Reviewerly/exHarmony huggingface dataset:
Low resource language specific pre-processing and features for sentiment analysis task

Sentiment analysis is a classification task where polarity of textual data is identified, i.e. to analyze whether a sentence or document expresses a negative, positive or neutral sentiment. Manipuri is a less privileged, highly agglutinative and tonal language. Despite being a scheduled language of Indian Constitution, it is also a resource constrained language. In this work, we report the sentiment analysis for Manipuri using different types of machine learning based approaches. The dataset used in our work is collected from local daily newspaper. The novelty of this work is that we carry out language specific pre-processing tasks such as transliteration, building negative morpheme based lexicon and filtering of noisy words. Using them as additional linguistic features in our models improves the classification result in terms of precision, recall and F-score. The ensemble voting of best three classifiers based on TF-IDF perform better than BM25 based classifiers and other stand-alone classifiers. Based on this result, we attempt to classify the sentiment of news articles during a certain period of time. Further, we report the finding of deep learning based approaches on the same dataset.
Towards Evaluating and Training Verifiably Robust Neural Networks

Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP trained networks: CROWN, a bounding method based on tight linear relaxation, often gives very loose bounds on these networks. We also observe that most neurons become dead during the IBP training process, which could hurt the representation capability of the network. In this paper, we study the relationship between IBP and CROWN, and prove that CROWN is always tighter than IBP when choosing appropriate bounding lines. We further propose a relaxed version of CROWN, linear bound propagation (LBP), that can be used to verify large networks to obtain lower verified errors than IBP. We also design a new activation function, parameterized ramp function (ParamRamp), which has more diversity of neuron status than ReLU. We conduct extensive experiments on MNIST, CIFAR-10 and Tiny-ImageNet with ParamRamp activation and achieve state-of-the-art verified robustness. Code is available at https://github.com/ZhaoyangLyu/VerifiablyRobustNN.
Balancing the Elicitation Burden and the Richness of Expert Input When Quantifying Discrete Bayesian Networks

Structured expert judgment (SEJ) is a method for obtaining estimates of uncertain quantities from groups of experts in a structured way designed to minimize the pervasive cognitive frailties of unstructured approaches. When the number of quantities required is large, the burden on the groups of experts is heavy, and resource constraints may mean that eliciting all the quantities of interest is impossible. Partial elicitations can be complemented with imputation methods for the remaining, unelicited quantities. In the case where the quantities of interest are conditional probability distributions, the natural relationship between the quantities can be exploited to impute missing probabilities. Here we test the Bayesian intelligence interpolation method and its variations for Bayesian network conditional probability tables, called "InterBeta." We compare the various outputs of InterBeta on two cases where conditional probability tables were elicited from groups of experts. We show that interpolated values are in good agreement with experts' values and give guidance on how InterBeta could be used to good effect to reduce expert burden in SEJ exercises.
Digitization of industrial engineering processes using the augmented reality technology: industrial case studies

Augmented Reality Technology is one of the key technologies of digital transformation in industrial and non-industrial areas. Due to the rapid development of display hardware and tracking systems, virtual and augmented reality applications are being developed today that would only have been possible in large research laboratories with huge efforts a few years ago. This paper presents the application of the augmented reality technology (AR) in the industrial field. Based on industrial case studies the implementation of two AR applications and their potential to digitize the product lifecycle processes will be discussed. The use cases were developed within cooperation projects between the University of Applied Sciences Karlsruhe and a fire truck manufacturer. The case studies focus on the following topics: digital product configuration management and production planning and assistance. The objective of the case studies is the investigation of the AR application in the industrial environment and its capability as digital transformation technology along the product lifecycle. The presented case studies deliver experiences and suggestions in terms of praxis-oriented development and deployment of the AR technology in the engineering field, which can be used for future AR research works.
Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball

In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) network. Based on the RBF analysis, the number of network layers and the number of hidden layer neurons are adjusted and optimized. In order to improve the prediction accuracy of shooting in basketball, a method based on. Through the analysis, it can be known that the accuracy of both the traditional RBFNN and the CIRAN-based online neural network for the prediction of shooting accuracy is above 70%. The prediction accuracy of the online neural network for shooting is higher than that of the traditional one. This is mainly because the online update function of the learning algorithm can better adjust the corresponding structure with the development of the game and has a better generalization ability. In addition, because the CIRAN learning algorithm introduces the hidden layer neuron deletion strategy, its network structure is simpler than that of the traditional one, the number of hidden layer neurons is less, and the running time required is less, which can better meet the real-time requirements and provide a more scientific method for basketball training.
Block Chain Platforms and Smart Contracts

A smart contract is a line of code that is inserted in blockchain and naturally executed when the predefined terms and conditions are met. Blockchain is a mutual, disseminated record on which the exchanges are carefully recorded and connected together, so they provide the complete history or provenance of the benefit. In the past, blockchain was associated only with digital currency, such as Bitcoin. However, blockchain innovations are now used in numerous businesses as a protected and savvy approach to create and manage dispersed databases and maintain records of online transactions of different types. Smart contracts can be used in various industries, including banking and financial services, where multiple lenders can provide loans with the same loan terms to multiple borrowers using smart contracts. In the energy sector, smart contracts are used for the general distribution of energy. The entertainment industry uses smart contracts to facilitate how music and movie rights are maintained according to the Digital Right Management System. Thus, smart contacts will become much more common in the very near future.
Design and Evaluation of a Deep Learning Algorithm for Emotion Recognition

Facial emotion recognition is one of the most interesting research areas where many researchers are actively participating over the past few decades. This paper attempts to discuss about the application of emotion recognition where seven different emotions such as happy, sad, neutral, angry, surprise, fear and disgust are obtained. Humans can produce thousands of emotions in different situations which have different meanings, intensities and complexities. By using convolutional neural network (CNN) algorithm, an accuracy of about 89%has been achieved. It is the simplest way of all. For better results deep learning and neutral networks have been used. Our proposed deep learning model helps us in focusing important features in humans face to detect emotion using multiple datasets such as FER-2013 and image dataset.
Transformation Driven Visual Reasoning

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of state driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget’s theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction. The resource of TVR is available at https://hongxin2019.github.io/TVR.
Test Automation with Grad-CAM Heatmaps - A Future Pipe Segment in MLOps for Vision AI?

Machine Learning (ML) is a fundamental part of modern perception systems. In the last decade, the performance of computer vision using trained deep neural networks has outperformed previous approaches based on careful feature engineering. However, the opaqueness of large ML models is a substantial impediment for critical applications such as in the automotive context. As a remedy, Gradient-weighted Class Activation Mapping (Grad-CAM) has been proposed to provide visual explanations of model internals. In this paper, we demonstrate how Grad-CAM heatmaps can be used to increase the explainability of an image recognition model trained for a pedestrian underpass. We argue how the heatmaps support compliance to the EU's seven key requirements for Trustworthy AI. Finally, we propose adding automated heatmap analysis as a pipe segment in an MLOps pipeline. We believe that such a building block can be used to automatically detect if a trained ML-model is activated based on invalid pixels in test images, suggesting biased models.
Morse glasses: an IoT communication system based on Morse code for users with speech impairments

The advent of internet of things has opened the opportunities for people with disabilities, increased their inclusion and productivity in their living society. Most of the invented smart sensing devices including the wearable ones for users with speech impairments are expensive and not affordable for patients in the low income countries such as Egypt. Morse Glasses is a cost efficient wearable device based on IoT technology and a modified Morse code that tracks the patient’s eyes blinks and translates it into a generated speech. A sequence of Morse encoded alphabets/sentences along with the frequently used ones is displayed and heard on any android supported device that is installed Morse Glasses mobile application. With cost less than 30$, patients with motor neuron diseases such as Amyotrophic Lateral Sclerosis (ALS) can communicate easily with the others, express their needs and simply live their life normally.
Stochastic Tree Ensembles for Regularized Nonlinear Regression

This article develops a novel stochastic tree ensemble method for nonlinear regression, referred to as accelerated Bayesian additive regression trees, or XBART. By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques from recursive partitioning algorithms, XBART attains state-of-the-art performance at prediction and function estimation. Simulation studies demonstrate that XBART provides accurate point-wise estimates of the mean function and does so faster than popular alternatives, such as BART, XGBoost, and neural networks (using Keras) on a variety of test functions. Additionally, it is demonstrated that using XBART to initialize the standard BART MCMC algorithm considerably improves credible interval coverage and reduces total run-time. Finally, two basic theoretical results are established: the single tree version of the model is asymptotically consistent and the Markov chain produced by the ensemble version of the algorithm has a unique stationary distribution.
CSBR: A Compositional Semantics-Based Service Bundle Recommendation Approach for Mashup Development

An increasing number of services are being offered which leads to difficulties in choosing appropriate services during mashup development. Currently, several service recommendation techniques have been developed for mashup creation, however, they are largely limited to suggesting services which have similar functionalities. The fundamental problem with these techniques is that they do not consider the large semantic gap between mashup descriptions and service descriptions. In this article, we propose a compositional semantics-based service bundle recommendation model (CSBR) to tackle this problem. CSBR is based on a semantic service package repository, which is constructed by mining the existing mashups. Specifically, the reusable service packages, which consist of multiple collaborative services, are annotated with composite semantics rather than their original semantics. Based on the semantic service package repository, CSBR can recommend a bundle of services that cover the functional requirements of the mashup as completely as possible. Extensive experiments are conducted on a real-world dataset and the results show CSBR achieves significant performance improvements in both precision and recall metrics over the state-of-the-art methods.
KnowGen: A Knowledge Generation Approach for Tag Recommendation Using Ontology and Honey Bee Algorithm

Diversity has recently been recognized as one of the main contributors in enhancing the efficiency of the recommendation system regarding user satisfaction. In Social Tagging-based systems, tag suggestion is used to suggest tags to an user for a resource. So far, diversity in tag recommendations has been disregarded in conventional tag recommendation methodologies. The proposed KnowGen model uses Synonymization with Word Embeddings and Ontology with data driven Knowledge Exploration and semantically aware by Honey Bee Algorithm. Hybridizing all these helps in yielding better accuracy. To assimilate users preferences for the recommendations, the MIcrosoft News Dataset (MIND) dataset is used. Efficiency of the model is estimated using Normalized Discounted Cumulative Gain (NDCG). The proposed model has achieved an Accuracy of 94.17% and tends to perform better than the other baseline models by attenuating the weakness of the common tag recommender systems.
Improved Lightcnn with Attention Modules for Asv Spoofing Detection

With the advent of many state-of-the-art speech synthesis or conversion techniques, speaker recognition system is confronted with the imperceptible interference caused by spoofed speech. In this paper, we realize that filter bank distributions of cepstral features in the frequency domain cause considerable influence to the system performance through experiments. Therefore, given attention mechanism may help to focus on key information, this paper presents improved light convolutional neural network (LCNN) with attention modules, separately named Squeeze-and-Excitation (SE) block, Convolutional Block Attention Module (CBAM) and Dual Attention Network (DANet). To our knowledge, we are the first to do systematic study of attention mechanisms in the field of speech anti-spoofing. Experimented on ASVspoof 2019 dataset, our proposed single system can get 22% min-tDCF reduction as well as 42% EER reduction over LCNN baseline and its performance can even rank fourth among all participating teams using fusion systems in ASVspoof 2019 competition.
Feature Detection and Description based on ORB Algorithm for FPGA-based Image Processing

Feature detection and description is a key concept of image processing and computer vision. Oriented FAST and Rotated BRIEF (ORB) algorithm is one of feature detector and descriptor that is widely used in image stitching and registration systems such as simultaneous localization and mapping (SLAM) systems. ORB algorithm has improved the speed of processing and robustness. It has scale-invariance and rotational invariance properties. However, there are some obstacles to implement on low-power devices such as its high computational intensity. This paper presents the feature detection and description algorithm based on ORB algorithm which adopted parallel and pipeline techniques to accelerate the system and be beneficial for designing FPGA architecture. Compared with the results obtained by cv.ORB() function, the proposed algorithm improves the parallelism and reduces the operation of the process but could maintain the consistency of the corner numbers and the accuracy.
A projection multi-objective SVM method for multi-class classification

Support Vector Machines (SVMs), originally proposed for classifications of two classes, have become a very popular technique in the machine learning field. For multi-class classifications, various single-objective models and multi-objective ones have been proposed. However,in most single-objective models, neither the different costs of different misclassifications nor the users' preferences were considered. This drawback has been taken into account in multi-objective models.In these models, large and hard second-order cone programs(SOCPs) were constructed ane weakly Pareto-optimal solutions were offered. In this paper, we propose a Projected Multi-objective SVM (PM), which is a multi-objective technique that works in a higher dimensional space than the object space. For PM, we can characterize the associated Pareto-optimal solutions. Additionally, it significantly alleviates the computational bottlenecks for classifications with large numbers of classes. From our experimental results, we can see PM outperforms the single-objective multi-class SVMs (based on an all-together method, one-against-all method and one-against-one method) and other multi-objective SVMs. Compared to the single-objective multi-class SVMs, PM provides a wider set of options designed for different misclassifications, without sacrificing training time. Compared to other multi-objective methods, PM promises the out-of-sample quality of the approximation of the Pareto frontier, with a considerable reduction of the computational burden.
A Sentiment Classification Method of Web Social Media Based on Multidimensional and Multilevel Modeling

Sentiment classification of web social media faces the problem of text context semantics missing. The existing research mainly solves the problem of text context semantic missing by mining language symbol information in web social media text, seldom considering the emoticon symbols and punctuation symbols in web social media text. Similar to language symbols, emoticons’ symbols and punctuation symbols in web social media text also contain certain sentiment information. In order to make full use of sentiment information contained in web social media to solve the problem of text context semantics missing, we propose a sentiment classification method of web social media based on multidimensional and multilevel modeling. By modeling web social media text from three dimensions (language symbols, emoticons’ symbols, and punctuation symbols) and three levels (words, sentences, and documents) based on a deep learning framework, in this article, we attempt to solve text context semantics missing faced by the sentiment classification of web social media and improve the accuracy of sentiment classification of web social media. The experimental results on Sina Weibo and Twitter datasets show that the average accuracy of our method is 0.9479, which achieves more than 5.86% performance compared with the existing sentiment classification methods.
Fraud Detection in Online Product Review Systems via Heterogeneous Graph Transformer

In online product review systems, users are allowed to submit reviews about their purchased items or services. However, fake reviews posted by fraudulent users often mislead consumers and bring losses to enterprises. Traditional fraud detection algorithm mainly utilizes rule-based methods, which is insufficient for the rich user interactions and graph-structured data. In recent years, graph-based methods have been proposed to handle this situation, but few prior works have noticed the camouflage fraudster’s behavior and inconsistency heterogeneous nature. Existing methods have either not addressed these two problems or only partially, which results in poor performance. Alternatively, we propose a new model named Fraud Aware Heterogeneous Graph Transformer (FAHGT), to address camouflages and inconsistency problems in a unified manner. FAHGT adopts a type-aware feature mapping mechanism to handle heterogeneous graph data, then implementing various relation scoring methods to alleviate inconsistency and discover camouflage. Finally, the neighbors’ features are aggregated together to build an informative representation. FAHGT shows a remarkable performance gain compared to several baselines on different datasets.