Reviewerly/exHarmony huggingface dataset:
Rock Classification with Features Based on Higher Order Riesz Transform

Abstract Most modern algorithms use convolutional neural networks to classify image data of different kinds. While this approach is a good method to differentiate between natural images of objects, big datasets are needed for the training process. Another drawback is the demand for high computational power. We introduce a new approach which involves classic feature vectors with structural information based on higher order Riesz transform. Following this way we create a framework specialized for texture data like images of rock cross-sections. The key advantages are faster computations and more versatile choices of the underlying machine learning tools while maintaining a comparable accuracy in comparison with state-of-the-art algorithms.
Modified invasive weed optimization-based path exploration for mobile robot

Purpose This study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments. Design/methodology/approach The existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions. Findings The proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms. Originality/value As per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.
Impact of dust accumulation and deposits on polycrystalline solar panel: Case of Senegal

This study focuses on the Sahel, one of the areas with the highest concentration of desert dust, with source areas having fine particle emissions in Chad, Niger, Mauritania, etc.It also includes areas of subsidence of air masses that promote the deposition of dust, such as Senegal.It turns out that the countries of Sahel are among the poorest in the world.They have a relatively low electrification rate.However, this area is also characterized by its good solar potential which makes it an ideal place for the installation of photovoltaic solar collectors.Senegal has launched the challenge to solve this problem of electrification by turning to renewable energies.But being a drop zone, the sensors on the ground will be impacted.This study defines the impact of the thickness of the dust deposit layer on a polycrystalline photovoltaic sensor.By looking at the behaviour of irradiation and aerosol deposition in Senegal with the ERA5 and Giovanni data, the intra-annual profile of deposition and irradiation in Senegal was established.By applying the results obtained on the climatology of these parameters to a monocrystalline solar photovoltaic collector, it was contrasted that the evolution of the power, as well as the yield of the collector was strongly impacted by the accumulation of the deposit after 5 years.The power delivered by the collector went from 59.779 W during the month of the 1st year of accumulation to 4.28 W during the last month of the 5th year of accumulation.The resulting yield is also affected.More detailed illustrations are given in this work.
The Effect of Parental Attention on Learning Outcomes in Elementary School Students

This study aims to determine the positive and significant influence and the magnitude of the effective contribution of parental attention on mathematics learning outcomes in elementary school students. This quantitative research employed the correlation method. The research samples were 260 public elementary school students in the Central Java, taken by cluster random sampling. Research data were collected by distributing questionnaires and test instruments. The data were then analyzed utilizing the SPSS version 25 application. From the regression analysis performed, the t-count value was 8.909 ≥ t-table of 1.969, and the Sig. was 0.000 ≤ 0.05. It indicates that parental attention had a positive and significant effect on students' mathematics learning outcomes. In addition, the correlation analysis results obtained r-count = 0.485 &gt; r-table (df = 260) = 0.121, with an effective contribution value of 23.5%. Thus, it is concluded that there was a significant influence and effective contribution of parental attention on the learning outcomes of public elementary school students in the Gebang Sub-district, Purworejo Regency.
Evaluation of Maximum Error in Jump Motion Prediction

Recent advances in machine learning algorithms have improved the accuracy of human behavior predictions. Such predicted behaviors are anticipated to be used to control avatars in virtual reality (VR) space, for example, to eliminate delays between remote locations. In such applications, not only the average prediction error, but also the maximum error and the conditions under which it occurs must be considered. In this study, we examine the abovementioned aspects comprehensively for a jumping motion, and confirm that the maximum error occurs at the frame immediately before the body begins the motion, and that the maximum error is approximately 35 cm for a jumping motion when predicting 0.5 s ahead, which is more than double the average error.
Future route presentation to autonomous mobile wheelchair passengers using the movement of vibrotactile stimuli

While autonomous wheelchairs reduce the burden on the passenger, the automation of the operation may make it difficult for the passenger to understand the route the wheelchair will take in the future. In this case, passengers may feel anxious or uncomfortable due to unexpected movements of the wheelchair. To reduce passengers' anxiety and discomfort, it is important to present information to passengers about the future route of the wheelchair when obstacles or pedestrians approach from the front. In previous studies, only the distance and direction to the next turning point were presented. We propose a comfortable and easy-to-understand route presentation method that presents the direction and avoidance width of the wheelchair's turn by changing the length of the haptic apparent motion according to the width of the wheelchair's turn. Preliminary simulated wheelchair driving experiments have confirmed the potential of the proposed method to improve passenger comfort.
Formation Optimization Using GA-SA Hybrid Algorithm of Multi-Agent Robots for Cooperative Object Transportation

This research considers the topic of cooperative object transportation by multiple carry robots, where the object weight is assumed to exceed the capacity of a single robot. In certain applications, such as warehouses and construction sites, the object may have arbitrary shape, uneven weight distribution, or discontinuous surfaces, such that simple robot formations may not be sufficient to achieve stable transport. A formation optimization method is proposed based on a hybrid genetic algorithm and simulated annealing (GA-SA) approach. Firstly, the weight distribution of the object is transformed into a two-dimensional cell representation. GA-SA is then applied to solve the combinatorial optimization problem of determining the robot formation that maintains stability during transport. The performance and validity of the proposed method is analyzed through simulation.
Efficiency of Reinforcement Learning using Polarized Regime by Variational Autoencoder

Reinforcement learning from low-dimensional state expressions extracted as features from images is more efficient than learning directly from high-dimensional images. The autoencoder (AE) is typically used to render an image into a low-dimensional state; reinforcement learning on latent variables encoded by variational autoencoder (VAE) give excellent results. Notably, VAE is known to have a polarized regime and disentanglement among the generated latent variables. However very few studies have attempted to improve the efficiency of reinforcement learning using a polarized regime. This paper demonstrates the capability of a VAE polarized regime to improve the efficiency of reinforcement learning using an interactive driving game. It was found that only some polarized latent variables contribute to image restoration and reinforcement learning. The results show that calculation times can be significantly shortened while maintaining performance by reducing the number of search points of ES (Evolution Strategy) via the selection of only the latent variables that contribute to reinforcement learning using the polarized regime.
3D Shape Reconstruction of Puppet Head from CT Images by Machine Learning

This paper discusses a method to reconstruct 3D shape of puppet’s heads from CT images by a machine learning. Our proposed method performs two classes segmentation for wood and other areas in CT images using the U-Net framework. By using this method, we can extract wood parts which consists of 3D shape of puppet’s head. As a result of experiments, some parts cannot be correctly discriminated due to the influence of metal parts. However, we can confirm that most of the wood parts can be extracted correctly.
Digital Watermarking Based on a Gradient Orientation Code

This paper proposes a novel digital watermark based on the gradient orientations of a digital image. The specific features of a digital image can be expressed pixelwise using a 3-bit gradient orientation code (GO code). As the first watermark, the 3-bit GO code, covering 8 gradient directions, are spread and embedded in the three least significant bit (LSB) planes of its RGB color channels. The GO code is used for detecting the tampered region in an image. As a second watermark, we also embed a down-sampled self-image into the three second least significant bit (LSB2) planes of the image. The second watermark, excluding the tampered region, is utilized for recovering the original image. Experimental results show that the proposed method can detect and recover image tamper that occupies slightly more than 50% of an image. The quality of reconstructed images is maintained over PSNR 40dB.
Evaluation of impact by control switching according to driving environment

In autonomous driving, safety and computational complexity are the most important topic in system design. However, computational complexity and safety guarantee are a trade-off relationship in vehicle control. Human drivers, on the other hand, change their driving methods according to driving conditions. In order to incorporate this idea, this paper formulates control methods that can be applied to various environments. Simulations were conducted to evaluate the performance of each method. As a result, the computation time could be reduced without compromising safety by combining control methods.
Design of finger-contact device reducing the amount of mouse pointer operation and typing

Mouse cursor operation and keyboard typing have the important roles in various situations such as document creation. Therefore, it is necessary to research and develop devices to improve the efficiency of mouse cursor and key typing operation. There are many research and developments about devices. However, it is difficult to increase the typing speed due to the flat keyboard layout like a standard keyboard. In addition, there are many devices that require remembering of keyboard layouts. Therefore, the purpose of this research is to design of finger-contact device that reduce the amount of finger movement and do not require re-learning of keyboard layouts. The device can type and operate a mouse cursor. I designed a device using a computer-aided design (CAD) and built it with 3D printing. Then, I optimized the identification of key typing with a machine learning algorithm. As for the identification accuracy, the Long Short-Term Memory (LSTM) was the highest result.
Shift-invariant grey wolf optimizer exploiting reference points and random selection of step-sizes

The grey wolf optimizer (GWO) is a popular metaheuristic method based on the leadership hierarchy of grey wolves to solve continuous optimization problems, which has been received a lot of attention from researchers because of its advantages over other metaheuristic methods and its simplicity. In this paper, we empirically and theoretically show that the method is shift-dependent on the coordinate representation of the optimization problem, and in addition, it is too specialized only to optimization problems having the optimal solution at the origin, which means that its searching ability is not necessarily effective to other problems. Then, we propose a new improved GWO which is invariant to the shift-transformation by using reference points and step-size selection based on a random permutation in its updating system. Finally, we show advantages of the proposed GWO by comparing it with other metaheuristic methods.
Simulation-based Scheduling by Bayesian Optimization based on Gaussian Process Regression with Rank Correlation Kernel

Bayesian optimization (BO), which approximates computationally complex simulations to speed-up optimization searches has been studied intensively. However, BO is difficult to apply to the scheduling problem due to the difficulties in feature engineering from schedules i.e., ordinal variables. In this paper, we propose a kernel for ordinal variables. This new kernel is based on the Spearman rank correlation coefficient, which expresses the similarity between ordinal variables. Applying the GPR model based on the proposed kernel to BO enables it to be directly applied to the scheduling problem. We compare BO using the proposed kernel with the most popularly used genetic algorithm (GA). The experimental results demonstrate that BO using the proposed kernel found a near-optimal solution significantly faster than the GA under small numbers of searches.
Intention Estimation in Kicking from a Seated Position

If intentions could be understood from small actions, intuitive actions could be performed in a limited space. Body-based inputs are intuitive; however, they also require a large amount of space. In this study, kicking motions in a seated position are measured and machine learning is used to predict the leg that was used to kick. It was not possible to predict the leg that would move before the movement started, and the movement of the center of gravity was smaller than that of kicking in a stand-up position. This suggests a correlation between the movement of the center of gravity and predictability of movement.
Disturbance Observable Reinforcement Learning that Compensates for Changes in Environment

Reinforcement learning is a method that acquires control rules by interacting with the system dynamics’ so-called environment. However, when applying the acquired control rules in the real world, tasks fail due to changes in the environment (e.g., friction and mass). Since reinforcement learning acquires optimal control rules under train environment, it is no longer optimal under changed environment. Therefore, we propose to infer the change of dynamics and compensate the action to act as if it is in the unchanged environment. We show that the change of environment can be seen as the change of input action. As a result, we used inverse dynamics to get changed input action and compared it with input action to infer the change of the dynamics. Experiments conducted using a mass-spring damping and pendulum simulation demonstrate that our method improves task performance under a changing affine non-linear environment.
Evaluating The Cyber-Security Culture of the EPES Sector

The Energy Sector is highly targeted by cyber threats because of its inherent value and profitability. Recent reported security incidents verify its key playing role in the entire economic and societal concurrent reality. This paper aims to assess the cyber-security culture status of European representatives in the entire electrical power supply chain during the coronavirus pandemic and the Ukrainian war. An evaluation campaign has been carefully designed and held from 3rd March 2022 to 18th March 2022. During that period, participants from different Electrical Power and Energy Systems (EPES) organizations participated in the campaign. Gathered results were analyzed and co-examined using different techniques revealing important findings regarding the cyber-security status and resilience of individuals and organizations in the European EPES sector.
Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing

This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. To avoid inference accuracy loss in inference task partitioning, we propose receptive field-based segmentation (RFS). To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers. In this scheme, the collaborative edge servers (ESs) only need to exchange small fraction of the sub-outputs after computing each fused block. In addition, to find the optimal solution of partitioning a CNN model into multiple blocks, we use dynamic programming, named as dynamic programming for fused-layer parallelization (DPFP). The experimental results show that DPFP can accelerate inference of VGG-16 up to 73% compared with the pre-trained model, which outperforms the existing work MoDNN in all tested scenarios. Moreover, we evaluate the service reliability of DPFP under time-variant channel, which shows that DPFP is an effective solution to ensure high service reliability with strict service deadline.
On Secure and Side-Channel Resistant Hardware Implementations of Post-Quantum Cryptography

Currently, many post-quantum cryptography schemes have been implemented on various hardware platforms in order to provide efficient solutions in cybersecurity services. As researchers and hardware developers focus primarily on designs providing small latency and requiring fewer hardware resources, their implementations could seldom omit protection techniques against various physical attacks. This paper studies potential attacks on the cryptography implementations that run on Field-Programmable Gate Array (FPGA) platforms. We mainly analyze how Post-Quantum Cryptography (PQC) implementations could be vulnerable on various platforms. Further, we aim at the FPGA-based implementations of National Institute of Standards and Technology (NIST)'s PQC competition finalists. Our study should present to developers the current overview of attacks and countermeasures that can be implemented on specific PQC schemes on FPGA platforms. Moreover, we present novel implementation of one universal countermeasure component and reveal additional resources that are needed.
Studying Co-occurrence Patterns

While the previous two chapters focused on studying community-environment associations, here we will focus on characterising associations between taxa within communities, as in Exercise 17.1.
Employing social network analysis to dark web communities

Deep web refers to sites that cannot be found by search engines and makes up the 96% of the digital world. The dark web is the part of the deep web that can only be accessed through specialised tools and anonymity networks. To avoid monitoring and control, communities that seek for anonymization are moving to the dark web. In this work, we scrape five dark web forums and construct five graphs to model user connections. These networks are then studied and compared using data mining techniques and social network analysis tools; for each community we identify the key actors, we study the social connections and interactions, we observe the small world effect, and we highlight the type of discussions among the users. Our results indicate that only a small subset of users are influential, while the rapid dissemination of information and resources between users may affect behaviours and formulate ideas for future members.
Toward a critical technical practice in disaster risk management: lessons from designing collaboration initiatives

Purpose Despite decades of social science research into disasters, practice in the field continues to be informed largely from a technical perspective. The outcome is often a perpetuation of vulnerability, as narrowly defined technical interventions fail to address or recognize the ethical, historical, political and structural complexities of real-world community vulnerability and its causes. The authors propose that addressing this does not require a rejection of technical practice, but its evolution into a critical technical practice – one which foregrounds interdisciplinarity, inclusion, creativity and reflexivity, as means to question the assumptions, ideologies and delimited solutions built into the technical tools for understanding risks. Design/methodology/approach The authors present findings from three events they designed and facilitated, aimed at rethinking the engineering pedagogy and technical practice of disaster risk management. The first was a 2-day “artathon” that brought together engineers, artists and scientists to collaborate on new works of art based on disaster and climate data. The second was the Understanding Risk Field Lab, a 1-month long arts and technology un-conference exploring critical design practices, collaborative technology production, hacking and art to address complex issues of urban flooding. The third was a 4-month long virtual workshop on responsible engineering, science and technology for disaster risk management. Findings Each of these events uncovered and highlighted the benefits of interdisciplinary collaboration and reflexivity in disaster risk modeling, communication and management. The authors conclude with a discussion of the key design elements that help promote the principles of a critical technical practice. Originality/value The authors propose “critical technical practice” which foregrounds principles of interdisciplinarity, inclusion, creativity and reflexivity, as a means to question the assumptions, ideologies and delimited solutions built into the technical tools for understanding climate and disaster risk.