Reviewerly/exHarmony huggingface dataset:
Regularization of Mixture Models for Robust Principal Graph Learning

A regularized version of Mixture Models is proposed to learn a principal graph from a distribution of D-dimensional datapoints. In the particular case of manifold learning for ridge detection, we assume that the underlying structure can be modeled as a graph acting like a topological prior for the Gaussian clusters turning the problem into a maximum a posteriori estimation. Parameters of the model are iteratively estimated through an Expectation-Maximization procedure making the learning of the structure computationally efficient with guaranteed convergence for any graph prior in a polynomial time. We also embed in the formalism a natural way to make the algorithm robust to outliers of the pattern and heteroscedasticity of the manifold sampling coherently with the graph structure. The method uses a graph prior given by the minimum spanning tree that we extend using random sub-samplings of the dataset to take into account cycles that can be observed in the spatial distribution.
A Comparative Analysis and Prediction over Bitcoin Price Using Machine Learning Technique

Bitcoin is one of the primary computerized monetary forms to utilize peer innovation to work with moment installments. The free people and organizations who own the overseeing figuring control and take part in the bitcoin network—bitcoin miners— are accountable for preparing the exchanges on the blockchain and are persuaded by remunerations (the arrival of new bitcoin) and exchange charges paid in bitcoin. These excavators can be considered as the decentralized authority implementing the believability of the bitcoin network. New bitcoin is delivered to the excavators at a fixed yet occasionally declining rate. There is just 21 million bitcoin that can be mine altogether. As of January 30, 2021, there are around 18,614,806 bitcoin in presence and 2,385,193 bitcoin left to be mined. This paper will predict the nature of bitcoin price because, according to the reports of the past few years. The year 2020-present appeared to be a good time for bitcoin because, in this time duration, bitcoin has seen huge ups and downs. This paper will use various Machine Learning Techniques for the predictive analysis of bitcoin to accurately predict the price's nature. As the price of bitcoin depends upon various factors and these factors directly affect the price, i.e., multiple factors of bitcoin are dependent on each other. After analyzing the results from multiple research papers and review papers, we discovered each algorithm has its advantages and disadvantages while predicting the bitcoin value. Keeping in mind all the findings, we will find algorithms that predict the bitcoin price accurately and without fewer disadvantages. So, if we go as per assumptions, regression would be the best choice for predicting the bitcoin value, but there are others algorithms also. So, in this paper, we will see the results of the multiple algorithms and then choose the correct algorithm after analyzing the results of all the implemented algorithms. This paper also includes the implementation of the comparison charts with each algorithm so that it will be easy to analyze the findings of each algorithm.
Artificial Intelligence enabled virtual sixth sense application for the disabled

The sixth sense is a multi-platform app for aiding the people in need that is people who are handicapped in the form of lack of speech (dumb), lack of hearing (deaf), lack of sight (blind), lack of judicial power to differentiate between objects (visual agnosia) and people suffering from autism (characterized by great difficulty in communicating and forming relationships with other people and in using language and abstract concepts). Our current implementation of the product is on two platforms, namely, mobile and a web app. The mobile app even works for object detection cases in offline mode. What we want to achieve using this is to make a better world for the people suffering from disabilities as well as an educational end for people with cognitive disabilities using our app. The current implementation deals with object recognition and text to speech and a speech to text converter. The speech to text converter and text to speech converter utilized the Web Speech API (Application Program Interface) for the website and text to speech and speech to text library for the mobile platform. The object recognition wouldn't fetch enough use out of a website. Hence, it has been implemented on the mobile app utilizing the Firebase ML toolkit and different pre-trained models, which are both available offline as well as online.
Breast Cancer Detection Using Machine Learning and Its Classification

Throughout the world, one of the most frequently occurring cancers is breast cancer. Thousands of women succumb to breast cancer every year. Due to the high fatality rate in humans, breast cancer is a cause for concern. It attracts many researchers and raises concern about current research and development. If breast cancer can be traced early on, it can improve prognosis and enhance the probability of survival. Early diagnosis not only can promote timely treatment for patients, but it also reduces the mortality rate. However, early tracing of the disease has turned into an increasingly serious concern due to the rapid increase in the world population. With rapid population growth, the chances of succumbing to breast cancer are extremely high. Statistically, it comes second on the list of most threatening cancers amongst women all over the world. Hence, precise detection and treatment of this ailment are of prime importance. The classification of patients into malignant or non-malignant groups is the subject of much research. In this direction, machine learning is highly regarded as a serious contender for the detection of breast cancer patterns. Machine learning offers distinct advantages in extracting sensitive information from breast cancer datasets. The information extracted helps in the early detection of this fatal disease. With the help of machine learning, a cancerous lump is traced at an early stage and is further narrowed down to a lump in one breast. The treatment can be started at an early stage to prevent the situation from getting worse and hence, making the treatment more effective. If the cancerous lump has already traversed to more body parts, then the treatment becomes complex and will require targeting all cancerous cells within the body. It’s also been claimed that this form of cancer is the easiest cancer to be cured only if detected at an early stage. This inference leads to the question that if it is easier to cure and the cause of so many deaths worldwide, why hasn’t its detection been ramped up yet? Integrating the concept of machine learning into the detection of cancer comes into play here. With data acquired over the years and highly efficient algorithms around the corner, machine learning can study the patterns and images of X-rays and point out anomalies that could suggest the occurrence of lumps.
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map

Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English.However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, different segmentation granularity of Chinese sentences, and imperfect performance of syntactic analyses.Furthermore, entire neural Chinese SC models have been under-investigated so far.In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. 1 Experimental results show that our SOM-NCSCM can significantly benefit from the deep investigation of similarity among data, and achieve a promising F1 score of 89.655 and BLEU4 score of 70.116, which also provides a baseline for further research on the Chinese SC task.
Machine Translation in Low-Resource Languages by an Adversarial Neural Network

Existing Sequence-to-Sequence (Seq2Seq) Neural Machine Translation (NMT) shows strong capability with High-Resource Languages (HRLs). However, this approach poses serious challenges when processing Low-Resource Languages (LRLs), because the model expression is limited by the training scale of parallel sentence pairs. This study utilizes adversary and transfer learning techniques to mitigate the lack of sentence pairs in LRL corpora. We propose a new Low resource, Adversarial, Cross-lingual (LAC) model for NMT. In terms of the adversary technique, LAC model consists of a generator and discriminator. The generator is a Seq2Seq model that produces the translations from source to target languages, while the discriminator measures the gap between machine and human translations. In addition, we introduce transfer learning on LAC model to help capture the features in rare resources because some languages share the same subject-verb-object grammatical structure. Rather than using the entire pretrained LAC model, we separately utilize the pretrained generator and discriminator. The pretrained discriminator exhibited better performance in all experiments. Experimental results demonstrate that the LAC model achieves higher Bilingual Evaluation Understudy (BLEU) scores and has good potential to augment LRL translations.
Hole-filling framework by combining structural and textural information for the 3D Terracotta Warriors

As one of the eight wonders in the world, the virtual restoration of Terracotta Warriors is of great significance to archaeology. However, some parts of fragments were corroded for thousands of years, resulting in the existence of several holes in most of the restored cultural heritage artifacts. Based on the structural and textural information, we present a framework for filling the hole. First, a method based on the Poisson equation was employed to fill the hole on the triangular mesh model. Then, to complete the surface color and texture information of the three-dimension (3D) model, make the hole patch, and the original model surface texture natural transition, the 3D problem is converted into two-dimension (2D) image inpaint problem, and a refined network is added into EdgeConnect to generate a higher resolution result. A set of experiments is performed to evaluate the performance of our proposed framework. We hope the proposed framework can provide a useful tool to guide the virtual restoration of other cultural heritage artifacts.
Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning
A focused linear logical framework and its application to metatheory of object logics

Abstract Linear logic (LL) has been used as a foundation (and inspiration) for the development of programming languages, logical frameworks, and models for concurrency. LL’s cut-elimination and the completeness of focusing are two of its fundamental properties that have been exploited in such applications. This paper formalizes the proof of cut-elimination for focused LL. For that, we propose a set of five cut-rules that allows us to prove cut-elimination directly on the focused system. We also encode the inference rules of other logics as LL theories and formalize the necessary conditions for those logics to have cut-elimination. We then obtain, for free, cut-elimination for first-order classical, intuitionistic, and variants of LL. We also use the LL metatheory to formalize the relative completeness of natural deduction and sequent calculus in first-order minimal logic. Hence, we propose a framework that can be used to formalize fundamental properties of logical systems specified as LL theories.
Energy Management of a Microgrid based on LSTM Deep Learning Prediction Model

Accurate and stable forecasting of total demand in micro-grid is essential for the proper operation of the energy management system. On the other hand, forecasting total renewable energy generation in micro-grid also plays a major role in scheduling the other conventional distributed generators in MG. Future power shortages in micro-grid and future excess energy generations from renewable energy sources are easily calculated when load and total renewable energy generation are forecasted. In this paper, the LSTM model is designed to forecast the total electric demand, total solar power and wind power production in micro-grid separately. To increases the performance accuracy and to reduce the complexity of the model due to a large amount of data, all three datasets are clustered in terms of seasons. So total demand and power productions are forecasted separately for all four seasons. These datasets are arranged in time series format and 2-steps predictions are employed in this study. MAE, MSE, RMSE, RSE and RAE are the performance metrics used to evaluate the model accuracy. This model of predicting both demand and fluctuating renewable energy resources enhances the energy management of microgrid.
Local Binary Pattern for Binary Object Classification using Coordination Number (CN)* and Hu's Moments

Hu's Moment corresponding to the Local Binary Pattern (LBP) features vector have been frequently used for binary objects pattern recognition and classification. Hu's Moments are used in a variety of applications due to its geometrically invariant features on object translation, rotation and scaling. In this paper, we have formulated the methodology of the Hu's Moment by using Local Binary Patterns. The minimum responding decimal values of the developed model for the Local Binary Pattern (LBP) corresponding to the Coordination Number (CN)* of each contour point of an object are used to compute Hu's seven moments. We have incorporated the local binary patterns corresponding to the coordination number of object contour points in a modified model for computing similarity between two binary objects. The experimental results conducted on Kimia-99 and MPEG-7 CE datasets demonstrate that our approach shows better results compared to the other existing algorithms.
How Blockchain Technology Can Transfigure the Indian Agriculture Sector

Blockchain and decentralized ledger gained fame because of its financial application bitcoin, but after exploring, researchers have found it has a wide range of different application areas beyond cryptocurrencies. This technology is all set to extremely transform various industries, such as law, banking, healthcare and real estate. One hardly explored sector that blockchain technology has the potential to transform completely is the Indian agriculture sector. More prominently, it has a large number of challenges that need to be overcome soon. Blockchain technology can help to transform Indian agriculture sector in many different ways. This chapter provides the current status of the Indian agriculture sector and proposes an architecture of how to use blockchain technology to transform this sector. The authors also present the limitations and challenges faced during the implementation of blockchain technology in agriculture along with its advantages. In the end, the authors list some areas in agriculture where blockchain technology can be used.
Multi-Scale Warping for Video Frame Interpolation

A novel video interpolation network to improve the temporal resolutions of video sequences is proposed in this work. We develop a multi-scale warping module to interpolate intermediate frames robustly for both small and large motions. Specifically, the proposed multi-scale warping module deals with large motions between two consecutive frames using coarse-scale features, while estimating detailed local motions by exploring fine-scale features. To this end, it takes multi-scale features from the encoder and estimates kernel weights and offset vectors for each scale. Finally, it synthesizes multi-scale warping frames and combines them to obtain an intermediate frame. Extensive experimental results demonstrate that the proposed algorithm outperforms state-of-the-art video interpolation algorithms on various benchmark datasets.
Interdisciplinary Trends of Digital Education in the COVID-19 Paradigm: Global Event Horizon

Transformative shifts in the knowledge economy of the XXI century, Industry 4.0 and Web 4.0 development and elaboration of networked society, emergency digitization of all social communicative spheres due to pandemic measures have imposed pressing revisions onto interdisciplinary and cross-sectorial job market demands of university level education, curriculum design and learning outcomes. The COVID-19 pandemic induced amplified digitalization measures in the higher education sphere, informed by the need to take quick comprehensive action in order to achieve the overarching result to transform educational and communicative scenarios into interdisciplinary digital, remote, and hybrid formats. The consequent functional tasks to meet this challenge in the educational sphere worldwide are estimated as 1) to adapt the existent educational scenarios to digital, remote and hybrid formats; 2) to upgrade e-competence and digital literacy of all stakeholders of the educational process and industry; 3) to activate complex interdisciplinary skillsets, otherwise latent or underutilized in the professional interaction; 4) to introduce functional technical solutions for facilitation of formal and informal educational workflow and communication. The findings of the comprehensive framework research project 'TRANSITION' disclose a wide scope of generalized theoretical and applied issues, permeating the social and educational context worldwide: global event horizon and paradigm shifts in the interdisciplinary trends of digital education in the Covid-19 timeframe and beyond; transformative changes and avenues of development of the network society and education as an interdisciplinary socio-cultural institution and industry in the digital age; global experiences, universal/generic challenges, technical advances and specific national gains in quality assurance of online and hybrid learning in the Covid-19 paradigm.
Generation and Extraction Combined Dialogue State Tracking with Hierarchical Ontology Integration

Recently, the focus of dialogue state tracking has expanded from single domain to multiple domains.The task is characterized by the shared slots between domains.As the scenario gets more complex, the out-of-vocabulary problem also becomes more severe.Current models are not satisfactory for addressing the challenges of ontology integration between domains and out-of-vocabulary problems.To address the problem, we explore the hierarchical semantics of the ontology and enhance the interrelation between slots with masked hierarchical attention.In state value decoding stage, we address the out-of-vocabulary problem by combining generation method and extraction method together.We evaluate the performance of our model on two representative datasets, MultiWOZ in English and CrossWOZ in Chinese.The results show that our model yields a significant performance gain over current state-of-the-art state tracking model and it is more robust to out-of-vocabulary problem compared with other methods.
MLHarness: A scalable benchmarking system for MLCommons

With the society’s growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common set of measures for ML/DL models with large scale open datasets under common development practices and resources so that people can benchmark and compare models’ quality and performance on a common ground. MLCommons has emerged recently as a driving force from both industry and academia to orchestrate such an effort. Despite its wide adoption as standardized benchmarks, MLCommons Inference has only included a limited number of ML/DL models (in fact seven models in total). This significantly limits the generality of MLCommons Inference’s benchmarking results because there are many more novel ML/DL models from the research community, solving a wide range of problems with different inputs and outputs modalities. To address such a limitation, we propose MLHarness, a scalable benchmarking harness system for MLCommons Inference with three distinctive features: (1) it codifies the standard benchmark process as defined by MLCommons Inference including the models, datasets, DL frameworks, and software and hardware systems; (2) it provides an easy and declarative approach for model developers to contribute their models and datasets to MLCommons Inference; and (3) it includes the support of a wide range of models with varying inputs/outputs modalities so that we can scalably benchmark these models across different datasets, frameworks, and hardware systems. This harness system is developed on top of the MLModelScope system, and will be open sourced to the community. Our experimental results demonstrate the superior flexibility and scalability of this harness system for MLCommons Inference benchmarking.
Improving Recommendation for Video Content Using Hyperparameter Tuning in Sparse Data Environment

As we are familiar with the increasing number and demand for online videos on the internet has led to difficulty in the extraction of required data. The recommendation system is used to filter or to collect information according to the user's preference. Nowadays, the use of recommendation systems has become important so that we can find more relevant data from huge sources. Based on the attributes such as genres, we can recommend important videos to different users. There are different video recommendation problems such as cold start, data sparsity, etc. Here in this paper, we are solving the data sparsity problem which is considered as the biggest problem as it can give poor results. There are different recommendation techniques that are used to solve various recommendation problems such as content-based filtering, real-time recommendation system, hybrid recommender system, single network-based recommendation system, collaborative filtering. We are using a single-network recommendation system to enhance the quality of recommendation.