Reviewerly/exHarmony huggingface dataset:
FortClash: Predicting and Mediating Unintended Behavior in Home Automation

Smart home inhabitants can specify trigger-condition-action rules to control the home's behavior. As the number of rules and their complexity grow, however, so does the probability of issues such as inconsistencies and redundancies. These can lead to unintended behavior, including security vulnerabilities and wasted resources, which harms the inhabitants' trust in the system. Existing approaches to handle unintended behavior typically require inhabitants to define all-encompassing, permanent solutions by modifying the rules. Although this is fitting in certain situations, some unforeseen situations might occur. We argue that the user always must have the last word to avoid unwanted behaviors, without altering the overall behavior. With FortClash, we present an approach to predict many different types of unintended behavior, and contribute four novel mechanisms to mediate them that rely on making one-time exceptions. With FortClash, inhabitants gain a new tool to deal with unintended behavior in the short-term that is compatible with existing long-term approaches such as editing rules.
Quantification on the Generalization Performance of Deep Neural Network with Tychonoff Separation Axioms

• A measure to evaluate model ability to distinguish classes irrespective of class imbalance and the threshold applied is important. • T 2 complexity quantifies the degree of separability between classes. • Under binary and multiclass classification dataset, the efficiency of the measure in comparison with other classification metrics is studied. Learning in Deep Neural Network (DNN) identifies the geometric structure of data. Hence there is a need for topological measures for quantifying this geometric structure. Based on the transformations in DNN, a metric is introduced with certain conditions to generate a base for a suitable topology. The locally indiscrete nature of the topology is applied to quantify the generalization gap in DNN approximations based on Tychonoff separation axioms. It develops a new measure T 2 complexity that quantifies the model’s ability to distinguish classes irrespective of the class imbalance and the threshold applied. The efficiency of the new measure on a real dataset for binary and multiclass classification problems compared with commonly used classification metrics Accuracy, Precision, Recall, F 1 score, Matthews Correlation coefficient, AUC-ROC score, and AUC-PR score. The experimental analysis shows that the proposed measure gives a necessary condition for an optimal classification model. The study shows that the measure T 2 complexity can be included as a performance metric to analyze the model’s ability to distinguish classes on any binary and multiclass classification problem when the existing measures show misleading results due to class imbalance.
A label-guided weighted semi-supervised neutrosophic clustering algorithm

The traditional neutrosophic clustering method only performs cluster analysis on the data itself, and often ignores the supervision information of data. In order to solve the above problems, a label-guided weighted semi-supervised neutrosophic clustering algorithm is proposed in the paper. On the one hand, the paired constraint information is used to construct the supervision weight coefficient and the distance measurement learning is combined to re-measure the degree of membership of the data and the cluster center; On the other hand, by minimizing the sum of squares of error between membership matrix and label matrix, the purpose of clustering results guided by label information is realized. Experiments on various data sets and comparisons with other clustering algorithms show that the new clustering algorithm can make full use of supervisory information and improve the accuracy of clustering.
Connecting the Dots in Self-Supervised Learning: A Brief Survey for Beginners

Abstract The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the “dots” of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.
Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix

The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology matching. To improve the quality of ontology alignment, in this work, the ontology matching problem is first defined as a sparse multi-objective optimization problem (SMOOP), and then, a multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented to adaptively measure the relevance of each individual’s genes to the objectives, which is applied in MOEA’s initialization, crossover and mutation to ensure the population’s sparsity and to speed up the the algorithm’s convergence. The experiment verifies the performance of MOEA-RM by comparing it with the state-of-the-art ontology matching techniques, and the experimental results show that MOEA-RM is able to effectively address the ontology matching problem with different heterogeneity characteristics.
Natural Language Processing based Machine Translation for Hindi-English using GRU and Attention

Machine translation, abbreviated as MT, is basically an extension of language computation that studies the concept of translating text or speech from one language to another. On the most rudimentary level, machine translation basically conducts mechanical reinstatement of words from one particular language into another, however, this alone rarely yields a good translation because it needs to recognize the closest match between the complete sentence and the target language. Not every term in one language has an equivalent word in another, and many words have many meanings. Unlike our everyday normal phrase-based translation systems, which are usually made up of many small and tiny sub-components that can be tweaked individually, NMT i.e., neural machine translation seeks to design and train a simple, massive, end-to-end neural network system that analyses a sentence and provides an accurate translation for the respective language. Neural Machine translation is a radical newer technique in the field of language translation and localization that trains neural models using deep neural networks and artificial intelligence. The main advantage of this approach is that you can train a single system directly with source and target text. This eliminates the need for special system pipelines required for statistical machine learning. This means you can perform training and translation on an end-to-end model at once.
Sports-Assisted Education Based on a Support Vector Machine and Genetic Algorithm

A sports-assisted education method based on a support vector machine (SVM) is proposed to address the problem of complex and variable sports actions leading to easy ghosting of target detection and high dimensionality of feature extraction, which reduces the low accuracy of sports action recognition. The ViBe target detection algorithm is improved by using Wronskian function and the “4-linked algorithm” seed filling algorithm, which effectively solves the ghosting problem and obtains clearer human sports targets. By using the genetic algorithm to fuse the eight-star model with sports action features extracted by the Zernike moment, redundant features are reduced and differentiability between different classes is ensured. Sports action classification was achieved by using a one-to-one construction of an SVM classifier. The results show that the proposed method can effectively recognize sports movements with an average recognition accuracy of more than 96%, which can assist physical education and has a certain practical application value.
Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

With the rapid development of the technologies of virtual reality (VR) and augmented reality (AR), the accurate visual query of 3D objects which are closer to real world has become a hotspot of study. There are a lot of redundancy problems in the existing axis-aligned bounding box model (AABB) and oriented bounding box model (OBB), which are used to represent 3D objects mostly at present, and cannot be used for the accurate visual query. For example, the results of the visual query must be accurate, and without any error in automatic driving technology in the automotive industry, otherwise, serious safety problems would be caused. The convex hull model could be used to solve those problems. However, there are few studies on visual query based on the convex hull model. A visual query method for 3D obstacle space based on the convex hull model is proposed in this paper. Firstly, the definitions of “query point-object visual body” and “combined obstacle” are proposed. Secondly, the visibility detection algorithm of the convex hull model is given in detail. The collision detection method of “query point-object visible body” combined with other obstacles and line-plane geometric calculation operations are used to detect the visibility of the convex hull model. Then, a 3D visual k-nearest neighbor algorithm based on the convex hull model is given in the paper. Finally, the algorithm is verified by experiments and compared with the traditional visibility detection algorithm, and the analysis of experimental results shows that the algorithm has a great performance and higher accuracy. The growth rate of the query time is smaller, and the speed is faster; especially, when there are fewer query points, the query speed can be increased by more than 50%.
A Fog Computing Architecture Integrating Blockchain and Internet of Things for Securing Bank Accounts

Blockchain technology provides high levels of data security, as well as decentralization, open and transparent network infrastructure, and cheap operational costs. Even in the generally conservative world of finance, these excellent traits make blockchain an extremely practical and in-demand solution and also in constrained banking business. Banking activities are inextricably linked to deposits and loans. Because the deposits are not controlled by a single company, a distributed ledger-based system for loans and deposits, is decentralized and cannot go bankrupt. Use the block chain technique to validate and trace transactions for online commerce. Block chains were created to handle certain transactions with specific validation needs. A value chain's systems all save blocks of time-stamped transactions. A blockchain is a decentralized ledger in which all participants can see each other's transactions. A blockchain is a distributed ledger that records asset trades between members of a community. The connection between two blocks is made using the SHA-256 algorithm. Block chain breaks data into little parts and distributes them around a network of computers instead of storing data in a single spot or uploading it to a cloud service. It is a decentralized digital transaction ledger with no central control point. Data will not be lost if one or two nodes fail because each computer (or node) has a full duplicate of the ledger. It effectively eliminates the middleman, as there is no need to seek the assistance of a third-parties to conduct a transaction. Instead of putting their faith Users can trust a decentralized, immutable ledger in a vendor or service provider. Also, use the fog server system to save duplicate storage and prevent unauthorized access.
Effect of Spatial Dropout on Mosquito Classification using VGGNet

Monitoring systems with automatic classification have been developed for years but never reached the human expert level. Convolutional neural networks (CNN) achieve state-of-the-art results in image classification tasks and can be applicable for real-time image classification. However, CNN usually may be overfitting during training due to relatively small datasets. In this work, we investigate the effects of transfer learning, spatial dropout, and image augmentation techniques in the VGGNet architectures. Our results show that VGG-16 achieves more than 96.9% of accuracy when proper techniques are applied to the networks. Moreover, combining the predictions from VGG-16 and VGG-19 increases accuracy to 97.2% on the test dataset.
Supply Chain Dapp using Blockchain and Microfinance

Humans' primary source of nutrition is food. Consuming a high-quality product is important in everyone's life and also food is essential to our survival. Traditional approaches do not provide consumers with the capacity to trace what they consume. The process can be made transparent from starting to end by utilizing the Blockchain technology. With the integration of blockchain technology, supply chain can provide consumers with advanced traceability and trus t in the final product. Recently, Ethereum is emerging as a peer-to-peer, decentralized blockchain platform with smart contract functionality. The major purpos e of this res earch study is to encourage the farmers to utilize microfinance in order to invest in their crops and to encourage farmers to utilize the capital offered by them without incurring debt. By verifying all prior production, an inves tment offer will be created for the farmers' crop.
Image Super-Resolution Method Based on Dual Learning

Existing super-resolution methods convert high-resolution images into low-resolution images, and use the synthesized images as input to train the model. However, it is difficult for synthetic low-resolution images to reflect the characteristics of real low-resolution images, resulting in poor model performance in practical applications. To address this problem, we propose a recurrent super-resolution framework, which consists of a degradation model and a reconstruction model. The degradation model degenerates the real high-resolution image into a more real low-resolution image, which is used as the input of the super-resolution reconstruction network, and then uses the reconstruction model to reconstruct the low-resolution image, and calculates the error with the original image. The generated high-resolution image is input into the degradation model again for degradation processing, forming a symmetrical and cyclic network structure, so that the super-resolution model has a better effect when reconstructing the real low-scoring image. In addition, the spatial attention mechanism is introduced into the generator network, which expands the receptive field of the convolution kernel, better extracts long-distance image features and improves the texture details of super-resolution images, which is consistent with the global.
The role of compute nodes in privacy-aware decentralized AI

Mobile devices generate and store voluminous data valuable for training machine learning (ML) models. Decentralized ML model training approaches eliminate the need for sharing such privacy-sensitive data with centralized entities by expecting each data owner that participates in an ML model training process to compute updates locally and share them with other entities. However, the size of state-of-the-art ML models and the computational needs for producing local updates in mobile devices prohibit the participation of mobile devices in decentralized training of such models. Split learning techniques can be combined with decentralized model training protocols to realize the involvement of mobile devices in model training while preserving the privacy of their data. Mobile devices can produce local updates by splitting the model they are training into multiple parts and delegating the processing of the computationally demanding parts to compute nodes. This work examines the impact of the number of available compute nodes and their interaction. We split ResNet101 ML model into 3,4, and 5 parts, keep the first and the last part in the data owner and assign the processing of the middle parts to compute nodes. Additionally, we analyze the training time when the compute nodes assist multiple data owners in parallel or are responsible for different model parts by forming a pipeline.
Impact of Preprocessing on Twitter Based Covid-19 Vaccination Text Data by Classification Techniques

Data from sources like twitter, organizations internal communications, Face book, blogs are keys in providing user the choice and scrutiny in organizational elevating. Text classification is used unrestrictedly to assign a fixed predefined grouping. This grouping is used for articles categorizing, chat organizing and brand mention on people opinion. There are number of method to classify, cluster or associate (Sentiment analysis activities) these data so as to obtain public opinion regarding particular topic. To perform sentiment analysis activities of the data collected from the source need to be processed. Garbage-in garbage-out is pertinent in relationships to data analysis. That is the output the analyzer gets from the analysis is completely depend on the input data provided. The inconsistent, jagged, inadequate, erroneous, data produce inaccurate results even though analyzer uses powerful algorithm. A preprocessing step is necessary to convert raw dirty data to trainable, understandable and analyzing data in the sentiment analysis algorithm. In this research work, twitter COVID data set is classified using LIBLINEAR and BayesNet classification techniques on both processed and unprocessed data. For processing, resample and Remove Useless filter is used and the results are compared. For the comparison purpose performance metrics like Fl-score and precision were also used to validate the used models with its results. Through this comparison analysis, the best performances of the dassification algorithms are suggested for further process.
A Multimodal Information Fusion Model for Robot Action Recognition with Time Series

The current robotics field, led by a new generation of information technology, is moving into a new stage of human-machine collaborative operation. Unlike traditional robots that need to use isolation rails to maintain a certain safety distance from people, the new generation of human-machine collaboration systems can work side by side with humans without spatial obstruction, giving full play to the expertise of people and machines through an intelligent assignment of operational tasks and improving work patterns to achieve increased efficiency. The robot’s efficient and accurate recognition of human movements has become a key factor in measuring robot performance. Usually, the data for action recognition is video data, and video data is time-series data. Time series describe the response results of a certain system at different times. Therefore, the study of time series can be used to recognize the structural characteristics of the system and reveal its operation law. As a result, this paper proposes a time series-based action recognition model with multimodal information fusion and applies it to a robot to realize friendly human-robot interaction. Multifeatures can characterize data information comprehensively, and in this study, the spatial flow and motion flow features of the dataset are extracted separately, and each feature is input into a bidirectional long and short-term memory network (BiLSTM). A confidence fusion method was used to obtain the final action recognition results. Experiment results on the publicly available datasets NTU-RGB + D and MSR Action 3D show that the method proposed in this paper can improve action recognition accuracy.
Piano Teaching Knowledge Graph Construction Based on Cross-Media Data Analysis and Semantic Network

With the rapid development of information technology and mobile Internet, digital image, text, audio, video, and other cross-media data are growing explosively, which has changed people's way of life and work. In view of the issues of negative studying effectivity and challenging attention of college students in the modern-day piano instructing process, this paper puts forward the application of knowledge Atlas technology in piano teaching and constructs a multimodal knowledge Atlas of piano teaching based on deep neural network, so as to make piano teaching more intelligent and improve students' learning efficiency and learning interest. How to realize the semantic association understanding of cross-media data is the core problem of cross-media semantic analysis. First, this paper introduces the basic rules of ontology construction and the basic method of establishing general knowledge graph are introduced. Then, taking the piano teaching content as an example, natural language sentences can be expressed and stored with cross-media data using semantic network. The mathematical understanding is extracted in accordance to the herbal language processing technology, and the entities are fused in accordance to the frequent semantic similarity detection between extraordinary entities, so as to decrease the redundancy and repetition fee of entities and the complexity of the graph. The fused new knowledge is processed according to the quality evaluation rules, the qualified part is added to the knowledge base, and then the above steps are iterated to update the database. The great overall performance of piano instructing understanding graph mannequin primarily based on semantic network is validated through experiments.