Reviewerly/exHarmony huggingface dataset:
RGB-Depth Cross-Modal Person Re-identification

Person re-identification is a key challenge for surveillance across multiple sensors.Prompted by the advent of powerful deep learning models for visual recognition, and inexpensive RGBD cameras and sensor-rich mobile robotic platforms, e.g.self-driving vehicles, we investigate the relatively unexplored problem of cross-modal re-identification of persons between RGB (color) and depth images.The considerable divergence in data distributions across different sensor modalities introduces additional challenges to the typical difficulties like distinct viewpoints, occlusions, and pose and illumination variation.While some work has investigated re-identification across RGB and infrared, we take inspiration from successes in transfer learning from RGB to depth in object detection tasks.Our main contribution is a novel cross-modal distillation network for robust person re-identification, which learns a shared feature representation space of person's appearance in both RGB and depth images.The proposed network was compared to conventional and deep learning approaches proposed for other cross-domain re-identification tasks.Results obtained on the public BIWI and RobotPKU datasets indicate that the proposed method can significantly outperform the state-ofthe-art approaches by up to 10.5% mAP, demonstrating the benefit of the proposed distillation paradigm.
Intersection Recognition Using Results of Semantic Segmentation for Visual Navigation

It is popular to use three-dimensional sensing devices such as LiDAR and RADAR for autonomous navigation of ground vehicles in modern approaches. However, there are significant problems: the price of 3D sensing devices, the cost for 3D map building, the robustness against errors accumulated in long-term moving. Visual navigation based on a topological map using only cheap cameras as external sensors has potential to solve these problems; road-following and intersection recognition can enable robust navigation. This paper proposes a novel scheme for intersection recognition using results of semantic segmentation, which has a high affinity for vision-based road-following strongly depending on semantic segmentation. The proposed scheme mainly composed of mode filtering for a segmented image and similarity computation like the Hamming distance showed that good accuracy for the Tsukuba-Challenge 2018 dataset constructed by the authors: perfect results were obtained for more than half intersections included in the dataset. In addition, a running experiment using the proposed scheme with vision-based road-following showed that the proposed scheme could classify intersections appropriately in actual environments.
A New Paradigm for a Marketplace of Services: Smart Communities in the IoT Era

We propose a vision and a new paradigm for a Marketplace of Services as an integral part of a Smart Community Infrastructure. The Smart Communities of the (near) future will provide a large number of services to be offered as utilities and sold on a metered basis. These services will be aggregated and synthesized from a hierarchy of resources produced and shared by the community itself. Smart Community members or visitors will purchase as much or as little of these services as they find suitable to their needs and are billed accordingly. The context and condition of the members and the environment play a major role in service offerings and adaptations. Smart Communities have four fundamental characteristics: sustainability, resilience, empathy-driven proactive intelligence, and emergent behavior. These fundamental characteristics are a direct consequence of the underlying platform construction and management of the marketplace and its underlying IoT infrastructure. We illustrate our vision using examples of services that the marketplace may offer. We also highlight some major research challenges that need to be resolved to make our vision reality.
Supersingular Isogeny Key Exchange for Beginners

This is an informal tutorial on the supersingular isogeny Diffie-Hellman protocol aimed at non-isogenists.
Deep Learning for Multimodal Fall Detection

Fall detection systems can help providing quick assistance of the person diminishing the severity of the consequences of a fall. Real-time fall detection is important to decrease fear and time that a person remains laying on the floor after falling. In recent years, multimodal fall detection approaches are developed in order to gain more precision and robustness. In this work, we propose a multimodal fall detection system based on wearable sensors, ambient sensors and vision devices. We used long short-term memory networks (LSTM) and convolutional neural networks (CNN) for our analysis given that they are able to extract features from raw data, and are well suited for real-time detection. To test our proposal, we built a public multimodal dataset for fall detection. After experimentation, our proposed method reached 96.4% in accuracy, and it represented an improvement in precision, recall and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score over using single LSTM or CNN networks for fall detection.
Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype

Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of various state-of-the-art classifiers. The feature dominance level is proportional to the difference in means of benign and malignant tumors. The experiments were done on original Wisconsin Breast Cancer Dataset (WBCD) with total nine features. It is found that the classifiers' performances for top 4 and top 5 dominant-based features are almost equivalent to performances for all nine features. Artificial neural network (ANN) is come forth as the best performing classifier among all with accuracies of 98.9% and 99.6% for top 4 and top 5 dominant features respectively. The error rate of ANN between all nine and top 4 &5 dominant features is less than 2% for four performance evaluation parameters namely sensitivity, specificity, accuracy and AUC. Thus, it can be stated that the dominance-based filtering approach is appropriate for selecting a sound set of features from the feature pool, consequently, helps to reduce computation time with no deterioration in classifier's performance.
Improving Recurrent Neural Networks for Offline Arabic Handwriting Recognition by Combining Different Language Models

In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.
Using Container Migration for HPC Workloads Resilience

We share experiences in implementing a containerbased HPC environment that could help sustain running HPC workloads on clusters. By running workloads inside containers, we are able to migrate them from cluster nodes anticipating hardware problems, to healthy nodes while the workloads are running. Migration is done using the CRIU tool with no application modification. No major interruption or overhead is introduced to the workload. Various real HPC applications are tested. Tests are done with different hardware node specs, network interconnects, and MPI implementations. We also benchmark the applications on containers and compare performance to native. Results demonstrate successful migration of HPC workloads inside containers with minimal interruption, while maintaining the integrity of the results produced. We provide several YouTube videos demonstrating the migration tests. Benchmarks also show that application performance on containers is close to native. We discuss some of the challenges faced during implementation and solutions adopted. To the best of our knowledge, we believe this work is the first to demonstrate successful migration of real MPI-based HPC workloads using CRIU and containers.
Research and Application of Key Technologies for Medical Image Intelligence Knowledge Discovery and Data Processing

Hospitals have accumulated a large amount of medical image data which need to be analyzed and integrated so as to be able to find the needed medical image in time, which is the basis of key technologies such as intelligent diagnosis of diseases. Meanwhile, through the analysis and integrated processing of medical images, the potential value of existing medical image data can be fully explored. In this paper, the key technologies in the intelligent image knowledge discovery system and the characteristics of medical image data are studied and improved. In this paper, the characteristics of knowledge discovery and medical image data are comprehensively considered, and RDM texture features are selected as the feature representation of medical images. An improved RDM operator is proposed and proved by experimental results. Experimental results show that the improved RDM coding method can improve the stability of medical image data expression.
Towards Improving Rate-Distortion Performance of Transform-Based Lossy Compression for HPC Datasets

As the size and amount of data produced by high-performance computing (HPC) applications grow exponentially, an effective data reduction technique is becoming critical to mitigating time and space burden. Lossy compression techniques, which have been widely used in image and video compression, hold promise to fulfill such data reduction need. However, they are seldom adopted in HPC datasets because of their difficulty in quantifying the amount of information loss and data reduction. In this paper, we explore a lossy compression strategy by revisiting the energy compaction properties of discrete transforms on HPC datasets. Specifically, we apply block-based transforms to HPC datasets, obtain the minimum number of coefficients containing the maximum energy (or information) compaction rate, and quantize remaining non-dominant coefficients using a binning mechanism to minimize information loss expressed in a distortion measure. We implement the proposed approach and evaluate it using six real-world HPC datasets. Our experimental results show that, on average, only 6.67 bits are required to preserve an optimal energy compaction rate on our evaluated datasets. Moreover, our knee detection algorithm improves the distortion in terms of peak signal-to-noise ratio by 2.46 dB on average.
Evaluation of Task Workload and Intrinsic Motivation in a Virtual Reality Simulator of Electric-Powered Wheelchairs

For some people with severe physical disabilities, the immediate driving of an Electric-Powered Wheelchair (EPW) appears as a safety problem, which can be solved by the use of a virtual reality (VR) simulator for safe-driving learning purposes. There are several VR environment approaches in the literature applied to EPW driving training, including several tests that were performed to validate these simulators. This work evaluates the influence of different display devices on task workload and intrinsic motivation of participants, using the Simcadrom EPW Simulator developed at UFES/Brazil. Results from two qualitative tests: Intrinsic Motivation Inventory (IMI) and NASA Task Load Index (NASA-TLX) were compared for three displays (Head Mounted Display – HMD, desktop screen, and video projector). The results show that the HMD provided the highest usefulness score. On the other hand, the desktop screen reported the lowest task workload.
Semantic Ontology-Based Approach to Enhance Arabic Text Classification

Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.
ORGANIZATION OF MONITORING IN THE QUALITY MANAGEMENT SYSTEM OF THE EDUCATIONAL PROCESS WHEN TRAINING OF SPECIALISTS

Purpose: The problem of the quality of education concerns not only Russia but also the entire world community. Many developed countries are concerned about the current state of education and are trying to improve their quality. One of the leading trends in the modernization of education in Russia is increasing attention to the problem of improving its quality, which requires the development of conceptual approaches to finding ways to solve it.&#x0D; Methodology: In pedagogical terms, the quality of education is considered from two positions: firstly, as the degree of compliance of the educational system with the established requirements and the quality of educational services provided by the educational institution; secondly, as the effectiveness of the educational process that meets the needs of social customers not only in terms of assimilation of a certain amount of knowledge by students, but also the development of their personality, cognitive and creative abilities.&#x0D; Result: The choice of an actual and effective educational program is interesting not only for the consumer but also for the educational organization (institution), as it is a quality program in many sectors of education that affects the competitiveness of the educational organization.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of the Organization of Monitoring in the Quality Management System of the Educational Process when Training of Specialists is presented in a comprehensive and complete manner.
Bangla Document Categorisation using Multilayer Dense Neural Network with TF-IDF

Document categorization is a classic NLP task which includes allocating documents based on their content into one or more predefined classes. In this paper, we proposed a model using multilayer dense neural network with TF-IDF as feature selection technique in terms of Bangla text document categorization. The proposed system is distributed into three consecutive steps: i) preprocessing raw text data and extracting feature using TF-IDF, ii) designing the model architecture and fitting the model to train set, and iii) evaluating model performance on test set based on accuracy and F1 score. It is observed from experiments that the proposed method exhibits higher accuracy (84.58%) and F1 score (0.84) compared to the other well-known classification algorithms (Support Vector Machine, Multinomial Naive Bayes and so on) for Bangla text document classification.
Compressive Sampling for Robust Video Watermarking based on BCH Code in SWT-SVD Domain

The security and confidentiality of the data can be guaranteed by using a technique called watermarking. In this study, compressive sampling is designed and analyzed on video watermarking. Before the watermark compression process was carried out, the watermark was encoding the Bose Chaudhuri Hocquenghem Code (BCH Code). After that, the watermark is processed using the Discrete Sine Transform (DST) and Discrete Wavelet Transform (DWT). The watermark insertion process to the video host using the Stationary Wavelet Transform (SWT), and Singular Value Decomposition (SVD) methods. The results of our system are obtained with the PSNR 47.269 dB, MSE 1.712, and BER 0.080. The system is resistant to Gaussian Blur and rescaling noise attacks.
Automatic Image Annotation and Deep Learning for Tooth CT Image Segmentation

Recently, convolutional networks show great ability dealing with the problem of biomedical imaging, such as tooth image segmentation. In this paper, we propose a novel tooth-based computer tomography (CT) image segmentation approach that integrates U-Net with a level set model. Compared with a single U-Net, our method uses the level set method to build the mask for CT images. This allows automatic annotation in our model, improving the efficiency on image segmentation. Furthermore, we make some changes to the origin U-Net structure for the feasibility to images of any sizes. Using the combination of these two models, our integrated method shows its superiority dealing with problems on tooth image segmentation, outperforming the U-Net or the level set model alone.
Probabilistic Deep Ordinal Regression Based on Gaussian Processes

With excellent representation power for complex data, deep neural networks (DNNs) based approaches are state-of-the-art for ordinal regression problem which aims to classify instances into ordinal categories. However, DNNs are not able to capture uncertainties and produce probabilistic interpretations. As a probabilistic model, Gaussian Processes (GPs) on the other hand offers uncertainty information, which is nonetheless lack of scalability for large datasets. This paper adapts traditional GPs regression for ordinal regression problem by using both conjugate and non-conjugate ordinal likelihood. Based on that, it proposes a deep neural network with a GPs layer on the top, which is trained end-to-end by the stochastic gradient descent method for both neural network parameters and GPs parameters. The parameters in the ordinal likelihood function are learned as neural network parameters so that the proposed framework is able to produce fitted likelihood functions for training sets and make probabilistic predictions for test points. Experimental results on three real-world benchmarks - image aesthetics rating, historical image grading and age group estimation - demonstrate that in terms of mean absolute error, the proposed approach outperforms state-of-the-art ordinal regression approaches and provides the confidence for predictions.
Categorized Representations and General Learning

In this study we investigate the connection between general statistical properties of data and its representations with native category structures that can be formed in unsupervised observation without significant prior knowledge either in the form of ground truth data, or pre-known categories. We use a deep autoencoder model and data representing samples of Internet datagrams to produce categorized representations of input data space, with emergent density structure that was measured and visualized. We then attempt to use the emergent structure to develop a general approach to learning of new concepts in an iterative environment driven process that requires very small ground truth data and can be triggered by a single encounter. In the experiments, iterative learning applied to several categories of applications resulted in better than random accuracy of classification for most studied categories and demonstrated a direction in which discovery and early learning of new concepts can be bridged to permanent learning, with potential applications in the fields where massive amounts of prior knowledge aren’t yet available, as well as in self-learning and general learning artificial systems.
Privacy preserving integrity checking of shared dynamic cloud data with user revocation

In Cloud computing, storage and maintenance of the data are provided as service to the client. Traditionally, when the clients store and maintain their data at their own server, the data are dynamic in nature and are often shared among groups of users. The clients desire the traditional flexibility when they shift their data to the Cloud. Introducing the flexibility of sharing dynamic data with storage outsourced to the Cloud needs addressing new security challenges and requires the provision for user revocation. This paper presents a secure and effective scheme for storage of shared dynamic data in untrusted cloud servers with provisions for privacy preserving integrity check by third-party auditor and for addition and revocation of users. This proposed scheme is based on CDH-based ring signature and vector commitment.
FPGA based architecture for securing IoT with blockchain

The Internet of Things (IoT) became widely utilized during last years, due to the large number of object that are connected to it - not only computers, but also humans, sensors and actuators. In the near future, a large number of objects will communicate to each other in a way never experienced before by the humankind. Connecting all these objects together will come with a wide area of challenges. One of the most important issues concerns trust. In majority of cases, centralized architectures are used to solve the problem, but the enormous number of object that can be connected in IoT will make almost impossible to manage and implement these solutions. Blockchain technology offers powerful solutions for decentralized architectures, which can be the future of IoT systems. A strong solution needs to be flexible to changes and must offer powerful resources to be successful. A hardware platform that uses FPGAs is suitable for those systems. In this paper we propose a solution for securing IoT systems using blockchain technology, implemented on a FPGA based architecture.
Augmented reality based on stem for supporting science literacy in vocational education

Abstract AR development is becoming very popular in various fields of science. Some researchers have demonstrated that AR has the potential in science, technology, engineering, and mathematics (STEM). Puse of total AR integrated STEM needs to be studied more deeply, how STEM-AR characteristics, and how the potential of AR-based STEM to support the scientific literacy of students. Searches through relevant sources, the online database of which seven ACM Digital Library, ERIC, ieeexplore, ISI Web of Science, ScienceDirect, Scopus, and Springer. The results showed that the use of STEM-AR potentially an effort to improve science literacy.