Reviewerly/exHarmony huggingface dataset:
Fingerspelling Identification for Chinese Sign Language via Wavelet Entropy and Kernel Support Vector Machine

Sign language recognition is beneficial to help the hearing-impaired and the healthy communicate effectively and help hearing-impaired people integrate into society, making their study, work, and life more convenient, especially in speech therapy and rehabilitation. Fingerspelling identification plays an important role in sign language recognition, which has unique advantages in expressing abstract content, terminology, and specific words, and can also be utilized as the basis of learning gesture recognition based on Pinyin rules. We proposed a WE-kSVM approach, carrying out on 10-fold cross-validation, and achieved an overall accuracy of 88.76 ± 0.59%. Maximum accuracy is 89.40% based on thirty categories. Here, Wavelet entropy technique can reduce the number of features and accelerate the training. Gaussian kernel (RBF) provided excellent classification performance. Meanwhile, 10-fold cross-validation prevented overfitting effectively. The experiment results indicate that our method is superior to the other five state-of-the-art approaches.
Study on Face Recognition Techniques

An accurate and efficient face recognition system is a more interesting topic in most industries and research areas. It is a type of biometric information process that is easily adaptable as compared to the tradition card recognition system. Generally, a face recognition system is preceded by a face detection technique. The face detection technique is the preliminary stage to detect a face in live images. In this paper, some face detection techniques are discussed such as finding skin likelihood image, skin segmentation, the morphological operation for extracting boundary regions, Haar-like features, and Ada-boost algorithm. This Haar-like feature algorithm continually searches its pattern from the particular face and which has better advantages over other techniques. After the face detection technique, the face recognition technology is applied on the detected face for further identification by using some classifiers.
Aspect based Fuzzy Logic Sentiment Analysis on Social Media Big Data

Social Media is good information medium for disclosing information of products and services of E-Commerce Business. This information is provided by customers themselves on their purchased product. Aspect based sentiment analyzes of feature/specification quoted by the customers. Such specifications/reviews are available in Twitter, Flipkart websites. This research work considered reviews of features/specifications on Twitter and Flipkart websites. Hence this work focused on analyzing the problems of customer on buying quality products. This work is automated to extracts the semantic based aspects or features and their opinion for the task of analyzing comments. Fuzzy logic and NLP are employed in this work with consider all categories of comments to measure their given weightage by customer tweets. For experimentation real time Twitter data and Flipkart data has been used. Experimental result and performance analysis report proved that aspect based sentiment analysis on social data on this work is performing well. In this work hidden information about the product specification has been retrieved and produced concise information to customers perceives about the product specification and eases the customers to take decision on the purchase quality product. And also assist the business organization to take decision on the product specification and improve their business according to their customer's opinion and expectation.
A Blockchain Based Distributed Storage System for Knowledge Graph Security

With the rapid rise of artificial intelligence, knowledge graph has also been widely concerned, and the security of knowledge graph has also followed. In the process of knowledge graph construction, process files are easy to be tampered and forged by malicious, so that the reasoning results are biased. The purpose of this experiment is to explore the availability and effectiveness of knowledge graph based on blockchain and distributed system storage and traceability. This paper proposes a scheme of process file storage and traceability of knowledge graph based on blockchain and distributed file storage system. Firstly, the process files of building knowledge graph are preprocessed by distributed file system to improve the storage efficiency and save resources; then the processed files are stored in the blockchain network to ensure the security and integrity of the files.
Dropout regularization in hierarchical mixture of experts

Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts is a hierarchically gated model that defines a soft decision tree where leaves correspond to experts and decision nodes correspond to gating models that softly choose between its children, and as such, the model defines a soft hierarchical partitioning of the input space. In this work, we propose a variant of dropout for hierarchical mixture of experts that is faithful to the tree hierarchy defined by the model, as opposed to having a flat, unitwise independent application of dropout as one has with multi-layer perceptrons. We show that on a synthetic regression data and on MNIST, CIFAR-10, and SSTB datasets, our proposed dropout mechanism prevents overfitting on trees with many levels improving generalization and providing smoother fits.
Multi-Objective Optimization Algorithm and Preference Multi-Objective Decision-Making Based on Artificial Intelligence Biological Immune System

The operating mechanism of the biological immune system is often used for the development of intelligent technology.This research introduces the multi-functional optimization algorithm of the biological immune system into the VR image segmentation, and proposes a multi-purpose VR image segmentation method with more stable and better segmentation performance.In order to combine it with the characteristics of the VR image itself, a complementary feature extraction method combining filters and gray-level symbiosis probability is used.In addition, in order to enable the algorithm to solve the segmentation problem of huge pixel images, the best solution is found through communication and exchange between subgroups.Use the excellent genes of the memory genes as the imported genes and introduce the inferior individuals to strengthen the mining of the best solution of Pareto at the boundary of the impossible field.In order to verify the performance of the algorithm, 3 synthetic texture images and 2 actual VR images are used, 8 constrained targets and 4 unconstrained target benchmark functions are selected to test the optimization function of PCMIOA.Multipoint parallel search uses two different search schemes, local and global.In this way, the domain value of the highest value can be searched globally, and the local best solution can be searched at the same time, realizing the global search mechanism.The relatively satisfactory target value is 98.25, and the deviation between the corresponding solution and the ideal solution is 0.093.The results of the research show that Multi -objective optimization algorithm is an excellent demonstration of the diversity of Pareto-oriented methods and solutions.Compared with the previous prediction methods, this method has higher prediction accuracy and robustness.The choice of decision makers can be taken into consideration, and subjective willfulness can be reduced to make decision results more realistic and reliable.
Development of Robust Automatic Speech Recognition System for Children's using Kaldi Toolkit

In this paper, the Punjabi children speech recognition system is developed using Subspace Gaussian mixture models (SGMM) acoustic modeling techniques. Initially, the system is dependent upon Mel-frequency cepstral coefficients (MFCC) approach for controlling the temporal variations in the input speech signals. Here, SGMM is integrated with HMM to measure the efficiency of each state which carries the information of a short-windowed frame. For handling the children speaker acoustic variations speaker adaptive training (SAT), based on vocal-tract length normalization and feature space maximum likelihood linear regression is adopted. Kaldi and open-source speech recognition toolkit is used to develop the Robust Automatic Speech Recognition (ASR) System for Punjabi Children's speech. S GMM accumulate the frame coefficients and their posterior probabilities and pass these probabilities to HMM which systematically fit the frame and output have resulted from HMM states. Therefore, the achievement of SGMM has gotten a large performance margin in Punjabi children speech recognition. A remarkable depletion in the word error rate (WER) was noticed using SGMM by varying the feature dimensions. The developed children ASR system obtained a recognition accuracy of 83.66% while tested by varying the feature dimensions to 12.
An Abstract Semantics of Speculative Execution for Reasoning About Security Vulnerabilities

Reasoning about correctness and security of software is increasingly difficult due to the complexity of modern microarchitectural features such as out-of-order execution. A class of security vulnerabilities termed Spectre that exploits side effects of speculative, out-of-order execution was announced in 2018 and has since drawn much attention. In this paper we formalise speculative execution and its side effects as an extension of a framework for reasoning about out-of-order execution in weak memory models. Our goal is to allow speculation to be reasoned about abstractly at the software level. To this end we encode speculative execution explicitly using a novel language construct and modify the definition of conditional statements correspondingly. Underlying this extension is a model that has sufficient detail to enable specification of the relevant microarchitectural features. We add an abstract cache to the global state of the system, and derive some general refinement rules that expose cache side effects due to speculative loads. The rules are encoded in a simulation tool, which we use to analyse an abstract specification of a Spectre attack and vulnerable code fragments.
Cryptanalysis of Masked Ciphers: A Not So Random Idea

A new approach to the security analysis of hardware-oriented masked ciphers against second-order side-channel attacks is developed. By relying on techniques from symmetric-key cryptanalysis, concrete security bounds are obtained in a variant of the probing model that allows the adversary to make only a bounded, but possibly very large, number of measurements. Specifically, it is formally shown how a bounded-query variant of robust probing security can be reduced to the linear cryptanalysis of masked ciphers. As a result, the compositional issues of higher-order threshold implementations can be overcome without relying on fresh randomness. From a practical point of view, the aforementioned approach makes it possible to transfer many of the desirable properties of first-order threshold implementations, such as their low randomness usage, to the second-order setting. For example, a straightforward application to the block cipher LED results in a masking using less than 700 random bits including the initial sharing. In addition, the cryptanalytic approach introduced in this paper provides additional insight into the design of masked ciphers and allows for a quantifiable trade-off between security and performance.
Interpreting Variational Autoencoders with Fuzzy Logic: A step towards interpretable deep learning based fuzzy classifiers

The emerging success of Deep Learning (DL) in various application areas comes also with the questions starting with "How"s and "Why"s. These questions can be answered if the DL methods are interpretable and thus provide a certain a degree of explanation. In this paper, we propose a DL framework that leverages the advantages of β-Variational Autoencoder (VAE) and Fuzzy Sets (FSs), which are disentanglement and linguistic representation, for the design of a novel DL based Fuzzy Classifier (FC). We first present a step-by-step design approach to construct the DL-FC which is composed of the encoder layer of β-VAE and a Fuzzy Logic System (FLS) followed by a softmax layer. The β-VAE is trained so that the semantic information of the high dimensional data is captured. The latent space of the β-VAE is clustered to extract FSs. The FSs are then used to define antecedents of the FLS that is trained with DL methods. We present results conducted on the MNIST dataset and showed that DL-FC is quite competitive with its deep neural network counterpart. We then try to provide an interpretation to the antecedents of FLS by examining the FSs, the latent traversals and heat-maps of each latent dimension. The results show that the antecedents of FLS can be defined with linguistic interpretations. Thus, for the first time in the literature, we showed that linguistic interpretations can be defined for the latent space of β-VAE with FSs.
A workload-adaptive mechanism for linear queries under local differential privacy

We propose a new mechanism to accurately answer a user-provided set of linear counting queries under local differential privacy (LDP). Given a set of linear counting queries (the workload) our mechanism automatically adapts to provide accuracy on the workload queries. We define a parametric class of mechanisms that produce unbiased estimates of the workload, and formulate a constrained optimization problem to select a mechanism from this class that minimizes expected total squared error. We solve this optimization problem numerically using projected gradient descent and provide an efficient implementation that scales to large workloads. We demonstrate the effectiveness of our optimization-based approach in a wide variety of settings, showing that it outperforms many competitors, even outperforming existing mechanisms on the workloads for which they were intended.
What Do We Know About Time Pressure in Software Development?

Time pressure means that time experienced by an individual is scarce in relation to the task demands at hand. In this article, we summarize findings and provide practitioner takeaways based on a systematic review of existing literature.
Hierarchical Memory Decoder for Visual Narrating

Visual narrating focuses on generating semantic descriptions to summarize visual content of images or videos, e.g., visual captioning and visual storytelling. The challenge mainly lies in how to design a decoder to generate accurate descriptions matching visual content. Recent advances often employ a recurrent neural network (RNN), e.g., Long-Short Term Memory (LSTM), as the decoder. However, RNN is prone to diluting long-term information, which weakens its performance of capturing long-term dependencies. Recent work has demonstrated memory network (MemNet) owns the advantage of storing long-term information. However, as the decoder, it has not been well exploited for visual narrating. The reason partially comes from the difficulty of multi-modal sequential decoding with MemNet. In this article, we devise a novel memory decoder for visual narrating. Concretely, to obtain a better multi-modal representation, we first design a new multi-modal fusion method to fully merge visual and lexical information. Then, based on the fusion result, during decoding, we construct a MemNet-based decoder consisting of multiple memory layers. Particularly, in each layer, we employ a memory set to store previous decoding information and utilize an attention mechanism to adaptively select the information related to the current output. Meanwhile, we also employ a memory set to store the decoding output of each memory layer at the current time step and still utilize an attention mechanism to select the related information. Thus, this decoder alleviates dilution of long-term information. Meanwhile, the hierarchical architecture leverages the latent information of each layer, which is helpful for generating accurate descriptions. Experimental results on two tasks of visual narrating, i.e., video captioning and visual storytelling, show that our decoder could obtain superior results and outperform the performance of conventional RNN-based decoder.
Classification of Indian Classical Dance Images using Convolution Neural Network

Indian classical dance is the combination of gesture of all the body parts. It has varied forms and is generally a combination of single hand mudra, double hand mudra, leg alignment, hip movement, eye movement, facial expression, and leg posture. Each dance form has unique gesture, using which, they can be classified. The costumes worn by dancers are also unique. This work proposes the identification and classification of Indian Classical Dance images using Deep Learning Convolution Neural Network (CNN). This work uses the dataset consisting of five dance classes namely Bharatanatyam, Odissi, Kathak, Kathakali, Yakshagana, the images of which are collected from the internet using Google Crawler. This system can be used for automated dance quizzes and can be used by anyone to find out how well he/she is familiar with the variety of dance forms in India given its varied postures and styles.
Research on an Improved KCF Target Tracking Algorithm Based on CNN Feature Extraction

Target tracking is one of the most concerned computer problems, but it is also challenging with few training samples, fast moving objects and some other issues. The kernelized correlation filter (KCF) algorithm proposed by the team of Joao F. Henriques had applied to address this problem for tracking successfully. The method has expanded the number of negative samples to enhance the performance of the tracker and used the fast Fourier transform to accelerate the calculation of the algorithm. However, the features used by the KCF have limited ability to express the target with complex background. We propose improved KCF algorithm for tracking. The pre-trained deep convolutional neural network (CNN) is introduced in extracting the layer information respectively to describe the spatial and semantic features of the target. Experiments are performed on OTB-2015 benchmark datasets, and the results show that in comparison with the existing tracking algorithms, the proposed improved algorithm can deal with the challenges much better performance compared to original KCF and KCF-S method.
Kinect v2 tracked Body Joint Smoothing for Kinematic Analysis in Musculoskeletal Disorders

Body joint monitoring is essential for disorder diagnosis and assessment of treatment effectiveness. Microsoft Kinect v2 is a low-cost and markerless human motion-tracking RGB-D sensor that provides spatial locations of tracked skeletal joints in the form of 3D coordinates. Sometimes, body tracking of kinect v2 produces erratic 3D coordinates, which affects the real-time tracking performance of the sensor. A careful study of the literature suggests that skeletal tracking of kinect v2 needs further exploration. This work proposes a filter combined with the concept of body kinematics to remove noise and enhances the quality of 3D coordinates in body frame data. Also, it generates "Motion Signature" of the tracked joint, which shows movement pattern for kinematic analysis, and helpful in joint monitoring of Musculoskeletal Disorders (MSD). The clinically relevant anatomical movement was executed, to evaluate the performance of the proposed filter. We compared Range of Motion (RoM) values obtained from the proposed filter with the gold standard goniometry. Results indicate that RoM values from the proposed filter are in high correlation with the goniometry with an Intraclass Correlation Coefficient values ranging between 0.95 to 0.98 authenticating that it improves the skeletal joint tracking of kinect v2.
Mathematical Modeling of Non-Gaussian Dependent Random Variables by Nonlinear Regression Models Based on the Multivariate Normalizing Transformations

The technique to construct nonlinear regression models based on the multivariate normalizing transformations and prediction intervals is considered. We demonstrate that there may be data sets for which the results of constructing the nonlinear regression models depend on firstly, which normalizing transformation is applied, univariate, or multivariate, and, secondly, are there any outliers in the data set. The application of multivariate normalizing transformations allows us to take into account the correlation between variables in the nonlinear regression model. We use the prediction intervals of nonlinear regressions to detect the outliers in the process of building the nonlinear regression models. In general, this process is iterative since we repeat constructing the model for new data after the outlier cutoff. The example of constructing a three-factor nonlinear regression model to simulate the non-Gaussian dependent random variable depend on three predictors is given. There are six iterations in the process of constructing this nonlinear regression model. We performed comparing the constructed model to the linear regression one and nonlinear regression models that are built by the univariate normalizing transformations. This model has larger values of multiple coefficient of determination, and prediction percentage, smaller values of the magnitude of relative error, and widths of the confidence and prediction intervals of regression compared to other models, both linear and nonlinear ones. Suitable values of prediction accuracy metrics for the constructed model may be explained better multivariate normalization of the data set and the outlier cutoff, which are used to build one.
Detection and 3D Modeling of Brain Tumor using Machine learning and Conformal Geometric Algebra

Medical imaging or processing involves a wide range of computations on which the accuracy of analysis depends on the most. So to enhance the accuracy of analysis we need to reduce the computational geometric problems. Usually problems occur during the image segmentation or shape approximation or 3D modeling, and volumetric data registration. Con-formal geometric algebra is an effective paradigm to all this problems. The images we obtain after the MRI scan is 2D image from which the tumor has to be identified. This paper provides a key to identify the growth of the tumor in the regions inside the brain and to develop a 3D modeling of the tumor for the future reference which may help the doctor in the treatment of the tumor affected patient effectively. For the accurate prediction of whether a tumor is there in the brain related regions or not 2D image obtained must be first taken for the noise removal.
Albert, An Intermediate Smart-Contract Language for the Tezos Blockchain

Tezos is a smart-contract blockchain. Tezos smart contracts are written in a low-level stack-based language called Michelson. In this article we present Albert, an intermediate language for Tezos smart contracts which abstracts Michelson stacks as linearly typed records. We also describe its compiler to Michelson, written in Coq, that targets Mi-Cho-Coq, a formal specification of Michelson implemented in Coq.