Reviewerly/exHarmony huggingface dataset:
Virtual laboratories, an educational resource as a strategy for quality education

Virtual laboratories are considered the simulation of a real laboratory, where the behavior of a certain is studied using mathematical models, and although it does not interact physically with the processes or systems, experimentation with simulated models it is comparable to reality. The objective is to recognize the benefits that virtual laboratories offer the student, as a tool in problem solving, linking theory with practice, and developing teamwork, all within distance learning. Therefore, a compilation of different sources of information regarding virtual laboratories was made, where it is stated that the interviewees who make use of this tool for their learning obtained a favorable response by identifying that they develop skills that prepare them for their experimental participation in a future. Therefore, it is considered a strategy for quality teaching.
Financial Software Perception Function Based on Big Data Intelligent Technology

Financial management is an important part of modern corporate governance, and the study of financial management information has also become an important subject of financial software research. In order to adapt to the frequent changes of the open environment and the needs of users, The financial system must be able to continuously adapt to changes in the internal operating environment of the software system and changes in external functional requirements. The purpose of this article is based on intelligent information technology and the rapid development of the Internet, apply intelligent information technology to the development of financial software perception functions, and designing the perception function of financial software to ensure the functional aspects of new financial software progress. This paper takes the perception function of financial software based on intelligent confidence technology as the research goal. With the support of big data intelligence data mining technology, the data model and algorithm studied in this paper are given, namely the Logistic algorithm and ID3 algorithm in data mining technology. Then the two algorithms are used to verify the data trends of a certain securities website on the Internet for two years as variables. Data experiments are used to prove the feasibility and practicability of the algorithm proposed in this paper. According to the data experiments in this article, The feasibility of the financial software perception function of the intelligent information technology proposed in this paper, substitute the real data of a website for two years into the data mining algorithm to get, which is based on the Logistic algorithm and the ID3 algorithm of the decision tree. The prediction accuracy of both exceeds 80%, and the accuracy of the decision tree algorithm reaches about 86%, which is better than the Logistic algorithm.
Research on Realization of Yi Speech Synthesis in Robot

As an ancient civilization with a history of more than 5,000 years, our country has formed a multi-ethnic and multi-lingual country through continuous research and progress. It uses multiple languages and scripts in its territory. Yi language belongs to the Han-Tibetan-Burman Yi ethnic group. It is a minority language with a complex and diverse dialect and mother tongue system. According to previous research results, modern Yi language can be divided into six major dialects: north, east, south, west, central and southeast. It includes 25 native languages, showing the diversity and complexity of modern Yi language. The southeast dialect of Yi language can be divided into three kinds of native languages: Sani, Asi and Azhe, This article focuses on the realization of Yi north dialect speech synthesis in robots, and understands speech synthesis technology and Yi speech on the basis of literature data, and elaborates the realization of Yi speech synthesis in robots by designing a Yi speech synthesis system, and test the designed system.
Faster Zero-shot Multi-modal Entity Linking via Visual-Linguistic Representation

Abstract Multi-modal entity linking plays a crucial role in a wide range of knowledge-based modal-fusion tasks, i.e., multi-modal retrieval and multi-modal event extraction. We introduce the new ZEro-shot Multi-modal Entity Linking (ZEMEL) task, the format is similar to multi-modal entity linking, but multi-modal mentions are linked to unseen entities in the knowledge graph, and the purpose of zero-shot setting is to realize robust linking in highly specialized domains. Simultaneously, the inference efficiency of existing models is low when there are many candidate entities. On this account, we propose a novel model that leverages visuallinguistic representation through the co-attentional mechanism to deal with the ZEMEL task, considering the trade-off between performance and efficiency of the model. We also build a dataset named ZEMELD for the new task, which contains multi-modal data resources collected from Wikipedia, and we annotate the entities as ground truth. Extensive experimental results on the dataset show that our proposed model is effective as it significantly improves the precision from 68.93% to 82.62% comparing with baselines in the ZEMEL task.
Depth based Indian Classical Dance Mudra's Recognition using Support Vector Machine

I honorary present, an interesting application of Indian classical dance hasta mudra recognition as computer vision techniques. The dance mudras form a complex human gesture that are complicated to interpret by a machine. To solve this problem, I proposed a new framework for Indian classical dance recognition using depth sensor. First, the dance mudras of various classical dance datasets were created by using Microsoft Kinect (depth) sensor. Second, extract histogram of oriented (HOG) features of dance mudras as input of depth image and third, dance mudras are classified by using support vector machine (SVM) as converting dance mudras to text labels. The proposed framework tested by 50 dance mudras form different classical dance videos. To find the performance of proposed algorithm tested with different features and state-of-the-art methods.
Implementing New Resources for STEM Education

In a rapidly changing world, especially amidst COVID-19, the educational goals must be refined and modified. This chapter derives from a study based on research findings and recognizes the individual aspects of 21st century skills that can pave the way for adaptation and transition utilizing applied skills and digital literacy. The 21st century skills (21CS) have also been in the spotlight about digital literacy, as the pandemic possessed the current need to continue instruction and learning despite the setback imposed. It was concluded that 21CS covers the requirements and works best when implemented into the current mode of instruction combined with STEM education and basic skills.
Progress of image caption: modelling, datasets, and evaluation

Image captioning is a popular topic in recent years. Researchers tried different methods to improve the performance of the image captioning system. In this paper, three methods, including the vision-based method, semantic-based method, and attention-based method, are introduced. These three methods aim to enhance the ability to describe images. In addition, we gathered, compared, and analysed various datasets from different articles in which those three types of methods are used. Analysing the data of three types of methods using datasets such as MSCOCO and Flicker 30K, we get the result that the semantic-based method is the most efficient.
Adaptive Transfer Learning via Fine-grained Multi-task Pre-training

Nowadays pre-training paradigm has been widely adopted for deep learning-based applications. In multiple pre-training tasks, conventional methods process them using naive Multi-Task Learning (MTL) technology. The pre-trained models are unavoidably influenced by the well-known negative transfer phenomenon of MTL, which will also make a negative impact on downstream tasks. To deal with this problem, we propose a novel Adaptive Pre-Training (APT) framework. In the pre-training stage, we adopt a task-sensitive MTL technology called Fine-grained Sharing Network (FSN), which trains a subnet mask for each task besides the network weights. In the fine-tuning stage, the most suitable subnet is selected for each downstream task. Therefore, different downstream tasks may be fine-tuned based on different network structures and benefit from the outcome of their most closely related pre-training task. According to our experiments, the proposed APT outperforms the conventional pre-training baseline at a clear margin. In addition, experiments show that, even without the subnet adaptation, the network weights pre-trained by FSN alone are of much higher quality and can be used in the framework of conventional pre-training to improve performance.
Brand Visual Image Design of Rural Tourist Attractions Based on Super-Resolution Reconstruction and Image Scaling Technology

Based on the super-resolution reconstruction and image scaling technology, this paper studies the brand visual Xinang design of rural tourist attractions, from the investigation of tourists' visual experience needs and intuitive feelings, the positioning of the brand image of accommodation, the mining and extraction of visual image design elements, and the design of visual image Research from a system perspective. A multi-frame super-resolution reconstruction algorithm for mapping between convex sets is proposed. The algorithm is based on the active viewpoint degradation model. After implementing the fuzzy kernel estimation steps of image registration and blind deblurring based on the SIFT algorithm, a scalable image progressive watermarking algorithm based on DWT-SVD is proposed, which can be used for scalable images the copyright protection is of great significance to the security technology of scalable images.
Research on the Identification of Organizational Entities in Fraudulent Texts Via Bi-GRU-Flat

The analysis of fraudulent texts requires the extraction of entities in the text. Organization is an important entity type in fraudulent texts. Recognizing and extracting the entity of organization is of great significance to text analysis. This paper proposes a deep learning network model based on Bi- GRU-Flat for identifying entities of organization in fraudulent texts. The model uses a modified ELMo module to perform deeper contextualized word representations on the input sequence, and then uses Flat's special position coding and lattice structure transformation to help extract more lexical information, and finally makes further constrained corrections to the output through the CRF layer. The experiments show that the model outperforms other related models in terms of precision, recall and F1 value, and the recognition effect is better.
Improved Cloud Auditing Protocol and Its Application for Pandemic Data Management

Data integrity verification mechanisms play an important role in cloud environments. Recently, a lightweight identity-based cloud storage audit scheme has been proposed; this paper points out security vulnerabilities of their OffTagGen algorithm. That is, the attackers such as malicious cloud servers can forge the tags, which can destroy data integrity. By improving the construction of OffTagGen algorithm, an improved security cloud auditing protocol is proposed in this work to better protect user’s privacy. The analysis shows that the new protocol is effective and resistant to attacks.
Systems and Software Interface Survey

ABSTRACT INCOSE formed the Systems and Software Interface Working Group (SaSIWG) in 2017, responding to the Corporate Advisory Board interest in software and problems identified in the systems and software interface (physical, logical, data, and human). This third SaSIWG paper presents a systems engineers, software engineers, and project managers survey discussing best practices and the priority challenges related to the interface between systems and software. We group and summarize the best practices mentioned by the 31 interviewees then address priority challenges and problems. Systems engineering done well includes an ever‐increasing amount of Model‐Based Systems Engineering. It also includes developing and holding to a vision, managing data, ensuring inter‐disciplinary work, planning systematic verification, and ensuring modularity. Systems engineering must evolve to meet new challenges and, most importantly, systems engineer expertise must include software engineering.
A Data Driven Framework to Teach Visualization Design

Teaching visualization design to beginners is a challenging and important activity. This work proposes a data-driven framework for teaching visualization design. The data-driven framework consists of three modules; data processing, data transformation and data embellishment. Each of these modules have a motivation and are characterised by sub-modules that help accomplish the motivation. We define and elaborate such a framework, speculate on an implementation of such a data-driven framework and finally propose a syllabus based on the data-driven visualization design framework.
Research on the Modulation Recognition Method OFDM Signal Based on Short-time Fourier Transform and Convolutional Neural Network

In order to improve the modulation recognition accuracy of the Orthogonal Frequency Division Multiplexing (OFDM) signal under low signal-to-noise ratio, an OFDM signal modulation recognition method based on short-time Fourier transform and convolutional neural network is proposed. Using the cyclic stationarity of the communication signal with the symbol interval as the cycle, the method firstly performs a short-time Fourier transform with a window period, and converts the received time domain signal into time-frequency image; Secondly, the time-frequency image as a shallow feature is sent to the VGG-16 convolutional neural network model for training, and the features of the OFDM signal and several single-carrier communication signals are automatically extracted in the two dimensions of time and frequency; Then, the Softmax function is selected in the output layer of the network to map the output of the neuron to the probability space to calculate the probability of the category of the signal, and completes the modulation classification of the OFDM signal and other signals. Finally, the modulation recognition process of OFDM signal and several single carrier signals with equal symbol interval are simulated, and the results show that the proposed method can realize the modulation recognition of OFDM signal. When the signal-to-noise ratio is -8dB, the modulation recognition accuracy of OFDM signal can reach 98.3%. Compared with other OFDM signal modulation recognition algorithms, and the influence of different signal-to-noise ratios, different convolution kernel sizes, different activation functions and different optimization algorithms on the system's modulation recognition performance are analyzed.
Clay Time - Tangible Interaction with Polymer Clay for Auditory Feedback

Malleable materials such as polymer clay hold the potential to create new Tangible interactions that engage the user to be hands on. Considering the properties of the material, this work explores the possibilities of introducing clay as a medium to interact and in turn trigger audio feedback that facilitates learning, exploration and collaboration. Through this design project, we developed Clay Time, an easy to assemble polymer clay interaction-based design that uses shapes moulded and placed on a mat as input to provide audio feedback through a mounted smart phone.
Measuring the Binary Interestingness of Images: A Unified Prediction Framework Combining Discriminant Correlation Analysis and Multiple Kernel Learning

In the modern content-based image retrieval systems, there is an increasingly interest in constructing a computationally effective model to measure the interestingness of images since the measurement of image interestingness could, more or less, improve the human-centered search satisfaction and the user experience in different applications. Typically, a couple of interestingness attributes (also called cues) are empirically selected and the corresponding features to capture these cues are elaborately leveraged in order to comprehensively represent the interestingness content underlying the images to be considered. How to make good use of these cues as well as the related features to arrive at an effective model to measure the interestingness of images is the core issue to be addressed in the big data era. In this paper, we propose a unified framework to predict the binary interestingness of images combining the discriminant correlation analysis (DCA)-based discriminative data fusion method and the multiple kernel learning (MKL)-based classification technique. More specially, on one hand, to reduce feature redundancy in describing the interestingness cues of images, the DCA or its multi-set variant is adopted to fuse multiple feature sets of the same type for individual cues by taking into account the class structure among the samples involved to describe three representative interestingness cues, i.e., unusualness, aesthetics and general preferences, with three sets of compact and discriminative features; on the other hand, to make good use of the heterogeneity from the three sets of high-level features for describing the interestingness cues, the SimpleMKL method is employed to enhance the generalization ability of the built model for binary interestingness measurement. Experimental results on the publicly-released interestingness evaluation data set have demonstrated the rationality and efficacy of the proposed framework where we conducted several groups of comparative studies across different interestingness feature combinations, different interestingness cues, as well as different feature types for the three interestingness cues.
Generation of Pseudo-random Sequences of the Maximum Period Using Elliptic Curves Transformations

A promising direction for constructing cryptographically stable pseudo-random sequence generators is an application of transformations in a group of points of elliptic and hypereliptic curves. This will allow building evidence-stable crypto algorithms, the problem of finding the private key in which is associated with solving a theoretically complex elliptic curve discrete logarithm problem. This paper proposes a method for generating pseudo-random sequences of the maximal period using transformations on elliptic curves. This method consists in the application of recurrent transformations with sequential formation of elements of points group of elliptic curves. This allows providing the maximum period of pseudo-random sequences with the reduction of the problem of finding the private key to the solution of the theoretically complex elliptic curve discrete logarithm problem. The block diagram of the device for generating pseudo-random sequences and the scheme for generating the internal states of the generator are given. We also present the results of statistical testing of some generators, which show that the generated sequences are indistinguishable in a statistical sense from truly random ones.
Information and Communications Technology and the Level of Income in Africa

Information and communications technology has been identified as a factor that drives income, economic growth, and development. Because of this, several studies have been conducted to ascertain ICT's effects on economic growth. However, such studies failed to examine whether real per capita income influences the effect of ICT on income level and whether the effect of ICT on income level differs among regions of African countries. This study, therefore, investigated the effect of ICT on income level. Specifically, it examined whether real per capita income influences the effect of ICT on income level and whether the effect of ICT on income level differs among the sub-regions of African countries. Thus, empirical models were estimated using the panel regression analysis with fixed-effect and random-effect estimators. The results show that ICT positively affects income level in Africa, and real income per capita influences the effect of ICT on income level. In contrast, the effect of ICT on income level differs among African regions. The effect is larger in Eastern, Southern, and Northern African countries than in other regions. Therefore, authorities should consider investment in ICT as a tool or mechanism for enhancing income.
New minerals and the first mineral occurrences in the Kupferschiefer (U, REE, HgS, chloride minerals PtAs<sub>2</sub>, Pt(Sb,Bi)<sub>2</sub>, PtBi<sub>2</sub>), Poland, and their genetic meaning

Abstract Main U minerals in the Lubin and Polkowice mines in decreasing order of abundance are: uraninite, brannerite, metazunerite, metauranospinite, uranospinite, thucholite, monazite-huttonite, coffinite, and becquerelite. All these minerals are associated mainly with noble metal mineralization at the redox interfaces. Main minerals of REE are: (i) brannerite, monazite-huttonite in Noble Metals Bearing Shale (NMBS), (ii) florencite in Mo-Re shales and (iii) minor phospates in Cu-black shales derived from oxidation of organic matter. The latter, however, are controlling no more than 1/5 of the REE budget. Chloride minerals and sulphates are ubiquitous phases associated with Cu sulphides. They are represented by chlorargylite, cotunnite and penfieldite. Halite and sylvite form cubic inclusions in gypsum, up to 3 mm in chevrel compound, where they occur together with thiosulphates, anna-bergite and erithrite and Ni-Co-diarsenides. Sylvite is most common in lensoidal accumulations of semi massive kaolinite + chalcocite + gypsum at the contact between black shale and the top part of white sandstone. Such lenses have been traced over a distance of up to 30 m in the Lubin West and Polkowice East mines. Sylvite and halite have been so far overlooked during investigations of the Kupferschiefer because of applying standard cuƫng and polishing procedures using water as a lubricant. The Au content in native gold may reach exceptionally up to 97.40 wt% in a dolomitic reef from the Lubin West Mine. Gold forms four characteristic micro to nano-textures. Organic matrix of thucholite indicates correlation of Pt-Ni-V suggesting substitution of Pt in tetrapyrrole ring. Sperrylite, geversite and insizwaite appear at the western perimeter of Cu-sulphide deposit, where the redox gradient is so strong that almost all organic matter is gone. Platinum has forms its own minerals trapped in microfractures in anilite-digenite-spionkopite-yarrowite-bornite composite grains present in the reddened NMBS. Celestine-barite solid solution is one of the main accessory minerals in the Kupferschiefer. It has Sr/Ba ratio around 2.5 and 7.5. Euhedral uraninite associated with celestine-barite yielded U/Pb age of 84 ± 1 Ma, based on four grains measurements.