Reviewerly/exHarmony huggingface dataset:
Reducing fear of water and aquaphobia through 360 degree video use?

Drowning is a serious public health problem threat claiming the lives of 372,000 people each year worldwide that can be linked to an individual’s ability to swim. Learning to swim requires limited fear of water. This exploratory study investigated the potential interests of 360° video use for reducing fear and apprehension that underpin aquaphobia. Two students aged 11–12 years old who were non-swimmers with a reluctance to enter the water (i.e., a refusal and/or fear of immersion or to immerse only part of the face or the body in water) participated in qualitative interviews while viewing 360° video of an aquatic environment at progressively deeper levels through a head-mounted display (HMD). Three main findings were identified. First, the use of a 360° video viewed in an HMD led students to live an original corporeal immersive experience, a kind of immersion in the pool but experienced outside the pool. Second, students felt a strong emotional engagement between anxiety and curiosity from exploring the aquatic environment. Third, during the viewing situation, students developed and acquired accurate perceptive cues and knowledge related to the aquatic environment. The implications of these findings highlight the benefits of 360° video use as a tool to enhance greater confidence and familiarity with the aquatic environment to support learning and reduce phobia in non-swimmers. Limitations of the study and future research directions are discussed.
Unsupervised Image Translation Using Multi-Scale Residual GAN

Image translation is a classic problem of image processing and computer vision for transforming an image from one domain to another by learning the mapping between an input image and an output image. A novel Multi-scale Residual Generative Adversarial Network (MRGAN) based on unsupervised learning is proposed in this paper for transforming images between different domains using unpaired data. In the model, a dual generater architecture is used to eliminate the dependence on paired training samples and introduce a multi-scale layered residual network in generators for reducing semantic loss of images in the process of encoding. The Wasserstein GAN architecture with gradient penalty (WGAN-GP) is employed in the discriminator to optimize the training process and speed up the network convergence. Comparative experiments on several image translation tasks over style transfers and object migrations show that the proposed MRGAN outperforms strong baseline models by large margins.
Smart-Card Technology for the Dental Management of Medically Complex Patients

Smart-card technology is believed to help healthcare industries in several ways, since it minimizes risks and medical errors, enables accurate patient identification, reduces administrative costs, improves efficiency, and facilitates prompt delivery of care to patients. The present study aims to highlight the adoption of a newly designed dental smart card for medically complex patients. The present smart card is an advance in patient identification, using a quick-response (QR) code to automatically report or receive certain types of responses from patients or physicians once illuminated by signals from QR readers. Further, the card provides general information about the patient's condition and physical details. The card is pocket sized and can be carried easily by the patient anywhere, alongside a digital copy of the card.
Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation

More than 7000 languages are spoken in the world today. Amharic is one of the languages spoken in the East African country Ethiopia. A lot of speech data is being made every day in different languages as machines are getting better at processing and have improved storing capacity. However, searching for a particular word with its respective time frame inside a given audio file is a challenge. Since Amharic has its own distinguishing characteristics, such as glottal, palatal, and labialized consonants, it is not possible to directly use models that are developed for other languages. A popular approach in developing systems for searching particular information in speech involves using an automatic speech recognition (ASR) module that generates the text version of the speech where the word or phrase is searched based on text query. However, it is not possible to transcribe a long audio file without segmentation, which in turn affects the performance of the ASR module. In this paper, we are reporting our investigation on the effects of manual and automatic speech segmentation of Amharic audio files in a spiritual domain. We have used manual segmentation as a baseline for our investigation and found out that sentence-like automatic segmentation resulted in a word error rate (WER) closer to the WER achieved on the manually segmented test speech. Based on the experimental results, we propose Amharic speech search using text word query (ASSTWQ) based on automatic sentence-like segmentation. Since we have achieved lower WER using the previously developed speech corpus, which is in a broadcast news domain, together with the in-domain speech corpus, we recommend using both in- and out-domain speech corpora to develop the Amharic ASR module. The performance of the proposed ASR is a WER of 53% that needs further improvement. Combining two language models (LMs) developed using training text from the two different domains (spiritual and broadcast news) allowed a WER reduction from 53% to 46%. Therefore, we have developed two ASSTWQ systems using the two ASR modules with WERs of 53% and 46%.
Optimizing information and communication technology applications in chemistry learning

Information and communication technology (ICT) is crucial to the educational growth of any country and the academic interactions and collaborations of students. The study determined the extent of ICT application in chemistry instruction in Anambra State, Nigeria, secondary schools. This study uses a descriptive survey. A total of 133 students and 12 chemistry teachers as samples. The t-test was used to test the research hypothesis. The study's findings revealed a low extent of ICT application in teaching and learning chemistry in Anambra State, Nigeria. Moreover, both the chemistry teachers and students agree that ICT is applied to a low extent for teaching and learning chemistry in Anambra State, Nigeria. The study also revealed that the use of outdated computers; lack of technical assistance; lack of time; lack of computer hardware/software; lack of electricity; broken down computers; lack of internet or slow connectivity, and high cost of computers are some of the challenges of ICT application while the provision/maintenance of adequate ICT software and hardware, exposure of chemistry teachers to workshops and conferences and provision of uninterrupted internet services and power supply by the government among others were proffered as possible strategies to eliminate these challenges.
Two-Stage Optimization Methods to Solve the DNA-Sample Allocation Problem

This paper deals with new methods capable of solving the optimization problem concerning the allocation of DNA samples in plates in order to carry out the DNA sequencing with the Sanger technique. These methods make it possible to work with independent subproblems of lower complexity, obtaining solutions of good quality while maintaining a competitive time cost. They are compared with the ones introduced in the literature, obtaining interesting results. All the comparisons among the methods in the literature and the laboratory results have been made with real data.
Quality measures for fully automatic CT histogram-based fat estimation on a corpse sample

In a previous article a new algorithm for fully automatic 'CT histogram based Fat Estimation and quasi-Segmentation' (CFES) was validated on synthetic data, on a special CT phantom, and tested on one corpse. Usage of said data in FE-modelling for temperature-based death time estimation is the investigation's number one long-term goal. The article presents CFES's results on a human corpse sample of size R = 32, evaluating three different performance measures: the τ-value, measuring the ability to differentiate fat from muscle, the anatomical fat-muscle misclassification rate D, and the weighted distance S between the empirical and the theoretical grey-scale value histogram. CFES-performance on the sample was: D = 3.6% for weight exponent α = 1, slightly higher for α ≥ 2 and much higher for α ≤ 0. Investigating τ, S and D on the sample revealed some unexpected results: While large values of τ imply small D-values, rising S implies falling D and there is a positive linear relationship between τ and S. The latter two findings seem to be counter-intuitive. Our Monte Carlo analysis detected a general umbrella type relation between τ and S, which seems to stem from a pivotal problem in fitting Normal mixture distributions.
The concept of creative doing as the basis for the organization of educational and design research work of university students

The purpose of this work is to define the term creative doing, comparing it with the terms creation and creativity; definition of the concept of creative doing and its structure, demonstration of examples of practical application of the concept of creative doing in design and research work of university students. When organizing the educational and research work of students, a project-activity approach was used. The illustration of successful creative projects is carried out in the course of organizing excursions with students to museums, estates, historical and memorable places by teachers. The concept of creative doing is illustrated by the example of the activities of Russian entrepreneurs of the late XIX - early XX centuries. Specific topics and stages of organizing the design and research work of students are proposed, which is the practical significance of this study.
A scheme for multipartite entanglement distribution via separable carriers

Abstract The ability to reliably distribute entanglement among the nodes of a network is an essential requirement for the development of effective quantum communication protocols and the realization of useful quantum networks. It has been demonstrated, in different contexts, that two remote systems can be entangled via local interactions with a carrier system that always remains in a separable state with respect to such distant particles. We develop a strategy for entanglement distribution via separable carriers that can be applied to any number of network nodes to achieve various entanglement distribution patterns. We show that our protocol results in multipartite entanglement, while the carrier mediating the process is always in a separable state with respect to the network. We provide examples showcasing the flexibility of our approach and propose a scheme of principle for the experimental demonstration of the protocol.
Security analysis on optical cryptosystem based on interference and phase-retrieval technique

• Evaluated the security strength of the interference and phase-retrieval technique combined cryptosystem; • Proposed COA with normalization and image smoothing techniques; • Demonstrated the security vulnerability of the cryptosystem based on interference and phase-retrieval technique. In this paper, the security strength of an optical cryptosystem based on interference and phase-retrieval technique has been evaluated. Compared to the conventional interference-based scheme in which private keys are generated according to the principle of vector decomposition, a modified error-reduction phase retrieval algorithm is employed to produce a plaintext-dependent key with the smaller size than that of the plaintext in the improved cryptosystem; thus, resulting in higher resistance against iterative attacks and lower burden of data transmission. However, it is found that only one phase-only mask (POM) served as the private key is unknown to illegal users, the key space is limited; thus, it is possible to investigate an iterative process to recover the unknown key with the aid of public keys and the arbitrarily given ciphertext as additional constraints. And employing the recovered private key, encoded information may be reconstructed. Based on this cryptoanalysis, the iterative ciphertext-only attack (COA) with normalization and image smoothing techniques (i.e., the median filter, the bilateral filter) is proposed to crack the enhanced scheme. Numerical simulations are carried out to validate the feasibility and effectiveness of the proposed COA.
Missing Person Identification using Machine Learning with Python

Abstract: With advances in computing and telecommunications technologies, digital images and video are playing key roles in the present information era. This system uses powerful python algorithm through which the detection and recognition of face is very easy and efficient. Human face is an important biometric object in image and video databases of surveillance systems. Detecting and locating human faces and facial features in an image or image sequence are important tasks in dynamic environments, such as videos, where noise conditions, illuminations, locations of subjects and pose can vary significantly from frame to frame. we want to identify the person based on face data base which we have already created in own data. After that we want to start identification of face using face recognition package. Finally, we will do comparison with data base and we will say weather that person is missing person or unknown person.
Analisis dan Perancangan Sistem Informasi Penjualan pada Toko XYZ

Along with the development of the times in the field of technology, companies are increasingly being triggered to use advanced technology as a tool or medium to survive and win the increasingly fierce competition. Internet technology has been proven to be one of the effective and efficient information media in disseminating information that can be accessed by anyone, anytime and anywhere. Internet technology has a huge effect on commerce or business. XYZ Store is a trading business engaged in the sale of plastic. The sales system at the XYZ store still uses a manual system and does not use more competent and computerized technology. The plastic sales transaction system that is done manually requires computerized data collection as a tool. Due to these problems, it is necessary to do research to find solutions that can solve problems at the XYZ store. The results of the research are the construction of a web-based plastic sales information system that makes it easier for users to place orders and payments through a computerized system that was built. Through this web information system, XYZ store can reach more customers from various provinces and cities in Indonesia.
Contrast-Controllable Image Enhancement Based on Limited Histogram

To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the values of the two metrics, information entropy and MS-SSIM, processed by the algorithms in this paper, are more accurate compared to the other six excellent algorithms.
Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data

A large amount of data transmission is one of the challenges faced by communication systems. In this paper, we revisit the intelligent receiver consisting of a neural network, and we find that the intelligent receiver can reduce the data at the transmitting end while improving the decoding accuracy. Specifically, we first construct a smart receiver model, and then design two ways to reduce the data at the transmitter side, namely, end-of-transmitter data trimming and equal-interval data trimming, to investigate the decoding performance of the receiver under the different trimming methods. The simulation results show that the receiver still has an accurate decoding performance with a small amount of trimming at the end of the transmitter data, while the decoding performance of the smart receiver is better than that of the conventional receiver with complete data when the data is trimmed at equal intervals. Moreover, the receiver with equally-spaced data cropping has a lower BER when the data at the transmitter side is reduced by the same data length. This paper provides a new solution to reduce the amount of data at the transmitter side.
Cryptocurrency Returns, Cybercrime and Stock Market Volatility: GAS and Regime Switching Approaches

This study examines the behaviour of cryptocurrencies’ returns to stock market volatility and cybercrime in the South African economy. The study makes us Generalized Autoregressive Score Model (GAS) investigate the time-varying correlation between cryptocurrencies’ returns and cybercrime, and cryptocurrencies’ returns and stock market volatility by making use of daily time series data on different four types of Cryptocurrencies, Bitcoin, Ethereum, Tether and BNB from January 2019 to December 2021. The study also makes use of the regime-switching approach to regime-switching impacts on the cryptocurrencies’ returns. The empirical results obtained showed that cybercrime, on average, has negative impacts on the cryptocurrencies’ returns and the time-varying correlation between stock market volatility and each of the cryptocurrencies’ returns is largely positive. The stock market volatility impact is found to be regime-switching dependent. The study recommends that efforts to reduce cybercrime activities must be reinforced to deepen the use of digital currencies and policy measures must be taken to ensure reduced or moderate stock market volatility.
High-speed integrated QKD system

Quantum key distribution (QKD) is nowadays a well established method for generating secret keys at a distance in an information-theoretic secure way, as the secrecy of QKD relies on the laws of quantum physics and not computational complexity. In order to industrialize QKD, low-cost, mass-manufactured and practical QKD setups are required. Hence, photonic and electronic integration of the sender's and receiver's respective components is currently in the spotlight. Here we present a high-speed (2.5 GHz) integrated QKD setup featuring a transmitter chip in silicon photonics allowing for high-speed modulation and accurate state preparation, as well as a polarization-independent low-loss receiver chip in aluminum borosilicate glass fabricated by the femtosecond laser micromachining technique. Our system achieves raw bit error rates, quantum bit error rates and secret key rates equivalent to a much more complex state-of-the-art setup based on discrete components.
National Security and Higher Education : Challenges, Risks and Threats

Globalisation raises the risk of unwanted leaks of information from higher education institutions and the use of the scientific results of one country for hostile purposes by another one. The national interests to be protected have their dynamics so national security has to also be in permanent development. Networking as a part of HUMINT has a growing importance among students and the teaching staff as well. The protective measures need urgent upgrading in order to prevent any possible risk on behalf of state and non-state actors. As the students are the most exposed to different radical ideas, the preventive measures have to be focused on them. It is also important to keep an eye on the laboratories accessible at the higher education institutions where electronic, technical and chemical tools and devices can be illegally elaborated for illicit purposes.
Framework for Detection of Fraud at Point of Sale on Electronic Commerce sites using Logistic Regression

Many businesses have been positively impacted by electronic commerce (ecommerce). It has enabled enterprises and consumers transact business digitally and experience diversity as long as the internet is accessible and there is a gadget to surf the internet. Several governments have gradually adopted electronic payment throughout the country. The Nigerian government has also done a lot of prodding toward the adoption of a cashless economy, which includes embracing ecommerce. As ecommerce expands, so does actual and attempted fraud through this channel. According to the Nigerian Central Bank, electronic fraud reached trillions of Naira by 2021. The purpose of this work was to employ logistic regression as a decision-making tool for detecting fraud in e-commerce platforms at either the virtual or physical point of sale. The main contribution of this research is a model developed using logistic regression for detecting fraud at the point of sale on electronic commerce platforms. The accuracy of the result is 97.8 percent. The result of this study will provide key decision makers in ecommerce firms with information on fraud patterns on their ecommerce platforms, this will enable them take quick actions to forestall these fraudulent attempts. Further research should be carried out using data from other developing countries.
Sentiment Analysis and Stance Detection on German YouTube Comments on Gender Diversity

This paper explores different options of detecting the stance of German YouTube comments regarding the topic of gender diversity and compares the respective results with those of sentiment analysis, showing that these are two very different NLP tasks focusing on distinct characteristics of the discourse. While an already existing model was used to analyze the comments’ sentiment (BERT), the comments’ stance was first annotated and then used to train different models – SVM with TF-IDF, DistilBERT, LSTM and CNN – for predicting the stance of unseen comments. The best results were achieved by the CNN, reaching 78.3% accuracy (92% after dataset normalization) on the test set. Whereas the most common stance identified in the comments is a neutral one (neither completely in favor nor completely against gender diversity), the overall sentiment of the discourse turns out to be negative. This shows that the discourse revolving around the topic of gender diversity in YouTube comments is filled with strong opinions, on the one hand, but also opens up a space for anonymously inquiring and learning about the topic and its implications, on the other. Our research thereby (1) contributes to the understanding and application of different NLP tasks used to predict the sentiment and stance of unstructured textual data, and (2) provides relevant insights into society’s attitudes towards a changing system of values and beliefs.
Reviewing ViBe, a Popular Background Subtraction Algorithm for Real-Time Applications

Background subtraction or foreground segmentation is commonly the first step in video processing pipelines, where foreground objects and moving entities are detected for further analysis.Background subtraction is still an open problem in computer vision due to the wide range of possible scenarios and challenging cases, such as weather conditions, illumination variation, dynamic backgrounds or low frame-rates.In this article, we review a well-known general algorithm for background subtraction named visual background extractor or ViBe.More than 10 years after its publication, ViBe is still widely used due to its simplicity and low computational load, as its authors claim it can process up to 200 fps.We analyze the algorithm's mechanism to segment foreground objects, comment on its strengths and drawbacks, and describe an effective variant of the method to quickly dissipate ghost effects using 3-frame difference.Furthermore, we provide an easy-to-use demo that allows to quickly test the performance of ViBe on custom data, modify its parameters and download the results.