Reviewerly/exHarmony huggingface dataset:
Adversarial Optimization Scheme For Online Tracking Model Adaptation In Autonomous Systems

Online tracking model updating is typically addressed as a regression problem, involving the minimization of the dispersion between the obtained tracker model response maps in each consecutive frame and some target distribution (e.g., Gaussian), using a closed-form solution. Inspired by the recent applications of Generative Adversarial Networks (GANs), we propose to solve this problem with an adversarial optimization scheme, by employing a GeneratorDiscriminator network pair. That is, the role of the Generator is assigned to the tracking model so that it produces response maps belonging to some target distribution, while an additional discriminator network is trained to identify if the tracker response maps produced by the generator belong to this target distribution, or not. Therefore, the tracker model exploits the discriminator network as an additional information pool about the target distribution. It is shown that this simple addition improves tracking performance in standard benchmark datasets, without significantly hurting training complexity, thus rendering the proposed method suitable for embedded system application such as in autonomous cars and Unmanned Aerial Systems.
Research and Development of the Convolutional Neural Network for Pneumonia Recognition in Radiographs

In this paper, we present the results and analysis of using the convolutional neural network (CNN) for pneumonia recognition in radiographs. The network architecture was developed, and the author's software implementation of the CNN model using TensorFlow / Keras framework is proposed. The classes in the dataset are imbalanced, and we used a combination of approaches to prevent overfitting problem such as increasing the cost of classification mistakes on the minority class, increasing the amount of data (data augmentation), exclusion of a certain percentage of neurons during training (dropout) and others. The model gave us the next results: accuracy is 0.91, precision is 0.9, recall is 0.97, roc-auc is 0.96. The obtained results allow making a conclusion about the effectiveness of using the developed CNN model for pneumonia recognizing.
A Neuro-Inspired Autoencoding Defense Against Adversarial Attacks

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperceptible to humans. The most effective current defense is to train the network using adversarially perturbed examples. In this paper, we investigate a radically different, neuro-inspired defense mechanism, aiming to reject adversarial perturbations before they reach a classifier DNN, using an encoder with characteristics commonly observed in biological vision, followed by a decoder restoring image dimensions that can be cascaded with standard CNN architectures. Unlike adversarial training, all training is based on clean images. Our experiments on the CFAR-10 and a subset of Imagenet datasets show performance competitive with state-of-the-art adversarial training, and point to the promise of bottom-up neuro-inspired techniques for the design of robust neural networks.
Priority-Based Shortest Job First Broker Policy for Cloud Computing Environments

Scheduling plays a vital role in enhancing the resource utilization and performance in the Cloud environment. The Broker Policy is responsible for Cloudlets’ scheduling that decides the order in which the Cloudlets are assigned to Virtual Machines. Traditional Data Center Broker Policy executes the Cloudlets in First Come First Serve (FCFS). Shortest Job First (SJF), as its name justifies, executes the Cloudlets in the increasing order of their length or execution time. However, neither FCFS nor SJF supports the concept of priority. Nevertheless, priority scheduling is typical in Real-Time scenarios where the priority plays a vital role in deciding the schedule for execution of tasks. This paper proposes a priority-based Shortest Job First Policy for Data Center Broker in Cloud environments termed as Priority_DCB that selects processes for execution in non-decreasing order of priority. If two or more Cloudlets have the same priority, their execution order will be the Shortest Job First. The proposed approach provides a simple and easy solution for executing the Cloudlets according to their priorities. CloudSim is used to implement the proposed approach and carrying out the simulations. Results show that the proposed approach is better than the other state-of-the-art Data Center Broker Policy in terms of Cloudlets’ turnaround time and waiting time.
Quality and Complexity Assessment of Learning-Based Image Compression Solutions

This work presents an analysis of state-of-the-art learning-based image compression techniques. We compare 8 models available in the Tensorflow Compression package in terms of visual quality metrics and processing time, using the KODAK data set. The results are compared with the Better Portable Graphics (BPG) and the JPEG2000 codecs. Results show that JPEG2000 has the lowest execution times compared with the fastest learning-based model, with a speedup of $1.46 \times$ in compression and $30 \times$ in decompression. However, the learning-based models achieved improvements over JPEG2000 in terms of quality, specially for lower bitrates. Our findings also show that BPG is more efficient in terms of PSNR, but the learning models are better for other quality metrics, and sometimes even faster. The results indicate that learning-based techniques are promising solutions towards a future mainstream compression method.
Planning Total Veener Production PT. XYZ

PT. XYZ is engaged in the manufacture and sale of wood veneers. Starting from the constant occurrence of over stock, now the company must make improvements to the production forecasting process so that over stock can be avoided. It can be seen that accurate production forecasting can create conditions for an effective and efficient production system. This study aims to obtain a more accurate forecast of material requirements using the Support Vector Regression (SVR) method, which is the result of the development of a Support Vector Machine (SVM) which has good performance in predicting time series data. Application of the Support Vector Regression (SVR) method with the RBF kernel in predicting the need for veneer production using the MATLAB application, it produces the smallest error rate with a MAPE of 5%, RMSE of 4364.63 and of 0.748274147. on 67 training data and 20 testing data.
Human development and governance in Africa: do good fences make good neighbours?

In this paper, we revisit the relationship between governance and human development in Africa during the period 2010-2019 taking into account the existence of spatial dependence and controlling the endogeneity problem through a Generalized Spatial Two Stage Least Squares (2SLS). The exploratory spatial data analysis reveals the existence of spatial dependence of human development and governance quality. Our empirical findings support that in Africa, “good fences make good neighbours” or proximity matters in the distribution of human development. Implications are discussed.
The Reform of Tax Accounting Course in Colleges and Universities Based on the Mixed Teaching Mode

The development of information technology has brought changes to education industry. Educational researchers combine information technology with teaching, and creatively put forward many new teaching models. Among them, the mixed teaching mode has attracted the attention of educational researchers. The idea of mixed teaching mode is to combine the traditional classroom teaching with the application of educational technology teaching. Through the complementary advantages of the two, teaching efficiency and effect can be improved. Taking tax accounting as an instance, this paper explains the specific application of hybrid learning mode in the teaching of tax accounting lesson in universities, constructs course system based on the task orientation of "knowledge" + "skill points" + "information technology", and organizes online learning and offline discussion of "multiple interactions". Let students have different experience in each teaching process, let students always be interested in improving learning effect. The paper proposes some suggestions to solve the problems from the perspective of mixed teaching mode, which may offer some help for the practice of tax accounting course.
Factors Affecting Development of Blockchain

Blockchain is a digital record of transactions made with cryptocurrencies. It is a newly occurred concept, whose appearance just started twelve years ago symbolized by the anonymous publication of the paper, Bitcoin: A Peer-to-Peer Electronic Cash System. There is limited research on this buoyant technology, thus this paper hopes to identify the factors that might have influence on the future development of blockchain to provide an explicit depicting of blockchain. To realize this goal, this paper identified three variables that may affect the future development of blockchain, including population acceptance, government policy, and technique support, and then constructed a model to describe development of blockchain.
Application of Data Mining on Sales of Leather Handicrafts in Sidoarjo with Apriori Algorithm to Help Marketing Strategy

Sidoarjo is one of the regencies that is developing in a forward direction. This is known by the increase in the tourism sector and Small and Medium Enterprises. One of them is Sidoarjo leather handicraft. UD Qory Jaya is a business actor in the leather retail industry. Orders that continue to increase make the turnover of goods uneven resulting in a buildup of stock in stores. Due to these problems the compilers took the initiative to deliver messages by carrying out a method of processing data using the Apriori algorithm. This method is used to maximize the sales potential of a combination system of goods so that the target item for sale is appropriate. From the results of research conducted by testing a minimum support of 15% and a minimum of 20% confidence, the association rules are produced if you buy a slingbag then buy men's leather shoes and vice versa. And from the test results with a minimum support of 70% and a minimum confidence of 70% an association rule is not generated because the existing data does not exceed the minimum value of support and minimum confidence. With the implementation of this method, researchers hope to have a better impact for developing future marketing strategies based on previously researched data.
Strategies to Create "Golden Courses" of Advanced Mathematics in Universities under the Background of Big Data

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Possible Future: Trend of Industrial Design Based on Artificial Intelligence

Artificial Intelligence (AI) is the latest achievement in the development of human technology. Intelligent design driven by artificial intelligence technology is the inevitable trend of future design development. Intelligent design has the characteristics of user experience as the center, universality to the industry and inevitable risk. It plays an important role in industrial practice and may bring out of control risk [1,2].
Text line extraction using deep learning and minimal sub seams

Accurate text line extraction is a vital prerequisite for efficient and successful text recognition systems ranging from keywords/phrases searching to complete conversion to text. In many cases, the proposed algorithms target binary pre-processed versions of the image, which may cause insufficient results due to poor quality document images. Recently, more papers present solutions that work directly on gray-level images [1,2,7,12,15]. In this paper, we present a novel robust, and efficient algorithm to extract text-lines directly from gray-level document images. The proposed approach uses a combination of two variants of Convolutional Neural Network (CNNs), followed by minimal energy seam extraction. The first ConvNet is a modified version of the autoencoder used for biomedical image segmentation [8]. The second is a deep convolutional Neural Network, working on overlapping vertical slices of the original image. The two variants are combined to one neural net after re-attaching the resulting slices of the second net. The merged results of the two nets are used as a preprocessed image to obtain an energy map for a second phase. In the second step, we use the algorithm presented in [2], to track minimal energy sub-seams accumulated to perform a full local minimal/maximal separating and medial seam defining the text baselines and the text line regions. We have tested our approach on multi-lingual various datasets written at a range of image quality based on the ICDAR datasets.
Towards Efficient and Data Agnostic Image Classification Training Pipeline for Embedded Systems

Nowadays deep learning-based methods have achieved a remarkable progress at the image classification task among a wide range of commonly used datasets (ImageNet, CIFAR, SVHN, Caltech 101, SUN397, etc.). SOTA performance on each of the mentioned datasets is obtained by careful tuning of the model architecture and training tricks according to the properties of the target data. Although this approach allows setting academic records, it is unrealistic that an average data scientist would have enough resources to build a sophisticated training pipeline for every image classification task he meets in practice. This work is focusing on reviewing the latest augmentation and regularization methods for the image classification and exploring ways to automatically choose some of the most important hyperparameters: total number of epochs, initial learning rate value and it's schedule. Having a training procedure equipped with a lightweight modern CNN architecture (like MobileNetV3 or EfficientNet), sufficient level of regularization and adaptive to data learning rate schedule, we can achieve a reasonable performance on a variety of downstream image classification tasks without manual tuning of parameters to each particular task. Resulting models are computationally efficient and can be deployed to CPU using the OpenVINO™ toolkit. Source code is available as a part of the OpenVINO™ Training Extensions ( https://github.com/openvinotoolkit/training_extensions ).
Output augmentation works well without any domain knowledge

Data augmentation is a method to compensate a lack of sufficient amount of training data by increasing variations of the training data. It is also used even when there is a huge amount of training data to improve a generalization performance on the test data. In this paper, we propose a new method, Output-Augmentation (OA), which we use to improve the generalization performance without data augmentation. It augments each original output (but not input) and produces an arbitrary number of outputs which average to the original output. Updating the parameters is done by using the gradient over both the original and the augmented outputs. We conclude that the proposed novel method strongly complements the existing ones by showing empirical evaluations where we see improvements of the generalization performance in the task of image classification.
Defending Against Noise by Characterizing the Rate-Distortion Functions in End-to-End Noisy Image Compression

There has been an increasing consensus that precise understanding of the rate-distortion (RD) characteristics plays a critical role in image and video coding. In this paper, we explore the RD behaviors of end-to-end image compression in the real-world application scenario that the images could be corrupted by noise at different levels. With the RD behaviors that all images share, we develop a deep learning driven pre-analytical model which fully exploits the properties of RD functions and allows us to improve the quality with economized coding bits. The proposed approach does not require any prior knowledge of the noise level, and could effectively defend against the noise through the end-to-end compression. Extensive experimental results show that the proposed scheme offers the best promise in predicting RD behaviors, and naturally avoids the unnecessary bits consumption.
Stacking Ensemble-based Automatic Web Page Classification

The World Wide Web is expanding at an exponential rate resulting in a vast amount of information. Therefore, search engines need an automated web page classification system to avoid manual classification. Web page classification plays a vital role in deciding the fate of a Focused web crawler by moving it towards the relevant section of the web. Multiple classifiers can be integrated to create a classifier ensemble for web page classification. The performance of the classifier ensemble depends upon the diversity. Diversity can be achieved either by integrating multiple heterogeneous base learners or providing different training subsets to each base classifier. A heterogeneous classifier ensemble results in better predictive performance than an individual classifier. Stacking ensemble method is proposed in this work, integrating Naïve Bayes, Support Vector Machine and Random Forest algorithms, as Stacking method, combines the advantages of individual base classifiers and the two-stage learning process of Stacking ensemble leads to better relevance prediction than individual classifiers. This study aims to improve the performance of the classification system by retrieving Indian academician web pages from foreign universities’ websites. The feature set for classification consists of URL text and web page content. Term Frequency-Inverse Document Frequency vectorization assigns feature weights to determine the relevancy of a web page. This work compares the performance of the proposed method with individual classifiers.
An Improved Cluster Load Balancing Scheduling Algorithm

People’s lives and work are closely connected to the network, and network activities take up an increasingly large portion of their daily lives, generating large amounts of data. This large amount of data puts tremendous pressure on server clusters, which leads to resource allocation problems. Existing load-balancing algorithms take simple factors into account and do not take into account the server load and the resource consumption of the request. This paper proposes a PSO-GA (Particle Swarm Optimization-Genetic Algorithm) based LVS (Linux Virtual Server) cluster load-balancing scheduling algorithm to quantify the different scheduling options by constructing a resource balance model and an adaptation function. The PSO-GA algorithm is used to solve the adaptation function to obtain the optimal weights. The load balancer schedules requests according to the weights to achieve Linux virtual server cluster load balancing.
Interactive and educational components of printed educational products for children (from the experience of French museums)

According to the author, interdisciplinary research devoted to the organization of the educational environment of the museum is of great interest to museum staff, teachers of educational organizations and parents today. The object of the research was the French illustrated guidebooks with game tasks for children. It is concluded that the analysis of French museum publications for children and scientific works devoted to their description reveals a rich palette of various play tasks related to the subject of the exposition, but their educational component is not sufficiently thought out by the authors and studied by scientists, which could become a topic for a separate study.
Recurrent and Convolutional Neural Network Based on Interest in Classifying Arabic Documents

Document classification is one of the essential assignments of (NLP) applications. Right now, present a tedious, convolutional neural network dependent on Arabic language consideration Document classification. In this model, we will apply many applications including the repeated neural network to capture information from each sentence to the maximum extent possible when learning word representations, as well as applying a convolutional neural network to obtain contextual information for the entire document. What's more, we additionally utilize a consideration layer that consequently controls words that assume key jobs in each sentence to catch key segments of sentences. We are exploring different avenues regarding two arrangements of normally utilized Arabic document assortments. Exploratory outcomes show that our recommended technique exceeds expectations the essential techniques for these informational indexes have a few pointers.
A Real-Time Single-Shot Multi-Face Detection, Landmark Localization, and Gender Classification

Face detection and gender classification by Deep Neural Networks can find application in areas such as video surveillance, customized advertisement, and human-computer interaction. This paper presents a real-time single-shot multi-face gender detector based on Convolutional neural network (CNN). The proposed method not only detects face but also classifies the gender of persons in the wild, meaning in images with a high variability in pose, illumination, and occlusion. To train and evaluate the results, a new annotated set of face images is created. Our experimental results show that the proposed method achieves excellent performance in term of speed and accuracy.
Hardware Dataflow for Convolutional Neural Network Accelerator

Convolutional Neural Network (CNN) is one the most used Artificial Neural Network in AI system. While this type of network managed to achieve high accuracy for many tasks, it requires high computational power which can't be handled by the general purpose CPU. GPU usually becomes the first choice to accelerate the CNN, but unfortunately, GPU has low energy efficiency which made it not suitable for edge computing application. To address this problem, current research focuses on ASIC and FPGA implementation of hardware accelerator which has better energy efficiency. In this paper, we propose hardware dataflow for CNN accelerator. This dataflow is scalable for multicore system and also can be implemented on ASIC or FPGA. We implemented the proposed dataflow on FPGA. The proposed dataflow managed to achieve 30 GOPS throughput in FPGA. We also provide the analytical framework for this dataflow which can be used to estimated energy consumption and data reusability under various computation workloads.
The development of students industrial training information system (FBM-STIS): impact towards process efficiency / Norhidayah Mohd Rashid … [et al.]

Faculty of Business and Management has developed Student Industrial Training Information System (FBM-STIS) to facilitate the faculty pertaining to student internship placement in line with Industrial Revolution 4.0 (IR 4.0) emerged worldwide. Internship is a must for most undergraduate programs and it is carried out in the final semester. Owing to the complexity of the process, FBM-STIS is hoped to provide ease and accurate information to the students and FBM administrative staff. FBM-STIS also enhances efficiency through the reduction of time for students to receive the Confirmation Letter (CL), and support Green Nation program through its paperless initiatives and reduction of carbon emission since students’ physical presence is not needed at the faculty. Above all, it assists UiTM in its strategy to ‘Jana and Jimat’ in that, FBM-STIS is able to reduce operational cost. With the latest technologies, the study hopes the system match with the preferences of Generation Z who are more IT literate that favor simple and prompt process and feedback.