Hasil untuk "Cybernetics"

C. Land

Lotfi El Hafi, Kazuma Onishi, Shoichi Hasegawa et al.

Cybernetic avatars (CAs) are key components of an avatar-symbiotic society, enabling individuals to overcome physical limitations through virtual agents and robotic assistants. While semi-autonomous CAs intermittently require human teleoperation and supervision, the deployment of fully autonomous CAs remains a challenge. This study evaluates public perception and potential social impacts of fully autonomous CAs for physical support in daily life. To this end, we conducted a large-scale demonstration and survey during Avatar Land, a 19-day public event in Osaka, Japan, where fully autonomous robotic CAs, alongside semi-autonomous CAs, performed daily object retrieval tasks. Specifically, we analyzed responses from 2,285 visitors who engaged with various CAs, including a subset of 333 participants who interacted with fully autonomous CAs and shared their perceptions and concerns through a survey questionnaire. The survey results indicate interest in CAs for physical support in daily life and at work. However, concerns were raised regarding task execution reliability. In contrast, cost and human-like interaction were not dominant concerns. Project page: https://lotfielhafi.github.io/FACA-Survey/.

Simeon Emanuilov, Aleksandar Dimov

This paper presents a novel approach for similarity search with complex filtering capabilities on billion-scale datasets, optimized for CPU inference. Our method extends the classical IVF-Flat index structure to integrate multi-dimensional filters. The proposed algorithm combines dense embeddings with discrete filtering attributes, enabling fast retrieval in high-dimensional spaces. Designed specifically for CPU-based systems, our disk-based approach offers a cost-effective solution for large-scale similarity search. We demonstrate the effectiveness of our method through a case study, showcasing its potential for various practical uses.

Elena Rovenskaya, Alexey Ivanov, Sarah Hathiari et al.

Abstract Economic and social interactions are shifting to digital platforms which grow into vast ecosystems where user engagement creates value for members while ecosystem orchestrators harvest massive revenue. The digital ecosystem business model succeeds by adeptly navigating fast-changing environments, including new technologies and volatile demands, through dynamic innovation in a decentralized decision-making setting. This renders digital platform ecosystems complex adaptive systems. Recognizing that natural ecosystems are a prime example of complex adaptive systems, we propose a systematic hierarchical framework for describing and understanding digital ecosystems, rooted in ecology and evolution. Our framework compares digital ecosystems hosted by societies to natural ecosystems embedded in biomes, products to species, and technologies and elements of business strategy to the genetic makeup of a species. As digital platforms face heightened scrutiny about their socio-economic power and societal value, our approach contributes to the development of deeper understanding and sustainable governance of the digital economy.

Guliashki Vassil, Kirilov Leoneed, Marinova Galia

The Job Shop Scheduling Problem (JSSP) attracts many researchers due to its combinatorial nature and its discovery in numerous practical applications. This type of problem is characterized by high computational complexity; therefore, solving large-sized problems is not accessible with exact optimization methods. Very often, real JSSP problems can be presented as Flexible Job Shop Scheduling Problems (FJSSP). For these problems, there are single-criterion and multi-criteria mathematical models. On the other hand, the ways to solve this type of problems include exact methods and heuristic or metaheuristic algorithms. This paper the aim to review the progress of research in the field of solving FJSSP over the last 10 years, as well as to show current trends for future scientific developments in this area.

Masikini Lugoma, Abel Omphemetse Zimbili, Masengo Ilunga et al.

This study uses Random Forest algorithm to model students' final year mark in an engineering technology module taught by the University of South Africa. The algorithm uses a supervised learning classification technique to map the different assessment marks and the final mark. Hence, the latter are labelled instances whereas the former constitute the features. Random Forest (RF) has been applied to Structural Analysis 3, which takes into consideration the graduate attribute concept or level of competence as far as assessments are concerned. Firstly, the RF is subjected to imbalanced binary classes, then balanced classes are achieved by Synthetic Minority Oversampling Technique (SMOTE) and class weights adjustment techniques. The results showed that SMOTE brought an improvement in accuracy of 3%. It was also revealed that an increase of 4, 15 and 9% in precision, recall and F1-Score were observed in predicting non-competent students. An increase of 4 and 3% was noticed in the case of the precision and F1-Score respectively in predicting competent students, whereas the recall did not display any change. Despite the RF with SMOTE overperformed standard RF and RF class weights adjustment, all three algorithms were good candidates in the prediction of student performance. RF-SMOTE could be suggested as a guiding instrument when dealing with imbalanced data.

Ihor Bezverbnyi

Introduction. This article proposes a novel approach to speech signal analysis based on the chirplet transform, which integrates the Hilbert – Huang transform with chirplet analysis. This method provides enhanced segmentation and feature extraction capabilities, enabling accurate identification of time-frequency characteristics in speech signals. It is proposed to overcome the limitations of traditional methods such as Short-Time Fourier transform and wavelet analysis, by offering a more adaptive solution tailored to the non-linear and non-stationary nature of speech signals. The purpose of the work is to develop a numerical-analytic method for phonetic analysis of speech signals. The central feature of the methodology is the combination of empirical mode decomposition from Hilbert – Huang transform with chirplet projections onto alternative nonlinear scales, such as the mel-scale. This approach ensures superior localization of dynamic changes in the frequency-time domain, while ensures superior with the perceptual characteristics of human hearing. By leveraging chirplet transforms, the proposed method enhances the detection of linguistic elements, including phonemes and other speech segments, even in the presence of overlapping components. Results. The practical implementation of this method is demonstrated through experimental analysis of speech signals. The results indicate an improvement in the accuracy of segmentation and noise suppression compared to conventional approaches. Time-frequency visualizations illustrate the adaptability of the method in handling complex speech signals with varying dynamic properties. Conclusions. This research contributes to advancements in speech analysis, recognition, and audio signal processing, offering potential applications in areas such as voice-controlled systems, linguistic studies, and speech recognition technologies. The proposed approach can be further refined and integrated with machine learning algorithms to automate the classification and analysis of speech segments. The article provides a foundation for future studies on the intersection of chirplet transforms and nonlinear signal processing, emphasizing their role in addressing real-world challenges in speech and audio technologies.

Adrian P. Wozniak, Mateusz Milczarek, Joanna Wozniak

With the growing popularity of machine learning, implementations of the environment for developing and maintaining these models, called MLOps, are becoming more common. The number of publications in this area is relatively small, although growing rapidly. Our goal was to review the current state of the literature in the MLOps area and answer the following research questions: What classes of tools are used in MLOps environments? Which tool implementations are the most popular? What processes are implemented within MLOps? What metrics are used to measure the effectiveness of MLOps implementation? Based on this review, we identified classes of tools included in the MLOps architecture, along with their most popular implementations. While some tools originate from DevOps practices, others, such as Model Orchestrators, Feature Stores, and Model Repositories, are unique to MLOps. We propose a reference MLOps architecture based on these findings and outline the stages of the model production process. We also sought metrics that would allow us to assess and compare the effectiveness of MLOps practices, but unfortunately, we were unable to find a satisfactory answer in this area.

Abdarahmane Traoré, Moulay A. Akhloufi

Violence and abnormal behavior detection research have known an increase of interest in recent years, due mainly to a rise in crimes in large cities worldwide. In this work, we propose a deep learning architecture for violence detection which combines both recurrent neural networks (RNNs) and 2-dimensional convolutional neural networks (2D CNN). In addition to video frames, we use optical flow computed using the captured sequences. CNN extracts spatial characteristics in each frame, while RNN extracts temporal characteristics. The use of optical flow allows to encode the movements in the scenes. The proposed approaches reach the same level as the state-of-the-art techniques and sometime surpass them. It was validated on 3 databases achieving good results.

Farhad Nazari, Darius Nahavandi, Navid Mohajer et al.

Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user's skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracy

K. Ebru, T. Suleymanov

Purpose of this study is to study the activities of modern Islamic financial institutions in Turkey.Results. The prospects for the development of Islamic banking in Turkey as one of the priority areas of state strategy are considered. The main characteristics and features of Islamic banking in Turkey, the history of creation and the development process are revealed. The main obstacles to the functioning of Islamic banks in the legal field are considered.Conclusions. Islamic banking in Turkey has advanced significantly in its development since its first formation.

Václav Diviš, Bastian Spatz, Marek Hrúz

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out-of-Distribution recognition. Although the original problem remains unsolved, the term “Out-of-Model Scope” detection offers a clearer perspective. The ability to detect Out-of-Model Scope inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting Out-of-Model Scope situations, the system’s robustness is enhanced and it is prevented from operating in unknown and unsafe scenarios. In this paper, we propose a novel approach for Out-of-Model Scope detection that integrates three sources of information: (1) the original input, (2) its latent feature representation extracted by an encoder, and (3) a synthesized version of the input generated from its latent representation. We demonstrate the effectiveness of combining original and synthetically generated inputs to defend against adversarial attacks in the computer vision domain. Our method, TRust Your GENerator (TRYGEN), achieves results comparable to those of other state-of-the-art methods and allows any encoder to be integrated into our pipeline in a plug-and-train fashion. Through our experiments, we evaluate which combinations of the encoder’s features are most effective for discovering Out-of-Model Scope samples and highlight the importance of a compact feature space for training the generator.

Yuhong Deng, Chongkun Xia, Xueqian Wang et al.

Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects. We present a novel framework, Graph-Transporter, for goal-conditioned deformable object rearranging tasks. To tackle the challenge of complex configuration space and dynamics, we represent the configuration space of a deformable object with a graph structure and the graph features are encoded by a graph convolution network. Our framework adopts an architecture based on Fully Convolutional Network (FCN) to output pixel-wise pick-and-place actions from only visual input. Extensive experiments have been conducted to validate the effectiveness of the graph representation of deformable object configuration. The experimental results also demonstrate that our framework is effective and general in handling goal-conditioned deformable object rearranging tasks.

Manolis Chiou, Mohammed Talha, Rustam Stolkin

This paper investigates learning effects and human operator training practices in variable autonomy robotic systems. These factors are known to affect performance of a human-robot system and are frequently overlooked. We present the results from an experiment inspired by a search and rescue scenario in which operators remotely controlled a mobile robot with either Human-Initiative (HI) or Mixed-Initiative (MI) control. Evidence suggests learning in terms of primary navigation task and secondary (distractor) task performance. Further evidence is provided that MI and HI performance in a pure navigation task is equal. Lastly, guidelines are proposed for experimental design and operator training practices.

S.D. Bazhitov, A.V. Larichev, A.V. Razgulin et al.

We discuss a problem of reconstructing (sectioning) multilayer object images in observed images obtained by focusing the imaging system on each layer and containing spurious blurry images of neighboring layers. The blurring model used describes a physical process of incoherent light scattering in the Fresnel approximation with a priori unknown parameters of the point spread function. We propose a method of "Boundary separation" of sectioning, which combines the use of a physical blur model with modern methods of blur estimating and edge detection. The results of testing the "Boundary separation" method on the data of physical experiments with different-scale model multilayer objects are analyzed and compared with the existing methods for solving the optical sectioning problem. It is concluded that the method is most effective on multilayer objects with clearly defined boundaries, on which the method has demonstrated almost complete restoration of the desired layers.

Kunal Abhishek, E. George Dharma Prakash Raj

The survey presents the evolution of Short Weierstrass elliptic curves after their introduction in cryptography. Subsequently, this evolution resulted in the establishment of present elliptic curve computational standards. We discuss the chronology of attacks on Elliptic Curve Discrete Logarithm Problem and investigate their countermeasures to highlight the evolved selection criteria of cryptographically safe elliptic curves. Further, two popular deterministic and random approaches for selection of Short Weierstrass elliptic curve for cryptography are evaluated from computational, security and trust perspectives and a trend in existent computational standards is demonstrated. Finally, standard and non-standard elliptic curves are analysed to add a new insight into their usability. There is no such survey conducted in past to the best of our knowledge.

Kunal Abhishek, E. George Dharma Prakash Raj

Short Weierstrass's elliptic curves with underlying hard Elliptic Curve Discrete Logarithm Problems was widely used in Cryptographic applications. This paper introduces a new security notation 'trusted security' for computation methods of elliptic curves for cryptography. Three additional "trusted security acceptance criteria" is proposed to be met by the elliptic curves aimed for cryptography. Further, two cryptographically secure elliptic curves over 256 bit and 384 bit prime fields are demonstrated which are secure from ECDLP, ECC as well as trust perspectives. The proposed elliptic curves are successfully subjected to thorough security analysis and performance evaluation with respect to key generation and signing/verification and hence, proven for their cryptographic suitability and great feasibility for acceptance by the community.

Ryo Fujiwara, Ryoma Fukuhara, Tsubasa Ebiko et al.

Renewable energy is an essential factor in guaranteeing the sustainability of society. In Japan, there have been developments to harness energy from the ocean. The Tsugaru strait, in the northern region of Japan, is an area that has attracted attention for this purpose. We propose a tidal/ocean power generator utilizing a Flaring Flanged Diffuser (FFD) to harness the power. However, for the power generators utilizing FFD to generate power at the optimal condition, design values based on the stream regimes need to be determined. In this paper, the objective is to forecast the design values of tidal/ocean power generators utilizing FFD. We are especially interested in the dimensions of the diffuser shape that relate to effective factors for increasing flow velocity. Fluid field data around FFD is obtained by experimentation. The fluid field data is measured by particle image velocimetry (PIV). The trained deep neural network can forecast design values from a given fluid field. Moreover, we can recognize correlations between the changes in design values and the increase of fluid velocity.