Reviewerly/exHarmony huggingface dataset:
Dynamic Recognition and Classification of Trajectories in SLRecon Adopted Artificial Intelligence in Kinect

We have proposed “SLRecon” a digital representation of the exoskeleton by Kinect software to analyze the movement of the hands and thus identifies the trajectories taken by the signs for further processing. Subsequently, the trajectories were considered for phases such as training, validation and testing of a neural network-based artificial intelligence algorithm. The network responsible for recognizing and classifying 5 important signs determined by an expert. The neural network is a multilayer perceptron that was trained using the backpropagation method. The training phase was performed with 6 subjects and additionally tested with 9 subjects. We also discussed the results from the simulation phase, which confirmed that the system achieved 99.6% efficiency in detection and classification, while it achieved 98.7% accuracy in the field test. Finally, we compared and validated our results with other methods .
An Approach of Statement Compression Using Classifier Algorithm with Improved Efficiency

Sentence compression is the task of diminishing the length of the sentence without changing the real meaning of the sentence. In common such as natural language handling sentence pressure is a significant issue where the point is to produce a little and syntactically right form of the genuine sentence. This is a functioning exploration territory due to different reasons like expanding infiltration of mobile phones or PDAs, utilization of web or internet sources through PDA or smart phones and expansion in on-line shopping through PDAs or smart phones. Now a day's people do not have time to read large text by using this sentence compression model we can generate the small and detailed paragraph which conveys the same meaning of the original paragraph. The present circumstance requests little however complete useful sentence which fits in little screen of advanced cell than huge sentences. We have developed three sentence compression models with three different classifiers they are GRU, XGBoost and LSTM. We have evaluated the three models based on three evaluation methods Accuracy, Compression rate and Grammar & Informativeness. We have compared all the three models based on their accuracies.
Illumination-Enhanced Crowd Counting Based on IC-Net in Low Lighting Conditions

The low lighting in some extreme conditions always affect the accuracy of the crowd counting and other vision tasks. The existing methods mainly rely on the generalization ability of deep-learning model to count the crowd number. But in extremely low lighting conditions, these methods are not efficient. To alleviate this issue, this paper proposes a novel approach, named Illumination-aware Cascading Network (IC-Net). The IC-Net can handle the low lighting conditions and generate a high-quality crowd density map. It contains two submodules, i.e., the Illumination Fusion Module and the Feature Cascading Module. The Illumination Fusion Module can fuse the low-illumination feature and the illumination enhanced feature to highlight the missing feature in darkness. The Feature Cascading Module is a cascading model and used to further express the illumination feature. It can generate the high-quality density map. In addition, a new dataset is collected, named Low Light Scenes Crowd (LLSC) dataset, which all come from extremely low illumination conditions. Experimental results on LLSC and benchmark show that the proposed method outperforms the existing state-of-the art methods in such extreme conditions.
Human Activity Recognition System

Modern human activity recognAition systems are mainly trained and used upon video stream and images data that understand the features and actions variations in the data having similar or related movements. Human Activity Recognition plays a significant role in human-to-human and human-computer interaction. Manually driven system are highly time consuming and costlier. In this project, we aim at designing a cost-effective and faster Human Activity Recognition System which can process both video and image in order to recognize the activity being performed in it, thereby aiding the end user in various applications like surveillance, aiding purpose etc. This system will not only be cost effective but also as a utility-based system that can be incorporated in a large number of applications that will save time and aid in various activities that require recognition process, and save a lot of time with good accuracy Also, it will aid the blind people in availing the knowledge of their surroundings.
Adversarial Representation Learning with Closed-Form Solvers

Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time. Existing methods learn model parameters iteratively through stochastic gradient descent-ascent, which is often unstable and unreliable in practice. To overcome this challenge, we adopt closed-form solvers for the adversary and target task. We model them as kernel ridge regressors and analytically determine an upper-bound on the optimal dimensionality of representation. Our solution, dubbed OptNet-ARL, reduces to a stable one one-shot optimization problem that can be solved reliably and efficiently. OptNet-ARL can be easily generalized to the case of multiple target tasks and sensitive attributes. Numerical experiments, on both small and large scale datasets, show that, from an optimization perspective, OptNet-ARL is stable and exhibits three to five times faster convergence. Performance wise, when the target and sensitive attributes are dependent, OptNet-ARL learns representations that offer a better trade-off front between (a) utility and bias for fair classification and (b) utility and privacy by mitigating leakage of private information than existing solutions.Code is available at https://github.com/human-analysis.
Enhanced Velocity-Driven Particle Swarm Optimization for Evolving Artificial Neural Network

research-article Share on Enhanced Velocity-Driven Particle Swarm Optimization for Evolving Artificial Neural Network Authors: Wei Li Xi'an University of Technology, China Xi'an University of Technology, ChinaView Profile , Haonan Luo Xi'an University of Technology, China Xi'an University of Technology, ChinaView Profile , Zhou Lei Xi'an University of Technology, China Xi'an University of Technology, ChinaView Profile , Junqing Yuan Xi'an University of Technology, China Xi'an University of Technology, ChinaView Profile , Wangshun Dong Xi'an University of Technology, China Xi'an University of Technology, ChinaView Profile , Lei Wang Shaanxi Key Laboratory for Network Computing and Security Technology, China Shaanxi Key Laboratory for Network Computing and Security Technology, ChinaView Profile Authors Info & Claims ICCAI 2021: 2021 7th International Conference on Computing and Artificial IntelligenceApril 2021 Pages 238–243https://doi.org/10.1145/3467707.3467743Online:24 September 2021Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
What's the Big Idea? Deep Learning Algorithms

Abstract Deep learning algorithms are modern “media darlings” – and for good reason, as Eran Raviv explains
DEVELOPMENT AND ANALYSIS OF A CONCEPTUAL MODEL OF BIOMETRIC INFORMATION FROM THE POINT OF VIEW OF THE EXISTING LEGISLATIVE AND REGULATORY FRAMEWORKS

The proposed work is devoted to the classification of biometric systems for recognizing subjects, which play an important role in modern information security systems, since they are currently one of the main means of identification and authentication of an individual. The classification schemes presented in this paper allow us to apply a systematic approach to the development of new methods of biometrics.
Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimation

Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis.
A Reversible Digital Watermarking Algorithm Based on Medical Images via Threshold Segmentation and Contrast Enhancement

With the popularization of the Internet, the information is more convenient to acquire and transmit via network. But the privacy protection under the digital environment should be paid more and more attention. Now, in the paperless hospital, the reversible digital watermarking technology is adopted to embed the electronic cases into medical images of patients as watermark form, which can ensure the privacy protection. However, the existing reversible digital watermarking algorithm usually pursues the peak signal-to-noise ratio, which makes it difficult to consider the embedding capacity and image quality. To solve these problems, a reversible digital watermarking algorithm based on threshold segmentation for image contrast enhancement is proposed. In the proposed model, the Ostu threshold segmentation is applied to the gradient amplitude of the image to obtain the pixel threshold, and the bidirectional scanning is used to determine the foreground area. Meanwhile, the square column displacement between peak-pixel and zero-pixel value is used to avoid pixel overflow. The redundant information is added in the process of embedding to ensure the accuracy of information extraction. The experimental results show that the algorithm can keep a higher embedding capacity and make the embedded image with a higher visual quality.
Low Resource Machine Translation

We discuss the burgeoning field of unsupervised machine translation, where words and phrases are translated between languages without any parallel corpora. We discuss popular methods, and applications to low-resource settings. We further investigate the application of polyglot training to this field and present new promising directions for unsupervised machine translation.
The Construction and Prospect of China National Bibliography

The national bibliography is a comprehensive and systematic catalog that gathers and records the information of all the publications in a country, and serves as the base for monitoring and managing all the information related. China National Bibliography in modern times has been continuously developed and improved, thanks to the legacies from its past and the experiences of other countries. The present paper first reviews the past and the present of China National Bibliography, then discusses the progress of researches on the application of Functional Requirements for Bibliographic Records (FRBR), and finally explains the survey on the feasibility of adopting the new cataloging standard RDA in China. Although the FRBRization of China National Bibliography, to a certain extent, facilitates the readers’ use, we argue that the FRBRization of the existing MARC data cannot realize all the ideas FRBR creates. There are some problems, such as the distinctions between works and expressions, the construction issues of the bibliographic and authority data. In the process of implementing localized RDA in the cataloging of foreign materials of all languages and types, the National Library of China conducted an investigation into the feasibility of implementing RDA in the cataloging of Chinese literature, finding that although there is not a vast gulf between RDA and current Chinese cataloging standards, a lot of problems do need to be resolved both theoretically and practically. First, the Chinese national standard “Resource Description” of the day and the CNMARC standards need to be improved. Second, it is urgent to reduce the increasing manual workload of the catalogers caused by adopting RDA. Third, both the staff force and the system reserve ability of the member libraries under Online Library Cataloging Center of China should be taken into account. Fourth, RDA would add new data elements, thus the initiation of this new standard simultaneously requires updating and transforming the integrated library system. Last but not the least, the application of RDA also requires the development of corresponding online toolkits to improve the cataloging efficiency.
The way of survival based on heuristic Dijkstra algorithm

For the “LifeAfter” survival game problem, in order to make it smooth customs clearance, players need to meet multiple conditions, so the survival problem is abstracted into the path planning problem under multiple constraints. Aiming at this problem, we create A-D algorithm based on heuristic A * algorithm and Dijkstra algorithm. In order to reduce the complexity of computer calculation, we greatly improve the operation efficiency of the algorithm by the method of space exchange time. For the three problems in the topic, we propose three different benefit functions to optimize the global, avoid falling into the local optimal solution, guide the player under the constraints of multiple conditions, as far as possible to make the objective function value the largest (respectively: the least supply point (the sum of campfire point and food point), the path, the end point of satiation and comfort is the largest).
Improving Automated Search for Underwater Threats Using Multistatic Sensor Fields by Incorporating Unconfirmed Track Information

Sonobuoy fields, comprising a network of sonar transmitters and receivers, are used to search for and track underwater targets. Although normally such fields are operated from a maritime patrol aircraft, automated scheduling and processing creates opportunities for employing them as autonomous sensor systems. The automated search mechanism considered in this work is controlled by modelling the presence of undetected threats in an Operational Area (OA) using a spatial probability density function (PDF), known as a threat map. The algorithm decides how to schedule waveform transmissions, known as pings, to efficiently search and clear the OA. A conventional approach is to update the threat map based on just the characteristics of the sonobuoy field and switch to a separate metric to track a target after track confirmation. In this study we address the phase when there are potential contacts which cannot yet be promoted to confirmed tracks. We develop a mechanism for probing the associated areas of interest while still remaining in the threat map driven search scheduling. To this end, we propose reinitialising the threat map after each transmission using an augmented PDF, where unconfirmed tracks are represented by weighted Gaussians. Simulations show that this approach significantly improves search performance, reducing the number of pings required to confirm a track, distance from a confirmed track to the target and the proportion of falsely confirmed tracks.
Improving Non-Autoregressive Machine Translation via Autoregressive Training

Abstract In recent years, non-autoregressive machine translation has attracted many researchers’ attentions. Non-autoregressive translation (NAT) achieves faster decoding speed at the cost of translation accuracy compared with autoregressive translation (AT). Since NAT and AT models have similar architecture, a natural idea is to use AT task assisting NAT task. Previous works use curriculum learning or distillation to improve the performance of NAT model. However, they are complex to follow and diffucult to be integrated into some new works. So in this paper, to make it easy, we introduce a multi-task framework to improve the performance of NAT task. Specially, we use a fully shared encoder-decoder network to train NAT task and AT task simultaneously. To evaluate the performance of our model, we conduct experiments on serval benchmask tasks, including WMT14 EN-DE, WMT16 EN-RO and IWSLT14 DE-EN. The experimental results demonstrate that our model achieves improvements but still keeps simple.
Cycle-Consistent World Models for Domain Independent Latent Imagination

End-to-end autonomous driving seeks to solve the perception, decision, and control problems in an integrated way, which can be easier to generalize at scale and be more adapting to new scenarios. However, high costs and risks make it very hard to train autonomous cars in the real world. Simulations can therefore be a powerful tool to enable training. Due to slightly different observations, agents trained and evaluated solely in simulation often perform well there but have difficulties in real-world environments. To tackle this problem, we propose a novel model-based reinforcement learning approach called Cycle-consistent World Models. Contrary to related approaches, our model can embed two modalities in a shared latent space and thereby learn from samples in one modality (e.g., simulated data) and be used for inference in different domain (e.g., real-world data). Our experiments using different modalities in the CARLA simulator showed that this enables CCWM to outperform state-of-the-art domain adaptation approaches. Furthermore, we show that CCWM can decode a given latent representation into semantically coherent observations in both modalities.
To the Guestion of the Formation of Cultural Literacy of Belarusian Junior Schoolchildren

The subject of the study in the article is the phenomenon of cultural literacy of junior schoolchildren in the context of the educational process in secondary schools of the Republic of Belarus. The relevance of the investigated problem is due to significant changes in the education system, which implies the development of the student’s personality, which is not simply subject knowledge and skills, but by a whole complex of universal skills that make up new, or functional literacy. In the context of this literacy, our interest causes cultural literacy as the ownership of cultural and marked information, which is part of the ordinary consciousness of most national culture carriers. The purpose of this article is to disclose a possible approach to the process of forming cultural literacy of primary school students. To achieve the goal, a theoretical analysis of scientific literature was used, as well as a descriptive method, which allows to transmit the specific features of realities that are carriers of culturally significant information. The methodological basis was the cultural, anthropocentric and competence concept of education. The formation of cultural literacy of younger schoolchildren requires use in the educational process of adequate scientific and methodological support. As a base and tool, we offer a dictionary of cultural realities, which includes the optimal information on the student of primary classes the amount of information on the extralinguistic and linguistic signs of world and national culture. We consider the working notebook for the student and guidelines for the teacher to be the recommended accompanying elements of the dictionary. Such an educational and methodical complex will help to ensure not only the formation of cultural literacy as an element of functional literacy, but also the education of the student who has the qualities of a sincere patriot and a citizen of their country.
BEEO: Semantic Support for Event-Based Data Analytics

Recent developments in data analysis and machine learning support novel data-driven operations. Event data provide social and environmental context, thus, such data may become essential for the workflow of data analytic pipelines. In this paper, we introduce our Business Event Exchange Ontology (BEEO), based on Schema.org that enables data integration and analytics for event data. BEEO is available under Apache 2.0 license on GitHub, and is seeing adoption among both its creator companies and other product and service companies. We present and discuss the ontology development drivers and process, its structure, and its usage in different real use cases.Resource Type: OntologyLicense: Apache 2.0DOI: https://doi.org/10.5281/zenodo.4695281Repository: https://github.com/UNIMIBInside/Business-Event-Exchange-Ontology
Research on Vehicle Fine-grained Image Classification Method Based on Deep Learning

The classification of vehicle fine-grained images cannot achieve satisfactory results due to the problems of visual angle, occlusion and local labeling. This paper studies the vehicle 3D bounding box construction method, combined with the characteristics of the rigid body symmetry of the vehicle, and uses projection transformation and posture normalization to synthesize a multi-featured vehicle image with the front or back, side and top surface of the vehicle, thus solve the problem that the perspective and occlusion issues of the vehicle cannot extract effective vehicle features to some extent; then studies the two-level attention algorithm, and uses the selective search method to automatically extract vehicle component features from the synthesized vehicle image, so as to solve the problem of vehicle local labeling to a certain extent.
‘Oshudh Poro’: A Mobile-Phone Application to Support Low-literate Rural Bangladeshi People’s Personal Medication Management [Poster]

Inability to read and understand the information on medicine packaging is a problem for many people across the world, especially in the Global South. As a solution to this, many of them reportedly often memorize the names and looks of their medicines. However, they often fail to distinguish between registered and unregistered/duplicate medicines and consume them without realizing, which further deteriorates their health conditions and even may lead to death. They frequently seek help from family members and friends to supervise their medicine intake, however, such helpers might still struggle to distinguish between registered and duplicate medicines if the texts written on the medicine’s packaging seem similar. To address this problem, we designed an android mobile-phone application, ‘Oshudh Poro’ scans medicine packaging, captures images of its unique registry number, matches the number to a database of registered medicines in the country, retrieves the relevant details upon finding the medicine as registered, converts them to Bengali audios and plays the details aloud. Thus, the users know which medicine are they taking, whether it is registered or unregistered and its details. Thus, low-literate users would gain more autonomy in managing their personal medication. We argue that this application would lead wellbeing-HCI to more sustainable design solutions as the application is built on existing skills of users and integrates only the existing features available to the intended users. Thus, our design contributes to wellbeing-HCI, sustainability research, and HCI4D.