Reviewerly/exHarmony huggingface dataset:
Philosophical and Methodological Foundations for Improving Digital Transformation and Implementing Artificial Intelligence

Nowadays, there is an evolving process of digital transformation and the introduction of artificial intelligence (AI) into a wide range of social systems. Usually, insufficient attention is paid to assessing the social consequences of such innovations. The underlying causes of that are related to the dominance of the technogenic model of civilization, the embodiment of which is the technocratic approach, and the use of this approach in the interests of the globalist project. In the development and implementation of digital technologies and AI, an ontological paradox arises, for overcoming which it is required to develop adequate philosophical and methodological foundations for assessing social innovations based on digital technologies. The article discusses the expediency of using three types of scientific rationality (classics, non-classics, post-non-classics) to overcome the limitations of the Western model of technogenic civilization and the use of a subjective approach corresponding to this rationality. It is fundamentally important that the three types of scientific rationality correspond to the key stages in the evolution of cybernetics and AI. The evolution of AI is analyzed from these positions and an approach is proposed to overcome the ontological paradox in digital transformations and the implementation of AI. In the context of the development of ideas on scientific rationality, the author considers the specifics of innovative models based on digital technologies and AI. The article examines the problem of the formation of an integrative field of knowledge as the ergonomics of digital transformations and AI, which will allow to take into account the rich ergonomic experience of a multi-criteria socio-humanitarian assessment of the use of computer technology and software: productivity, safety, satisfaction, and development. In the conclusion, the article considers the basic positions of the configurator, that is, of the devise for assessing innovations based on digital technologies and AI, including assessing of scientific, methodological and organizational issues and persons concerned.
Understanding AI-Generated Personal Narratives as Design Material for Socially Engaging Things

Requirements of interaction design are becoming increasingly sophisticated and complex. Interaction designers should consider not only usability, intuitiveness, and aesthetics but also the quality of the social experience of interactive things. However, traditional design approaches for social experiences have limitations in terms of sustainability, diversity of outcomes, and adaptability. In this paper, we propose utilizing AI-generated personal narratives as new material for socially engaging things and share our understanding of it from a user study. We first generated and qualitatively analyzed 100 different personal narratives to understand the diversity of AI-generated personal narratives and derived 20 types as a result. We developed a software prototype generating personal narratives. We then conducted a user study using the prototype of sharing platform and the representative cases of each type and investigated user experiences through in-depth interviews. Our findings show that an AI-generated personal narrative reframes the relationship between people and things as if there were social relationships between people and people. People perceived the thing as a social entity. Depending on the contents of the narrative, they felt intimate or distant. Finally, we discuss the requirements and make suggestions for AI-generated personal narratives to provide a satisfactory social experience.
Composite Reversible Data Hiding Scheme for Secure Image Reconstruction

Intense growth of information technologies has ameliorated the means of access to digital information. Digital image processing manages the digital content to store, share more efficiently with lesser time and space complexity. However, these approaches beach the privacy of digital content. Recent research focuses on protecting digital content from illegal use and distribution by hiding reversible data scheme to handle the risk of privacy violations for digital content. In this paper, a Composite Reversible data hiding (CRDH) scheme is proposed. CRDH used the Integer Wavelet Transform (HAAR transform) with the HH band's Eigenvalue decomposition. The proposed CRDH initially applied the IWT transformation on the cover image (CI) and parsed it into four subsequent frequency sub-bands namely LL, HL, LH, and HH. Confidential data of the proposed scheme are embedded by combining the HH band of the cover image's individual values with the encrypted Eigenvalues of the confidential data. The selection of casing art is such a manner where values lie within a range. The Confidential data image and HH band's frequency band are approximately identical; therefore, changing the individual values significantly does not trigger the quality of the confidential data image and the HH band's content. The proposed scheme's main objective is to develop a data hiding scheme that prevents the verification of digital information by maintaining a high rate of PSNR. The PSNR of the current technology is less than 50% for the entire available data set. The PSNR value indicates the image's visual quality, where the PSNR improves the higher image quality, so hiding data is necessary for the scheme that prevents authentication and maintaining a high rate of PSNR. The proposed method achieves this goal, gains a PSNR rate of over 50%, and reaches 57% for Tiffany and high SSIM which represent the quality of image.
Spatial Agent-based Architecture Design Simulation Systems

This paper presents case studies and analysis of agent-based reinforcement learning (RL) systems towards practical applications for specific architecture/engineering tasks using Unity 3D-based simulation methods.Finding and implementing sufficient abstraction for architecture and engineering problems to be solved by agent-based systems requires broad architectural knowledge and the ability to break down complex problems.Modern artificial intelligence (AI) and machine learning (ML) systems based on artificial neural networks can solve complex problems in different domains such as computer vision, language processing, and predictive maintenance.The paper will give a theoretical overview, such as more theoretical abstractions like zero-sum games, and a comparison of presented games.The application section describes a possible categorization of practical usages.From more general applications to more narrowed ones, we explore current possibilities of RL application in the field of relatable problems.We use the Unity 3D engine as the basis of a robust simulation environment.
Collaborative Filtering Matches Decision Templates: A Practical Approach to Estimate Predictions

Collaborative Filtering stands as an underlying strategy to reasonably deal with large-scale problems like scalability and high sparsity. In the classifier fusion context, one could benefit from adopting such a strategy to learn decision templates effectively for the sake of computation efficiency. This paper introduces a framework that explores collaborative filtering-based latent factors models for fast decision template generation, assuming it has a sparse matrix structure. Experiments conducted over five general-purpose public datasets and statistically assessed have demonstrated its feasibility for building decision templates under low sparsity conditions and datasets labeled with fewer classes. Under such conditions, the proposed framework showed competitive recognition rates, significantly reducing computational costs, particularly when distance-based classifiers are employed for ensemble learning purposes.
Robustness of Mask-RCNN at High Ambiguous Environments

Since instance segmentation are popular in the field of unmanned driving, object recognition and instance segmentation in the case of high ambiguity are lacking at the present stage, this paper compares the training results of mask-RCNN algorithm under normal conditions and low visibility with fuzzy conditions. Firstly, for the dataset part, video downloaded from YouTube and be divided into pictures frame by frame by video processing software, which are used as datasets under normal conditions. Then, images are blurred by Fourier transform, which are used as training sets under low visibility conditions. After that, the dataset needs to be normalization, which is to divide the pixel value of the picture by 255 and map them to 0-1 and to improve the training speed. Then we test the data through mask-RCNN, through the tensor Flow library, and train the results on Colabortary. And this algorithm is based on Feature Pyramid Network (FPN) and ResNet101 backbone. Meanwhile there is an Area of Interest (ROI) Pool layer that processes of different sizes into a uniform size output. Through the test of these two datasets, the results show that mask-RCNN's test accuracy can reach more than 99% under normal visibility, and the error rate is extremely low.
A Research of Mosaic Force Elements Orchestration Based on Process Calculus

Mosaic warfare system includes networks, digital mosaic elements, artificial intelligent systems and autonomous unmanned systems. Mosaic force elements were created for force elements in cyberspace. Networks connect all physical force elements and digital force elements. Command & control relationships and the operation mechanism will be mapped into cyberspace, promoting the combination of dynamic force elements. This research focuses on the orchestration of digital force elements based on web service combination theory tool. The attributes and quality of digital force element are defined, and then a semantically expanded process calculus is utilized to conduct formal modeling and validate force agent orchestration. Numerical results show that the proposed method improves the overall quality of force element combination.
Exploring the Technology and Problems of Artificial Intelligence Education Applications

The rapid development of artificial intelligence technology and its use in the field of education continues to spread, bringing about high speed development in education. However, there are inevitably some problems in the actual use of AI in education (e.g., simplifying and narrowing it down). Analyze and discuss the problems in the process of using the common techniques of AI educational applications: the problem of simplicity of execution leading to behaviorism, the problem of information cocoon caused by algorithmic recommendations, the problem of teachers' AI anxiety, the problem of ethics of AI educational applications, and the problem of emotional deficiency. The only way to better integrate technology into educational practice is to view it correctly and to grasp the degree of its use.
Text Document clustering using partial Fractionation and Bisecting K-means

In the era of big data, text clustering has come up as an important technique in fields such as web documents collection, news recommendation systems, health data collection, business data management, health data management, etc. In that context, this paper presents an automatic text document clustering technique to identify distinct groups in large number of unordered documents. The text documents of one group are very much similar to each other and dissimilar to documents in other groups. The grouping criterion is based on a function whose value is either maximized or minimized. In this paper, a new method for the calculation of initial set of clusters is proposed using fractionation method, and a new similarity measure is also proposed using the combination of cosine, link, neighbours, and Gaussian function. Using these functions, both local and global similarity is considered. This work describes an approximate algorithm based on clustering which will try to overcome the drawback of k-means of uncertain number of iterations by fixing the number of iterations, without losing the precision. The proposed approach uses the neighbours and links along with Gaussian function in different characteristics of the clustering algorithm for finding initial clusters in text document clustering. The experimental results performed using sports data documents show that performance of the proposed technique is very promising.
The Power Analysis Based on Mean–Ridge Regression

Comparing the common methods in side channel analysis such as correlation power analysis (CPA), the non-profiled power analysis based on machine learning achieves better attack effect, which increases the analysis accuracy rates by constructing and training a power leakage model. In order to construct an assumption of power leakage model which fits better with the measured power traces, a non-profiled power analysis method - based on mean-ridge regression - was proposed. It constructed the initial dynamic power leakage model by using the measured power consumption for the purpose of calculating the optimal ridge parameters and optimal weight coefficients of ridge regression. Then, the initial dynamic power leakage model was averaged to construct a power leakage model according to mean-ridge regression, which reduced the model overhead and the noise impaction. Finally, the key recovery process for the PRSENT algorithm was achieved by calculating the relationship between the model and the measured power consumption. The experimental results show that: compared with correlation power analysis and the existing method based on ridge regression, the power analysis based on mean-ridge regression expands the correlation coefficients between the model and the measured power traces, increases the attack accuracy, and reduces the number of power traces used for key recovery.
ERM: Energy-Based Refined-Attention Mechanism for Video Question Answering

Spatiotemporal attention learning remains a challenging video question answering (VideoQA) task as it requires a sufficient understanding of cross-modal spatiotemporal information. Existing methods usually leverage different cross-modal attention mechanisms to reveal potential associations between video and question. While these methods effectively remove irrelevant information from the spatiotemporal attention, they ignore the pseudo-related information within the cross-modal interaction attention. To address this problem, we proposed a novel energy-based refined-attention mechanism (ERM). ERM leverages the significant difference distribution as a discriminative criterion derived from question-guided cross-modal interaction information to determine question-related and question-irrelated cross-modal interaction information. The specific method is to measure the linear separability between the target neuron and other neurons in the neural network to confirm the importance of neurons. In addition, to solve the statistical bias caused by the differences between different modes in video tasks, the ERM proposed in this paper has learnable parameters. The correlation between different modes can be learned adaptively through learnable parameters. The advantages of the proposed ERM are that it is more flexible and modular while remaining lightweight. With the help of the ERM, we construct a lightweight VideoQA model that efficiently integrates the cross-modal feature representations in an energy-based manner. To evaluate the effectiveness of our method, we carried out extensive experiments on five publicly available datasets and compared them with state-of-the-art VideoQA methods. The experiment results demonstrate that our method brings a noticeable performance improvement compared to state-of-the-art VideoQA methods. ERM can be flexibly integrated into different VideoQA methods to improve their Q&A performance.
Improved Generalized Successive Projection Algorithm for Generalized Separable Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) is a widely used technique for dimensionality reduction, and generalized separable NMF (GSNMF) can learn the representation with better interpretability, as it decomposes the given matrix based on the row features and the column features at the same time. But in some cases, the GSNMF algorithm faces the 0- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> problem, where only one perspective of feature can be developed. This paper modified the generalized successive projection scheme, and proposes an improved generalized successive projection algorithm (IGSPA) to avoid the 0- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> problem. To verify the effectiveness of our method, we conduct extensive experiments on three commonly used face datasets. Compared with the existing methods, numerical experiments show that our method has superior performance.
Is Ant Colony System better than FFD for VM placement in a heterogeneous cluster?

First fit decreasing (FFD) is the most popular heuristic for virtual machine (VM) placement problems. However, FFD does not perform as much in a heterogeneous cluster environment. Moreover, FFD and other heuristics, such as best fit decreasing (BFD), are limited to handle the VM placement problem effectively when multiple resources are considered together. In this study, we analyze the reason why the ant colony system performs better than FFD for VM placement in a heterogeneous cluster. We verified our logical observations through experimental comparisons with other heuristics.
Maximum Mean Discrepancy Adversarial Active Learning

The aim of active learning is to reduce the sampling costs. However, the uncertainty approaches based on probability given by the neural network model is not reliable, and it is prone to make overconfident predictions for outlier samples. In this paper, we provide a maximum mean discrepancy adversarial learning-based active learning strategy. Our approach utilizes the structural information of unlabeled samples during training to estimate their relationship with the structure of labeled samples in order to distinguish unlabeled from labeled samples. In addition, we introduce IsoMax into active learning as a way to make active learning more sensitive to outliers and to alleviate the problem of overconfidence in outliers at the beginning of active learning sampling. The query strategy combines the criteria of uncertainty and source domain discrepancy. On three separate picture classification datasets, CIFAR10, SVHN, and MNIST, the approach is assessed. The outcomes demonstrate our approach's superiority over other techniques.
Human Interpretation of Inter-robot Communication

We investigated how a human observer interprets the motion of a robot while communicating with another robot. We generated a variety of motion patterns for a robot running on wheels and evaluated the ease of human interpretability of these motions. The results showed that although the interpretation results for motoins imitating human head gestures were relatively accurate, motions that imitated hand gestures tended to be interpreted less accurately.
Collecting a Custom Database for Image Classification in Recommender Systems

Nowadays machine learning and deep learning are widely used techniques for data analyses that require large amounts of labeled data. Moreover, with the migration from data as a service to data as a product the businesses are facing a complicated problem to collect the correct data from scratch that are suitable for data analyses and knowledge extraction using machine learning and deep learning-based approaches. The data collection and generation of custom databases is an active research topic aimed to overcome the major bottleneck for today’s business when machine learning and deep learning is to be applied for data analyses. In the paper a methodology for data collection is suggested that provide a structured approach for dataset collection allowing to overcome some of the challenges connected with the data collection activity. The suggested methodology is applied for collection of custom databases for solving an image classification problem in recommender systems.
Journey on Flattening the Curve: The Actors and Networks of Indonesian Contact Tracing System

The contact tracing system implemented in Indonesia is called SILACAK, a modified version of DHIS2. SILACAK as a contact tracing system platform created a dashboard following national guidelines, which is a tool to monitor the COVID-19 cases in Indonesia and support decision making. The role of SILACAK has throughout several journeys and some changes that can be implemented by several actors involved such as the developer team, government, health workers, and also society. To analyze the connection between all aspects that affect the SILACAK system, A framework is needed to deeply review which can be a historical artifact on the system. Actor- network theory (ANT) is the best practice to know about how networks come into being, to trace what associations exist, how they move, how actors are enrolled into a network, how parts of a network form a whole network and how networks achieve temporary stability.
Stealth Attacks: A Natural and Robust Physical World Attack Against Object Detectors

Recent studies have shown the vulnerability of deep neural networks (DNNs) to adversarial examples induced by added imperceptible perturbations to the input images. Most of the attack studies focused on target classifiers, but few aimed at target detectors that are more challenging. This paper proposes a more natural and robust adversarial attack scheme against practical object detectors. First, we extract the target area through image semantic segmentation, and perturbations are only added to the extracted target area to generate more practical adversarial examples. Then, style transfer is adopted to make the generated adversarial examples more natural-looking. Finally, we improve our robustness against adversarial examples by simulating angle, lighting, distance, and background changes. Our generated adversarial examples have proved more successful and robust in adversarial attacks than other methods. This paper sheds light on the potential threat of adversarial examples to the real physical world and hopefully provides guidance for defending against such natural and robust adversarial example attacks.