Reviewerly/exHarmony huggingface dataset:
Face Identification Using Conditional Generative Adversarial Network

Abstract Most of research studies that have dealt with face corrupted images to the level of being unrecognizable by a human are based on full image reconstruction. In some real scenarios, a single corrupted image might need to be recognized among a limited number of available clean images. This study deals with face identification from artificially corrupted images with various kinds of noises. The work proposes a face identification conditional generative adversarial network (FI-CGAN) model to identify faces based on the CGAN. The proposed models reconstruct the corrupted image based on available clean images to map the corrupted image to a specific label. The classification is made using the nearest neighbor method with peak signal-to-noise ratio, mean squared error and structural similarity index as metrics. The study used the Specs on Faces dataset and achieved a satisfactory performance for face identification.
Dual-Channel Domain Adaptation Model

Document-level cross-domain sentiment analysis aims to leverage useful information in the source domain to help infer document-level sentiment on the target domain. The existing cross-domain sentiment analysis methods neglect complex syntactic structure and diversified semantic information of document text in different domains. Therefore, we proposed a novel dual-channel domain adaptation model (DCDA) for document-level cross-domain sentiment analysis. It consists of feature extraction module and domain adaptation module. The dual-channel feature extraction module adopts hierarchical attention structure to extract context channel features at word level and sentence level. In addition, different attention strategies are implemented at different levels, which enables accurate assigning of the attention weight. GAT is used to extract syntactic channel characteristics of documents. We adopt adversarial mutual learning in the domain adaptation module. It learns the domain-invariant features by using adversarial network learning, and makes full use of the information of the target domain to improve the classification effect by mutual learning. Experiments on multiple public datasets demonstrate the effectiveness of DCDA.
Shared Memories Driven by the Intrinsic Memorability of Items

When we experience an event, it feels like our previous experiences, our interpretations of that event (e.g., aesthetics, emotions), and our current state will determine how we will remember it. However, recent work has revealed a strong sway of the visual world itself in influencing what we remember and forget. Certain items—including certain faces, words, images, and movements—are intrinsically memorable or forgettable across observers, regardless of individual differences. Further, neuroimaging research has revealed that the brain is sensitive to memorability both rapidly and automatically during late perception. These strong consistencies in memory across people may reflect the broad organizational principles of our sensory environment, and may reveal how the brain prioritizes information before encoding items into memory. In this chapter, I will discuss our current state-of-the-art understanding of memorability for visual information, and what these findings imply about how we perceive and remember visual events.
Current Status and Application Analysis of Graph Database Technology

With the massive growth of data, the correlation between data becomes more and more complex. Under this background, graph database develops rapidly. By analyzing the concept, model and structure of graph database, the characteristics of graph database are summarized. The three key technologies of graph database are expounded and analyzed in detail. The current graph database products are compared and analyzed, and the domestic and foreign research statistics are analyzed, and the current graph database application scenarios are summarized. Finally, the trend of graph database development in the future is proposed.
Evaluation of Dataset Distribution and Label Quality for Autonomous Driving System

Nowadays, some well-known open source datasets used to train models in autonomous driving domain, which still have some data quality problems, and greatly affect the accuracy of training results. Therefore, it is meaningful to study the influence of dataset's quality. According to ISO/IEC 25012:2008(E), this paper mainly evaluates the dataset distribution and the dataset label quality. Dataset distribution is the equilibrium between the features of the dataset, and the dataset label quality includes missing, duplicate and inconsistent label. We used the noise data learning model O2U-Net [1] to see the impact on accuracy by simulating dataset quality problems, such as changing the equilibrium and making noise labels. The experimental results show that adding “car” and other common features in automatic driving filed, had a higher accuracy in the model, while missing label and inconsistent label have the most serious impact on the accuracy of the model. In terms of the determination of dataset quality, more attention should be paid to the equilibrium of interested feature in the autonomous driving field, as well as the problems of missing label and inconsistency label. Finally, this paper integrates a data quality testing platform, which can well show the quality problems of autonomous driving datasets.
Weather image classification using convolutional neural network with transfer learning

Weather condition is an important factor that is considered for various decisions. In the industrial world, weather classification is very useful, such as in development of self-driving cars, smart transportation systems, and outdoor vision systems. Manual weather classification by humans is inconsistent and takes a long time. Weather forecast information obtained from the internet is not real time at a specific location. Weather image has unique characteristics because one type of weather can be like another. Computer vision is a branch of computer science to recognize or classify images that can assist in classifying weather images that do not depend on weather forecast information from the internet. This study aims to classify weather images using Convolutional Neural Network (CNN) with Transfer Learning. Four CNN architectures, MobileNetV2, VGG16, DenseNet201, and Xception were used to perform weather image classification. Transfer learning was used to speed up the process of training models to get better performance faster. The proposed method will be applied to the weather image which consists of six classes, cloudy, rainy, shine, sunrise, snowy, and foggy were classified in this study. The experiment result with 5-cross validation and 50 epochs showed that the Xception has the best average accuracy of 90.21% with 10,962 seconds of average training time and MobileNetV2 has the fastest average training time of 2,438 seconds with 83.51% of average accuracy.
Transfer Learning Based Facial Recognition System

Facial recognition has become an important factor in our life because of its usage in various applications. These applications include unlocking smartphones, smart advertising, prevent retail crimes; tracking school attendance, recognizing important dignitaries at events, find missing persons and others. Apart from its uses, it also has various challenges to encounter such as different poses, background, noise, illumination and others. We have various Convolutional Neural Network (CNN) models and learning methods employed which gives optimal results. These models have various layers in them which extract important information from the input image, reduce dimensionality and pass this information to further layers. This paper focuses on two aspects facial detection and classification using deep learning. The work proposes a deep learning model to capture these aspects. Experimentation takes images from the database, applies Image Generator for Augmentation, trains the model for a certain number of epochs where the model adjusts its weights using backpropagation to give better accuracy. Performance evaluation of the model shows that it has been successful in predicting the faces of individuals correctly with an accuracy of 89%.
Using data mining as a tool for anomaly detection in food safety audit data

The integrity of third-party food safety audits has been constantly challenged by food safety incidents of certified food businesses. Integrity programs have been established for food safety certification program owners (CPOs) to monitor the involved parties’ performance and find anomalies in audit data. To find such anomalies in a large amount of data is labour intensive, and no standard approach has been established. This paper explored data mining approaches and leveraged algorithms to automate integrity checks. Furthermore, this paper provides initial validation of a suitable algorithm. Out of three potentially suitable algorithms, the couple-biased random walk (CBRW) algorithm was chosen as the basic algorithm to find anomalies in audit data. This algorithm was adjusted and expanded to show contributing factors for a potential anomaly enabling integrity managers to find the reason for potential anomalies faster. Three experts validated the sample findings of the algorithm and discussed these findings in detail. The validation showed an 80% accuracy of the algorithm and brought up findings that were not known before by the experts. The findings justify further exploration of data mining for anomaly detection in food safety audits.
Learning-Based Optimization for Online Approximate Query Processing

Approximate query processing (AQP) technique speeds up query execution by reducing the amount of data that needs to be processed, while sacrificing the accuracy of the query result to some extent. AQP is essentially a trade-off between the accuracy of the query result and the query latency. However, the heuristic AQP optimization and error control mechanism used by the existing AQP system fails to meet the accuracy requirements of users. This paper proposes a deep learning-based error prediction model to guide AQP query optimization. By using this model, we can estimate the errors of candidate query plans and select the appropriate plans that can meet the accuracy requirement with high probability. Extensive experiments show that the AQP system proposed in this paper can outperform the state-of-the-art online sampling-based AQP approach.
Website Fingerprinting on Access network and Core Gateway

Website fingerprinting (WF) is a traffic analysis technique that enables attacker passively inspecting user’s traffic to infer which website the user is accessing, especially sensitive sites, has been proven effective in laboratory open-world environment experiment. Several WF claim a high performance, but implicitly assume that clients only visit web pages, which fundamentally limits its practicability. We want to investigate their usefulness in open-real-world. We proposed a two-stage scheme and realized a high performance of Tor traffic identification and website fingerprinting attack on the access network and core gateway, which is a more realistic application scenario. Then we analyzed the classification performance of using statistical features and packet sequence features on the access network and core network. We can conclude that our method works fine on both networks, but improving performance on core gateway is difficult.
Use of Digital Tools for Checking Uniqueness by Students and Academic Staff of the Borys Grinchenko Kyiv University: Problems and Solutions

The article examines the level of mastery by the Borys Grinchenko Kyiv University students, master’s students, postgraduates, academic staff and researchers of the digital tools allowing to check the uniqueness of academic texts. The anti-plagiarism software most popular among the respondents was identified; its advantages and shortcomings, as well as the difficulties that arise when using it were analyzed. Proposals on how to increase the level of mastery of skills in self-regulation of educational and scientific activity, in particular writing own academic texts, for all participants in the Borys Grinchenko Kyiv University educational and scientific process were developed. Based on results of the survey, an algorithm for detecting the absence/presence of academic plagiarism in the student research papers submitted to the Ukrainian Competition of Student Research Papers in the Fields of Knowledge and Specialties was developed and launched, indicating the responsibility of all actors of the process for observance of the principles of academic integrity.
Cybersickness-aware Tile-based Adaptive 360° Video Streaming

In contrast to traditional videos, the imaging in virtual reality (VR) is 360&#x00B0;, and it consumes larger bandwidth to transmit video contents. To reduce bandwidth consumption, tile-based streaming has been proposed to deliver the focused part of the video, instead of the whole one. On the other hand, the techniques to alleviate cybersickness, which is akin to motion sickness and happens when using digital displays, have not been jointly explored with the tile selection in VR. In this paper, we investigate Tile Selection with Cybersickness Control (TSCC) in an adaptive 360&#x00B0; video streaming system with cybersickness alleviation. We propose an <tex>$m$</tex>-competitive online algorithm with Cybersickness Indicator (CI) and Video Loss Indicator (VLI) to evaluate instant cybersickness and the total loss of video quality. Moreover, the algorithm exploits Sickness Migration Indicator (SMI) to evaluate the cybersickness accumulated over time and the increase of optical flow to improve the tile quality assignment. Simulations with a real network dataset show that our algorithm outperforms the baselines regarding video quality and cybersickness accumulation.
Collaboration of Dermatologists and ML Models to Obtain Improved Predictions

Skin cancer is often diagnosed visually. An early detection of malignant melanomas and the removal afterwards can prevent death. It has been shown that convolutional neural networks (CNNs) outperform experienced dermatologists. Despite the rapid successes in machine learning (ML), most physicians still have little confidence in the prediction of these ML models.In this paper, we propose to use a heat map to (i) establish confidence in the predictions of ML model, and (ii) avoid possible errors. In our analysis, we found that shaded areas or hairs in the image were incorrectly detected as melanoma. A dermatologist in collaboration with an ML model will thereforealways be better than just the dermatologist or just the model alone.
A Robustness-Oriented Data Augmentation Method for DNN

The application of deep neural networks (DNN) is extensive and has achieved great success in many fields. However, the security and reliability are two great challenges for the system based on DNN. The robustness is an important attribute to measure the security and reliability of DNN models. We observe that further training the model through the small variation of the training dataset can improve the robustness while maintaining the accuracy on the testing dataset. Based on this observation, we propose a general DNN training framework, STYX,conducting on the alternative training dataset. We have carried out experiments on several commonly used datasets, whose results show that STYX can improve the model's robustness and keep its accuracy on the testing dataset as much as possible.
Human Pose Estimation Based on GAN and DCGAN Models

As an important application based on the field of computer vision, the various human movements and gestures can be reconstructed by detecting the key articulation points of the human body. It is mainly used in human behavior recognition, human-computer interaction, and attitude tracking. However, the current human pose estimation models have many challenges, such as difficulty detecting the non-typical articulation points of the human body and inaccurate locating of the extremities. They are prone to error or lack of information in complex situations. This paper proposed GAN and DCGAN models to tackle this issue, which can improve the accuracy of human posture prediction. This paper mainly focuses on the contribution of Generative Adversarial Network to the detection of human key articulation points, revising the original model posture and obtaining a model that is closer to the real posture of the human body. The experimental results demonstrated that the model's accuracy is improved to a certain extent after using the DCGAN model. Furthermore, we note that in most cases, the performance of the proposed model is superior to others.
Real-Time Focused Extraction of Social Media Users

In this paper, we explore a real-time automation challenge: the problem of focused extraction of Social Media users. This challenge can be seen as a special form of focused crawling where the main target is to detect users with certain patterns. Given a specific user profile, the task consists of rapidly ingesting Social Media data and early detecting target users. This is a real-time intelligent automation task that has numerous applications in domains such as safety, health or marketing. The volume and dynamics of Social Media contents demand efficient real-time solutions able to predict which users are worth to explore. To meet this aim, we propose and evaluate several methods that effectively allow us to harvest relevant users. Even with little contextual information (e.g., a single user submission), our methods quickly focus on the most promising users. We also developed a distributed microservice architecture that supports real-time parallel extraction of Social Media users. This modular architecture scales up in clusters of computers and it can be easily adapted for user extraction in multiple domains and Social Media sources. Our experiments suggest that some of the proposed prioritisation methods, which work with minimal user context, are effective at rapidly focusing on the most relevant users. These methods perform satisfactorily with huge volumes of users and interactions and lead to harvest ratios 2 to 9 times higher than those achieved by random prioritisation.
Training a Robotic Arm Movement with Deep Reinforcement Learning

This paper introduces a general experimental design scheme for conditions and parameter settings of robotic arm control under the specific task when using Deep Deterministic Policy Gradient(DDPG) algorithm to train the robotic arm for completing the control task. Based on the Coppelia simulation tool, this paper builds an interactive reinforcement learning environment for robotic arm control tasks, and designs two different control tasks to verify the validity of experimental design schemes. Conclusions in this paper provide an important reference for finding suitable environmental design and parameter settings for using DDPG to train a manipulator and improving the training effect.
Dancing With Wolves: An Intra-Process Isolation Technique With Privileged Hardware

Intra-process memory isolation is a cornerstone technique of protecting the sensitive data in memory-corruption defenses, such as the shadow stack in control flow integrity (CFI) and the safe region in code pointer integrity (CPI). In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SEIMI</small> , a highly efficient intra-process memory isolation technique for memory-corruption defenses. The core is to use the efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Supervisor-mode Access Prevention (SMAP)</i> , a hardware feature that is originally used for preventing the kernel from accessing the user space, to achieve intra-process memory isolation. To leverage SMAP, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SEIMI</small> creatively executes the user code in the privileged mode. In addition to enabling the new design of the SMAP-based memory isolation, we further develop multiple new techniques to ensure secure escalation of user code. Extensive experiments show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SEIMI</small> outperforms existing isolation mechanisms, including the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memory Protection Keys</i> (MPK) based scheme and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memory Protection Extensions</i> (MPX) based scheme.
Large-Scale Computer Simulation of the Performance of the Generalized Nets Model of the LPF-algorithm

Large-scale simulation of the throughput (TP) of an existing LPF-algorithm (Longest Port First) for crossbar switch are presented. The throughput for Generalized Nets (GN) model of algorithm is studied for uniform independent and identically distributed Bernoulli traffic. The presented simulations are executed on the AVITOHOL supercomputer located at the IICT, Bulgaria. The modeling of the TP utilizes LPF for a switch with $$N\in [2,60]$$ commutation field size. Problems arise due to the time complicatedness of the implementation of the LPF-algorithm $$(O(N^{4.7})$$ ). It is necessary to reduce the time complexity without introducing distortions into the results of simulation. One variant of the LPF with simplified random selection of a starting cell of weighting-matrix are discussed.
The Use of Arabic Language COVID-19 Tweets Analysis in IoT Applications

Social media platforms have become one of the most powerful tools for organizations and individuals to publish news and express thoughts or feelings. With the increasingly enormous number of internet users in Saudi Arabia, the need raised to analyze Arabic posts. Since the emergence of COVID-19 in the latest 2019, it lefts economies and businesses counting the cost while governments fight the spread of the virus with new compartmentalization measures. Keeping in view the importance of quick and timely data analysis and sharing for policy actions, Artificial intelligence (AI) has played a crucial role in facilitating the exchange of views and information between scientists and decision-makers during the Coronavirus pandemic, and they continue to do so. This work mined to these content-related tweets to see how people’s feelings and expressions are changing. The results of this analysis can be used with integration with several IoT technologies to reduce the impact of covid-19 and drive new decisions in this field. For this goal, we proposed a Machine Learning (ML) models that can classify both of the sentiment and topic of Modern Standard Arabic (MSA) tweets and achieve high accuracy results.
Starting Point Selection and Multiple-Standard Matching for Video Object Segmentation With Language Annotation

In this study, we investigate language-level video object segmentation, where first-frame language annotation is used to describe the target object. Because a language label is typically compatible with all frames in a video, the proposed method can choose the most suitable starting frame to mitigate initialization failure. Apart from extracting the visual feature from a static video frame, a motion-language score based on optical flow is also proposed to describe moving objects more accurately. Scores of multiple standards are then aggregated using an attention-based mechanism to predict the final result. The proposed method is evaluated on four widely-used video object segmentation datasets, including the DAVIS 2017, DAVIS 2016, SegTrack V2 and YouTubeObject datasets, and a novel accuracy measured as mean region similarity is obtained on both the DAVIS 2017 (67.2%) and DAVIS 2016 (83.5%) datasets. The code will be published.