Reviewerly/exHarmony huggingface dataset:
Automatic summarization of Instagram social network posts by combining semantic and statistical approaches

The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title of “automatic text summarization”. Manual processing and summarization of large volumes of textual data is a very difficult, expensive, time-consuming, and impossible process for human users. Text summarization systems are divided into extractive and abstract categories. In the extractive summarization method, the final summary of a text document is extracted from the important sentences of the same document without any kind of change. In this method, it is possible to repeat a series of sentences repeatedly and interfere with pronouns. But in the abstract summarization method, the final summary of a textual document is extracted from the meaning of the sentences and words of the same document or other documents. Many of the performed works have used extraction methods or abstracts to summarize the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this research, by developing a crawler, extracting the popular text posts from the Instagram social network, suitable pre-processing, and combining the set of extractive and abstract algorithms, the researcher showed how to use each of the abstract algorithms. and used extraction as a supplement to increase the accuracy and accuracy of another algorithm. Observations made on 820 popular text posts on the Instagram social network show the accuracy (80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) of the proposed system.
New Texture Descriptor Based on Improved Orthogonal Difference Local Binary Pattern

Local descriptor plays an important role in Content-Based Image Retrieval (CBIR) and face recognition. Almost all local patterns are based on the relationship between neighboring pixels in a local area. The most famous local pattern is Local Binary Pattern (LBP), in which the patterns are defined based on the intensity difference between a central pixel and its neighboring in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> local window. Orthogonal Difference Local Binary Pattern (OLDBP) is an extended version of LBP which is introduced recently. In this paper, ODLBP is improved. In the proposed method each <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> local window is divided into two groups and then local patterns of each group are extracted and finally, the feature vector is provided by concatenating of groups patterns. To evaluate the proposed method, three datasets Yale, ORL and GT are used. Implementation results show the powerful of the proposed method comparing to ODLBP. The proposed method is more faster than the ODLBP while its precision and recall are slightly higher than the ODLBP method.
Fusion of deep-learning models for multi-view image classification

Advances in Convolutional Neural Networks (CNN) have demonstrated state of the art performance in the tasks of image classification and object detection over the past decade. While significant progress has been made in development of more efficient networks, the computational and memory requirements still exceed practical limits in many applications. Additionally, the pose variability in such applications requires even larger training datasets for the network to generalize to all possible scenarios. The goal of this work is to develop an architecture for fusion of multiple views of a single target to provide robust classification with a lightweight backbone network used across all agents. Motivated by approaches to ensemble learning, we demonstrate that multiple weak learners with computationally efficient networks can combine to enhance classification accuracy. Three methods of fusion are considered: decision fusion, feature fusion, and multi-scale feature fusion. A novel network architecture is developed and implemented for each approach then trained and evaluated using synthetic data. For the feature fusion models, a custom training scheme is developed to minimize classification error while maintaining a common feature extraction backbone across agents. This conforms to a distributed classification use case where each agent has no prior knowledge of its position relative to target. Finally, we discuss the requirements for shared data of each approach in the context of applications with limited communication bandwidth.
Human-machine cooperative AI decision-making with heterogeneous data

Many techniques have been developed for sensor and information fusion, machine and deep learning, as well as data and machine analytics. Currently, many groups are exploring methods for human-machine teaming using saliency and heat maps, explainable and interpretable artificial intelligence, as well as user-defined interfaces. However, there is still a need for standard metrics for test and evaluation of systems utilizing artificial intelligence (AI), such as deep learning (DL), to support the AI principles. In this paper, we explore the elements associated with the opportunities and challenges emerging from designing, testing, and evaluating such future systems. The paper highlights the MAST (multi-attribute scorecard table), and more specifically the MAST criteria ―analysis of alternatives‖ by measuring the risk associated with an evidential DL-based decision. The concept of risk includes the probability of a decision as well as the severity of the choice, from which there is also a need for an uncertainty bound on the decision choice which the paper postulates a risk bound. Notional analysis for a cyber networked system is presented to guide to interactive process for test and evaluation to support the certification of AI systems as to the decision risk for a human-machine system that includes analysis from both the DL method and a user.
Centralized multi-sensor multi-target data fusion with tracks as measurements

Tracking systems often provide sets of tracks rather than raw detections obtained from sensors. Integrating these track sets into other tracking systems is challenging because the usual sensor models do not apply. In this work we present a method for fusing track data from multiple sensors in a central fusion node. The algorithm exploits the covariance intersection algorithm as a pseudo-Kalman filter which is integrated into a multi-sensor multi-target tracker within a Bayesian paradigm. This makes it possible to (i) integrate the proposed fusion method seamlessly into any existing tracker; (ii) modify multi-target trackers to take a set of tracks as a set of measurements; and (iii) perform gating to enable data association between tracks. The described method is demonstrated in simulations using several target trackers within the Stone Soup tracking framework.
Kazakh Handwritten Text Recognition Using Computer Vision and Neural Network

This paper presents a novel approach to digitizing handwritten text in the Kazakh language. Despite the numerous methods available to recognize handwritten characters, such as Convolution Neural Networks, Recurrent Neural Networks, and Connection Temporal Classification algorithms, these approaches have not been entirely successful in recognizing Kazakh script. Our investigation into the limitations of these existing methods reveals that visual feature extraction alone is insufficient for accurately recognizing Kazakh handwritten text. As a result, we developed a Kazakh Handwritten Text Recognizer (HTR) that combines the strengths of each method. Our experimental results demonstrate that our approach improves the recognition accuracy of Kazakh handwritten text. This paper presents a promising solution for digitizing handwritten Kazakh text and paves the way for further research in this area.
Conceptual approach to the formation of the digital twin of the Destination Earth (first glance)

The article proposes the concept of creating a digital twin of our planet based on the proof of the hypothesis - “Is the digital twin of the Earth beneficial for us for all the inhabitants of the planet?”. This hypothesis involves consideration of the primary approach to the model of the Earth's digital twin based on the use of conceptual information approaches of various authors in industry. A global approach is considered here, since the digital twin of the planet consists of many digital twins of all physical processes and various tasks of human life. Purpose: to form the first version of the concept of a high-precision digital model of the Earth for its modeling. Research method: analysis, comparison and conclusion on the existing concepts of the formation of digital twins Originality: for the first time, the concept of the global task of forming a digital twin of Destination Earth and the idea of forming a global hub of digital twins are put forward. Results: proof of the hypothesis about the possibility of obtaining a digital twin of the Earth.
Synthetic Audio Data Generation Algorithm for the Diarization Problem

In this paper an approach to building a dataset for the diarization problem is considered. A pipeline is proposed for fine-tuning a pretrained diarization model on synthetic data in order to increase diarization accuracy for Russian language. The suggested algorithm for processing audio data allows to add random portions of silence between adjacent audio segments, make several segments partially overlap and add specific type of noise to the audio track. Diarization error rate metric is calculated on the generated audio files by utilizaing Python’s pyannote diarization model. The results show that the artificially generated synthetic audio data is adequate and closely resembles real audio tracks. The proposed method involves speech extraction, concatenation, normalization, audio track formation, and noise overlaying. Our experiments have shown that generating longer and more diverse conversations leads to better quality data.
Multimodal data fusion using signal/image processing methods for multi-class machine learning

As the world progresses further into the digital era, we see a growing utility for combining datasets gathered on different devices and receivers as well as on varying time ranges, for use in machine learning. However, machine learning classification introduces a requirement for standardized data, which in turn hampers the ability to utilize diverse sets of data at a given timestamp. In this paper, we investigate the application of various signal pre-processing techniques (Daubecheis wavelet, discrete cosine and discrete fourier transform among others) for multi-modal, multi-class machine learning. Following the pre-processing, the multi-faceted signals are represented solely by features generated from first order statistics, eigen decomposition, and linear discriminant. Utilizing these generated features, as opposed to the signals themselves, these diverse datasets may now be combined as input to machine learning methods. Furthermore, we apply Fisher’s linear discriminant ratio and Random Forest feature importance metrics for feature ranking and feature space reduction followed by a comparison of the approaches. Our work demonstrates that dissimilar datasets with common classes may be combined using the proposed methods with a classification accuracy ≥ 95%. This paper demonstrates that the feature space may be reduced by approximately 60% with ≤ 5% loss in classification accuracy, and in some cases, a slight increase in classification accuracy.
How Can We Improve Data Quality for Machine Learning? A Visual Analytics System using Data and Process-driven Strategies

ML (Machine learning) models are used to mine inconspicuous information in big data. The model and data quality influence the performance of a machine-learning model. However, it is inefficient to modify the model, which is a black box, and low-quality data tends to cause biased learning of the model. Therefore, it is crucial to improve the data quality. Different techniques have been used to improve data quality depending on the data conditions and the data quality issues. Therefore, improving data quality is time-consuming and challenging for users with insufficient knowledge of data. Visual analytics techniques have been proposed to focus on decision support to improve data quality. However, existing studies are complicated for users to consider a comprehensive DQI (Data Quality Improvement) method for generating data suitable for ML models. Also, it remains limited in that users must directly consider all combinations of DQI processes. This paper presents a novel visual analytics system that manages data quality for use in ML models. The proposed system suggests an optimal quality improvement process with visualization techniques such as heatmap, histogram, and scatter plot to support DQI.
Preserving Privacy in Image Database through Bit-planes Obfuscation

The recent surge in computer vision applications has caused visual privacy concerns to people who are either users or exposed to an underlying surveillance system. To preserve their privacy, image obfuscation lays out a strong road through which the usability of images can also be maintained without revealing any visual private information. However, prior solutions are susceptible to reconstruction attacks or produce non-trainable images even by leveraging the obfuscation ways. This paper proposes a novel bit-planes-based image obfuscation scheme, called Bimof, to protect the visual privacy of the user in the images that are input into a recognition-based system. By incorporating the chaotic system for non-invertible noise with matrix decomposition, Bimof offers strong security and usability for creating a secure image database. In Bimof, it is hard for an adversary to recover the original image, withstanding a malicious server. We conduct experiments on two standard activity recognition datasets, UCF101 and HMDB51, to validate the effectiveness and usability of our scheme. We provide a rigorous quantitative security analysis through pixel frequency attacks and differential analysis to support our findings.
Toward Compliance Implications and Security Objectives: A Qualitative Study

Presently, personal data protection and supplementary methods delivering the required level of confidentiality may not fulfil the existing European Union legal standards. Moreover, it still has numerous research gaps. Therefore, the most crucial objective demanded by multiple stakeholders is securing the correct application of privacy-enhancing technology (PET) methods concerning General Data Protection Regulation (GDPR) compliance. However, no review and analysis of the overlap exist between the PET and compliance domains focusing on security goals and the anonymization level. Consequently, this study focuses on the implications of security goals and Data Privacy Impact Assessment (DPIA) introduced in GDPR to enhance personal data security from the respondent’s group standpoint, namely data protection officers, software developers, privacy legal practitioners and privacy researchers. Additionally, the consequence of different levels of anonymization, security goals, and PET method (Differential Privacy) concerning appropriate compliance will be studied. More importantly, our research method relies on a literature review and a detailed survey with a specific group of respondents. Therefore, this review provides a snapshot of the current situation between the needs of a particular group of respondents and security goals, with an additional focus on different anonymization levels and compliance requirements.
Interactive Transformations and Visual Assessment of Noisy Event Sequences: An Application in En-Route Air Traffic Control

Real-world event sequence data, such as activity logs, eye-tracking data, simulation data, and electronic health records, often share characteristics such as a large alphabet of events, fragmentation, noise, and high complexity which makes them difficult to analyze in their raw form. Because of this, simplification and preprocessing through various data transformations are commonly required before the data can be effectively visualized and analyzed. Existing methods for such data transformation are either manually applied and rely heavily on user expertise, or use algorithmic approaches to apply bulk operations which can imply the loss of potentially important information without users being aware. To bridge this gap, we propose a visual analytics approach that aims to successively increase the quality of noisy event sequences by supporting an interactive, context-aware application of data transformations. This is achieved by providing cues concerning the potential loss of information that transformation operations may imply and allowing users to explore, and visually assess their impact on the data. Therefore, a central feature of the approach is that users can tune the data transformation process so that important identified data characteristics are preserved. We motivate the proposed approach in the domain of air traffic control and illustrate it through a usage example, using event sequences derived by merging eye-tracking and simulator data from a human-in-the-loop simulation experiment with 14 air traffic controllers.
Framework for Automated Attendance &amp; Attention Tracking to Address Learning Gaps Due to Pandemic

The sudden shift to online education due to the pandemic has led to widespread learning gaps in the student population, increasing the need for individualistic attention to student learning as we transition back to physical classrooms. One can take advantage of the advancements in technology to help in this process by developing tools that can assist teachers decrease the amount of administrative workload and improve their ability to track the learning and understanding of every student in the classroom. The current work proposes a framework for accurate, low-cost, and scalable computer visionbased attendance and attention tracking in physical classroom environment to tackle this problem.
Structured Training as an Enabler for Women in ICT: A Case Study from Bangladesh

Gender gap in technology and workspace has gained attention in recent years. Different initiatives have been taken to engage women in technology learning and usage space to prepare women for the workspace. This study looks at the three year-long initiative a particular voluntary organization has taken to engage, and train women all across Bangladesh. This impact study involves n=30 participants who joined the activities of the organization through focus group discussions and one-to-one interviews. The participants shared that these events have boosted their confidence and enhanced their skills and networking in their relevant fields. The findings demonstrate that structured learning helped women develop skills and resources required to transition into professional life. The study contributes to the existing literature by providing insights on the potential of such initiatives to increase women’s participation in ICT in the context of Bangladesh where women have limited access to technology.
Virtual Simulation based Visual Object Tracking via Deep Reinforcement Learning

The current research field of object tracking has become noticeably popular among researchers where AI techniques take place with high-level accuracy. An algorithm with multifunctional abilities had proposed in different proposals in recent years. We proposed a tracking technique integrated with a virtual reality simulator – the AirSim (Areal Informatics and Robotics Simulation) City Environ model using one of the DRL models to control with a drone agent to examine a realistic environment. Additionally, the suggested method had tested via the two public: VisDrone2019 and OTB-100 datasets to compare with conventional strategies to show better performance among recent works.
Encryption of Military Maps ImagesUsing Peter De Jong Chaotic Maps and Lightwaght Encryption

Because of the importance of the military maps in the transmission, and to keep them secure, a method for image encryption combining the chaotic map Peter DeJong map with a lightweight encryption algorithm is proposed to encrypt the military map images. Peter De Jong map is used to generate keys that are used with the lightweight encryption algorithm. The proposed method used Peter De Jong's map to generate numbers used in the pixel's permutation of the image. Then divide the image into blocks to apply the key numbers of lightweight encryption, then combine all blocks to get the encrypted image. The technique has been applied to a set of militarymap images. The tests used two approaches represented by generated key and encrypted image, and the results were efficient in all tests such as the correlation of the encrypted image in horizontal, vertical, and diagonal directions for original and encrypted images. The obtainedresult showsno correlations between the original and encrypted image. The quality of the images has been evaluated by calculating different metries for the original and encrypted images, such as MSE, PSNR, and SSIM. The Entropy of the encrypted image was measured, and it was the same as the original image values. The proposed method achieved an appropriate complexity commensurate with the requirements of the times in the transmission of images.
Websites Performance Evaluation Based on Software Engineering Metrics of Multi-Level Testing

Websitesplay an essential role in different domains like education, business, and entertainment. Therefore, designing an efficient website has been focused to obtain a high degree of performance. For that reason, many previous works have been studied that evaluate the performance of websites depending on quality metrics evaluation such as structure, multimedia, and performance. This paper proposed a software engineering tool that evaluates the performance and the quality of a website, the tool is divided into four layers: The programming layer, the analysis layer, the information, and data layer, and the main user interface layer. The university of Mosul website was selected as a case study, and its performance has been compared with other universities. As well as, the GTmatrix tool has been used to validate the results and to compare with the proposed tool, the performance evaluation obtained 60% by using the proposed tool and 63% using GTmatrix tool, while the structure gives 56% using the proposed tool and obtained 50% using GTmetrix. Finally, the proposed tool provides different suggestions to improve the quality of websites and to keep websites running smoothly.
Analysis of Energy Efficiency Indicators in IoT-based Systems

This article examines several use cases for IoT in industries. It can contribute to the efficient use of power, data exchange between devices and the network. When it comes to smart transportation, ensuring the safety of drivers and passengers in the IoT is paramount. Similarly, healthcare is open to a number of potential benefits offered by the IoT in terms of patient health monitoring and early diagnosis. For this purpose, IoT provides non-disruptive monitoring of machines used in various industries, thereby increasing equipment life and operational efficiency.