Reviewerly/exHarmony huggingface dataset:
Modern Information and Communication Technologies in the Advanced Education of Children

Scientific and technical progress in the modern world is increasing its pace and is actively being introduced into educational process. Accordingly, the impact of information and communication technologies on the younger generation cannot be avoided. The level of educational needs of modern students in the system of additional education of children is qualitatively new, so there is a need to create informational and educational environment as an effective means to meet educational needs of students in modern conditions.The purpose of the work is to develop and determine the effectiveness of the method of using modern information technologies in educational process of additional education for children. Therefore, the article discusses the use of information and communication technologies in education, theoretical foundations of the method of using modern information technologies in additional education of children. The article assesses the effectiveness of information and communication technologies implementation into the system of additional education for children. The authors conducted an experiment in which 26 children from the third year of study in the Navigation Circle of the city of Nizhny Novgorod took part. In order to investigate classes’ effectiveness at the Center providing "Navigation" course from the inside, the authors carried out a rapid survey. The authors found that students tend to positively perceive the general nature of navigation lessons at the Center. The Center developed a method for conducting practical work using multimedia simulators, after introduction of which the survey among students was repeated. The authors obtained the following results: high levels of interest and student assessment of these classes is obvious for all selected items. The students began to take a more positive view of the lessons at the Center by navigation, responded to their novelty and non-traditional character, which effectively expressed itself in increasing learning motivation.The developed method of using information and communication technologies in the system of additional education of children can be proposed for implementation in the activity of a navigator teacher, taking into account the advanced methodological recommendations.
Reducing Cybersickness in 360-Degree Virtual Reality

Abstract Despite the technological advancements in Virtual Reality (VR), users are constantly combating feelings of nausea and disorientation, the so-called cybersickness . Cybersickness symptoms cause severe discomfort and hinder the immersive VR experience. Here we investigated cybersickness in 360-degree head-mounted display VR. In traditional 360-degree VR experiences, translational movement in the real world is not reflected in the virtual world, and therefore self-motion information is not corroborated by matching visual and vestibular cues, which may trigger symptoms of cybersickness. We evaluated whether a new Artificial Intelligence (AI) software designed to supplement the 360-degree VR experience with artificial six-degrees-of-freedom motion may reduce cybersickness. Explicit (simulator sickness questionnaire and Fast Motion Sickness (FMS) rating) and implicit (heart rate) measurements were used to evaluate cybersickness symptoms during and after 360-degree VR exposure. Simulator sickness scores showed a significant reduction in feelings of nausea during the AI-supplemented six-degrees-of-freedom motion VR compared to traditional 360-degree VR. However, six-degrees-of-freedom motion VR did not reduce oculomotor or disorientation measures of sickness. No changes were observed in FMS and heart rate measures. Improving the congruency between visual and vestibular cues in 360-degree VR, as provided by the AI-supplemented six-degrees-of-freedom motion system considered, is essential for a more engaging, immersive and safe VR experience, which is critical for educational, cultural and entertainment applications.
Optimal Oblivious Parallel RAM

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Optimal Oblivious Parallel RAMGilad Asharov, Ilan Komargodski, Wei-Kai Lin, Enoch Peserico, and Elaine ShiGilad Asharov, Ilan Komargodski, Wei-Kai Lin, Enoch Peserico, and Elaine Shipp.2459 - 2521Chapter DOI:https://doi.org/10.1137/1.9781611977073.98PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract An oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (STOC '87 and J. ACM '96), is a technique for hiding RAM's access pattern. That is, for every input the distribution of the observed locations accessed by the machine is essentially independent of the machine's secret inputs. Recent progress culminated in a work of Asharov et al. (EUROCRYPT '20), obtaining an ORAM with (amortized) logarithmic overhead in total work, which is known to be optimal. Oblivious Parallel RAM (OPRAM) is a natural extension of ORAM to the (more realistic) parallel setting where several processors make concurrent accesses to a shared memory. It is known that any OPRAM must incur logarithmic work overhead (in the balls and bins model). Despite the significant recent advances for constructing ORAM, there is still a significant gap for OPRAM: all existing OPRAM schemes incur a poly-logarithmic overhead either in total work or in depth. Our main result closes the aforementioned gap and provides an optimal OPRAM. Specifically, assuming one-way functions, we show that any Parallel RAM with memory capacity N can be obliviously simulated in space O(N), incurring only O(log N) blowup in (amortized) total work as well as in depth. Our transformation supports all PRAMs in the CRCW (concurrent read, concurrent write) mode and the resulting simulation is in the CRCW mode as well. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
MSCCS: A Monero-based security-enhanced covert communication system

Existing public blockchain-based covert communication systems are suffering the issues of insufficient robustness, anti-temper modification, and anonymity at both the transaction and network layers. In this paper, to overcome this problem, we propose a novel Monero-based security-enhanced covert communication system, in which a new storage-type covert channel is developed. This channel makes Monero transaction amount as data carrier for covert communication. Then, we devise two new algorithms, respectively for resisting Eclipse attacks and the two existing node crawling attacks. Extensive simulation experiments show that the developed new covert communication channel can achieve higher robustness, anti-detection, and anonymity. The new security-enhanced algorithms can effectively mitigate Eclipse attacks by 37.6%, and the two existing node crawling attacks by 21.1% and 17.1% respectively.
Near-Optimal Quantum Algorithms for String Problems

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Near-Optimal Quantum Algorithms for String ProblemsShyan Akmal and Ce JinShyan Akmal and Ce Jinpp.2791 - 2832Chapter DOI:https://doi.org/10.1137/1.9781611977073.109PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We study quantum algorithms for several fundamental string problems, including Longest Common Substring, Lexicographically Minimal String Rotation, and Longest Square Substring. These problems have been widely studied in the stringology literature since the 1970s, and are known to be solvable by near-linear time classical algorithms. In this work, we give quantum algorithms for these problems with near-optimal query complexities and time complexities. Specifically, we show that: Longest Common Substring can be solved by a quantum algorithm in Õ(n2/3) time, improving upon the recent Õ(n5/6)-time algorithm by Le Gall and Seddighin (2020). Our algorithm uses the MNRS quantum walk framework, together with a careful combination of string synchronizing sets (Kempa and Kociumaka, 2019) and generalized difference covers. Lexicographically Minimal String Rotation can be solved by a quantum algorithm in n1/2 + o(1) time, improving upon the recent Õ(n3/4)-time algorithm by Wang and Ying (2020). We design our algorithm by first giving a new classical divide-and-conquer algorithm in near-linear time based on exclusion rules, and then speeding it up quadratically using nested Grover search and quantum minimum finding. Longest Square Substring can be solved by a quantum algorithm in time. Our algorithm is an adaptation of the algorithm by Le Gall and Seddighin (2020) for the Longest Palindromic Substring problem, but uses additional techniques to overcome the difficulty that binary search no longer applies. Our techniques naturally extend to other related string problems, such as Longest Repeated Substring, Longest Lyndon Substring, and Minimal Suffix. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Predictive Modeling of Clinical Events with Mutual Enhancement Between Longitudinal Patient Records and Medical Knowledge Graph

In recent years, with the better availability of medical data such as Electronic Health Records (EHR), more and more data mining models have been developed to explore the data-driven insights for better human health. However, there are many challenges for analyzing EHR such as high-dimensionality, temporality, sparsity, etc., which make the data-driven models less reliable. Medical knowledge graph (MKG), which encodes comprehensive knowledge about the medical concepts and relationships extracted from medical literature, holds great promise to regularize the data-driven models as prior knowledge. Nonetheless, the MKGs are typically not complete, which limits its utility in helping with the data mining process. In this paper, we propose a mutual enhancement framework MendMKG for predictive modeling of clinical events with both EHR and MKG. In particular, MendMKG first conducts a self-supervised learning strategy to simultaneously pre-train a graph attention network for embedding nodes and complete the MKG. It iteratively performs (1) an embedding-based knowledge graph completion module to derive missing edges, (2) and a reconstruction module of unlabeled EHR data to select high-quality ones from these edges, which would be further appended to the MKG to update the embedding model. Through the iterations, the two modules mutually benefit each other. Then, MendMKG uses the pre-trained graph attention network and the updated MKG to generate the visit embeddings to represent patient’s historical visits, and predict the diagnosis in future visit, through a fine-tuning approach. Experimental results on real world EHR corpus are provided to demonstrate the superiority of the proposed framework, compared to a series of state-of-the-art baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source code and knowledge graph data have been anonymously uploaded to https://github.com/1317375434/MendMKG.
Discrete Bidirectional Associative Memory Neural Networks of the Cohen–Grossberg Type for Engineering Design Symmetry Related Problems: Practical Stability of Sets Analysis

In recent years, artificial intelligence techniques have become fundamental parts of various engineering research activities and practical realizations. The advantages of the neural networks, as one of the main artificial intelligence methods, make them very appropriate for different engineering design problems. However, the qualitative properties of the neural networks’ states are extremely important for their design and practical performance. In addition, the variety of neural network models requires the formulation of appropriate qualitative criteria. This paper studies a class of discrete Bidirectional Associative Memory (BAM) neural networks of the Cohen–Grossberg type that can be applied in engineering design. Due to the nature of the proposed models, they are very suitable for symmetry-related problems. The notion of the practical stability of the states with respect to sets is introduced. The practical stability analysis is conducted by the method of the Lyapunov functions. Examples are presented to verify the proposed criteria and demonstrate the efficiency of the results. Since engineering design is a constrained processes, the obtained stability of the sets’ results can be applied to numerous engineering design tasks of diverse interest.
Older Adults’ Use of Extended Reality: A Systematic Review

Research has pointed to the potential of extended reality (XR), including virtual, mixed, and augmented reality, for broadly impactful benefits, including learning, physical activity and health, and psychosocial aspects such as increased empathy and reduced loneliness. More research is needed to evaluate the outcomes of XR in new populations of users, including older adults. The purpose of this systematic review is to summarize the extant literature that centers on older adult use of XR in order to identify key themes, as well as identified benefits and barriers to XR use. In total, 17 studies from 2015 to 2019 met the inclusion criteria, namely that the study collected data from a sample of adults at least 60 years of age interacting with a 3D virtual environment. Through qualitative analysis of the corpus, six thematic areas were identified: Socialization, Physical Rehabilitation, Driving Simulation, Cognitive Training, Reminiscence, and Assessment . Furthermore, the unique contexts and outcomes of XR use, preferences for XR use, and reported occurrences of cybersickness in older adults are highlighted. Research after 2019 is also noted. This review is intended to guide future research supporting deployment of XR with older adults, emphasizing the psychosocial impacts of the technology. Systematic Review Registration: (website), identifier (registration number)
A Novel Twin Support Vector Machine with Generalized Pinball Loss Function for Pattern Classification

We introduce a novel twin support vector machine with the generalized pinball loss function (GPin-TSVM) for solving data classification problems that are less sensitive to noise and preserve the sparsity of the solution. In addition, we use a symmetric kernel trick to enlarge GPin-TSVM to nonlinear classification problems. The developed approach is tested on numerous UCI benchmark datasets, as well as synthetic datasets in the experiments. The comparisons demonstrate that our proposed algorithm outperforms existing classifiers in terms of accuracy. Furthermore, this employed approach in handwritten digit recognition applications is examined, and the automatic feature extractor employs a convolution neural network.
Automatic learning for object detection

To alleviate the burden of manual image annotation, we propose an automatic learning method to enable object detection. This method mainly consists of the following three aspects: (1) a novel synthetic data generation strategy, which can automatically generate large-scale synthetic data with bounding-box annotations using only semantic concepts of target categories; (2) self-training paradigm combined with synthetic data generation strategy, which mines more information from the unannotated real data through iterative training to improve the performance of the object detector; (3) a simple and effective pseudo box filtering method, which can purify the quality of pseudo boxes during training. Without using any annotations (i.e., image-level annotations and bounding-box annotations) from the PASCAL VOC dataset, our proposed method can obtain 59.3% and 55.1% mAP on PASCAL VOC 2007 and PASCAL VOC 2012, respectively. We also demonstrate the effectiveness of our method on several datasets, including CUB-200–2011, FGVC Aircraft, Stanford Cars, Bird-Aircraft-Car-Dog, and CBCL StreetScenes.
iReview: an Intelligent Code Review Evaluation Tool using Biofeedback

Code reviews and software inspections are essential for building reliable software. However, current code reviews practice in the software industry (e.g., acceptance or rejection of pull requests with Git) deviates considerably from classic (and expensive) Fagan's inspections. Modern code reviews are lightweight and asynchronous and do not rely on a group of inspectors and inspection meetings any longer. The modern style of code reviews is much more flexible and cost-effective. Still, these advantages come with the price of reducing the quality of code reviews, as a single reviewer generally makes them with all the inherent and the very human limitations of one single look. The reviewer could be distracted, overloaded, under stress, or not even fully understand the code under review. This paper proposes a new tool (iReview) that evaluates the code review quality using biometric measures gathered from code reviewers (often called Biofeedback). Biometric measures such as Heart Rate Variability (HRV) and eye movement dynamics are used to assess the reviewer's comprehension of the code under review. iReview evaluates the quality of each review globally and indicates the code regions that have not been well-reviewed, explaining why those code regions should be reviewed again. The tool uses Artificial Intelligence techniques to classify the code regions into good and bad reviews based on various biometric and non-biometric features. The first results show that iReview can predict the review quality of medium or complex programs with an accuracy ranging from 75% to 87% in detecting bad reviews (i.e., code regions classified as bad reviewed still have undetected bugs). This tool is expected to improve software reliability by ensuring that good reviews have been carried out despite the current lightweight reviewing processes.
MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare

In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA–MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA–MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.
TrustBuilder: A non-repudiation scheme for IoT cloud applications

The IoT cloud computing paradigm is emerging for IoT applications. In such a paradigm, how to guarantee non-repudiation service provisioning has attracted research efforts recently. While existing solutions could work for distributed IoT cloud applications, storage, computation, and economic cost is still a practical challenging concern. In this paper, we propose a new, cost-lower non-repudiation scheme for IoT cloud applications. The proposed scheme guarantees that neither the IoT client nor the cloud could repudiate a service enjoyment and provisioning. Specifically, the proposed scheme employs a blockchain to achieve non-repudiation. First, when the cloud provides a service, it encrypts the service, stores a cryptographic hash digest of the encrypted service on the blockchain, and then sends the encrypted service to the IoT client. Second, the IoT client needs to acknowledge the hash digest on the blockchain to obtain the service. Third, the cloud sends the decryption key to the blockchain under the IoT client’s public key to finish a service provisioning. We show that the proposed scheme achieves non-repudiation fairly. We prototyped, evaluated, and open-sourced the proposed scheme. Experimental results confirmed the efficiency of the proposed scheme and the speedup compared with the state-of-the-art solution.
Smartphone English Language Learning Challenges: A Systematic Literature Review

Innovations and advancements in modern technologies have induced changes in every sphere of human activity, including education. Smartphones represent modern, state-of-the-art gadgets which aim at fostering the teaching and learning process of EFL (English as a foreign language) learners. Students may experience and enjoy considerable benefits of these smart devices such as ubiquity, portability, around the clock availability, or numerous technological features. Despite offering overwhelming advantages to language learners, both students and teachers need to take into account challenges they often meet and face. Being in its infancy, this phenomenon appears to be underexplored since majority of studies focus on benefits of mobile learning rather than on challenges. Concentrating on pitfalls, this review article attempts to examine original, peer-reviewed research articles written on smartphone English language learning challenges by reviewing pertinent articles in the two acknowledged databases, Scopus and Web of Science. The research results reveal that the reviewed studies contain identified drawbacks to varying degrees, while approximately one third of articles did not present any challenges. The detected pitfalls, such as smaller dimensions of smartphones, Internet connectivity issues, readiness level of both practitioners and learners, or unbalanced development of the four language skills (listening, reading, writing, speaking), are further addressed and discussed, followed by suggesting the possible solutions to these issues. Finally, it is emphasized that further research needs to be conducted in this regard to shed more light on challenges that both students and practitioners need to tackle.