Reviewerly/exHarmony huggingface dataset:
Implementation of a Human Activity Monitoring System through IoT Sensor and Blynk Cloud Platform

Human activity monitoring system plays a major role in the application of surveillance. It can be analyzed through cameras, sensors, and microphone. The traditional approach requires a human intervention for validating the human movement recorded by a surveillance camera and microphone. Therefore, the sensor based approaches are developed to make an alert signal through a buzzer or light, irrespective of the threshold value given to its output. But such sensor based technique also requires a human attention in the monitoring room. The motive of the proposed concept is to address such limitations by connecting the sensors with an Internet of Things (IoT) network and cloud platform for remote recording and monitoring purposes. The proposed work utilizes the Blynk IoT application and cloud server for the analytics.
Hybrid CNN-SVM Model for Face Mask Detector to Protect from COVID-19

The 2019 coronavirus outbreak (COVID-19) has had a huge impact on humanity. By May 2021, nearly, 172 million people worldwide were affected by the infectious spread of COVID-19. While the distribution of vaccines has already begun, mass distribution around the world has yet to take place. According to the World Health Organization (WHO), wearing a face mask can significantly reduce the spread of the COVID-19 virus. However, even improper wearing of face mask can prevent the purposes and lead to the spread of the virus. Under the influence of public health and the global economy, an effective Covid-19 pandemic strategy requires a lot of attention of humanity. To prevent the spread of such deadly virus, intelligent techniques are required. In the proposed work, an intelligent face mask detector framework is proposed based on deep learning concept which can classify the person who wear mask from those who are not wearing mask. In the proposed work, a hybrid model of convolution neural network with support vector machine is used for designing the mask detector. The performance of the proposed method is evaluated on real-world masked face recognition dataset (RMFD) and medical mask dataset (MDD). When implemented, it has been found that the proposed method can achieve high accuracy (99.11%). The excellent performance of the proposed model is very suitable for video surveillance equipment also.
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs

Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.Existing models treat knowledge selection as a sentence ranking or classification problem where each sentence is handled individually, ignoring the internal semantic connection among sentences in background document.In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.Our document semantic graphs preserve sentence-level information through the use of sentence nodes and provide concept connections between sentences.We apply multitask learning for sentence-level knowledge selection and concept-level knowledge selection jointly, and show that it improves sentencelevel selection.Our experiments show that our semantic graph based knowledge selection improves over sentence selection baselines for both the knowledge selection task and the end-to-end response generation task on HollE (Moghe et al., 2018) and improves generalization on unseen topics in WoW (Dinan et al., 2019). 1 * Work done as an intern at Amazon Alexa AI. 1 See https://www.amazon.science/publications/enhanced-knowledge-selection-for-g rounded-dialogues-via-document-semantic
A Four-Dimensional Space-Time Automatic Obstacle Avoidance Trajectory Planning Method for Multi-UAV Cooperative Formation Flight

Trajectory planning of multiple unmanned aerial vehicles (UAVs) is the basis for them to form the formation flight. By considering trajectory planning of multiple UAVs in formation flight in three-dimensional space, a trajectory planning method in four-dimensional space-time is proposed which, firstly, according to the formation configuration, adopts the Hungarian algorithm to optimize the formation task allocation. Based on that, by considering the flight safety of UAVs in formation, a hierarchical decomposition algorithm in four-dimensional space-time is innovatively put forward with spatial positions and time constraints both considered. It is applied to trajectory planning and automatic obstacle avoidance under the condition of no communication available between UAVs in the formation. The simulation results illustrated that the proposed method is effective in cooperative trajectory planning and automatic obstacle avoidance in advance for multiple UAVs. Meanwhile, it has been tested in a Swarm Unmanned Aerial System project and boasts quite significant value in engineering applications.
Bangla Handwritten Character Recognition Using Convolutional Neural Network

Recognizing handwritten characters is more challenging than recognizing printed characters. The size and shape of a handwritten character written by different people are not the same. The function of character recognition is complicated by the numerous variants in writing styles. Bangla handwritten character recognition had already been the focus of many researchers. Due to its specific characteristics of feature extraction and classification, the convolutional neural network (CNN) has recently shown notable progress in the fields of image-based recognition, video analytics, and natural language processing. As a result, this research provides a deep convolutional neural network (DCNN)-based mechanism for recognizing Bengali handwritten characters. In the field of pattern recognition, one of the most efficient ways to achieve higher accuracy or a lower error rate is to use a deep, optimized architecture that can process a large amount of data. Therefore, this paper has used DCNN for feature extraction and classification. This paper compares their accuracies by applying the DCNN model over three datasets: BanglaLekha-Isolated dataset, CMATERdb dataset, and our created dataset. By applying DCNN in researchers created dataset, accuracy has achieved up to 93.07%. This paper has also worked on simple CNN, DCNN, more advanced VGG-16 models for classification. And finally, the accuracies obtained from all the datasets have been compared.
Secure and Trusted Collaborative Learning Based on Blockchain for Artificial Intelligence of Things

Empowered by promising artificial intelligence, the traditional Internet of Things is evolving into the Artificial Intelligence of Things (AIoT), which is an important enabling technology for Industry 4.0. Collaborative learning is a key technology for AIoT to build machine learning (ML) models on distributed datasets. However, there are two critical concerns of collaborative learning for AIoT: privacy leakage of sensitive data and dishonest computation. Specifically, data contains sensitive information of users, which cannot be openly shared for model learning. Furthermore, to protect the privacy of data or other selfish purposes, participants of collaborative learning may behave dishonestly, submitting dummy data or incorrect model computation. Therefore, it is important to guarantee privacy preservation of data and honest computation on collaborative learning. Our work tackles the two concerns wherein a model demander can securely train ML models with sensitive data and can regulate the computation of participants. To this end, we propose a secure and trusted collaborative learning framework called TrusCL. The framework guarantees privacy preservation via a delicate combination of homomorphic encryption (HE) and differential privacy (DP), achieving the trade-off between efficiency and accuracy. Furthermore, based on blockchain, in our design, the key steps of secure collaborative learning are recorded on blockchain so that malicious behaviors can be effectively tracked and choked in a timely manner to facilitate trusted computation. Experimental results validate the trade-off performance of Trus-CL between model training efficiency and trained model accuracy.
Are Discrete Units Necessary for Spoken Language Modeling?

Recent work in spoken language modeling shows the possibility of learning a language unsupervisedly from raw audio without any text labels. The approach relies first on transforming the audio into a sequence of discrete units (or pseudo-text) and then training a language model directly on such pseudo-text. Is such a discrete bottleneck necessary, potentially introducing irreversible errors in the encoding of the speech signal, or could we learn a language model without discrete units at all? In this work, we study the role of discrete versus continuous representations in spoken language modeling. We show that discretization is indeed essential for good results in spoken language modeling. We show that discretization removes linguistically irrelevant information from the continuous features, helping to improve language modeling performances. On the basis of this study, we train a language model on the discrete units of the HuBERT features, reaching new state-of-the-art results in the lexical, syntactic and semantic metrics of the Zero Resource Speech Challenge 2021 (Track 1 - Speech Only).
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation

Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enjoy portions of overlaps in users and items, while the distribution of different scenarios is different. The key point of multi-scenario modeling is to efficiently maximize the use of whole-scenario information and granularly generate adaptive representations both for users and items among multiple scenarios. we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios. (2) Insufficient exploitation of entire space samples. (3) Item's multi-scenario representation disentanglement problem. In this paper, we propose a Scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. Specifically, we design a Multi-Layer Scenario Adaptive Transfer (ML-SAT) module with scenario-adaptive gate units to select and fuse effective transfer information from whole scenario to individual scenario in a quite fine-grained and decoupled way. To sufficiently exploit the power of entire space samples, a two-stage training process including pre-training and fine-tune is introduced. The pre-training stage is based on a scenario-supervised contrastive learning task with the training samples drawn from labeled and unlabeled data spaces. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. Extensive experimental results on public and industrial datasets demonstrate the superiority of the SASS model over state-of-the-art methods. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests. SASS has been successfully deployed on multi-scenario short video recommendation platform of Taobao in Alibaba.
The support of the process of training pre-service mathematics teachers by means of cloud services

The training of pre-service mathematics teachers is a complex process due to the specifics of the field. Informatization of education affects all the areas, and pre-service mathematics teachers can not be left out. The article is devoted to the problem of supporting the process of professional training of pre-service mathematics teachers by means of cloud services. Examples of the use of cloud technologies are given. The analysis of a survey of pre-service mathematics teachers on the use of information and communication technologies in the training process is done.
Using Twitter in Ukrainian sociology majors training

The article deals with the problem of using cloud technologies in the training of sociology students in Ukraine. The popularity of Twitter in Ukraine is analyzed. The possibilities of using Twitter as a learning tool in classroom are discussed. List of recommended tweeters, including Ukrainian resources as well as resources related to population censuses is proposed. The article offers examples of student activities for Social Statistics and Demographics courses. The article demonstrates that new forms of student’s activity related to data analysis introduced by academics and practitioners (building art objects and storytelling based on data; shared data collection by citizens through mobile devices, “play with data” modern data visualization services) can be realized with Twitter resources and can help overcome the barriers that arise while studying quantitative methods.
E-learning resources for successful math teaching to pupils of primary school

Ukrainian primary schools are undergoing significant changes as for Reform ‘New Ukrainian School’, it reflects rapid updating information technology and high level of children’ informational activity. Primary schools are basically focused on development subject knowledge and general study skills. One of the ways of their developing is to use tools and apps. There are the examples of using interactive tools and apps for teaching Math for young learners by teachers-to-be in the article. The article presents as well the experimental data about training teachers-to-be to use tools and apps. Interactive tools and apps provide real task variability, uniqueness of exercises, operative assessment of correction, adjustment of task difficulty, a shade of competitiveness and gaming to the exercises. To create their own apps teachers-to be use the tools that are the part of the integrated Microsoft Office package using designing environments, and other simple and convenient programs. The article presents experimental data about the results of training teachers-to-be to create apps. A set of criteria for creation apps was made and checked at the experimental research such as ability to develop apps, knowledge and understanding the functional capabilities of apps, knowledge of tools for creating apps and their functional capabilities, ability to select and formulate tasks for young learners, ability to assess adequately the quality of the developed apps.
Modified Lévy flight distribution algorithm for global optimization and parameters estimation of modified three-diode photovoltaic model

Abstract Many real-world problems demand optimization, minimization of costs and maximization of profits, and meta-heuristic algorithms have proficiently proved their ability to achieve optimum results. This study proposes an alternative algorithm of Lévy Flight Distribution (LFD) by integrating Opposition-based learning (OBL) operator, termed LFD-OBL, for resolving intrinsic drawbacks of the canonical LFD. The proposed approach adopts OBL operator for catering search stagnancy to ensure faster convergence rate. We validate the usefulness of our approach through IEEE CEC’20 test suite, and compare results with original LFD and several other counterparts such as Moth-flame optimization, whale optimization algorithm, grasshopper optimisation algorithm, thermal exchange optimization, sine-cosine algorithm, artificial ecosystem-based optimization, Henry gas solubility optimization, and Harris’ hawks optimization. To further validate the efficiency of LFD-OBL, we apply it on parameters optimization of Solar Cell based on the Three-Diode Photovoltaic model. The qualitative and quantitative results of all the experiments performed in this study suggest superiority of the proposed method.
Magneto-optical diffractive deep neural network

We propose a magneto-optical diffractive deep neural network (MO-D 2 NN). We simulated several MO-D 2 NNs, each of which consists of five hidden layers made of a magnetic material that contains 100 × 100 magnetic domains with a domain width of 1 µ m and an interlayer distance of 0.7 mm. The networks demonstrate a classification accuracy of &gt; 90% for the MNIST dataset when light intensity is used as the classification measure. Moreover, an accuracy of &gt; 80% is obtained even for a small Faraday rotation angle of π /100 rad when the angle of polarization is used as the classification measure. The MO-D 2 NN allows the hidden layers to be rewritten, which is not possible with previous implementations of D 2 NNs.
A Survey on Particle Swarm Optimization for Association Rule Mining

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.
A robust encryption watermarking algorithm for medical images based on ridgelet-DCT and THM double chaos

Abstract With the help of big data, cloud computing, artificial intelligence and other technologies, the informatization and intelligence of the wisdom medical have been gradually realized. However, with the transmission and storage of massive amounts of medical images in the cloud, information security issues have become increasingly prominent. The privacy of patients is at risk of disclosure, theft and tampering, which has become an important challenge restricting the development of wisdom medical. How to protect the personal information of patients in the cloud environment has become an urgent problem to be solved. Medical image watermarking technology is an effective method to solve this problem. Combining the characteristics of Tent chaos and Henon chaos, this paper designed a Tent-Henon-Map double chaos watermarking encryption method and designed a medical image encryption watermarking algorithm based on ridgelet-DCT transform. The watermark images were encrypted by the Tent-Henon-Map double chaos which had the characteristics of sensitive initial values and large key space. Then, the feature vectors of the medical images were extracted through ridgelet-DCT transform. On the basis of ordinary watermarking technology, combined with zero watermarking, third-party concepts, and cryptographic technology, watermarking had a good ability to resist image processing attacks. The experimental results showed that the key space of the algorithm was $${10}^{116}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mn>116</mml:mn> </mml:msup> </mml:math> , which had better encryption and hard to crack. The time of watermark embedding and extraction were only 0.336 s and 0.439 s, with lower computational cost. And under high-strength conventional attacks and geometric attacks, the NC values of the algorithm were all greater than 0.55, which could effectively extract watermark information. It shown that the algorithm proposed had good robustness against conventional and geometric attacks It shown that the algorithm proposed had good robustness against conventional and geometric attacks, while taking into account the security.
USV path following controller based on DDPG with composite state-space and dynamic reward function

Reinforcement learning (RL) is suitable for the design of unmanned surface vessels (USV) path-following controllers for its model-free and unsupervised learning. However, the USV state transition is not strictly consistent with Markov properties and the state-space, action-space, reward function strongly affect the performance of the RL-based controller. According to the dynamics and kinematic characteristics of USV, we design a new state-space to reduce the influence of large inertia and state hysteresis on the RL agent training. A comprehensive reward function is proposed to avoid the RL-based controller falling into local optimum according to the task decomposing. A dynamic threshold is used in the reward function to accelerate the training speed while ensuring tracking accuracy. Finally, the effectiveness of the proposed RL-based controller is evaluated by means of simulation and actual USV path following.
De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician confidentiality. This paper presents an end-to-end de-identification framework to automatically remove PII from Australian hospital discharge summaries. Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia. Our end-to-end de-identification framework consists of three components: (1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number; (2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and (3) De-identification: removing PII from the hospital discharge summaries. Our results showed that the ensemble model combined using the stacking Support Vector Machine (SVM) method on the three base-models with the best F1 scores achieved excellent results with a F1 score of 99.16% on the test set of our corpus. We also evaluated the robustness of our modelling component on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the token-level majority voting method on all six base-models, achieved the highest F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64% at binary token-level matching compared to two state-of-the-art methods. The end-to-end framework provides a robust solution to de-identifying clinical narrative corpuses safely. It can easily be applied to any kind of clinical narrative documents.