Reviewerly/exHarmony huggingface dataset:
Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation

Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for learning process. This paper tries to find a way for continual learning which does not require prior high-cost training data construction by imitating a biological memory model. We employ SDR (sparse distributed representation) for information processing and semantic memory model, which is known as a representation model of firing patterns on neurons in neocortex area. This paper proposes a novel memory model to reflect remembrance of morphological semantics of visual input stimuli. The proposed memory model considers both memory process and recall process separately. First, memory process converts input visual stimuli to sparse distributed representation, and in this process, morphological semantic of input visual stimuli can be preserved. Next, recall process can be considered by comparing sparse distributed representation of new input visual stimulus and remembered sparse distributed representations. Superposition of sparse distributed representation is used to measure similarities. Experimental results using 10,000 images in MNIST (Modified National Institute of Standards and Technology) and Fashion-MNIST data sets show that the sparse distributed representation of the proposed model efficiently keeps morphological semantic of the input visual stimuli.
Formation of Visual Thinking of Students in the Information Environment

The text reveals the research directions of development problems of visual thinking and visual literacy of students in the process of mastering the information space through visual activity, conducted directly by the author of this article. An attempt was made to select the pedagogical conditions of the organization of the educational process and the basic competencies of students, allowing the teacher to find effective educational forms and directions of work adequate to the mental age characteristics of children in the process of mastering different types and directions of artistic and creative activity within the framework of polyart education. Their implementation in practice will allow students to get the necessary level of knowledge, ideas and skills, corresponding to the education of a competent and thinking viewer who can read and understand the graphic “text” and visual images created by artists, directors, screenwriters, etc. The author highlights the important components of visual literacy, depending on the age and interests of students; clarifies the concepts of “visual thinking” and “visual literacy” from the perspective of a variety of artistic activities in the learning process; the author defines and formulates competencies aimed at the formation of visual literacy through the development of the visual art alphabet in the context of integrated learning; identifies the pedagogical conditions for the introduction of the presented model into the training system; reveals the dynamics of the visual literacy development based on the development directions of students based on the mental characteristics of the children’s age.
It is Time to Laugh: Discovering Specific Contexts for Laughter with Attention Mechanism

Based on previous studies on the co-occurrence between the types of laughter and conversation contents, this paper uses the attention mechanism to further study the types of laughter and the specific wording in the conversation. We assume that the types of laughter also co-occurs with specific words, as well as a certain pause, in the conversation. We have established a neural network learning model based on the attention mechanism. The results of our model show that certain words in the conversation are related to specific types of laughter.
Automated Human Tracking: An AI based Modular Approach

This paper has proposed a novel end-to-end automated system for the task of human tracking. Based on the research carried out, a modular approach has been provided for implementing such a system. The proposed solution comprises of Deep-learning (specifically Face recognition), Database, Path Finding, UI and Bio-mapping concepts. The primary source of data assumed are CCTV Footage. The dataset used for our experiments were collected by comprising a single image and few videos of a several people. This research work attempts to provide the recent location history and geographical location of a person given just a “single” image of that person. Given how important human tracking is to law-enforcement agencies, businesses and healthcare systems, it is important to improve the efficiency of the current tracking system which heavily relies on manual intervention. Thus, this research work proposes an End-to-End automated human tracking system ’God’s Eye’ as a tool, which can help in providing rapid and accurate results by reducing human involvement. God’s Eye aims to be a robust, low-cost, hardware-independent and seamless solution for human tracking in the present times. Implementing such a system at scale can be widely beneficial to the society and it is considered as a step in the direction of building a smarter & safer cities.
Incorporating Clustering Techniques into GAMLSS

A method for statistical analysis of multimodal and/or highly distorted data is presented. The new methodology combines different clustering methods with the GAMLSS (generalized additive models for location, scale, and shape) framework, and is therefore called c-GAMLSS, for “clustering GAMLSS. ” In this new extended structure, a latent variable (cluster) is created to explain the response-variable (target). Any and all parameters of the distribution for the response variable can also be modeled by functions of the new covariate added to other available resources (features). The method of selecting resources to be used is carried out in stages, a step-based method. A simulation study considering multiple scenarios is presented to compare the c-GAMLSS method with existing Gaussian mixture models. We show by means of four different data applications that in cases where other authentic explanatory variables are or are not available, the c-GAMLSS structure outperforms mixture models, some recently developed complex distributions, cluster-weighted models, and a mixture-of-experts model. Even though we use simple distributions in our examples, other more sophisticated distributions can be used to explain the response variable.
Medical Insurance Medication Anomaly Detection based on Isolated Forest Proximity Matrix

With the continuous development of the medical insurance system and the continuous increase in the number of insured persons, medical insurance fraud has also intensified. Traditional fraud detection methods are no longer suitable for the needs of medical insurance fraud review. Aiming at the problem of drug reselling in medical insurance fraud, this paper uses the proximity matrix of the isolated forest to improve the LDOF algorithm, and proposes a new medical insurance medication anomaly detection algorithm. Through comparative experiments, our algorithm not only improves the accuracy of LDOF, but also improves the computational efficiency, and shows good performance in the application of actual medical insurance data sets.
Well log analysis for lithology and fluid contacts in Rovuma Basin – Mozambique: Application of cluster and discriminant analyses

This study applies the cluster and discriminant analyses in geophysical well log data from the Rovuma sedimentary Basin - Mozambique. The main objective was to determine the lithological profile and fluid contacts in reservoirs. Well log data from five wells drilled on the same basin were used. For the discrimination, a reference well was chosen for training, and the obtained functions from it were then applied to the remaining wells. The classification process comprehended three main phases, namely, the separation of shale/non-shale layers along the entire logged section, separation of water/hydrocarbon within reservoirs and the separation of oil/gas within hydrocarbon bearing zones. The two methods, cluster analysis and discriminant analysis, were applied in parallel and the results are compared in each classification phase. The quality of reservoirs was also assessed by applying cutoffs in relation to shale content and effective porosity, delineating net reservoirs. In general, both methods converged to the same lithological model and fluidtypes in reservoirs. Gas has been indicated as the most predominant hydrocarbon in the basin.
Application of adaptive wavelet thresholding to recovery geoacoustic signal pulse waveforms

Recorded geoacoustic signals often contain noise and interference. Their appearance is caused by various reasons, e.g. of propagation environment heterogeneity, weather condition influence, human activity, etc. So, geoacoustic emission signals contain a persistent background noise that changes in intensity over time. This noise significantly distorts the geoacoustic pulse waveforms and thus complicates analysis of the signal characteristics. The article presents results of estimating the geoacoustic signal background noise. On the basis of these estimates, a method of adaptive wavelet thresholding is proposed to remove noise from the signal and recovery the single pulse waveforms. In conclusion, the results of a computational experiment are presented. They confirm effectiveness of using the chosen method for the geoacoustic signal preprocessing. The work was carried out as part of the implementation of the state task AAAA-A21-121011290003-0.
The Research and Analysis of Student Health Status on the Basis of Physic Fitness Test’ Data

In recent ten years, the serious problem of Chinese college students’ physical health shows a downward trend, which has aroused great concern from all walks of life. Aiming at this problem, this paper studies the physical test scores and health status of students from Grade 15 to Grade 18 at Guangxi University of Chinese Medicine. Firstly, the data are cleaned and sorted, and the indicators of Guangxi University of Chinese Medicine college students’ physical test are analyzed descriptively by using matplotlib package in python software. Then, correlation analysis and cluster analysis are carried out on each index of the physical test, which can be divided into two clusters. Then using Apriori algorithm for comprehensive comparative analysis, taking each index of physical fitness test as the antecedent and "total score = pass" as the consequent, the first six strong association rules are found out, and it is concluded that there are differences in several conditions of male and female students’ test indicators under the condition of "total score = pass". The indexes of speed, endurance and flexibility have great influence on college students’ physical fitness, while the BMI and vital capacity are not important factors affecting their physical fitness. By analyzing the results, we can strengthen the weak links in school education and promote the adjustment and reform of physical education in Guangxi University of Chinese Medicine (GXTCMU).
Population Resizing Model on Fitness Improvement Genetic Algorithm (PRoFIGA) Optimation on Food Scheduling

Health is a gift that humans should be grateful for. One way to be grateful can be done by implementing a healthy lifestyle through daily eating patterns. There are many types of diet, one of which is a balanced nutritional diet, namely the regulation of eating patterns by preparing portions of food ingredients consisting of various kinds of food ingredients based on daily nutritional needs. A varied food menu with different nutritional content will be difficult to regulate the exact calorie needs. Therefore we need a method that can help solve this problem. One method that can be used is the PRoFIGA model genetic algorithm. In this paper, the PRoFIGA genetic algorithm is implemented into a system. The result of this paper is scheduling a balanced nutritional diet with food recommendations according to the calories needed using the PRoFIGA genetic algorithm.
Smart Grid Data Security Sharing Mechanism Based on Alliance Blockchain

The safe sharing of power data by the power dispatch center is a key issue that needs to be solved urgently. To solve this problem, this paper proposes a data-sharing architecture based on the alliance blockchain, which is composed of an authentication service center and multiple power dispatch centers. In order to improve the security of power data, a secure storage mechanism and a secure sharing mechanism for power data are designed. When designing a security sharing mechanism, in order not to reduce the security level of power data during power data sharing, a power data sharing mechanism based on federated learning is proposed. In terms of algorithm performance evaluation indicators, two indicators of model training time and algorithm accuracy are used for evaluation, verifying that the algorithm in this paper has achieved good results in the accuracy of data recognition models under the premise of improving data security sharing.
Web Page Information Extraction Service Based on Graph Convolutional Neural Network and Multimodal Data Fusion

Information extraction and its service is a hot topic. Many works focus on extracting information from a certain web page and ignore the localization of the webpage which contains useful information. Nevertheless, developing a holistic system to extract information consists of locating a webpage and extracting information from that webpage, and these two steps are indispensable. For instance, extracting lecture news from universities' websites is a typical hard task that need to locate web pages and extract news information from them. Due to different layouts and visual appearances, statistic-based methods and visual based methods failed to find them. In this study, we propose an all-holistic method to locate lecture news on the university website. Graph Convolutional Network (GCN) is applied to fuse the multimodal data, which could learn useful features from different views, the linked relationship, the visual similarity, and the semantic of web pages. Firstly, we apply the link model to explore the parent-child relationship between web pages, then calculate the similarity of parent-child pages using a visual model and obtain the semantic features based on the BERT model. Specifically, the visual similarity features are learned based on triplet loss function which imposes the Convolutional Neural Network (CNN) model to learn similar parts in the same group. Lastly, these features are fused into the GCN model to find a certain webpage and it can be adaptive to various university websites. The experiments conducted on 50 websites show our method outperforms state-of-the-art.
Diachronic delta: A computational method for analysing periods of accelerated change in literary datasets

Abstract In his recently published book Distant Horizons (2019), Ted Underwood argues that literary change takes place incrementally. By analysing a dataset of English language fiction obtained from the digital research library HathiTrust, Underwood demonstrates that from the early 18th to the 21st century, literature written in English evinces a steady increase in words associated with physical description, concrete nouns as well as sensory perception and that this increase is accompanied by a proportional decrease in words associated with politics, economics as well as religion (Underwood 25). This article represents an effort to test Underwood’s incremental hypothesis by operationalizing Peter W.H. Smith and W. Aldridge’s cosine distance-based emendation to John Burrows’ ‘Delta’ method (Smith, P. W. H. and Aldridge, W. (2011). Improving authorship attribution: optimising burrows’ delta method. Journal of Quantitative Linguistics 18(1): 63–88). By applying cosine Delta across time rather than as it is conventionally applied, across text (Evert, Stefan et al. Understanding and Explaining Delta Measures for Authorship Attribution. Digital Scholarship in the Humanities 32 (suppl_2) (2017): ii4–ii16), we can identify particular years as being associated with extensive amounts of ‘novelty’ (which years introduce the most amount of distance from their predecessors) and also possess extensive amounts of ‘resonance’ (are relatively proximate to their successors). According to Alexander T.J. Barron et al.’s method for analysing the parliamentary debates of the French revolution, agents within a dataset which have high amounts of novelty and high amounts of resonance are both innovative and influential, as they are significantly different from the years which come before and relatively similar to the years which come after. We might refer to these years as ‘breaks’ and contrast their behaviour with the longue-durée approach otherwise prevalent within the computational literary studies discourse. Though, as this article demonstrates, Underwood’s hypothesis of incremental change remains robust when considering the changes which come over the novel from the mid-18th to the early 20th century, applying this method to poetic and dramatic production over the same period of time, thereby examining the distinct ways in which novelty and resonance behave, allow us to modulate Underwood’s hypothesis and to understand literary history within a more conjunctural framework.
Blockchain as an Instrument for Land Ownership and Authorization of Services

Land disputes in India take on an average about 20 years to be resolved. To make matters worse, land-related disputes account for nearly two-thirds of all pending court cases in India. Disputes related to land ownership affect agriculture as land is used as collateral for obtaining loans by farmers. Such issues also affect development of new infrastructure due to nonavailability of encumbrance free land, demands for higher compensation by landowners, and filing of large number of arbitration cases by landowners. Land related disputes impact urbanisation too given the acute shortage for housing in the country. Last, but not the least generation of black money through benami transactions could be pre-empted and eliminated by digitisation of land records and their regular upgradation. While the problem is humongous, digitization is, perhaps, only the first step in the right direction. The solution needs to ensure that all existing land records are accurate and free of any encumbrances. It also needs to be more all-encompassing to address issues around land ownership, so far has not addressed. Blockchain due to its characteristics of trust and immutable ledger could be the potential solution to address this problem. The paper explains how Blockchain can help improve transparency, trust and efficiency of land ownership data.
Learning Path of Distance Education in Regional Universities: Challenges and Opportunities

The global online transition has become a real challenge to the traditional form of education which have led universities to overhaul the delivery of education services. A sociological study was conducted, based on the study of the relationship of academic teaching staff, their assessment of the effectiveness of the measures taken by the universities in organizing the transition to DL format of education. The study was attended by lecturers from regional universities in Russia, Kyrgyzstan and Mongolia. The results of the study revealed the most organized and optimistic category of lecturers from Mongolian universities and focused on the main problems in the implementation of the DL of all universities at the ATS level. The normalization of the epidemiological situation in the future will make it possible for universities to define their approaches to the use of DLT and their role in the traditional face-to-face education system in accordance with the needs of the students and the characteristics of social rehabilitation of the economic level of development of each country.
Robot Path Planning Based on Q-learning Algorithm

When Q-learning algorithm is applied to robot path planning, the state information needs to be discretized first, and the process of discretization needs to occupy a large amount of memory space, resulting in low learning efficiency and slow convergence speed. A path planning algorithm based on BN (Batch Normalization) and Q-learning is proposed to solve the problems of low learning efficiency and slow convergence speed. The experimental results show that the algorithm can make the algorithm converge on the premise of ensuring the robot to find the best path without collision. The convergence speed of the improved algorithm is increased by 25%, and the learning reward value is increased by 34%.
Ensembled Neural Network for Static Hand Gesture Recognition

Sign language was originally created to fulfil the gap of communication between the speech impaired. However, with recent advances, we can see the applications of sign language in a variety of different fields such as automated vehicle movements, assistant systems, human robot interaction. In this paper, the main focus is towards creating a system that is highly efficient in detecting the hand sign gestures. To achieve these results, numerous deep learning techniques and advanced models have been used in this paper. The models were trained on ASL (American Sign language) dataset in which the highest validation accuracy was 99.56&#x0025; achieved with Resnet 50 architecture. Other pre-trained models used also received similar validation accuracies. A combined voting classifier is also implemented to achieve the maximum accuracy. All the models were finally tested on the HGM -4 dataset which is a totally new dataset with a different set of images and our models obtained decent results here also as shown in Table 1.
Data on minerals and crystallinity index of quartz in rock samples collected from Paleolithic archaeological site of Attirampakkam, Tamil Nadu

In the present work, rock samples were collected from Paleolithic archaeological site of Attirampakkam, Tamil Nadu, India to assess the mineralogical composition using Fourier transform infrared-spectroscopic (FT-IR) technique and X-Ray Diffraction Spectrometry (XRD). The quartz, kaolinite, montmorillonite, calcite, orthoclase, microcline and illite minerals are identified in rock samples and crystallinity index of quartz (SiO2) is estimated for all the samples by comparing the ratio of intensity of the characteristic peak at 778 and 695 cm-1 using FT-IR spectrum. In rock samples, calculated crystallinity index of quartz is greater than the 1 from FT-IR spectrum and it shows that the distribution is disordered in nature. Additionally, some more minerals such as hematite and rutile are identified in rock samples by X-ray diffraction technique. This extensive study shows that archeological rock samples are wide variation in mineral composition.