Reviewerly/exHarmony huggingface dataset:
Assessing phoneme distribution for speech modeling

Phonetically balanced texts are used to study different voice and speech characteristics. In the context of clinical work and research, these texts provide a standard for quantifying perceptual, acoustic, or aerodynamic assessments. Recent modeling efforts are being devoted to describing long-term speech behaviors based on a collection of sustained phonemes. However, comprehensive descriptions of phoneme distributions representative of connected speech are not readily available. Thus, the present study introduces a method to estimate phoneme distributions using text data mining, as an alternative to existing power law methods. The procedure used for the decomposition of texts into phonemes, the estimation of the phonetic distributions and the comparisons between different texts, conversational speech, and standard reading passages are discussed. The results are presented using histograms and R-squared determination coefficients for the case of the English language, although the approach can be easily applied for other languages. A discussion of the proposed method, results, and limitations is presented.
Internal and External Framework of Blockchain

Blockchain innovation can possibly change collaborations between states, organizations and residents in a way that was inconceivable simply 10 years prior.Though very often grouped with technologies such as artificial intelligence (AI) or IoT (Internet of Things), the technology is unique in its foundational nature. Unlike other technologies, which have the potential to deliver completely new services to citizens and other stakeholders alike, blockchain has the potential to revamp currently existing processes to unlock new sources of efficiency and value. [1] At present blockchain is, maybe, quite possibly the most encouraging technology, and it have an extremely high potential to change the present plans of action. It is basically a disseminated information base of records, or a public record, everything being equal (or computerized occasions) that have been executed and divided between taking an interest parties. Every exchange in the public record is confirmed by the agreement of a greater part of the members' processing power in the whole framework. Therefore, blockchain has the potential to significantly change the way financial institutions, for example, operate today. As an early evidence note that Bitcoin is one of the most commonly used applications based on blockchain technology. Also, due to the attractive characteristic of decentralization, persistency, anonymity and auditability, it has further far-reaching implications in a wide range of industries, sectors, and application areas, such as Internet of Things (IoT), medical health, and smart contracts, respectively. As a result, blockchain technology has received considerable attention in both the academic and the industrial communities [2].
Effects and methodology for grid subdivision of CT-based texture for unsupervised clustering

t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means have been increasingly utilized for dimension reduction and graphical illustration in medical imaging (e.g., CT) informatics. Mapping a grid network onto a slide is a prerequisite for implementing cluster analysis. Traditionally, the performance of cluster analysis is driven by hyperparameters, however, grid size which also affects performance is often set arbitrarily. In this study, we evaluated the effect of varying grid sizes, perplexity and learning rate hyperparameters for unsupervised clustering using CT images of renal masses. We investigated the impact of grid size to cluster analysis. The number of clusters was determined by Gap-statistics. The grid size selections were 2x2, 4x4, 5x5, and 8x8. The results showed that the number of output clusters increased with decreasing grid sizes from 8x8 to 4x4. However, when grid size reached 2x2, the model yielded the same cluster number as 8x8. This finding was consistent across different hyperparameter settings. Additional analyses were conducted to understand the nesting structure between the cluster membership (the mutually exclusive cluster number assigned to each grid in a cluster analysis) from large (8x8) grid and small (2x2) grid, although both grid size selections yielded the same number of clusters. We report that the cluster membership between large grid and small grid is only partially overlaid. This suggests that additional pattern/information is detected by using the small grid. In conclusion, the grid size should be treated as another hyperparameter when using unsupervised clustering methods for pattern recognition in medical imaging analysis.
VENTSEG: efficient open source framework for ventricular segmentation

Despite advances in deep learning methods aimed at cardiac ventricular segmentation, most algorithms have drawbacks due to low prediction accuracy with images from different MR scans to those trained. It leads to a process that requires time-consuming correction by technicians or specialists. The time in this process is significant mainly due to the large number of image sets to be processed. The lack of description of the algorithms has not allowed repeatability, while commercial software is difficult to access for clinical use or research. However, in cardiac segmentation research, several solutions have already been proposed. This paper presents an opensource cardiac functionality segmentation and evaluation framework, which contemplates a diverse database for network training, a multi domain network architecture that allows model generalization, and pre-and postprocessing algorithms that improve prediction results. The prediction evaluation of the framework shows that Ventseg is 3.66% superior to the trained model and the similarity percentages in the tested MR scores are over 84%. On the other hand, the inter-observer variability analysis, with anonymized data, shows in the different metrics that Ventseg is on par with cardiac segmentation specialists. Finally, the efficiency calculated in an intra-observer test indicates that our framework reduces manual segmentation time by approximately 80%.
An improved saliency model of visual attention dependent on image content

Many visual attention models have been presented to obtain the saliency of a scene, i.e., the visually significant parts of a scene. However, some mechanisms are still not taken into account in these models, and the models do not fit the human data accurately. These mechanisms include which visual features are informative enough to be incorporated into the model, how the conspicuity of different features and scales of an image may integrate to obtain the saliency map of the image, and how the structure of an image affects the strategy of our attention system. We integrate such mechanisms in the presented model more efficiently compared to previous models. First, besides low-level features commonly employed in state-of-the-art models, we also apply medium-level features as the combination of orientations and colors based on the visual system behavior. Second, we use a variable number of center-surround difference maps instead of the fixed number used in the other models, suggesting that human visual attention operates differently for diverse images with different structures. Third, we integrate the information of different scales and different features based on their weighted sum, defining the weights according to each component's contribution, and presenting both the local and global saliency of the image. To test the model's performance in fitting human data, we compared it to other models using the CAT2000 dataset and the Area Under Curve (AUC) metric. Our results show that the model has high performance compared to the other models (AUC = 0.79 and sAUC = 0.58) and suggest that the proposed mechanisms can be applied to the existing models to improve them.
Estimation of 3D point spread function of a widefield microscope by stochastic least square minimization method

The point-spread function (PSF) plays a fundamental role in deciding the resolution of an optical microscope. One way to find the experimental PSF is to image sub-resolution nanobead. However, the PSF can also be approximated numerically by knowing the optical arrangement of the imaging system. In this paper, we estimate the experimental three-dimensional PSF of a widefield microscope by taking several z-stacks of an arbitrary target, followed by application of our 2D PSF estimation scheme to each z slice. Here we use a standard resolution test target as the arbitrary target whose geometrical-optics predicted image can be easily constructed.
Sharing and Cooperation of Improved Cross-Entropy Optimization Algorithm in Telemedicine Multimedia Information Processing

In order to improve the efficiency of medical multimedia information sharing, this paper combines cloud computing technology and SOA (service-oriented architecture) technology to build a medical multimedia information sharing system. Building a medical information sharing platform requires integrating information resources stored in information systems of medical institutions and nonmedical information systems related to medical information and forming a huge resource pool. It is important to mine and analyze the information resources in the resource pool to realize the sharing and interaction of medical information. To this end, this paper proposes a gain-adaptive control algorithm with online adjustable parameters and investigates the extension of the mutual entropy optimization algorithm in the control domain and its integrated processing capability in the process of medical multimedia information processing. In addition, this paper constructs a medical multimedia information sharing and collaboration platform with medical multimedia information sharing and telemedicine as the core and verifies the effectiveness of the platform through experiments. The simulation results and comparison results with other systems prove that the system in this paper can realize fast data processing, retrieve and analyze massive data, and meet the demand of remote intelligent diagnosis under the premise of safety and stability. Meanwhile, the system in this paper can help hospitals achieve fast and accurate diagnosis, which has strong theoretical and practical values.
Real-Time Symbolic Transcription and Interactive Transformation Using a Hexaphonic Nylon-String Guitar

Abstract GuiaRT is an interactive musical setup based on a nylon-string guitar equipped with hexaphonic piezoelectric pickups. It consists of a modular set of real-time tools for the symbolic transcription, variation, and triggering of selected segments during a performance, as well as some audio processing capabilities. Its development relied on an iterative approach, with distinct phases dedicated to feature extraction, transcriptions, and creative use. This article covers the motivations for this augmented instrument and several details of its implementation, including the hardware and strategies for identifying the most typical types of sound produced on a nylon-string guitar, as well as tools for symbolic musical transformations. This acoustic–digital interface was primarily designed for interactive exploration, and it has also been effectively used in performance analyses and as a pedagogical tool.
A Voronoi-Based Semantically Balanced Dummy Generation Framework for Location Privacy

Location-based services (LBS) require users to provide their current location for service delivery and customization. Location privacy protection addresses concerns associated with the potential mishandling of location information submitted to the LBS provider. Location accuracy has a direct impact on the quality of service (QoS), where higher location accuracy results in better QoS. In general, the main goal of any location privacy technique is to achieve maximum QoS while providing minimum or no location information if possible, and using dummy locations is one such location privacy technique. In this paper, we introduced a temporal constraint attack whereby an adversary can exploit the temporal constraints associated with the semantic category of locations to eliminate dummy locations and identify the true location. We demonstrated how an adversary can devise a temporal constraint attack to breach the location privacy of a residential location. We addressed this major limitation of the current dummy approaches with a novel Voronoi-based semantically balanced framework (VSBDG) capable of generating dummy locations that can withstand a temporal constraint attack. Built based on real-world geospatial datasets, the VSBDG framework leverages spatial relationships and operations. Our results show a high physical dispersion cosine similarity of 0.988 between the semantic categories even with larger location set sizes. This indicates a strong and scalable semantic balance for each semantic category within the VSBDG’s output location set. The VSBDG algorithm is capable of producing location sets with high average minimum dispersion distance values of 5861.894 m for residential locations and 6258.046 m for POI locations. The findings demonstrate that the locations within each semantic category are scattered farther apart, entailing optimized location privacy.
Blockchain-Based Medical Cyber Physical Systems With Decentralized Threshold signature Scheme

Medical cyber physical systems are information applications of medical industry.A lagrge amount of medical data is stored in MCPS,and there are many challenges in the secure store and data sharing.Using blockchain technology into medical Cyber Physical system has become popular.Blockchain has remarkable features such as tamper proof and privacy protection, and has the function of protecting data in the medical Cyber Physical system.In this paper,we propose a hybrid blockchain,which applied private blockchain and consortium blockchain, After the medical source data is hashed, a hash tree is generated and stored in the private chain of the hospital. The hospital server extracts information to build a new transaction on the consortium chain.the system ensure the secure storage and fast access of data.Still,a threshold signature system is proposed.Aiming at the situation that medical accidents are easy to occur in multidisciplinary joint consultation in the medical process, this paper proposes to use threshold signature for joint consultation.Using the security and tamper-proof of the threshold signature, when the consensus is reached,treatment can be carried out and the medical data is uploaded to the consortium blockchain. The security analysis and performance analysis show that the scheme has advantages in safety and performance and is suitable for the medical environment.
Semantic Communities from Graph-Inspired Visual Representations of Cityscapes

The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors. This allows an agent to localise itself in future tasks under different environmental circumstances in an urban area. The proposed semantic grouping technique utilizes the Leiden Community Detection Algorithm (LeCDA), which is a novel and efficient method of low computational complexity and exploits semantic and topometric information from the observed scenes. The presented experimentation was carried out on a novel dataset from the city of Xanthi, Greece (dubbed as Gryphonurban urban dataset), which was recorded by RGB-D, IMU and GNSS sensors mounted on a moving vehicle. Our results exhibit the formulation of a semantic map with visually coherent communities and the realisation of an effective localisation mechanism for autonomous vehicles in urban environments.
Spectroscopic Characterization of Impactites and a Machine Learning Approach to Determine the Oxidation State of Iron in Glass‐Bearing Materials

Abstract We investigated a suite of impact glass‐bearing rocks using a multi‐analytical approach including visible‐near‐infrared diffuse reflectance spectroscopy, Mössbauer spectroscopy, and powder X‐ray diffraction. In order to better understand and interpret the obtained results, we built a database containing physical, chemical, and spectroscopic information on glasses and glass‐bearing materials using new results from this study and published works. We used the database to explore systematic relationships between parameters of interest and finally we applied several machine learning algorithms (support vector machine, random forests, and gradient boosting) to test the possibility to regress the oxidation state of iron from chemical and spectroscopic information. Our results show that even small amounts of mafic crystalline phases have a big influence on the spectral features of glass‐bearing rocks. Samples without mafic crystalline inclusions show the typical spectrum of glasses (two broad and shallow bands roughly centered around 1,100 and 1,900 nm) with minor variations due to bulk chemistry. We described a non‐linear relationship between average reflectance (average reflectance value between 500 and 1,000 nm), FeO + TiO 2 content, grain size, and Fe 3+ /Fe TOT . We tested the relation for the finer grain size (0–25 μm), and we qualitatively assessed how it is affected by grain size, Fe 3+ /Fe TOT , and crystal content. Finally, we developed a machine learning pipeline to regress the Fe 3+ /Fe TOT of glass‐bearing materials using the proposed database. Our machine learning calculations give satisfactory results (MAE: 0.0321) and additional data will enable the application of our computational strategy to remotely acquired data to extract chemical and mineralogical information of planetary surfaces.
A lightweight blockchain-based framework for medical cyber-physical system

A medical cyber-physical system (MCPS) combines intelligent medical devices with a network. Nowadays, MCPS is extensively used in the healthcare system. While sharing patient data into MCPS, data security and privacy are significant challenges in healthcare as most of the devices suffer from data leaking, data manipulation, data forgery, denial of service attacks, etc. In this work, a reputation-based blockchain framework has been proposed to ensure patient data security while preserving privacy. A detailed security analysis discusses the possible attacks and the countermeasures of the attacks. The result and performance analysis of the proposed framework show low latency in data sharing and high throughput with an increasing number of users in the network. The calculated results established that the average latency time is reduced to (46–50%) while throughput is increased by (44–50%) as the number of users increases.
SEVERAL OPPORTUNITIES FOR IMPLEMENTING THE TRAINING IN “COMPUTER MODELING AND INFORMATION TECHNOLOGIES” IN THE 7TH GRADE

The article discusses some programming environments that can be applied in the 7th grade “Computer Modeling” classes. Attention is paid to the new active programs in the discipline “Computer Modeling and Information Technologies”. A study was conducted to track the attitudes of teachers related to teaching a scripted text programming language in 7th grade.
Transferred Low-rank Discriminative Sub-dictionary Learning Method for Information Identification and Understanding in IoT-based Natural Language Processing

As Internet of Things (IoT) technology progresses rapidly, there is an increasing demand for automatic identification and understanding of natural language data. However, data labeling requires large amounts of effort and cost. Most intelligent algorithms rely on the assumption of uniform distribution of data, which brings great challenge to IoT-based natural language processing. To solve this problem, this study develops a transferred low-rank discriminative sub-dictionary learning (TLDSL) method. The TLDSL method learns a shared subspace through the maximum mean discrepancy (MMD) strategy that minimizes the distribution difference of sparse coefficients between the source and target domains. By learning the common sub-dictionary of the two domains, TLDSL reveals the intrinsic connection and establishes a bridge between the two domains, thus completing the knowledge transfer. By introducing the sub-dictionary incoherence, TLDSL can avoid the atomic correlation between different sub-dictionaries. In addition, the sparse coefficients are constrained in low rank representation, which can improve the model discrimination ability while preserving the global data structure. Experiments show that the TLDSL method can be effectively performed on cross-domain text classification and handwritten digit recognition.
The heterogeneous impact of particulate matter on solar performance in a megacity: The case of Seoul

Megacities play an essential role in accelerating the energy transition. Megacities need to decarbonize energy systems by taking transformative actions on photovoltaic (PV) plants. However, particulate matter 10 (PM10) accumulated on PVs may decrease the amount of photovoltaic power generation (PPG), preventing the further expansion of PV installation. This study identifies the heterogeneous effects of PM10 by using quantile regression and by including small-scale PVs to understand subgroup differences across the distribution of PPG. This differs from previous studies, which only addressed its homogeneous effects. The results show that the direction of PM10′s impact on PPG is consistent with previous studies and that the effect of PM10 is different for each quantile. It turned out that accumulated PM10 had a heterogeneous effect on the PPG quantile, and the absolute value of PM10 was the largest when the PPG quantile was low. This enables a richer explanation of the impact of PM10 on PPG. By identifying possible obstacles related to PM10, this research contributes to facilitating renewable-led municipal energy policy and proposes effective management to improve the reliability of solar energy systems.
Novel Video Benchmark Dataset Generation and Real-Time Recognition of Symbolic Hand Gestures in Indian Dance Applying Deep Learning Techniques

A computational approach towards promoting, preservation, and dissemination of knowledge in the domain of cultural heritage, is one of the research areas that has a wide scope. There has been a seismic shift in the way many sectors in society have adapted themselves to the pandemic situation, be it healthcare, food, education, diplomacy, and performing arts. Virtual learning and performing have become the need of the hour in the field of performing arts as well. The objective of this work is threefold; first, this creates benchmark datasets to be shared to make a beneficial impact and for a meaningful engagement by capturing, recognising, and classifying the multimedia content for hastamudras (hand poses) in Bharatanatyam, an Indian classical dance form, which plays a significant role in the conservation of intangible cultural heritage, second, as tutoring system to aspiring learners and third, to build video recommendation systems to promote art as a tool for building an international relationship and further elevate the significance of soft-power through performing arts. This article proposes applying the deep-learning techniques of CNNs as a critical technology to recognise the correct mudra. Experimental results on our challenging mudra dataset through the MobileNet architecture show 85%–95% accuracy in real-time, which outperforms the Sebastien-Marcel dataset. The time taken to process an image is 0.172 seconds, and the result is significant considering that the images are dynamic. This work proves the accuracy of the proposed method, which significantly outperforms another CNN-based Inception v3 model.