Reviewerly/exHarmony huggingface dataset:
Modified CNN model-based Forgery Detection applied to Multiple-Resolution Tampered Images

The crucial problem of forensic techniquesis is how to detect/recognize tampered images through public media platforms under the attactks of subjective modifications. Because of many accessible photoshop programs, an image/video such as in Facebook, Instagram, Reddit Twitter, etc. can be easily tampered to falsify the information within the image. Accoding to the requirement of an efficient method for detecting fake images, we have developed modifed CNN models which are combined with the super-resolution approach to solve this issue. In the paper, we present an appropriate method using CNN models to detect tampered images with the increase in resolutions of the tampered areas, the proposed model can detect and point out the areas that have been tampered. The ResNet50 and mUNet modified models are used for classification and segmentation respectively. With the developed models, the results were given with an accuracy of at least 90% on the evaluation sets.
Power skill training knowledge base construction based on knowledge map and improved FP-Growth

This paper designs the power skill training knowledge base model based on the knowledge mapping, which includes two levels and four basic elements, based on the analysis of the characteristics of power grid skill training. An improved FP-Growth algorithm considering interest value coefficient is proposed, which can filter the invalid learning access out of the frequent items. In this paper, the power skill training knowledge base is preliminarily established. The learning data of some trainees in the knowledge base are selected to update the resource association relationship. The updated training knowledge base enriches the resource association relationship, realizing the resource mining and integration among multiple posts and capabilities. The power skill training knowledge base can provide an intelligent and personalized skill training platform for power technical staff, to improve the skill level of staff, and further ensure the safe and reliable operation of the power grid.
Helmet Detection Algorithm Based on the Improved YOLOv5 and Dynamic Anchor Box Matching

To solve the problems of low recognition accuracy and undetectable helmet of small targets in helmet detection in complex scenes, a helmet detection algorithm based on improved YOLOv5 and dynamic anchor box matching is proposed to improve the detection efficiency of small helmets in complex scenes. Firstly, by adding a small target detection layer in the YOLOv5 network, the detection accuracy of small targets is preliminarily improved; Secondly, convolution block attention model (CBAM) is added to the feature extraction network to enhance the information transmission between feature layers and the recognition of foreground and background by the neural network; Finally, to further improve the detection rate of small target helmet, the accuracy of a priori frame matching is enhanced by dynamic topK anchor frame matching. The weight of pre-training on the COCO data set is fused for detection and recognition to improve the generalization and accuracy of detection. The experimental results show that in the helmet data set constructed in this paper, the detection accuracy of helmets is 98.2&#x0025;, and the helmet detection of small targets is realized.
The Impact of Mixed Reality in Education System-the Classroom Environment

In the mysterious realm full of emerging technologies, there is a fantastic role played by Mixed Reality techniques. The blending of real and virtual worlds that allows individualized user control opportunities of the environment around user remarks the unique feature of Mixed Reality. The current work shows the effort of many researchers who have tried to explore the field of Mixed Reality in their own planned ways for the creation of Mixed Reality smart classroom. The objective is to introduce, how Mixed Reality device can act as real-time analytical tool for the teacher in a classroom to enhance the learning process of the students. The first part introduces the advent of Mixed Reality with some defined theories that are widely accepted and a glimpse of approaches done by many researchers. Later, an overview of an attempt to create a Mixed Reality classroom, two different hypotheses incorporating the same Mixed Reality tool called Lumilo were experimented with the assistance of AIED and ITS systems. Followed by the results of the hypotheses, aiming one of them to be employed in the classroom environment where both teachers and students were most benefitted. This type of environment resulted in better modes of education when compared to the existing approaches of Mixed Reality classroom.
Chinese Character Learning Information Platform Based on Multimedia Information Technology

MIT (multimedia information technology) has brought profound changes to education, and MIT teaching has become an effective teaching method. Based on the teaching concept of MIT and curriculum integration, the connotation and goal of MIT Chinese and the Chinese character learning information platforms are clarified. In this paper, the design of a Chinese character learning information platform based on MIT is chosen as the study object. Starting with the investigation and analysis of the application of multimedia teaching resources by teachers in oral Chinese teaching, the problems in the construction of Chinese teaching resources are summarized. This paper combines the characteristics of domain ontology with those in the process of word segmentation to disambiguate ambiguous fields by calculating the strength of semantic association, so as to maximize the speed of ambiguity identification and resolution without losing AR (accuracy rate). After experiments, the system found a balance between RR (recall rate), AR, and the speed of Chinese character segmentation.
Defense Against Explanation Manipulation

Explainable machine learning attracts increasing attention as it improves the transparency of models, which is helpful for machine learning to be trusted in real applications. However, explanation methods have recently been demonstrated to be vulnerable to manipulation, where we can easily change a model's explanation while keeping its prediction constant. To tackle this problem, some efforts have been paid to use more stable explanation methods or to change model configurations. In this work, we tackle the problem from the training perspective, and propose a new training scheme called Adversarial Training on EXplanations (ATEX) to improve the internal explanation stability of a model regardless of the specific explanation method being applied. Instead of directly specifying explanation values over data instances, ATEX only puts constraints on model predictions which avoids involving second-order derivatives in optimization. As a further discussion, we also find that explanation stability is closely related to another property of the model, i.e., the risk of being exposed to adversarial attack. Through experiments, besides showing that ATEX improves model robustness against manipulation targeting explanation, it also brings additional benefits including smoothing explanations and improving the efficacy of adversarial training if applied to the model.
Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

With the explosion of social media, the Web, Internet of Things, and the proliferation of smart devices, large amounts of data are being generated each day. However, traditional data management technologies are increasingly inadequate to cope with this growth in data. NoSQL has become increasingly popular as this technology can provide consistent, scalable and available solutions for the ever-growing heterogeneous data. Recent years have seen growing applications shifting from traditional data management systems to NoSQL solutions. However, there is limited in-depth literature reporting on NoSQL storage technologies for big graph and their applications in various fields. This chapter fills this gap by conducting a comprehensive study of 80 state-of-the-art NoSQL technologies. In this chapter, we first present a feature analysis of the NoSQL solutions and then generate a data set of the investigated solutions for further analysis in order to better understand and select the technologies. We perform a clustering analysis to segment the NoSQL solutions, compare the classified solutions based on their storage data models and Brewer's CAP theorem, and examine big graph applications in six specific domains. To help users select appropriate NoSQL solutions, we have developed a decision tree model and a web-based user interface to facilitate this process. In addition, the significance, challenges, applications and categories of storage technologies are discussed as well.
Face animation making method based on facial motion capture

Facial motion capture technology is applied to 3D animation, games, CG films and virtual film and television. This article introduces three types of 3D face reconstruction methods and the method of facial motion capture data-driven facial animation by introducing the data collection and data optimization of Dynamixyz, a facial capture system without markers and Vicon Cara, a facial motion capture system with markers. Face animation based on face capture is realized, which makes face animation more efficient, natural and realistic.
Performance analysis of different distribution of Python and TensorFlow to efficiently utilize CPU on HPC Cluster

In recent years, Artificial Intelligence (AI) and Deep Learning (DL) Research and Development (R&#x0026;D) work has picked up amongst academia, research community and industry. Training Deep Neural Network model (DNN) requires huge amount of data and computing resources, specially Graphics Processing Unit (GPU) accelerator along with DL framework like TensorFlow and programing language like Python. Different distribution of framework and programing language is available and optimized for GPU and Central Processing Unit (CPU). Many literature, article and studies are published for DNN training on GPU. However, only few article and studied are available for DNN training on CPU especially in HPC Cluster. This paper presents performance analysis and comparison between different distribution of Python and TensorFlow to verify which combination run optimally on available CPU only node of a HPC cluster. We used ResNet50 [1], ResNet101 [1] and Inceptionv3 [2] neural network model of tf_cnn_benchmarks [3] for performance comparison. We further tune best identified software combination using distributed training technique, across single and multiple nodes. We did performance comparison based on different processor and architectures. We were able to show up to 7x performance improvement using Intel Distribution for TensorFlow on single node and up to 15.7X speedup on 16 nodes on different CPU architecture.
How the Events in the Life of Painters Influence the Colors of their Paintings

Out of the desire to observe the interdisciplinary usefulness of Computer Science, we decided to begin a study about the surrounding world. Starting from the visual world, we outlined a basic question: Do events we experience in our lives influence our artistic capabilities? We begin by collecting all the necessary data about several painters and then extracting the predominant colors of their paintings. Using Machine Learning techniques, we compared the efficiency of different algorithms, measuring the efficiency score in the CIELAB Color Space. Then, based on Natural Language Processing elements and painters&#x0027; biographical information, we built a timeline of the events from the painter&#x0027;s lives, enriched with the predominant colors used by them. Wanting to observe the human perspective about the feelings derived from the paintings, we build a small form-like application to extract this information from volunteers in the field. Correlating the algorithms&#x0027; results with those extracted from human subjects, we showed that periods of happiness and unhappiness bounded with the colors used by painters. These results lead us to the conclusion that this topic deserves further study in order to obtain more accurate information and to be able to move on to its use in practical applications.
Reduction of Alert Fatigue using Extended Isolation Forest

Alerts are tasks that continually monitor active queries to look for and report on specific events or conditions like system performance, security incidents, and threats for a system or network. Companies with an extensive IT infrastructure often deal with many alerts per day, varying from a routine host or network performance notifications to security incidents raised by Network Security Devices. With an increase in cyberattacks, Network Security Devices play a vital role in detecting critical incidents and threats. However, more often than not, these security incidents are frequently occurring low threat alerts. As the number of alerts skyrockets, it becomes increasingly tedious to sift through all the alerts generated and identify critical one. This may result in longer response time or overlooking important alerts, which is referred to as alert fatigue. Aiming to tackle this problem, our paper proposes a solution to reduce alert fatigue by identifying and highlighting anomalous alerts using Extended Isolation Forest, an isolation-based anomaly detection technique. Our model reduces the number of alerts received at the Security Operations Center (SOC) by 82.15%. The security analyst needs to monitor only 17.85% of the 50,000 total alerts received from the IDS.
Understanding Learners’ Interests in Cybersecurity Competitions on Reddit

Cybersecurity competitions have been recognized as an effective approach to engaging students with real-world challenges. This work aims to explore the cybersecurity competition contestants' interests and opinions by collecting and analyzing Reddit data. Reddit is a leading hub for students and professionals to exchange information and share experience. We identified a list of keywords of interest and gathered over three thousand relevant public posts. Both qualitative and quantitative data analysis methods are employed to gain insight into learners' interests and opinions from the data. Our results of analyzing Reddit posts provide a complementary understanding of learners' interests and opinions compared with the existing survey studies of cybersecurity competitions. The frequency analysis results show that the popular topics include training tools, career advice, penetration testing, CTF, and certifications. Although survey studies are more targeted, our analysis of Reddit posts provides a more complete picture of how learners' sentiment is impacted by competitions. In the end, our analysis results enable us to discuss several practical implications of the findings and directions of future work.
High-Resolution Representation Learning for Human Pose Estimation based on Transformer

Abstract Human pose estimation requires accurate coordinate values for the prediction of human joints, which requires a high-resolution representation to effectively improve accuracy. For some difficult joint prediction tasks, it is not only necessary to look at the characteristics of the joint points themselves, but also to make judgments in combination with the context of the whole image. Generally, the resolution will be reduced when the context information is obtained. In this process, it will inevitably lose some spatial information and make the prediction inaccurate. In this paper, we propose a high-resolution human pose estimation network based on Transformer to reduce the impact of spatial information loss on keypoints estimation. In detail, we use low-level convolution neural network to extract low-level semantics from the image, and then the Transformer is used to capture the image context to further predict the key points of the human body, obtain the high-resolution representation. The experiments show that our network can accurately predict the positions of keypoints, we achieve state-of-art results on the COCO keypoint detection dataset.
Smart Camera for Enforcing Social Distancing

The COVID-19 pandemic presents an unprecedented challenge to public health, food systems and the demand and supply chains. &#x201C;Coronavirus&#x201D; spreads when an infected person coughs, sneezes or talks, and droplets from their mouth are launched into the air and inhaled by people in the vicinity. Mid-2021 witnessed the production and supply of effective vaccines against Coronavirus, and around 4.5 billion vaccine doses have been utilised globally, reducing fatalities significantly. Given the Government&#x2019;s plans to ease quarantine restrictions for schools, offices, and public places, Social Distancing has become even more critical than ever before. This project incorporates Computer Vision techniques using the high-performance YOLOv4 library, DSFD Face detector, Deep Learning Darknet and Pre-trained ResNet models, and RaspberryPi to create a plug-and-play extension for CCTV cameras established in public places. The system uses the frame by frame information of CCTVs to detect people and classify violations of Social Distancing norms. The device also performs real-time Face Mask Detection, and this technique is robust to varying geometries of face masks and degrees of natural illumination. In case of a detected violation of Social Distancing norms, a buzzer blares in the background. The timestamp of violation with the snapshot of the frame highlighting the associated people is sent to a database and emailed to a centralised server for further investigation.
Real Object Registration Algorithm Based on SURF and RANSAC

In recent years, with the rapid development of smartphone devices and mobile cellular network technology, real-time virtual reality feedback software and games based on mobile phone cameras have developed rapidly. However, current existing real-scene item matching algorithms usually need to rely on Internet connections or require the high-performance computing power of devices to meet the demand. For this reason, this paper proposes a real-scene item registration algorithm based on the SURF algorithm, which has the advantages of good real-time performance, low calculation amount, and easy implementation. It can be adopted and further developed in real-scene software for item recognition and registration used in games and intelligent robots.
OCR Error Correction Using BiLSTM

Language models have critical importance in the pre- and post-processing of optical character recognition (OCR). The quality of documents and scanners is important for OCR systems, with inferior quality leading to more erroneous output. For long time intervals of sequences, long short-term memory (LSTM) fulfills the requirements because it can solve problems with long-term dependencies. In this study, we evaluate the performance of error correction for OCR data using LSTM. The results show that we have good performance for correcting error words by using bidirectional LSTM (BiLSTM). We obtain 98.13&#x0025; better performance in correcting error words by using OCRd data and 97.18&#x0025; better performance by using social media data. In this respect, we show that the method we have applied can be used for error corrections.
Recurrent Affine Transform Encoder for Image Representation

This paper proposes a Recurrent Affine Transform Encoder (RATE) that can be used for image representation learning. We propose a learning architecture that enables a CNN encoder to learn the affine transform parameter of images. The proposed learning architecture decomposes an affine transform matrix into two transform matrices and learns them jointly in a self-supervised manner. The proposed RATE is trained by unlabeled image data without any ground truth and infers the affine transform parameter of input images recurrently. The inferred affine transform parameter can be used to represent images in canonical form to greatly reduce the image variations in affine transforms such as rotation, scaling, and translation. Different from the spatial transformer network, the proposed RATE does not need to be embedded into other networks for training with the aid of other learning objectives. We show that the proposed RATE learns the affine transform parameter of images and achieves impressive image representation results in terms of invariance to translation, scaling, and rotation. We also show that the classification performance is enhanced and is more robust against distortion by incorporating the RATE into the existing classification model.
Text Understandingform Natural Images with Enhanced Classiftcation using Genetic Algorithm

In recent times, text retrieval as content from natural images is regarded as challenging and popular in computer vision. This paper illustrates understanding of natural scene text, having unconstrained scene properties in the vein of unstructured background, variant font characteristics, undesirable illumination and much more. These properties make scene text challenging, which encourages us to investigate this research area. To perform natural scene text understanding following steps have been carried out. Firstly, text extraction is done, which include contrast enhancement using L-U-V channel, Maximally Stable Extremal Region (MSER) for text region detection and Stroke Width Transform (SWT) for obtaining connected components and segmentation. After that, novel binary classification approach for classifying text and non text regions using genetic algorithm with integrated Support Vector Machine (SVM). Secondly, character based text recognition and labeling is employed using novel Convolution Neural Network (CNN) model. Finally, the CNN outcome is collected into the text file to examine that it matches to scene text or not. In order to observe the significance of the intended methodology, standard datasets SVT, IIIT5K and ICDAR 2003 have been used. After experimentations, it has been found that proposed methodology performs well while comparing with existing benchmark techniques in terms of accuracy.
A Framework for Visual Relation Detection Exploiting Global Context

Visual relation detection (VRD) is crucial for comprehensive image understanding which requires capturing the interactions between detected objects. However, the inference of the relations between objects is challenging due to the lack of richer context or semantic information. Most of the previous works on VRD focus on local context or simple semantic information. Attending to gather richer information, we develop a dismountable VRD framework that combines the global features and traditional local features from both vision and semantics. Specifically, we first investigate how to construct an efficient global context. Then a dual attention model (DAM) is proposed to gather necessary information from the global context in different modalities. At the reasoning stage, four kinds of features are fused to predict pairwise relations between objects in the image. Experimental results on Visual Genome (VG) dataset validate the effectiveness of our model.
Path Planning of Armor Cash Carrier Based on Intelligent Algorithm

The vehicle used to transport the funds of banks, is called an armor cash carrier. The armor cash carrier starts from the central bank, dispatches cash to different branches throughout the day, and returns to the central bank. Before the process, the shortest path needs to be planned efficiently. In this paper, an intelligent algorithm named as simulated annealing (SA) algorithm is applied to the path planning. By using the SA algorithm, the path searching process can jump out of the local optimal solution, and get the global optimal solution. Experimental results are reported to verify the effectiveness and efficiency of the path planning based on the SA algorithm.