Reviewerly/exHarmony huggingface dataset:
On Learning Interpreted Languages with Recurrent Models

Abstract Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified data sets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalize to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.
Trusted Compliance Enforcement Framework for Sharing Health Big Data

COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Compliance Enforcement Framework for sharing large velocity Health datasets. This framework, built using Semantic Web technologies, defines a Trust Score for each participant in the data exchange process and includes ontologies combined with policy reasoners that ensure data access complies with health regulations, like Health Insurance Portability and Accountability Act (HIPAA). We have validated our framework by applying it to the Centers for Disease Control and Prevention (CDC) Contact Tracing Use case by exchanging over 1 million synthetic contact tracing records. This paper presents our framework in detail, along with the validation results against Contact Tracing data exchange. This framework can be used by all entities who need to exchange high velocity-sensitive data while ensuring real-time compliance with data regulations.
Large-Scale Mean-Variance Optimization and Chunking Algorithm

We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization problems --- with weight upper bound constraints and without short sales --- when the size of mean-variance portfolios is much smaller than the number of assets, which is almost always the case. Our novel algorithm is built on the novel representation of mean-variance models in terms of the support vector data description --- an unsupervised machine learning algorithm designed for a one-class classification problem --- and the chunking algorithm, a decompositionalgorithm for support vector machine.
Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>α</mi> </math> is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy.
Overview Of Tensor Layout In Modern Neural Network Accelerator

Modern neural network accelerator is widely used to accelerate the training or inference in neural network related works, such as CNN, RNN, NLP, etc. The tensor is the fundamental unit to store the neural network's data. In the paper, we profile two prevalent tensor layout NHWC and N(C/x)HWx to see different layout's influence to L1/L2 cache. We design a profiling framework which includes block splitting engine, tensor data loading engine and memory with hierarchy structure. Experimental results can show the different L1/L2 cache configurations' influence for cache hit/miss count, load/store count and evict count, etc., which can guide the basic hardware design for L1/L2 cache in modern neural network accelerator.
Spatial-Temporal Attention Network with Multi-similarity Loss for Fine-Grained Skeleton-Based Action Recognition

In skeleton-based action recognition, the Graph Convolutional Networks (GCNs) have achieved remarkable results. However, in fine-grained skeleton-based action recognition, existing methods can't distinguish highly similar sample pairs well. This is because that the current methods do not pay attention to highly similar sample pairs and can't model complex actions through the spatial-temporal separation framework. In order to solve the above problems, we combine the multi-similarity loss function to weight the difficult sample pairs, and propose a novel ST-Attention module to construct the nodes connection between spatial and temporal. Finally, we used the Spatial-Temporal Attention Network with Multi-Similarity Loss (STATT-MS) to conduct experiments on NTU-RGBD-60, FSD-10 and UAV-human datasets and achieved state-of-the-art performance.
Private Distributed K-Means Clustering on Interval Data

K-means clustering has been heavily employed to mine valuable insights from interval data. Nevertheless, serious privacy leakage concerns are stumbling blocks impeding its widespread application. To quantify the privacy of small and large-scale interval data, we introduce two notions of &#x03B1;-Condensed Local Differential Privacy and &#x03F5;-Local Differential Privacy, and propose two distance-aware perturbation mechanisms of &#x03B1;-exponential and square wave mechanisms. Rigorous theoretical analysis proves that our proposed mechanisms satisfy these two privacy notions. The experimental results built on multiple synthesized and real datasets show that our proposed mechanisms can provide more accurate clustering results than prior work, such as Randomized Response, Generalized Randomized Response, and Optimized Local Hash.
Role of Statistical Methods in Pedagogical Research

The increasing role of statistical methods in social sciеnces and particularly in pedagogy cannot be argued. Especially in quantitative research, these methods provide a high degree of objectiveness and precision that cannot be achieved with other methods or procedures. That is why educationalists have to acquire deeper knowledge of the specifics of statistical methods and the features of their application. In the analysis of these aspects of pedagogical research in this article, the most commonly used statistical methods in them are outlined. For this purpose, the author has explored 96 doctoral dissertations with regard to the application of different statistical methods for data processing.
Mining High Utility Sequences with a Novel Utility Function

Mining high utility sequential patterns (HUSP) is a popular data mining task. The goal is to find all subsequences that yield a high utility (e.g. high profit) in a quantitative sequence database (QSDB). Traditional algorithms for this task have many uses but a major limitation is that they rely on the maximum or minimum utility measures for calculating the utility of a pattern, thus assuming either a best or worst case scenario. These measures are unsuitable for many real-life applications such as business decision-making. To address this issue, this paper introduces a novel utility function (NUF) to calculate the utility of a sequence in each input sequence, which provides a trade-off between the above two extreme cases. A novel upper bound on NUF is designed as well as search space pruning strategies to eliminate unpromising candidate patterns early. These contributions are integrated into a novel efficient algorithm named FHNewUSM to discover frequent HUSPs with NUF. An experimental study with both real-life and synthetic databases shows that the proposed algorithm is efficient for mining HUSPs with NUF in terms of execution time, memory consumption and scalability.
Web Service Clustering Technique based on Contextual Word Embedding for Service Representation

Due to extensive use of Internet and IoT, the demand Web services and APIs are increasing day by day and there is a proliferation of services on internet in terms of quality and quantity both. It raises the need of service management. Web service clustering plays a vital role in service management as it reduces the search space and time. Word2vec word embedding is highly demanded in these days as it can capture the semantic similarity but it does not bother the context and it effects the clustering performance. In this paper, Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) embedding is used in the vector space representation of services so that with the semantic meaning, context of the features can be also analyzed and services can be efficiently mapped in vector space. To analyze the performance of embedding, K-Means clustering is applied and results are compared with the different state-of-art techniques based on standard evaluation measures. The experimental results shows that accuracy of the proposed model is increased by approximately 49% in comparison of a model in which word2vec model is utilized.
Facial Features Extraction for Age Estimation Based on a Neural Network

Abstract The determination of a person’s age using computer-based programming relies on biometric features such as facial features and fingerprints. These technologies, which require the intense study of age progression mechanisms in the human face and the factors that influence face aging patterns, have increasingly attracted attention due to their various real-world applications. In this paper, a feature selection method (FSM) has been adopted to identify the most suitable local binary pattern (LBP) features for age estimation. The selected features were then classified using a back-propagation neural network (BPNN). Extracted features were classified using a MATLAB-based artificial neural network (ANN), in which a back-propagation network (BPN) was used. The proposed BPN network contains four layers of neurons: an input layer, two hidden layers, and an output layer. Each layer contains nine neurons in addition to the bias. Experiments in this work were conducted using three types of image datasets: standard FG-NET, privately-collected, and real-time captured image dataset. Experimental results demonstrate the significant role of using a back-propagation neural network (BPNN) with FSM, which produced an estimation accuracy of up to 93.22% after two rounds, while without FMS, estimation accuracy reached 82.966% at most, depending on the original LBP features. However, the accuracy using the images captured by the connected camera reached no more than 71%.
A Recommender System Based on Model Regularization Wasserstein Generative Adversarial Network

A recommender system (RS) commonly adopts a High-dimensional and sparse (HiDS) matrix to describe user-item preferences. Collaborative Filtering (CF)-based models have been widely adopted to address such an HiDS matrix. However, a CF-based model is unable to learn the property distribution characteristic of user’s preference from an HiDS matrix, thereby its representation ability is limited. To address this issue, this paper proposes a Model Regularization Wasserstein GAN(MRWGAN) to extract the distribution of user’s preferences. Its main ideas are two-fold: a) adopting an auto-encoder to implement the generator model of GAN; b) proposing a model-regularized Wasserstein distance as an objective function to training a GAN model. Empirical studies on four HiDS matrices from industrial applications demonstrate that compared with state-of-the-art models, the proposed model achieves higher prediction accuracy for missing data of an HiDS matrix.
Mirror Distillation Model with Focal Loss for Chinese Machine Reading Comprehension

Machine comprehension of text is a critical problem in natural language processing (NLP). However, there are severe data imbalance problems in extractive machine reading comprehension (MRC) tasks: unbalanced data will lead to differences between training and testing. Without a balanced label, the learning process tends to meet to a point, strongly biasing towards the class and the majority of labels. The most used cross-entropy criteria are accuracy-oriented, mainly focusing on the majority class to achieve high accuracy. Another problem is that the training of the model only introduces correct knowledge, which is not accurate enough for answer prediction. To address these issues, we propose to use focal loss in replacement of the standard cross-entropy objective for data imbalanced MRC tasks, which uses weight coefficients to adjust the weight of positive and negative samples and distinguish between easy and difficult samples. We propose using the same model to perform mirror distillation so that the teacher model can provide the correct and wrong knowledge for the student model to learn, obtain better model generalization ability, and improve the accuracy of answer prediction. Our results show that our mirror distillation method significantly improves the performance of the model. Moreover, our results also prove that the focal loss effectively solves the problem of data imbalance in the task.
Predicting the impact of online news articles – is information necessary?

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features - ranging from the semantic relations through readability measures to the article's digital content - are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.
Robust DCF object tracking with adaptive spatial and temporal regularization based on target appearance variation

To alleviate the boundary effect and constrain the update of the tracking model, current object tracking methods based on Discriminative Correlation Filter (DCF) usually introduce spatial and temporal regularization constraints in the filter training objective function. However, these regularization constraints with fixed coefficients greatly limit the adaptability of the tracker with respect to target appearance variation. This paper proposes a spatial-temporal regularization model based on the real-time target appearance variation for the filter training, improving the adaptability of the filter related to target appearance variation. Moreover, the filter training objective function with the adaptive spatial-temporal regularization is proposed to enhance the robustness of the filter. Finally, an iterative optimization method based on the alternating direction method of multipliers (ADMM) is proposed to update the filter, and the convergence proof of the optimization method is also presented. Comparison experiments with some representative trackers including ASRCF,ARCF, CSR_CF, DSAR_CF and SSR_CF etc. on OTB2015, UAV123 and LaSOT databases show that the proposed algorithm effectively improves the tracking accuracy.
Design and Application of Pre-school Music Teaching System in Moodle Platform

As an indispensable medium between teaching theory and teaching practice, teaching mode can not only effectively guide teaching activities in theory, but also formulate effective operation process and Implementation Countermeasures for specific teaching practice. Music curriculum model based on cloud computing and data mining technology is a new type of music teaching mode, which is closely around the technical guideline of “learning with technology”, based on the education cloud architecture integrated by multimedia music curriculum resources, and reasonably using data mining technology, integrating information technology into interactive music teaching, It not only helps students to better understand the teaching content and grasp the internal laws of the knowledge learned, but also effectively solves the shortcomings of the traditional teaching mode which is based on the teacher's teaching, playing demonstration and students’ imitation. In this paper, the effective use of database, education cloud and other information means, in-depth research based on cloud computing and data mining technology music curriculum model, in order to provide schools, teachers and students with better information services.
Designing and Searching for Lightweight Monocular Depth Network

Depth sensing is extremely notable in some tasks of robot and autonomous driving. Nowadays, monocular depth estimation based on deep learning becomes a research focus in computer vision. However, most of the current work is seeking for more complex models to get higher accuracy, which can not achieve real-time inference on mobile or embedded systems. Therefore, we aim to design a lightweight model in this paper. At first, we improve the state-of-the-art model, FastDepth, producing FastDepthV2, which has higher accuracy and lower latency on the NYU Depth v2 dataset. Besides, since designing artificial networks takes time and effort, we make it automatic to design lightweight models in monocular depth estimation, using neural architecture search (NAS). Further, inspired by the architecture of MobileNetV2, a factorized hierarchical search space for encoder is used in this paper. In the meanwhile, we incorporate the accuracy and multiply-add operations of a model together into the searching objective, and use gradient-based reinforcement learning algorithm in the searching iterations. The controller in the reinforcement learning framework has converged after more than 1000 searching iterations, and three network architectures with the best performance are obtained. Under the same conditions of training and testing, two of them perform better than FastDepthV2.
WoKnack – A Professional Social Media Platform for Women Using Machine Learning Approach

Today’s generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system.
Real-Time Sign Language Converter for Mute and Deaf People

Deaf people may get irritated due to the problem of not being able to share their views with common people, which may affect their day-to-day life. This is the main reason to develop such system that can help these people and they can also put their thoughts forward similar to other people who don’t have such problem. The advancement in the Artificial intelligence provides the door for developing the system that overcome this difficulty. So this project aims on developing a system which will be able to convert the speech to text for the deaf person, and also sometimes the person might not be able to understand just by text, so the speech will also get converted to the universal sign language. Similarly, for the mute people the sign language which they are using will get converted to speech. We will take help of various ML and AI concepts along with NLP to develop the accurate model. Convolutional neural networks (CNN) will be used for prediction as it is efficient in predicting image input, also as lip movements are fast and continuous so it is hard to capture so along with CNN, the use of attention-based long short-term memory (LSTM) will prove to be efficient. Data Augmentation methods will be used for getting the better results. TensorFlow and Keras are the python libraries that will be used to convert the speech to text. Currently there are many software available but all requires the network connectivity for it to work, while this device will work without the requirement of internet.Using the proposed model we got the accuracy of 100% in predicting sign language and 96% accuracy in sentence level understanding.
Enhancing Database Security for Facial Recognition using Fernet Encryption Approach

With the evolving technologies in Artificial Intelli-gence (AI), Cloud Service, IoT and many other domains, activities and work have become easier for humans. The video surveillance system can also be enhanced with the help of these three main domains. In the proposed system, the database containing all the known face image file data with their corresponding name has been created on cloud platforms. The data sent to the cloud database has also been secured by encrypting it using Fernet cryptography technique. This ensures that the data on the cloud services is secured as the key required for decrypting the database data is present on the client side only. The known faces that are stored on the cloud service platform saves system space and make the database remotely accessible. It recognizes each face in the video file and matches them with the cloud database image files and helps to distinguish the known and unknown faces in a video file. The proposed work can be used to extract the key region of interest from a video file by the time stamp that are collected along with the matching known faces from the video. Also, the approach can be applied in places where facial biometric scan is used, and in security and surveillance system at various places like shopping mall, markets with a little modification. The identified faces from the video are highlighted by a blue box with the corresponding name at the top of the box and a notification is also sent to the client device with timestamp.