutes the unmarked value of T713, while MZ1 K′AL requires contextual or graphemic disambiguation, the latter facilitated by means of phonetic complements (e.g., k′a or la ) or lexical determinatives (e.g., T617/1M3). Syntagmatic contextual associations resulting from frequent proximity to other signs was a common factor in the establishment of certain signs as lexical determinatives. The question of MR2 mi , which can be analyzed as either a digraph or a case of a “phonetic determinative” (distinct from “phonetic complement”) in the holding site of T713, is left open to future research.
Rank AGS Identification Scheme and Signature Scheme

The identification protocol is a type of zero-knowledge proof. One party (the prover) needs to prove his identity to another party (the verifier) without revealing the secret key to the verifier. One can apply the Fiat–Shamir transformation to convert an identification scheme into a signature scheme which can be used for achieving security purposes and cryptographic purposes, especially for authentication. In this paper, we recall an identification protocol, namely the RankID scheme, and show that the scheme is incorrect and insecure. Then, we proposed a more natural approach to construct the rank version of the AGS identification protocol and show that our construction overcomes the security flaws in the RankID scheme. Our proposal achieves better results when comparing the public key size, secret key size, and signature size with the existing identification schemes, such as Rank RVDC and Rank CVE schemes. Our proposal also achieves 90%, 50%, and 96% reduction for the signature size, secret key size, and public key size when compared to the Rank CVE signature scheme.
Feasibility and Acceptance of Augmented and Virtual Reality Exergames to Train Motor and Cognitive Skills of Elderly

The GAME2AWE platform aims to provide a versatile tool for elderly fall prevention through exergames that integrate exercises, and simulate real-world environments and situations to train balance and reaction time using augmented and virtual reality technologies. In order to lay out the research area of interest, a review of the literature on systems that provide exergames for the elderly utilizing such technologies was conducted. The proposed use of augmented reality exergames on mobile devices as a complement to the traditional Kinect-based approach is a method that has been examined in the past with younger individuals in the context of physical activity interventions, but has not been studied adequately as an exergame tool for the elderly. An evaluation study was conducted with seniors, using multiple measuring scales to assess aspects such as usability, tolerability, applicability, and technology acceptance. In particular, the Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to assess acceptance and identify factors that influence the seniors’ intentions to use the game platform in the long term, while the correlation between UTAUT factors was also investigated. The results indicate a positive assessment of the above user experience aspects leveraging on both qualitative and quantitative collected data.
Quantized Information in Spectral Cyberspace

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time-frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.
Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism

Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video frequency of sports events, when letting it analyze and judge the behavior trend of athletes, often faced with the limitations of large-scale data sets and hardware, it takes a lot of time, and the accuracy of the results is not high. Therefore, we propose a deep clustering learning network for motion recognition under the self-attention mechanism, which can efficiently solve the accuracy and efficiency problems of sports event analysis and judgment. This method can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people on the sports field for identification, etc., by using the long and short-term memory network (LSTM), and combine the motion coding information in the key frames with the deep embedded clustering (DEC) to better analyze and judge the complex behavior change types of athletes. In addition, by using the self-attention mechanism, we can not only analyze the whole process of the sports video macroscopically, but also focus on the specific attributes of the movement, extract the key posture features of the athletes, further enhance the features, effectively reduce the amount of parameters in the calculation process of self-attention, reduce the computational complexity, and maintain the ability to capture details. The accuracy and efficiency of reasoning and judgment are improved. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of inference and prediction. It is proved that the method is effective and feasible in the analysis and reasoning of sports videos.
A Convolutional Neural Network with a Wave-Based Convolver

In this paper, we demonstrate that physical waves can be used to perform convolutions as part of a state-of-the-art neural network architecture. In particular, we show that the damping of waves, which is unavoidable in a physical implementation, does not diminish their usefulness in performing the convolution operations required in a convolutional neural network (CNN), and the damping only slightly decreases the classification accuracy of the network. These results open the door for wave-based hardware accelerators for CNNs.
Unsupervised Image Dedusting via a Cycle-Consistent Generative Adversarial Network

In sand–dust weather, the quality of the image is seriously degraded, which affects the ability of advanced applications to image using remote sensing. To improve the image quality and enhance the performance of image dedusting, we propose an end-to-end cyclic generative adversarial network (D-CycleGAN) for image dedusting, which does not require pairs of sand–dust images and corresponding ground truth images for training. In other words, we train the network in an unpaired way. Specifically, we designed a jointly optimized guided module (JOGM), comprised of the sandy guided synthesis module (SGSM) and the clean guided synthesis module (CGSM), which aim to jointly guide the generator through corresponding discriminator adversarials to reduce the color distortion and artifacts. JOGM can significantly improve image quality. We propose a network hidden layer adversarial branch to perform adversarials from inside the network, which better supervises the hidden layer to further improve the quality of the generated images. In addition, we improved the original CycleGAN loss function and propose a dual-scale semantic perception loss in feature space and a color identity-preserving loss in pixel space to constrain the network. Extensive experiments demonstrate that our proposed network model effectively removes sand dust, has better clarity and image quality, and outperforms the state-of-the-art techniques. In addition, the proposed method can help the target detection algorithm to improve its detection accuracy and capability, and our method generalizes well to the enhancement of underwater images and hazy images.
Medical Devices with Embedded Sensor Systems: Design and Development Methodology for Start-Ups

Embedded systems have become a key technology for the evolution of medical devices. However, the regulatory requirements that must be met make designing and developing these devices challenging. As a result, many start-ups attempting to develop medical devices fail. Therefore, this article presents a methodology to design and develop embedded medical devices while minimising the economic investment during the technical risk stages and encouraging customer feedback. The proposed methodology is based on the execution of three stages: Development Feasibility, Incremental and Iterative Prototyping, and Medical Product Consolidation. All this is completed in compliance with the applicable regulations. The methodology mentioned above is validated through practical use cases in which the development of a wearable device for monitoring vital signs is the most relevant. The presented use cases sustain the proposed methodology, for the devices were successfully CE marked. Moreover, ISO 13485 certification is obtained by following the proposed procedures.
An Online Hashing Algorithm for Image Retrieval Based on Optical-Sensor Network

Online hashing is a valid storage and online retrieval scheme, which is meeting the rapid increase in data in the optical-sensor network and the real-time processing needs of users in the era of big data. Existing online-hashing algorithms rely on data tags excessively to construct the hash function, and ignore the mining of the structural features of the data itself, resulting in a serious loss of the image-streaming features and the reduction in retrieval accuracy. In this paper, an online hashing model that fuses global and local dual semantics is proposed. First, to preserve the local features of the streaming data, an anchor hash model, which is based on the idea of manifold learning, is constructed. Second, a global similarity matrix, which is used to constrain hash codes is built by the balanced similarity between the newly arrived data and previous data, which makes hash codes retain global data features as much as possible. Then, under a unified framework, an online hash model that integrates global and local dual semantics is learned, and an effective discrete binary-optimization solution is proposed. A large number of experiments on three datasets, including CIFAR10, MNIST and Places205, show that our proposed algorithm improves the efficiency of image retrieval effectively, compared with several existing advanced online-hashing algorithms.
DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery

In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net for learning the visual content of images and image acquisition artifacts, and a JPEG artifact learning module for learning compression artifacts in the discrete cosine transform (DCT) domain. The backbone model of a network based on pre-training, such as CAT-Net, a representative network for image forgery detection, has a relatively large number of parameters, resulting in overfitting in a small dataset, which hinders generalization performance. Therefore, in this paper, the learning module is designed to learn the characteristics according to the DCT domain in real time without pre-training. In the experiments, the proposed network architecture and training method of DRRU-Net show that the network parameters are smaller than CAT-Net, the forgery detection performance is better than that of RRU-Net, and the generalization performance for various datasets can be improved.
A Privacy-Enabled, Blockchain-Based Smart Marketplace

Advancements in sensor-enabled devices led to the emergence of resource trading models for smart communities, such as the smart marketplace (SMP). Most of the proposed SMP architectures are based on blockchain technology, which has a public ledger to achieve transparency. Consequently, safeguarding the participant’s anonymity, untraceability, and transactional data privacy during trading becomes a challenging task. Most of the existing solutions to achieve anonymity are based on multiple account mapping, which is prone to identity-based attacks, and cryptographic techniques are used to achieve transactional data privacy, which often has a high computational overhead. In this work, we propose a lightweight privacy-enabled message exchange mechanism to accomplish our privacy goals in a blockchain-based SMP. Evaluation of the scheme was conducted to measure its resilience toward safeguarding participants’ anonymity, untraceability, and transactional data privacy during trading cycles. Statistical game theory-based security analysis and simulation based performance analysis of the proposed scheme showed that it achieved the desired privacy goals with a low computational overhead compared with existing state-of-the-art schemes.
Coupling RetinaFace and Depth Information to Filter False Positives

Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace or YOLOv7, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The method proposed by the authors is tested on a dataset of 549 images, which includes 614 upright frontal faces. The outcomes of the evaluation demonstrate that the method effectively minimizes false positives without compromising the overall detection rate. These findings suggest that incorporating depth information can enhance the accuracy of face detection.
DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering

In tag-aware recommender systems, users are strongly encouraged to utilize arbitrary tags to mark items of interest. These user-defined tags can be viewed as a bridge linking users and items. Most tag-aware recommendation models focus on improving the accuracy by introducing ingenious design or complicated structures to handle the tagging information appropriately. Beyond accuracy, diversity is considered to be another important indicator affecting the user satisfaction. Recommending more diverse items will provide more interesting items and commercial sales. Therefore, we propose a diversified tag-aware recommendation model based on graph collaborative filtering. The proposed model establishes a generic graph collaborative filtering framework tailored for tag-aware recommendations. To promote diversity, we adopt two modules: personalized category-boosted negative sampling to select a certain proportion of similar but negative items as negative samples for training, and adversarial learning to make the learned item representation category-free. To improve accuracy, we conduct a two-way TransTag regularization to model the relationship among users, items, and tags. Blending these modules into the proposed framework, we can optimize both the accuracy and diversity concurrently in an end-to-end manner. Experiments on Movielens datasets show that the proposed model can provide diverse recommendations while maintaining a high level of accuracy.
The Design of a Low-Cost Sensing and Control Architecture for a Search and Rescue Assistant Robot

At a disaster site, unforeseen circumstances can severely limit the activities of rescue workers. The best solution is for a cooperative team of robots and rescue workers to complete the rescue work. Therefore, in this paper, we propose a simple and low-cost sensing and control architecture for a search and rescue assistant robot using a thermal infrared sensor array, an ultrasonic sensor, and a three-axis accelerometer. In the proposed architecture, we estimate the location of human survivors using a low-cost thermal IR sensor array and generate and control the trajectory of approaching the searched human survivors. Obstacle avoidance and control are also possible through 3D position estimation of obstacles using 1D ultrasonic sensor integration. In addition, a three-axis accelerometer is used to estimate the tilt angle of the robot according to terrain conditions, and horizontal control of the storage box angle is performed using this feature. A prototype robot was implemented to experimentally validate its performance and can be easily constructed from inexpensive, commonly available parts. The implementation of this system is simple and cost-effective, making it a viable solution for search and rescue operations. The experimental results have demonstrated the effectiveness of the proposed method, showing that it is capable of achieving a level storage box and identifying the location of survivors while moving on a sloped terrain.
A Knowledge Inference and Sharing-Based Open-Set Device Recognition Approach for Satellite-Terrestrial-Integrated IoT

Satellite-terrestrial-integrated internet of things (IoT) is an inevitable trend in future development, but open satellite link and massive IoT device access will bring serious security risks. However, most existing recognition models are unable to discover and reject malicious IoT devices since they lack the decision information of these unauthorized devices during training. To address this dilemma, this paper proposes a knowledge inference and sharing-based open-set recognition approach to protect satellite-terrestrial-integrated IoT. It proceeds in two steps. First, knowledge inference, where we construct ideal substitutes for unauthorized devices after reasonable inference on the training set, aims to compensate the model’s missing decision information. Second, knowledge sharing, where we inherit the existing knowledge and modify the model’s decision boundaries through model expansion and knowledge distillation, achieves accurate open-set recognition. Experiments on the ORACLE dataset demonstrated that our approach outperforms other state-of-the-art OSR methods in terms of accuracy and running time. In short, our approach has excellent performance while only slightly increasing computational complexity.
A new approach for mobile robot path planning based on RRT algorithm

A novel route planning method based on the RRT algorithm is proposed in this study. To enhance the structure of the state tree, a general-purpose pseudorandom number generator is inserted into RRT. In addition, the proposed method includes a distance restriction that helps reduce the number of possible candidate nodes. MATLAB has been utilized to examine the efficacy of the proposed technique using three different types of maps. The simulation results demonstrate that the revised method is advantageous for route planning due to its superior convergence and efficacy over the original.
Business Transformation Projects

The role of requirements engineering (RoRE) is central for the implementation of projects in general and is especially crucial for business transformation projects (or simply projects) because transformation activities incur major changes in the existing sets of archaically defined requirements. Requirements engineering (RE) is a complex part of the project because it consists of many related communication, cross-functional knowledge, and dependencies, like the need for RE to interact with executive management, business users, business architects, implementation developers, and other project actors. In this chapter, the authors will try to propose an RoRE concept (RoREC) that can support project managers (or simply managers) in transforming the enterprise and managing RE activities. RE activities are not just about assembling a huge set of business and non-functional requests and features in the form of document(s) and delivering a repository of methodology-based diagram(s).