Reviewerly/exHarmony huggingface dataset:
Recognition of Obscure Objects Using Super Resolution-Based Generative Adversarial Networks

Object recognition has achieved a good progress in computer vision, but still it is a difficult task in case of low-resolution images, because traditional discriminant features in high resolution usually disappear in low resolution. In surveillance systems, the region of interests gets blurred due to the distance between the camera and object and also due to illumination effects. In low-resolution images, objects appear very small and blurred, thus making recognition of those objects tedious. Super resolution for natural images is a classic and difficult problem in image and video processing. But rapid developments in deep learning have recently sparked interest in super resolution of images. In this paper, generative adversarial network (GAN) that has been successfully employed in generating images and realistic textures with fine details has been extended to the application of image super resolution. The paper aims at improving the resolution of the obscure objects to improve the classification accuracy of the system. This is done by detecting obscure objects using RCNN, then improves its resolution using GAN network and finally classifies the improved images using AlexNet. The experiment is conducted using a MSCOCO dataset and collected shoe database in Google COLAB. The super resolved images increase the classification accuracy by 16%.
Resource Prediction Algorithm for IoT Devices

The prediction of resources in IOT platform plays important role in the network environment for the deployment application such as workload management, capacity planning, and dynamic allocation. In this paper we develop a new prediction model for the predicting multiple resources as per requirement needs. The new model is ARMA (Auto Regressive Moving Average) is to predict with highest accuracy of resource needs of different application in terms of, Bandwidth, RAM, Storage, CPU utilization. The new model is also predicting the throughput and response time, which intern enable the clients to have good scaling decision. The new model gives us a optimal solution as compared to other models. IoT is able to greatly improve the resource utilization of smart devices and promote the harmony between man-made and natural environments. Utilized as resources which is to run a composite service that supports user tasks. This heterogeneous device interaction causes difficulties to interact with the devices while gathering real time information management and monitoring from the environment.
Feature Learning Capacity Assessment of Deep Convolutional Generative Adversarial Network for Action Recognition in a Self-Supervised Framework

Feature learning has always been a critical and most important problem in the field of computer vision. Most of the research community is addressing the problem of feature learning using supervised learning which requires a lot of manually annotated data. In this paper, a self-supervised framework is proposed to evaluate the feature learning capability of the discriminator of a deep convolutional generative adversarial network (DCGAN) via action classification. The DCGAN is trained on action videos of the UCF101 dataset without using any label information and then the trained discriminator is extracted from the DCGAN network. The trained discriminator is used to generate feature vectors. The action classification is performed by finding the similarity between these feature vectors using multiple similarity measures. The experimental results prove that discriminator is a good feature vector generator as the maximum number of action classes are classified correctly without using any annotated data.
GloVe-FRCNN: Comprehensive network algorithm for Vespa mandarinia image-text extraction and classification

In this paper, a synthesis algorithm of text classification and image recognition is proposed. This algorithm directly extends the original image recognition method. And its novelty is based on the fusion algorithm of image recognition and text classification as a new mathematical derivation approach, which uses stepwise linear fitting to fuse multiple models. We use the new fitting method, which makes use of the idea of fitting the model with different weights, to expand the training data. The principle of the method is introduced in detail, and the experimental results are given.
Inter – intra observer variability using deep learning and traditional image processing for breast cancer

Breast cancer is one of the most common life-threatening diseases that affects women globally. Saudi Arabia is also one of the countries that suffer from a serious number of this disease among women. In terms of diagnosis modalities, a mammogram is the first line for detecting breast cancer. In addition, breast cancer can be screened by real-time ultrasound images, which are of relatively less quality and have more impact (noninvasive) images. Therefore, the purpose of this study is to develop image enhancement techniques using deep learning and image processing techniques. The main goal is to improve the ultrasound images in order to help radiologists screen the disease more accurately. For this study, ninety female patients of ages between 15 - 77 years are considered. These patients were already diagnosed using ultrasound with breast lesions. The images are visually graded and evaluated by two trained radiologists, both pre- and post-enhancement. In particular, two parameters were considered; 1) BI-RAD categories, 2) Breast cancer classification. The agreement between radiologists and post-enhancement was assessed using simple kappa and weighted kappa statistics. Moreover, sensitivity and specificity are also calculated.
Graph Matching in Graph-Oriented Databases

Modern graph database management systems use graph structures for semantic queries with nodes, edges, and properties to connect to and store information. Due to their schema-less nature, inappropriate data migration and manipulation can lead to severe data loss during the data query process. Data migration in graph databases strongly depends on graph matching methods to detect similar entities. This article describes a graph matching mechanism based on similarity measures to efficiently migrate data and avoid data loss within the different entities of the graph database.
Spatial-Channel Attention-Based Class Activation Mapping for Interpreting CNN-Based Image Classification Models

Convolutional neural network (CNN) has been applied widely in various fields. However, it is always hindered by the unexplainable characteristics. Users cannot know why a CNN-based model produces certain recognition results, which is a vulnerability of CNN from the security perspective. To alleviate this problem, in this study, the three existing feature visualization methods of CNN are analyzed in detail firstly, and a unified visualization framework for interpreting the recognition results of CNN is presented. Here, class activation weight (CAW) is considered as the most important factor in the framework. Then, the different types of CAWs are further analyzed, and it is concluded that a linear correlation exists between them. Finally, on this basis, a spatial-channel attention-based class activation mapping (SCA-CAM) method is proposed. This method uses different types of CAWs as attention weights and combines spatial and channel attentions to generate class activation maps, which is capable of using richer features for interpreting the results of CNN. Experiments on four different networks are conducted. The results verify the linear correlation between different CAWs. In addition, compared with the existing methods, the proposed method SCA-CAM can effectively improve the visualization effect of the class activation map with higher flexibility on network structure.
Machining technology of shaft parts based on CAD technology

With the continuous development of manufacturing industry, the requirements of process design with system intelligence and informatization are higher and higher. Traditional manual technology can not meet the modern requirements, modern intelligent computer-aided process design is more and more widely used. The purpose of this study is to use CAD technology with process shaft technology. In this study, different radii of shaft components were selected and horizontal tensions of 600 kN and 1200 kN were applied to test their effects and efficiency. We compared the processing efficiency and accuracy of conventional manual processing technology and computer-assisted design. In this study, a parametric process planning template with constraint knowledge is established for each part group and stored in the process planning template library. A parametric process plan was derived using the 3d model and the feature parameters extracted from the specific manufacturing environment knowledge base. The results show that the amplitude frequency characteristic of the whole workpiece increases first and then decreases from the chuck to the tip, and the amplitude frequency characteristic amplitude is the largest at the distance of 3/4 from the chuck. The maximum value appears at 174.47Hz and the amplitude is 0.000427N/mm. Compared with manual machining, the machining time of CAD machining mode is reduced by 22.94%, the metal removal rate is increased by 34%, and the cutting efficiency is significantly improved. It is concluded that the computer-aided design technology in this study plays a more accurate and time-saving role in the processing of shaft parts and contributes to the intelligent part processing.
Attribute-Based Encryption

In today’s world data is growing enormously. With the advancement in data, there is need of effective data security mechanism to handle this stored or transmitted information. This necessity introduced science of cryptography. Being an effective cryptographic system attribute-based encryption (ABE) became hot topic of research among the researchers. ABE is a kind of public key cryptography system. It uses ciphertext and private key of user for the encryption purpose. This scheme hooked up ciphertext with attributes (e.g., e-mail id, country name) which ensures high level of protection. This ABS scheme ensures data confidentiality and exclusive access policy. This chapter studies basic ABS system followed by brief introduction of few popular algorithms like Public Key Encryption, Public Key Infrastructure, and Identity Based Encryption. It also enlightened two major types of ABE, i.e., Key Policy-ABE (KP-ABE) and Ciphertext Policy-ABE (CP-ABE). Finally, it is concluded with security model of ABE with its comparative popular schemes.
Combined Classification Models Applied to People Personality Identification

The popularization of social networks has considerably increased the volume of data generated from the interaction between people. Understanding this data can be useful both for companies and governments and for users. This work proposes to study how to infer the behavior of people on social networks from published comments, specifically using the Myers-Briggs Typological Indicator (MBTI) in a social network focused on discussions on behavioral issues. The analysis carried out employs Natural Language Processing (NLP) techniques, resampling of the data set and classification algorithms combined by Majority Vote. The results showed 90% efficiency of the combiner with the use of random oversampling. SVM and KNN were the best individual classifiers regardless of the resampling technique used. Although smaller compared to the best individual classifier, the combination approach shows a decrease in the misclassification for INFJ and INFP classes up to 11% and 34%, respectively.
A Comprehensive Analysis of Methodologies used for Object Detection and Localization

In this world depending more on automation, Object localization and Object detection have revolutionized the field of computer vision. Computer vision is essential for an autonomous vehicle, facial recognition, health care, manufacturing, and agriculture. As computer vision is used in real-time, the accuracy performance of the model for object localization and detection is crucial. The purpose of this study is to analyze various methodologies used for object detection and localization. In this paper, a comprehensive survey of object localization and detection techniques has been detailed. This paper also compares performance metrics and discusses the limitations of models used for object detection and object localization.
Linear regression with partially mismatched data: local search with theoretical guarantees

Abstract Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression in which the predictor-response pairs are partially mismatched. We use an optimization formulation to simultaneously learn the underlying regression coefficients and the permutation corresponding to the mismatches. The combinatorial structure of the problem leads to computational challenges. We propose and study a simple greedy local search algorithm for this optimization problem that enjoys strong theoretical guarantees and appealing computational performance. We prove that under a suitable scaling of the number of mismatched pairs compared to the number of samples and features, and certain assumptions on problem data; our local search algorithm converges to a nearly-optimal solution at a linear rate. In particular, in the noiseless case, our algorithm converges to the global optimal solution with a linear convergence rate. Based on this result, we prove an upper bound for the estimation error of the parameter. We also propose an approximate local search step that allows us to scale our approach to much larger instances. We conduct numerical experiments to gather further insights into our theoretical results, and show promising performance gains compared to existing approaches.
Extracting Insights From Bitcoin Transactions

Bitcoin is the most well-known cryptocurrency. It was first released in 2009 by Satoshi Nakamoto. Bitcoin serves as a decentralized medium of digital exchange, with transactions verified and recorded in the blockchain. The latter is a public immutable distributed ledger that operates without the need of a trusted record keeping authority or a central intermediary. It provides OLTP capabilities with both atomic transactions and data durability guarantees for blockchain transactions. Blockchain ledgers were not designed to perform analytics questions. The availability of the entire bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analyzing the transactions to obtain insights on users/entities patterns and transactions patterns. For these purposes, the authors need to store and analyze cryptocurrency transactions in a data warehouse. In this chapter, they investigate public blockchain datasets, and they overview different data models for setting up a data warehouse appliance of cryptocurrencies.
Characterization of the use of google classroom in the 2020 COVID-19 Year, by the students of system engineering of the TECNM Campus, Instituto Tecnologico de Mexicali

During this year 2020, the COVID-19 pandemic has prompted people to look for online communication tools such as Google classroom, The objective of this quantitative research is to measure the impact on users of the computer systems engineering career of the Institute Tecnológico de Mexicali with 395 enrolled students and make a diagnosis, based on the results of a survey applied to 65 students.
Hybrid Security Risk Assessment Model

Cybersecurity risk management often uses experience-based data to quantify the potential risks of new security technologies based on their exploitability and impact. However, use of such data may be limited and is rarely reusable because it often contains confidential information. This paper proposes a new approach using the Department of Homeland Security’s public National Vulnerability Database (NVD) for information on known vulnerabilities, and MITRE’s public Common Attack Pattern Enumeration and Classification (CAPEC™) tools as the basis of a risk scoring system.
Cross-Scene Person Trajectory Anomaly Detection Based on Re-Identification

In this work, we consider the cross-scene person trajectory anomaly detection problem, which detects the anomalous trajectories across multiple nonoverlapping scenes. This problem is highly significant for public security, but it is still underexplored. Since the trajectory is not continuous across nonoverlapping camera views, we take use of person reidentification (re-ID) to associate the same pedestrian in different scenes while mitigating its inaccuracy by a directional probabilistic graph. To better distinguishing normal samples from anomalies, We formulate a maximized margin graph autoencoder (MMGAE) model, and the reconstruction error of the MMGAE is regarded as an anomaly indicator for the sample. To verify the effectiveness of our approach, we collected and labeled a new dataset. we also explore the impact of the re-ID performance on the anomaly detection problem and the effect of an inaccurately constructed graph on the MMGAE.
A Brain-Like Computational Model Based on a Shared Memory

Recently, scientists have found great similarities between human brains and those of primates. We propose a brain-like computational model after significantly studying the problem solving systems of the behavioral control in the primate brain. Instead of working on the specific roles of tens of billions of neurons and how they might be connected with each other, we mainly focus on the accumulative and overall performance of the brain subsystems, each of which is modeled by a computational system or a certain algorithm. We propose a four-level computational system, where the four levels of the model are independent with each other, at the same time, one level is also connected to the others. The connectivity among levels has been studied as well. The four parallel subsystems are different in many ways, such as the sensory inputs, computational power, model complexity, and so on. However, all systems share a same working memory which stores past experience in all states and helps to make better decision at a near future. The model has been simulated in a challenging foraging problem. The experimental results show that the model works well on various environments with spares or dense obstacles.
Experiencing Embodied Sonic Meditation Through Body, Voice, and Multimedia Arts

This chapter introduces the Embodied Sonic Meditation practice by elaborating three proof-of-concept case studies, their social impact, and an evaluation framework that examines the body-sound relationship. Embodied Sonic Meditation is a sonic and multimedia art practice based on the combination of sensing technology and human sensibility. It is based on Tibetan contemplative culture and arts, Pauline Oliveros’ of “Deep Listening” practice, and embodied cognition. It invites new ways of using sensorimotor coupling through auditory feedback to create novel artistic expressions and to deepen our engagement in the world that we are living in. This series of works demonstrates that through the practice of “Embodied Sonic Meditation” and her own work, a media artist can not only promote traditional underrepresented culture but also project these cultural values and identities into the cutting-edge art and technology of contemporary society.
Robustness to Approximations and Model Learning in MDPs and POMDPs

In stochastic control applications, typically only an ideal model (controlled transition kernel) is assumed and the control design is based on the given model, raising the problem of performance loss due to the mismatch between the assumed model and the actual model. In some further setups, an exact model may be known, but this model may entail computationally challenging optimality analysis leading to the solution of some approximate model being implemented. With such a motivation, we study continuity properties of discrete-time stochastic control problems with respect to system models and robustness of optimal control policies designed for incorrect models applied to the true system. We study both fully observed and partially observed setups under an infinite horizon discounted expected cost criterion. We show that continuity can be established under total variation convergence of the transition kernels under mild assumptions and with further restrictions on the dynamics and observation model under weak and setwise convergence of the transition kernels. Using these, we establish convergence results and error bounds due to mismatch that occurs by the application of a control policy which is designed for an incorrectly estimated system model to the actual system, thus establishing results on robustness. These entail implications on empirical learning in (data-driven) stochastic control since often system models are learned through empirical training data where typically the weak convergence criterion applies but stronger convergence criteria do not. We finally view and establish approximation as a particular instance of robustness.
Zero Knowledge Adversarial Defense Via Iterative Translation Cycle

Image classification networks based on deep learning are found to be vulnerable to the carefully designed adversarial examples. The existing approaches are still far from solving this problem. In this paper, we propose iterative translation cycle GAN (ITC-GAN) to jointly optimize generators and discriminators, so as to defense against adversarial examples with zero knowledge of adversarial attacks. We train two generators to form an iterative translation cycle which changes deep features of the input images and then reconstructs them. As the cycle of image translation can be conducted iteratively, the noises of adversarial examples are gradually eliminated. We only use clean images to train the whole ITC-GAN, so our method is not coupled to specific attack methods and specific classifiers. We conduct experiments on MNIST, CIFAR10, and ImageNet50. The experimental results demonstrate that ITC-GAN is more robust and flexible than state-of-the-art methods in different adversarial settings.