Reviewerly/exHarmony huggingface dataset:
Applied Mathematics and Informatics Bachelor's and Master's Educational Programs Continuity During Updating Educational Standards

The education system in the modern world is built from the steps - junior school, high school, college, university (bachelor's, master's, postgraduate). Teachers and psychologists are concerned about the continuity of these levels of education for the education of employed professionals. The paper is devoted to the analysis of the short-term continuity of the educational programs of undergraduate and graduate programs in applied mathematics and computer science in the context of updating the survey. The implementation has been studied of the mechanisms of succession of professional and research components of the training of students of the directions 01.03.02 - “Computational mathematics and mathematical modeling” and 01.04.02 - “Mathematical modeling” in the educational practice of the North Caucasus Federal University. Curricula, the general vectors of training are considered, examples of reporting in areas (final theses and exams, papers in scientific journals) are given. A mechanism is proposed for the formation of a set of professional competencies for bachelor's and master's degree graduates in the specialty “Mathematical Modeling” correlated with the requirements of professional standards. The most productive forms of continuity of the research component of educational programs are identified in the field of applied mathematics and computer science at NCFU.
Offering Outworld Experiences to In-Patients With Dementia Through Virtual Reality: Mixed Methods Study

Research has suggested that institutionalization can increase the behavioral and psychological symptoms of dementia. To date, recent studies have reported a growing number of successful deployments of virtual reality for people with dementia to alleviate behavioral and psychological symptoms of dementia and improve quality of life. However, virtual reality has yet to be rigorously evaluated, since the findings are still in their infancy, with nonstatistically significant and inconclusive results.Unlike prior works, to overcome limitations in the current literature, our virtual reality system was co-designed with people with dementia and experts in dementia care and was evaluated with a larger population of patients with mild to severe cases of dementia.Working with 44 patients with dementia and 51 medical experts, we co-designed a virtual reality system to enhance the symptom management of in-patients with dementia residing in long-term care. We evaluated the system with 16 medical experts and 20 people with dementia.This paper explains the screening process and analysis we used to identify which environments patients would like to receive as an intervention. We also present the system's evaluation results by discussing their impact in depth. According to our findings, virtual reality contributes significantly to the reduction of behavioral and psychological symptoms of dementia, especially for aggressive, agitated, anxious, apathetic, depressive, and fearful behaviors.Ultimately, we hope that the results from this study will offer insight into how virtual reality technology can be designed, deployed, and used in dementia care.
Face Recognition and Raspberry Pi Powered Smart Door Unlocking System

Security plays a major role in the well-being of people. It is not possible to hire a security guard in person and always ensure his presence on our premises. It is an inefficient investment. The main role of security personnel is to stay on patrol, monitor, inspect and defend against any breach of security. A smart lock is an electromechanical lock that allows entry based on the authorization device that gives it instructions to lock and unlock. This system is proved to be inefficient as well These locks are either based on Pin, Bluetooth, WIFI. We propose a smart locking system that unlocks based upon face recognition that pic up specific, distinctive details about a person’s face. This is a powerful library that can run even by taking up a single picture of the person provided that the facial features are distinctly identified. The model is proven to be reaching accuracy levels of 99.7% according to the Centre for Strategic and International Studies (CSIS). The database is stored on the cloud consisting of all the authorized personnel that can pass through the door. The client holds the power of customizing the entry and exit of an individual through an application on his mobile.
NaijaFaceVoice: A Large-Scale Deep Learning Model and Database of Nigerian Faces and Voices

The fusion of two or more traits in multimodal biometrics generally improves recognition accuracy. The question is, by how much? Large-scale databases are better suited for training deep learning models for better generalization and accuracy. Therefore, a large-scale multimodal database is beneficial. However, publicly available large-scale multimodal databases are scarce, especially for faces and voices. Again, because a face image is 2-D while a voice is 1-D, there is the challenge of the best way to fuse both. Therefore, improvements owing to fusion have hitherto yielded marginal improvements. This study proposes a semi-automated curation algorithm for the extraction of the faces and voices of target individuals in videos to create a large-scale face-voice database. The curation technique involves observing the positions at the time of the occurrence of the target subject’s faces and voices in videos. These positions are supplied to a MATLAB2017b script that detects the faces in the observed regions, crops, resizes, auto-labels, and writes them to the disk. A second MATLAB2017b script, extracts the audio content within the observed regions, auto-labels, and writes the voice segments to the disk. The created database named NaijaFaceVoice consists of 2,656 subjects with over 2 million faces and 195 hours of utterances. The database was employed to develop a large-scale recognition system that leveraged Convolutional Neural Networks. Robust fusion methods incorporating the proposed Spectrogram-Voting concept significantly improved performance achieving a record equal error rate of 0.0003519%, an improvement by a factor of over 450.
Multi-robot cooperative autonomous exploration via task allocation in terrestrial environments

Cooperative autonomous exploration is a challenging task for multi-robot systems, which can cover larger areas in a shorter time or path length. Using multiple mobile robots for cooperative exploration of unknown environments can be more efficient than a single robot, but there are also many difficulties in multi-robot cooperative autonomous exploration. The key to successful multi-robot cooperative autonomous exploration is effective coordination between the robots. This paper designs a multi-robot cooperative autonomous exploration strategy for exploration tasks. Additionally, considering the fact that mobile robots are inevitably subject to failure in harsh conditions, we propose a self-healing cooperative autonomous exploration method that can recover from robot failures.
Detecting Rotational Symmetry in Polar Domain Based on SIFT

Symmetry is everywhere, found in objects around us, whether artificial or natural, and acts as a mid-level cue for both human and machine perception of the chaotic real world. From real-world symmetries, humans take advantage of various tasks, but their computational treatment remains elusive. This study proposes a novel approach for detecting rotational symmetry and the order of rotation within a single object digital image. The proposed method relies on the extraction of Scale Invariant Features Transform (SIFT) features and the robust centroid point. The centroid is computed on the basis of extracted features, to be drawn in xy-plan so that the centroid is on the origin. Later on, converted to the polar domain to facilitate the extraction of rotationally symmetric pairs and the order of rotation. The symmetry exhibited by each pair in the transform domain is the function of the features’ location, orientation, magnitude, and descriptor vector. Experimental results show that the approach correctly identifies the rotational symmetry if enough features are detected and the centroid is robust one.
Didactical Considerations for Further Education Learning Factories: a Three Case Study

Industrial upskilling becomes increasingly essential as higher skill requirements and workforce scarcity spread throughout the manufacturing industry. While hiring and consulting strategies often require a significant investment of time and expense before seeing performance improvement or low knowledge absorption, the upskilling of in-house workers often results in an immediate, long-term benefit for companies. However, industrial upskilling strategies have often underperformed due to a misalignment with the learning requirements for the enrolled workers. Learning factories have shown promising results, but the way to teach and facilitate learning still needs to be tailored for this industrial upskilling. This article investigates how upskilling works in three learning factories under the MADE Learning Factory Initiative. The study examines the didactical dimension compared to the adaptability and authenticity of the technology used during instruction. The results show that the learning factories’ dimensions must be considered together with the didactics to ensure authenticity, adaptability and learning. Lastly, the article discusses balancing these considerations with the targeted industrial learners based on lessons learned from the MADE Learning Factory Initiative.
A Gene-Based Algorithm for Identifying Factors That May Affect a Speaker’s Voice

Over the past decades, many machine-learning- and artificial-intelligence-based technologies have been created to deduce biometric or bio-relevant parameters of speakers from their voice. These voice profiling technologies have targeted a wide range of parameters, from diseases to environmental factors, based largely on the fact that they are known to influence voice. Recently, some have also explored the prediction of parameters whose influence on voice is not easily observable through data-opportunistic biomarker discovery techniques. However, given the enormous range of factors that can possibly influence voice, more informed methods for selecting those that may be potentially deducible from voice are needed. To this end, this paper proposes a simple path-finding algorithm that attempts to find links between vocal characteristics and perturbing factors using cytogenetic and genomic data. The links represent reasonable selection criteria for use by computational by profiling technologies only, and are not intended to establish any unknown biological facts. The proposed algorithm is validated using a simple example from medical literature-that of the clinically observed effects of specific chromosomal microdeletion syndromes on the vocal characteristics of affected people. In this example, the algorithm attempts to link the genes involved in these syndromes to a single example gene (FOXP2) that is known to play a broad role in voice production. We show that in cases where strong links are exposed, vocal characteristics of the patients are indeed reported to be correspondingly affected. Validation experiments and subsequent analyses confirm that the methodology could be potentially useful in predicting the existence of vocal signatures in naïve cases where their existence has not been otherwise observed.
Analyzing Social Media Sentiment: Twitter as a Case Study

This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.
Medical Image Watermarking for Tamper Detection in ROI Using LWT and Hashing

Security is one of the important characteristics while transmitting the data particularly medical images and patient information over cyberspace without any loss of information. In the proposed method LWT, DCT and SHA-256 hashing techniques are used to provide security to the patient data and to detect tampers in ROI of the medical images. In this paper, medical image is segmented into three regions, region of interest (ROI), region of non interest (RONI) and BORDER regions. ROI is an area that has an important impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper proposed ROI based tamper detection that embeds hash value of ROI into RONI for authentication and also embed the patient information into RONI for providing security. The experimental results of the proposed method on various medical image databases proved to identify the tampers in medical images. Compared with the existing technique, the proposed method offered high Peak Signal to Noise Ratio (PSNR) approximately 59db for watermarked medical images.
La seconda coniugazione del passato remoto (-ei/-etti): preferenze dei parlanti italiani nella scelta delle forme flesse alternative

The second inflexional Paradigm of the passato remoto (-ei/-etti): The current preferences of Italian speakers regarding the choice of alternative inflected forms The Italian preterite (passato remoto, PR) is characterized by a complex inflexional morphology, both from the diachronic and the synchronic point of view. It is also the only verb tense that, according to the grammars, has two series of interchangeable endings ‒ for regular verbs ending in -ere: -ei, -é, -erono versus -etti, -ette, -ettero. This article is part of a wider research on the effective use of PR in contemporary Italian and shows what the current preferences are in the selection of this two series of endings amongst native Italian speakers. I will discuss the results of a questionnaire in which 3675 informants participated, and in particular: a) criteria used for the choice of the analyzed verbs, b) construction of the questionnaire, c) data processing. The data will show that: 1) there are a very small number of verbs that prefer the endings -ei, 2) all verbs ending in -stere require the endings -etti, and 3) there is a clear correspondence between the responses given to the questionnaire and the occurrences of the PR forms in the corpora.
COST ESTIMATE FOR THE MONTHLY SET UP AND OPERATION OF A TELECONSULTATION ROOM

ABSTRACT Objective: to estimate the cost of setting up and monthly operation of a teleconsultation room. Method: descriptive study, carried out by means of a survey of direct and indirect costs for setting up and monthly operation of a teleconsultation room in Niterói - RJ - BR. The data analysis was based on discrimination of the type and quantity of equipment needed to set up the room; and on budgets obtained through research in public tenders and commercial stores. To estimate the running costs, the following were considered: monthly electric power consumption; costs with telephone network, materials, and supplies; and with nurses. Results: the minimum cost to set up the room was R$14,904.17 reais. The average monthly cost for 30 hours per week with three nurses was R$14,706.95. Conclusion: it is believed that teleconsultation can improve access to health care, quality of care and reduce costs related to the health system.
A Sentence Level Classification of Telugu News Document using Sentiment Analysis

In recent years, sentiment analysis-based categorization in low-resource languages and regional languages has become a hot topic in natural language processing. Researchers are more interested in categorizing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, and others. To the best of our knowledge, no microscopic study on Indian languages has been published to yet due to a lack of annotated data. Using Telugu sentiment analysis, we presented a two-phase classification technique for Telugu news phrases in this work. It first recognizes subjectivity categorization, which categorizes statements as Positive, Negative, or Neutral. Sentiment Classification is the next step, which divides subjective statements into positive and negative categories. We get an accuracy of 81 percent for sentiment analysis categorization using this method.
A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets

The widespread application of multimedia technologies such as video surveillance, online meetings, and drones facilitates the acquisition of a large amount of data that may contain facial features, posing significant concerns with regard to privacy. Protecting privacy while preserving the semantic contents of facial images is a challenging but crucial problem. Contemporary techniques for protecting the privacy of images lack the incorporation of the semantic attributes of faces and disregard the protection of dataset privacy. In this paper, we propose the Facial Privacy and Semantic Preservation (FPSP) model that utilizes similar facial feature replacement to achieve identity concealment, while adding semantic evaluation to the loss function to preserve semantic features. The proposed model is versatile and efficient in different task scenarios, preserving image utility while concealing privacy. Our experiments on the CelebA dataset demonstrate that the model achieves a semantic preservation rate of 77% while concealing the identities in facial images in the dataset.
No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network

When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces a structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher-quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. In order to facilitate the model’s ability to extract both global and local features, we introduce a multi-scale feature fusion module. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.
Performance Comparison of Depth Limited Search and A* Algorithm: A Case Study

Search algorithms are an essential component of many artificial intelligence applications. Depth Limited Search and A* Search are two prominent search algorithms that have been widely used in various domains, including robotics, game playing, and natural language processing. The research paper presents an overview of these two algorithms, their architecture, pros and cons, and the areas of their application. A detailed comparison of these two algorithms in terms of their performance, efficiency, and effectiveness in solving various search problems is also analysed. Finally, the paper presents a case study for two approaches. Experimental results depicted that after repetitive execution of each algorithm and graph data, it was detected that while the A-star search was able to find the cheaper cost almost always, Depth Limited Search was able to find the route faster with limited node visits.
A Study of Collaborative Trajectory Planning Method Based on Starling Swarm Bionic Algorithm for Multi-Unmanned Aerial Vehicle

This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV’s physical model properties.
Any-Shot Learning From Multimodal Observations (ALMO)

In this paper, we propose a framework (ALMO) for any-shot learning from multi-modal observations. Using training data containing both objects (inputs) and class attributes (side information) from multiple modalities, ALMO embeds the high-dimensional data into a common stochastic latent space using modality-specific encoders. Subsequently, a non-parametric classifier is trained to predict the class labels of the objects. We perform probabilistic data fusion to combine the modalities in the stochastic latent space and learn class conditional distributions for improved generalization and scalability. We formulate ALMO for both few-shot and zero-shot classification tasks, demonstrating significant improvement in recognition performance on the Omniglot and CUB-200 datasets as compared to state-of-the-art baselines.