Reviewerly/exHarmony huggingface dataset:
CNN Based Model for Malaria Diagnosis with Knowledge Distillation

Malaria is a deadly disease caused by plasmodium parasites and carried by female anopheles mosquitoes. Bangladesh is one of the major malaria prone country with poor infrastructures, unable to diagnose Malaria rapid at a low cost. Here we propose a custom8 layers CNN architecture that is only 233.60KB in size meaning it can easily fit and work into a low cost smartphone. The model has been optimized through different preprocessing methods and model pruning techniques like knowledge distillation ensuring that the accuracy does not reduce due to smaller model size. The accuracy received from the optimized model is~ 96.51% when tested on NIH data-set containing images of infected and uninfected cells. Proposed model is a big step towards a complete mobile based rapid diagnostic platform that can be used in any resource restricted and hilly areas of Bangladesh.
Digital Quarantine—A Case Study on How CoViD-19 Accelerated Digital Transformation at Our School of Arts and Design

Despite the fact that the so-called digital transformation is being seen as one of the megatrends of the past and ongoing decades, there does not seem to be a common picture of how schools of art and design should address this phenomenon or even actively shape the digital shift. The case study discussed in this paper, therefore, sets out to identify the main areas that schools of arts and design could engage with in order to become active players in digital transformation. It does so by conducting both, a series of qualitative interviews as well as an online survey that reached out to representants of the design and arts community at Lucerne School of Applied Sciences and across Europe. Initially, this sample was meant to be compared to a sample originating from the computer science community. Due to the lockdown of European Universities caused by the CoVid-19 crisis, this second part of the study had to be postponed. Instead, the paper completes the findings of the qualitative study conducted in the design and arts community with a series of observations made during the sudden shift to remote and e-learning at our own university of Applied Sciences and Arts. As a result, the emerging topics from the study get critically reflected with the fact that our school of arts and design suddenly became a "remote" school that had to shift exclusively to the use of digital tools and media.
Image Enhancement Effects On Adult Content Classification

Adult content filtering is an essential part of digital media platforms. With the extensive usage of social media, it becomes harder to overcome this problem. Traditional methods consist of human supervision and standalone image processing techniques. These approaches are not accurate enough according to the massive size of the social media generated content. Also, the methods are not discriminative enough on a variety property of the images. Colour, shadow, frequency features of the images can vary, even the context is the same according to lumination features. The problem can be solved more accurately with deep learning techniques. Notably, the specific type of deep learning architecture called convolutional neural network is suitable for the problem space. In this study, the state of the art model has been used with transfer learning to test image enhancement effect on the success of the architecture. Colour vivid, sharpness value incrementation and histogram equalization approaches have been tested for adult content classification problems.
Real-Time Possessing Relationship Detection for Sports Analytics

In this paper, we propose a novel algorithm for relationship detection. This task involves the tracking of a target object and human pose. The target object is tracked with a visual object tracker. The human poses are estimated via a keypoint detector while the person identities are preserved with a simple yet effective IoU tracker. Finally, a possessing relationship inference is made based on the position information of the tracked target and humans. This algorithm meets the real-time requirement by running at over 20 FPS and we give an application illustration in sports analytics.
Research on peasants poverty alleviation under ecological constraint based on neural network algorithm and network features

Aiming at the actual problems encountered in the specific poverty alleviation work, this article designs a management system specifically designed for poverty alleviation workers to solve poverty alleviation data sharing and online editing and uploading of poverty alleviation logs. Based on the neural network and network characteristics, a system model is constructed, and the application of structural disturbance theory in dynamic networks is studied. Moreover, in this study, the dynamic change information between time-series networks is taken into account for structural disturbances. By combining structural disturbances and local topology, a new similarity measurement method suitable for dynamic networks is proposed. In addition, this study proposes an algorithm based on evolutionary clustering and density clustering to detect the structure of dynamic communities. Finally, this study compares the proposed method with the classic method in the artificial network and the real network and analyzes the performance of the research model through data analysis. The research results show that the model constructed in this paper has good performance.
Semi-supervised cross-modal representation learning with GAN-based Asymmetric Transfer Network

In this paper, we proposed a semi-supervised common representation learning method with GAN-based Asymmetric Transfer Network (GATN) for cross modality retrieval. GATN utilizes the asymmetric pipeline to guarantee the semantic consistency and adopt (Generative Adversarial Network) GAN to fit the distributions of different modalities. Specifically, the common representation learning across modalities includes two stages: (1) the first stage, GATN trains source mapping network to learn the semantic representation of text modality by supervised method; and (2) the second stage, GAN-based unsupervised modality transfer method is proposed to guide the training of target mapping network, which includes generative network (target mapping network) and discriminative network. Experimental results on three widely-used benchmarks show that GATN have achieved better performance comparing with several existing state-of-the-art methods.
Explainability in threat assessment with evidential networks and sensitivity spaces

One of the main threats to the underwater communication cables identified in the recent years is possible tampering or damage by malicious actors. This paper proposes a solution with explanation abilities to detect and investigate this kind of threat within the evidence theory framework. The reasoning scheme implements the traditional “opportunity-capability-intent” threat model to assess a degree to which a given vessel may pose a threat. The scenario discussed considers a variety of possible pieces of information available from different sources. A source quality model is used to reason with the partially reliable sources and the impact of this meta-information on the overall assessment is illustrated. Examples of uncertain relationships between the relevant variables are modelled and the constructed model is used to investigate the probability of threat of four vessels of different types. One of these cases is discussed in more detail to demonstrate the explanation abilities. Explanations about inference are provided thanks to sensitivity spaces in which the impact of the different pieces of information on the reasoning are compared.
Contrasting granite metallogeny through the zircon REE composition: Perspective from data mining

Yanshanian magmatism develops well and has obvious metallogenic specificity in the Qinzhou–Hangzhou bays of South China. Zircon, in granite, has great potential in the study of metallogenic specificity. Moreover, with the development of in-situ zircon analysis technology, a significant amount of data on zircon composition has become available in recent years. Hence, to further elucidate the distinctive diagnostic signatures of zircon signatures regarding metallogenic fertility and the preference for metallogenic types of granites, this paper exhaustively explores all possible bivariate and ternary discrimination diagrams using a set of 13 rare-earth elements with published zircon data from Yanshanian, South China. This results in 4095 binary diagrams and 12,485 ternary diagrams, ranked according to their success in classifying the training data. One-hundred binary graphs and 300 ternary graphs with the highest CH index values were selected, from which the graphs were picked out. The results reveal that there is no suitable diagram to distinguish between mineralization and barren granites. Further, in the diagrams to distinguish the type of mineralization, there are indeed some overlapping parameter ranges (such as Gd/Yb and Ce/Gd); however, it still can be observed that some binary and ternary graphs containing Eu or Ce (e.g. Eu/Gd, Eu/Tb and Ce/Sm) can discriminate between samples related to porphyry Cu and samples related to W–Sn (Sn). Some diagrams (i.e. Dy/Lu–Er/Lu, Gd/Dy–Er/Yb) also have been observed to distinguish between ore-forming types of granites, the underlying geochemical mechanism of which has not been fully understood. In addition, our results suggest that the linear discrimination analysis (LDA) method outperforms any of the traditional discrimination diagrams in discriminating ore-bearing and fertile granites. The results of geochemical data mining in this paper can be used as prospective indicators to provide a scientific basis for the study of Yanshanian hydrothermal deposits in South China. Moreover, the compositions of zircon are relatively simple to obtain, and the LDA methods described in this study could potentially be used as a useful exploration tool.
Nurse care activity recognition based on convolution neural network for accelerometer data

Human activity recognition on sensor data plays a vital role in health monitoring and elderly care service monitoring. Although tremendous progress has been noticed to the use of sensor technology to collect activity recognition data, recognition still remains challenging due to the pervasive nature of the activities. In this paper, we present a Convolution Neural Network (CNN) model by our team DataDrivers_BD in "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data" which is quite challenging because of the similarity among the tasks. On the other hand, the dissimilarity among the users patterns of working for a particular task. Since CNN can retrieve informative features automatically, it has become one of the most prominent methods in activity recognition. Our extensive experiment on nurse care activity recognition challenge dataset also achieved significant accuracy of 91.59% outperforming the existing state of the art algorithms.
LinkLearn: Blockchain Technology as a Learning Tool

Higher education institutions will remain gatekeepers of opportunity if industry, governments, and students value their existing credentials. Keeping up with technological changes and new learning models may contribute to making learning environments more effective and desirable. With appropriate learning models like computer-based learning, contact time can be changed from absorbing information to situations where students inquire, think and challenge their lecturers and each other. A blockchain is a digital ledger of economic and financial transactions but is not limited to these kinds of transactions as it can record ‘virtually everything of value’. Blockchain is a disruptive technology that will influence our daily lives and organizations such as education, business, and governance. The focus of this study was on a model, called LinkLearn, that implements blockchain principals and used as a learning tool. The research question formulated for this research was whether LinkLearn can be used as a learning tool. A qualitative research process was followed, and lecturers were interviewed to obtain their views about LinkLearn in education. The outcomes of this study show that lecturers are positive about LinkLearn as a learning tool. The results of this study are important as it shows that the LinkLearn model has the potential to transform the way students are engaged, motivated and evaluated.
A Pronoun Analysis of Null Arguments in Korean

This paper aims to account for null arguments in Korean under pro analysis.Korean is a radical pro-drop language; therefore pro is extremely productive.Thus, if pro analysis can capture interpretations of null arguments without postulating a separate operation such as argument ellipsis, it will be a good example of Occam's razor.We highlight the premise that pro is a silent form of an overt pronoun.This paper shows that the pronoun analysis of null arguments is both conceptually and empirically advantageous.Sloppy readings and nonspecific readings for mass nouns are regarded as data apparently supporting argument ellipsis.However, we show that the data can be reanalyzed under parallelism between null arguments and overt pronouns.We also show that only pronoun analysis can give a solid explanation to scope interaction between disjunction and negation and ellipsis-resistant constituents.We further discuss some advantages of pronoun analysis in comparison with another type of pro analysis, N(P) substitution analysis.
Modelling and design of a drain cleaning robot

Abstract Underground sewers collect waste from domestic and industrial buildings. These pipes are designed to carry only liquid and semi-solid wastes such as soap water and human excrement. However due to improper disposal, solid waste materials such as rags, plastic waste, condoms, sanitary napkins, white cement etc. are also found. Since the pipes are not meant to carry these wastes, they cause blockages. Apart from sewer drains housing societies with sewage treatment plants (STPs) also face this problem. Clearing these blocks either require a human worker, which endangers human life due to presence of toxic gases, or a high-pressure water hose cum suction machine which is very expensive. The aim of this paper is to design an automatic robot which will be able to clear blocks quickly at a much lower cost without any threat to human life. The robot will be lowered into the sewer pipes through a manhole. It will propel itself till it reaches the block then clear the block. The robot is tethered by data cable for communication and to prevent it from flowing away. A rope is used to retrieve the robot once the block is cleared.
Siamese BERT Model with Adversarial Training for Relation Classification

Relation classification is a very important Natural Language Processing (NLP) task to classify the relations from the plain text. It is one of the basic tasks of constructing a knowledge graph. Most existing state-of-the-art methods are primarily based on Convolutional Neural Networks(CNN) or Long Short-Term Memory Networks(LSTM). Recently, many pre-trained Bidirectional Encoder Representation from Transformers (BERT) models have been successfully used in the sequence labeling and many NLP classification tasks. Relation classification is different in that it needs to pay attention to not only the sentence information but also the entity pairs. In this paper, a Siamese BERT model with Adversarial Training (SBERT-AT) is proposed for relation classification. Firstly, the features of the entities and the sentence can be extracted separately to improve the performance of relation classification. Secondly, the adversarial training is applied to the SBERT architecture to improve the robustness. Lastly, the experimental results demonstrate that we achieve significant improvement compared with the other methods on real-world datasets.
Supply Chain Financial Model Innovation Based on Block-chain Drive and Construction of Cloud Computing Credit System

There are three stages in the development of supply chain finance. At present, online supply chain finance serves the supply chain business with its significant advantages. Due to the information asymmetry caused by the joint tampering of transaction information, supply chain finance is facing the bottleneck of credit crisis. The financing difficulty of small and medium-sized enterprises has affected their survival. As a new forward-looking technology, blockchain has the characteristics of decentralization, information tamperability and traceability. This paper introduces blockchain technology into the supply chain financial service system, reflects the whole chain business logic through blockchain network, proposes the supply chain financial model based on blockchain technology, constructs the cloud computing credit service system based on blockchain network, and effectively relieves the supply chain Chain credit crisis promotes the development of supply chain financial business.
Classification of rock types using multivariate analysis in the Dezak area, SW Iran

The geochemical multivariate techniques were applied to recognize different geological units and categorized rock types which accompanied by different penalties. Knowing changes and classification of raw materials with macroscopic and microscopic quantification have direct effect on economic and efficiency in a precipitated calcium carbonate plant. Based on lithological changes, the Darian Formation was divided into four rock units based on a total of 492 chip samples collected from the study area. The main aim objective of this study was to classify the rock types in this area using a multivariate analysis combining the R-mode factor analysis and K-Means method. Due to the opposite vector orientation of SiO2, Al2O3, Fe2O3, K2O, MgO, Na2O, MnO, TiO2, P2O5, SrO, and SO3 versus CaCO3, the lowest geochemical similarity and maximum separation distance were observed between CaCO3 and other parameters using the R-mode factor analysis, which has also been verified by the Ward’s hierarchical agglomerative clustering method. Complementary Q-mode analysis was applied to determine the position of the samples (eigenvalues) with respect to the variables (eigenvectors). In order to geochemical classification of the samples, the K-Means clustering method classified the samples taken from the study area into four geochemical groups. The second group, which comprised a total of 422 composite samples, contained high-concentration CaCO3 samples with an average concentration greater than 97%. The average SiO2 concentration in these samples was 1%, and the sum of Al2O3 and Fe2O3 was about 0.38%. Therefore, the materials falling in this group can be appropriately used as raw materials to feed the plant. Using clr-transformed data for R- and Q-mode analysis, the eigenvectors and eigenvalues show sub-compositionally coherent and closure effect is overcomes. The total variance which is explained by the first factor decreases from 78.6% in real R-mode analysis to 61.7% in clr transformation. Due to major occurrence of the first dimension on geochemical changes, the samples categorized as richest (the best data set), contaminated (the worst data set), and transmission zone.
Non-Abelian quantum adiabatic dynamics and phase simulation with classical resonant oscillators

It is known that the dynamics and geometric phase of a quantum system could be mapped mathematically into a classical system of coupled oscillators without loss of physics. In this work, we show that this quantum-classical mapping can also be used to simulate non-Abelian dynamics and phase of the quantum system with energy degeneracy by interpreting the quantum degeneracy as the resonance between the eigenfrequencies of the classical oscillators. The quantum evolution and Wilczek-Zee phase in the degenerate subspace can also be described by the classical non-Abelian evolution and a non-Abelian geometric angle in the resonant subspace. By studying the adiabatic evolution in this classical non-Abelian case, our results can also be used to generalize the concept of averaging principle and Hannay's angle into the classical system with resonant eigenfrequencies. The quantum-classical mapping of a tripod-scheme Hamiltonian with two degenerate dark states is employed to illustrate the classical non-Abelian dynamics and geometric angles.
FIRMACA-Fuzzy intelligent recommendation model using ant clustering algorithm for social networking

Error rate is a challenging parameter to be met for most of the recommendation systems where there is a high probability of item movement among the data clusters. In social networking applications, it is often necessary to produce apt user recommendations where ant-based clustering techniques provide best optimal solutions to the clustering problems. However, the existing ant clustering algorithms lack in efficient local search procedures. Also, they are in need of intelligent fuzzy rule improvements for effective cluster partitioning. Therefore, a new model named Fuzzy Intelligent Recommendation Model using Ant Clustering Algorithm (FIRMACA) is proposed in this paper where a combination of biased exploration and fuzzy rules is used for ant clustering. Experimental analysis shows improvement in recommendation metrics—precision, recall, normalized discounted cumulative gain (NDCG) (≤ 5%) and significant reduction (≤ 1%) in misclassification error rate (MER) compared to contemporary ACO-based algorithms. The selected clusters are optimized globally and locally to pull out the best possible redefined clusters.
Sequential Inverse Covariance Intersection Fusion Estimation for Non-uniform Sampling Systems with Fading Measurements

This paper is concerned with the state estimation problem for non-uniform sampling systems subject to fading measurements. The concerned non-uniform sampling scheme is that the state is updated uniformly and the measurements are sampled randomly. Moreover, the fading measurement phenomena may occur in different sensor measurement channels where the independent random variables obeying different certain probability distributions over different known intervals are employed to describe this phenomena. Firstly, a new state space model is established to depict the dynamics at the measurement sampling points within a state update period. Then, based on single sensor measurements, the non-augmented state estimator is proposed by applying an innovation analysis approach. Finally, the sequential inverse covariance intersection fusion estimation algorithm is proposed for the multi-sensor case. The simulation research verifies the effectiveness of the proposed estimation algorithms.
Working Pattern Recognition of Airborne Fire Control Radar for Unbalanced Data

A working pattern recognition model of airborne fire control radar for unbalanced data is proposed in this paper, which consists of weight oversampling and proportional voting random forest. Generally, there are two methods for solving the problem of the unbalanced data: data preprocessing and integrated learning. Most of the current methods have the problems such as data redundancy during oversampling, overfitting during identification, and unreasonable decision-making principles. The proposed model is made of the weight oversampling algorithm in data processing, and the proportional voting random forest in recognition algorithm. Simulation results show that, compared with traditional methods, the model proposed in this paper has greatly improved the recognition effect and recognition efficiency. It solves the problem of radar working pattern recognition in the case of unbalanced samples to a certain extent.