Reviewerly/exHarmony huggingface dataset:
Fast template matching in multi-modal image under pixel distribution mapping

A scheme that can achieve fast template matching by both traditional methods and neural network methods, called Hybrid Matching by Pixel Distribution Mapping (HMPDM), is proposed for the matching difficulty caused by the problem of nonlinear intensity differences between multi-modal images. The HMPDM scheme first extracts the distribution information of image pixels through the developed expanded Slice Transform (eSLT) matrix, to overcome the nonlinear intensity differences between visible and thermal infrared images; and then maps the eSLT matrices corresponding to different modal images into correlation surface images with the same modality through traditional integer mapping mechanism or neural network mapping mechanism; finally, template matching between visible and thermal infrared images is achieved by evaluating the similarity of correlation surface images through the Zero-mean Normalized Cross-Correlation (ZNCC) algorithm. The fast and classic ZNCC algorithm ensures the real-time performance of the HMPDM scheme in the most time-consuming sliding window matching stage. The traditional integer mapping mechanism and the neural network mapping mechanism enable the HMPDM scheme to use the traditional method and the neural network method alone to achieve template matching, or to combine the two methods to achieve template matching. The comprehensive utilization of traditional methods and neural network methods makes the HMPDM scheme adaptable to various platforms with limited hardware resources. The experimental results show that, on a hardware platform with limited resources, it only takes 0.18 s for a 64 × 64 template image to slide on a 256 × 256 query image to achieve template matching; and it only takes at least 1.2 s for neural network training on the CPU. The matching performance is also better than many popular multi-modal image template matching algorithms.
Semantic-aligned reinforced attention model for zero-shot learning

Zero-shot learning (ZSL) aims to recognize unseen images from invisible classes, by transferring semantic knowledge from visible classes to invisible classes. Such as, although humans have never seen a zebra, if we know that “a horse with stripes is a zebra”, then we can easily recognize it when we see a zebra. Given semantic descriptions, the human can capture intrinsic visual clues from different channels or appearance factors (e.g., color, texture) on salient parts. But computers are not smart enough to recognize it with high accuracy, they still need to make progress in the learning of semantic-aligned visual representations. Therefore, we propose a semantic-aligned reinforced attention (SRA) model to improve the attributes localization ability. We aim to discover invariable features related to class-level semantic attributes from variable intra-class vision information, and thereby avoid misalignment between much visual information and simple semantic representations. Specially, during the localization of spatial attention, we develop an efficient constraint directly on feature map to ensure the intra-attention compactness and inter-attention dispersion characteristics like human gaze. While for the channel, we proposed a novel attributes attention cross entropy loss to exploit the supervision effect of each semantic attribute subset. Experiments on three ZSL benchmarks, i.e., CUB, SUN and AWA2, indicate the competitiveness of our proposed method against the state-of-the-art ZSL methods.
A Formal Theory of AI Trustworthiness for Evaluating Autonomous AI Systems

Advances in the frontier of intelligence and system sciences have triggered the emergence of Autonomous AI (AAI) systems. AAI is cognitive intelligent systems that enable non-programmed and non-pretrained inferential intelligence for autonomous intelligence generation by machines. Basic research challenges to AAI are rooted in their transdisciplinary nature and trustworthiness among interactions of human and machine intelligence in a coherent framework. This work presents a theory and a methodology for AAI trustworthiness and its quantitative measurement in real-time context based on basic research in autonomous systems and symbiotic human-robot coordination. Experimental results have demonstrated the novelty of the methodology and effectiveness of real-time applications in hybrid intelligence systems involving humans, robots, and their interactions in distributed, adaptive, and cognitive AI systems.
Accuracy Improvement of Semantic Segmentation Trained with Data Generated from a 3D Model by Histogram Matching Using Suitable References

Visual navigation based on the results of semantic segmentation requires high classification accuracy. Previous research has proven that a classifier of semantic segmentation trained upon a dataset generated from a 3D model performs well when the input images are also generated from a 3D model. However, when the input images are real 2D images captured at the same location by a camera mounted on a robot, the average classification accuracy deteriorates. To overcome this issue, a novel scheme is proposed to improve the classification accuracy of semantic segmentation when the training data is generated from a 3D point cloud. The key features of the proposed scheme are filling in the missing data by inpainting and domain adaptation by histogram matching. To evaluate the proposed scheme, datasets composed of real images captured during a variety of seasons, weathers, and times were created. Experimental results showed that ICNet trained upon our dataset could provide accurate results for visual navigation.
A novel hybrid sampling method based on CWGAN for extremely imbalanced backorder prediction

Product backorder is a common problem in supply chain management systems. It is essential for entrepreneurs to predict the likelihood of backorder accurately to minimize a company’s losses. However, existing methods are hard to achieve satisfactory results since the number of backorders and non-backorders are extremely imbalanced. Besides, the backorder data’s attributes are complex to oversample them effectively. To address these problems, a novel hybrid sampling method is proposed to help predict extremely imbalanced backorder. The Randomized Undersampling (RUS) and a Conditional Wasserstein Generative Adversarial Network (CWGAN) are innovatively introduced into backorder prediction. First, RUS is used to reduce the majority non-backorder samples. Second, CWGAN is served as an oversampling technique to generate high-quality backorder samples. It utilizes unique structures in the generator and the discriminator to effectively model both numerical and categorical variables. Finally, the training dataset is balanced, and the Random Forest Classifier (RFC) is adopted to make backordering prediction. In the experiments of Kaggle’s dataset ‘Can you predict product backorder?’, our proposed method is superior to all benchmark methods in terms of standard evaluation metrics. The results show that our proposed product backorder prediction model is effective.
Continuous Documentation: Automating Document Preparation with your DevSecOps Pipeline

End item deliveries to government customers are usually accompanied by a multitude of required documents, typically in print-ready formats such as Microsoft Word or Adobe Portable Document Format (PDF). Preparing these documents requires tedious manual collation and re-formatting of data from a multitude of data sources, which takes a significant amount of labor, is error-prone, and incurs lengthy review and approval cycles.How can we modernize our document preparation to support continuous release and delivery? Continuous Documentation (CDoc)! By leveraging the Authoritative Sources of Truth (ASOTs) for data already within our DevSecOps pipelines, we can extend the concept of “Documents as Code” (DaC) to reliably and repeatably automate document preparation using a suite of Free and Open-Source Software (FOSS) tools. Continuous Documentation ensures documents are ready for delivery and release in the print-ready formats customers expect at the same time as the software they accompany.
Enhanced MapReduce Performance for the Distributed Parallel Computing: Application of the Big Data

Now a days and previous years, the increase in the volume of data has accelerated and this requires more storage places with the increase of data, as big data has a huge number of users and cloud computing, and these users need to access data securely and privately from any device at any time. Therefore, it is important to provide a safe flow of data in the Internet of Things (IOT records file) and to reduce its size in a way that does not affect its purpose or its purpose. The most important field of data mining is the search for items and repetitive data inside storage locations. Apriori algorithm was the most common algorithm for finding a set of repeated elements from data. This needs to delete a group of data that is repeated more than once and create a number of new groups after deleting the repeated ones, which leads to an increase in the storage space and an increase in the speed of its performance. In this paper, we implemented the MapReduce Apriori (MRA) algorithm on the Apache Hadoop cluster that includes two functions (Map and Reduce) to find the repeated sets of k-elements.
Federated Learning on Android - Highlights from Recent Developments

This article provides recent development highlights of federated learning and its use on Android-based devices, including a discussion related to frameworks used to allow its application. Some already working examples using federated learning for different data models are provided, and a discussion of its advantages.
Embodied-AI Wheelchair Framework with Hands-free Interface and Manipulation

Assistive robots can be found in hospitals and rehabilitation clinics, where they help patients maintain a positive disposition. Our proposed robotic mobility solution combines state of the art hardware and software to provide a safer, more independent, and more productive lifestyle for people with some of the most severe disabilities. New hardware includes, a retractable roof, manipulator arm, a hard backpack, a number of sensors that collect environmental data and processors that generate 3D maps for a hands-free human-machine interface.The proposed new system receives input from the user via head tracking or voice command, and displays information through augmented reality into the user’s field of view. The software algorithm will use a novel cycle of self-learning artificial intelligence that achieves autonomous navigation while avoiding collisions with stationary and dynamic objects. The prototype will be assembled and tested over the next three years and a publicly available version could be ready two years thereafter.
CNAS: Constrained Neural Architecture Search

Neural Architecture Search (NAS) paves the way for the automatic definition of neural networks architectures. The research interest in this field is steadily growing with several solutions available in the literature. This study introduces, for the first time in the literature, a NAS solution, called Constrained NAS (CNAS), able to take into account constraints on the search of the designed neural architecture. Specifically, CNAS is able to consider both functional constraints (i.e., the type of operations that can be carried out in the neural network) and technological constraints (i.e., constraints on the computational and memory demand of the designed neural network). CNAS has been successfully applied to Tiny Machine Learning and Privacy-Preserving Deep Learning with Homomorphic Encryption being two relevant and challenging application scenarios where functional and technological constraints are relevant in the neural network search.
TiBERT: Tibetan Pre-trained Language Model

The pre-trained language model is trained on large-scale unlabeled text and can achieve state-of-the-art results in many different downstream tasks. However, the current pre-trained language model is mainly concentrated in the Chinese and English fields. For low resource language such as Tibetan, there is lack of a monolingual pre-trained model. To promote the development of Tibetan natural language processing tasks, this paper collects the large-scale training data from Tibetan websites and constructs a vocabulary that can cover 99.95% of the words in the corpus by using Sentencepiece. Then, we train the Tibetan monolingual pre-trained language model named TiBERT on the data and vocabulary. Finally, we apply TiBERT to the downstream tasks of text classification and question generation, and compare it with classic models and multilingual pre-trained models, the experimental results show that TiBERT can achieve the best performance. Our model is published in http://tibert.cmli-nlp.con
DeepFarm: AI-Driven Management of Farm Production using Explainable Causality

American agriculture has been afflicted by numerous outlier events in the past decade, such as several natural disasters, cyber-attacks, trade wars, and a global pandemic. Such unprecedented black-swans have created outcome uncertainties throughout the food supply chain, starting at the farm level for agricultural producers and aggregating at the consumption level for households and international trade flows. The primary drivers behind the shocks in agricultural productivity include strong weather-related events, transitory transportation disruptions, shipping delays, and policy shifts. This paper presents DeepFarm, an Artificial Intelligence (AI)-enabled framework to measure and manage uncertainties while evaluating multiple cause-effect scenarios in agricultural farm production. We deploy Deep Learning (DL) models to predict the impact of crop yield during outlier events such as extreme weather events and cyber-attacks. Additionally, we use a causal inference-based approach to quantity the impact of such events affecting the critical phases of farm production. Models are developed; experiments are performed; the results are recorded, evaluated, and discussed. Our results suggest that DeepFarm can effectively forecast and quantity the impact of outlier events on crop yield across different regions in the US.
Reducing 0s bias in video moment retrieval with a circular competence-based captioner

The current study addresses the problem of retrieving a specific moment from an untrimmed video by a sentence query. Existing methods have achieved high performance by designing various structures to match visual-text relations. Yet, these methods tend to return an interval starting from 0s, which we named "0s bias". In this paper, we propose a Circular Co-Teaching (CCT) mechanism using a captioner to improve an existing retrieval model (localizer) from two aspects: biased annotations and easy samples. Correspondingly, CCT contains two processes: (1) Pseudo Query Generation (captioner to localizer), aiming at transferring the knowledge from generated queries to the localizer to balance annotations; (2) Competence-based Curriculum Learning (localizer to captioner), training the captioner in an easy-to-hard fashion guided by localization results, making pairs of the false-positive moment and pseudo query become easy samples for the localizer. Extensive experiments show that our CCT can alleviate "0s bias" with even 4% improvement for existing approaches on average in two public datasets (ActivityNet-Captions, and Charades-STA), in terms of [email protected],IoU=0.7. Notably, our method also outperforms baselines in an out-of-distribution scenario. We also quantitatively validate CCT's ability to cope with "0s bias" by a proposed metric, DM. Our study not only theoretically contributes to detecting "0s bias", but also provides a highly effective tool for video moment retrieval by alleviating such bias.
Federated Learning paradigm in E-health systems: An overview

Every day, professionals generate and use massive healthcare data to save, treat and ameliorate the lives of patients. The healthcare industry has adopted cloud-based solutions to solve several problems in a cost-effective manner. Therefore, privacy and security mechanisms should be deployed to protect valuable medical information from unauthorized access. Much of the work in literature in recent years has focused on using artificial intelligence techniques such as deep learning and federated learning to solve various problems in the health field. Federated learning (FL) is a special technique for machine learning for privacy preservation. This study aims to compare the traditional centralized training approach and FL to show the advantages of using FL in the medical field and prove that FL can be adopted for security and data latency in e-health systems. The results obtained showed the feasibility of FL when compared to traditional methods used in the aspect of securing data and latency in the medical field.
Content-Based Image Retrieval Using Fused Convolutional Neural Networks

Multimedia plays a significant role in our day-to-day life. Large image databases easily exist due to the spread of social websites, cloud storage, and smartphones. Searching by text is normal and easy, but searching by image’s content is more intelligent and sensitive. Therefore, this study develops a content-based image retrieval algorithm that achieved high image retrieval accuracy. Intelligent systems can help in this way and work effectively. This study investigates the three proposed models using deep learning algorithms. These models were applied on three databases of different sizes: Corel1K, Corel8K, Aloi74K, and Cifar-100. The first method uses a convolution neural network (CNN) for feature extraction and image classification. The second method uses CNN and long short-term memory (LSTM), and the last one employs CNN and gated recurrent unit (GRU). The three proposed algorithms have significantly reduced the time complexity. Additionally, they have achieved high image retrieval accuracy compared to state-of-the-art models. The proposed CNN model achieved 96%, 89%, and 98%, for Corel1K, Corel8K, and Aloi74K respectively. The CNN + LSTM model achieved CBIR accuracies of 96.5%, 90.5%, and 98.5% for Corel1k, Corel8k, and Aloi74k respectively. Finally, the CNN + GRU model achieved CBIR accuracies 97%, 91.5%, and 99% for Corel1k, Corel8k, and Aloi74k respectively.
Voice Print Recognition Check-in System Based on Resnet

With the development of modern technology and the rise of artificial intelligence, the application scenarios of identity authentication technology are becoming more and more complex, especially the current situation of the spread of the novel coronavirus, the traditional identity authentication technology can no longer meet people's actual needs, and the society urgently needs a secure and convenient identity authentication technology. Voice print recognition technology is an identity recognition method that uses specific feature extraction methods to extract the features that can identify the speaker's identity from the original speech input, and then uses these features to identify the speaker's identity. Aiming at the above problems, this paper proposes a deep learning-based voicing recognition algorithm, which is based on the theoretical knowledge of deep learning. The research work includes the following aspects: providing convolutional network to extract features; The "speech feature template library" is established by massive training. Research on matching and recognition algorithm; Research on check-in system based on voice print recognition. Based on this algorithm, this paper designs and implements a voiceprint recognition check-in system with clear interactive interface. The system has the functions of adding members, refreshing the voice library, checking in with the voice print, clearing the results and so on. The system interface will display the data of the voice print library and the historical check-in record, as well as the current recognition results, accuracy and check-in time. The average recognition rate of the system is about 95%, which can meet the requirement of practical application.
Towards automated Android app internationalisation: An exploratory study

Android has become the most popular mobile platform with over 2.5 billion active users who use many different languages across many different countries. In order for Android apps to be useable by all of them, app developers usually need to add an internationalisation feature that adapts the app to the users' linguistic and cultural requirements. Such a process, including the translation from the default language to up to thousands of languages, is usually achieved via manual efforts and hence is resource-intensive, time-consuming, and error-prone. Automated approaches are hence in demand to help developers mitigate such manual efforts. Since there are millions of apps proposed already for Android users, we are interested in knowing to what extent internationalisation has been supported. Our experimental results show that Android apps, at least the ones released on online markets, have mostly been equipped with internationalisation features, with the number of supported languages varies significantly. By mapping the actual term translations among different languages, we further find that the translations tend to be consistent among different apps, suggesting the possibility to learn from this data to achieve automated app internalisation. To explore this idea we implemented a Transformer-based prototype approach Androi18n, that learns from developers' practical translations to achieve automated mobile app text translations. Experimental results show that Androi18n is effective in achieving our objective, and its high performance is generic across the translations of different languages.
ChSO-DNFNet: Spam detection in Twitter using feature fusion and optimized Deep Neuro Fuzzy Network

The usage of social networking platforms for interaction and meeting has grown significantly. However, as social networks continue to expand, Twitter has emerged as a vital social media for real life and has become a major source of spam. To address the aforementioned problems, spam identification on social media becomes an increasingly crucial task. The features that are present in the high dimension data of social networks cannot be used effectively by the existing approaches. In order to filter the spam information in social media, a Chimp Sailfish Optimization-based Deep Neuro Fuzzy Network (ChSO-based DNFN) is proposed. The proposed method effectively performs well under high dimensional data in real platform environment using deep learning classifier. It is more robust and generates optimal result and also reduces the computational complexity problems. Additionally, the proposed approach demonstrated improved performance in terms of metrics like precision, recall, and F-measure, which were measured using a 5k continuous dataset and yielded values of 0.894, 0.903, and 0.898, respectively.
Smart Rollators as a Cost-Effective Solution for Personalized Assistance Healthcare Ecosystem in Elderly Communities

Nowadays, the ratio of elderly people per caregiver is increasing, and the statistics confirm that this trend will continue for decades. Healthcare systems need to change to support this unbalance. The have to move to enhanced, value-added and cost-effective services to patients, professionals and caregivers. Industry is already involved in this process, supported by new technologies such as artificial intelligence, deep learning, data analytics and robotics. A parallel approach for Healthcare -called Health 4.0- now opens new opportunities. Quality and personalized services require large amounts of data, which needs to be gathered in a transparent way. Assistive devices are already extensively used among the aging people. Transforming them into smart assistive devices provides the opportunity to monitor different parameters along their usual routines. In this paper, we explore the use of smart rollators in a ROS2 ecosystem to provide user condition services to the caregivers in order to reason about gait abnormalities and functional fitness conditions.