Reviewerly/exHarmony huggingface dataset:
SRDRN-IR: A Super Resolution Deep Residual Neural Network for IR Images

Human perception is only capable of perceiving a few objects outside the range of wavelengths for visible light in the electromagnetic spectrum. It restricts humans' ability to discriminate things in a variety of situations, such as dim light or under smoke and fog. The development of thermographic imaging technology has made it possible to see items that are invisible to the naked eye. This enables its usage in many sectors, such as defence, agriculture, healthcare, etc. Thermal cameras have low spatial resolution in comparison to the same-range RGB cameras due to hardware constraints. A deep neural network architecture, SRDRN, is proposed in this study for Super-Resolution (SR) of IR images. SRDRN uses the channel splitting concept with residual learning for computationally efficient super resolution. The viability of the proposed design is validated by analysing it with the available thermal image datasets.
Enhancing RISC-V Vector Extension for Efficient Application of Post-Quantum Cryptography

We present a cryptography extension built on RISC-V Vector Extension for efficient application of lattice-based post-quantum cryptography, offering custom instructions that can perform vectorized operations on polynomials of variable length and data width. We use micro-operation architecture to simplify the execution of variable-latency vector instructions and propose fracturable modular arithmetic units to support operations on variable coefficient width. On this basis, a vector unit is designed, achieving significant speed-up compared to the state-of-the-art counterparts for number-theoretic-transform-based polynomial multiplication. This cryptography extension is further integrated into the gem5 simulator to evaluate CRYSTALS-Kyber and CRYSTALS-Dilithium; results outperform the state-of-the-art implementations with more than 2.3 × improvement in cycle count.
Human Posture Transition-Time Detection Based upon Inertial Measurement Unit and Long Short-Term Memory Neural Networks

As human-robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control.
Segmented composite design of robust single-qubit quantum gates

Over the past few decades, quantum information processing research has focused heavily on error-mitigation schemes and error-correcting codes. However, while many proposed schemes have been successful in mitigating errors, most of them are perturbative and assume deterministic systematic errors, leaving studies of the problem considering the full noise and errors distribution scarce. In this work we introduce an error-mitigation scheme for robust single-qubit unitary gates based on a composite segmented design that accounts for the full distribution of the physical noise and errors in the system. We provide two optimization approaches to construct these robust segmented gates, perturbative and nonperturbative, which address all orders of errors. We demonstrate the effectiveness of our scheme in the photonics realm for the realization of dual-rail directional couplers. Specifically, we show that the three-segment composite design for the fundamental single-qubit unitary operations reduces the error by an order of magnitude for a realistic distribution of errors. Moreover, we demonstrate that the two approaches are compatible for small errors and significantly reduce the overhead of modern error-correction codes. Our methods are rather general and can be applied to other realizations of quantum information processing units.
A Classification Scheme for Hostile Design

A discussion is emerging over what is called “hostile design,” among other names, i.e., the construction of public-space objects in ways that exclude particular usages, and with an alleged effect of discrimination against already vulnerable populations. Critics outline various examples, from spikes added to a ledge to deter loiterers from sitting, to armrests added to a bench to discourage the unhoused from using it as a place to sleep. In an effort to sharpen the notion of hostile design as a critical tool, I develop an original typology, one in which the various examples are organized in terms of the mechanisms through which their hostility is enacted. This sets up reflections on some variables of hostile design—such as their level of “conspicuousness,” and their “domain of effect”—that cut differently across these categories.
A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.
Applying automated machine translation to educational video courses

Abstract We studied the capability of automated machine translation in the online video education space by automatically translating Khan Academy videos with state-of-the-art translation models and applying text-to-speech synthesis and audio/video synchronization to build engaging videos in target languages. We also analyzed and established two reliable translation confidence estimators based on round-trip translations in order to efficiently manage translation quality and reduce human translation effort. Finally, we developed a deployable system to deliver translated videos to end users and collect user corrections for iterative improvement.
Recognising Image Shapes from Image Parts, not Neural Parts

This paper describes an image processing method that makes use of image parts instead of neural parts. Neural networks excel at image or pattern recognition and they do this by constructing complex networks of weighted values that can cover the complexity of the pattern data. These features however are integrated holistically into the network, which means that they can be difficult to use in an individual sense. A different method might scan individual images and use a more local method to try to recognise the features in it. This paper suggests such a method and it is conjectured that this method is more ‘intelligent’ than a traditional neural network. The image parts that it creates not only have more meaning, but they can also be put into a positional context and allow for an explainable result. Tests show that it can be quite accurate, on some handwritten digit datasets, but not as accurate as a neural network. The fact that it offers an explainable interface however, could make it interesting.
An Image Stereo Matching Algorithm with Multi-Spectral Attention Mechanism

With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot's ability for autonomous navigation during self-driving. In this paper, we address the issue of low matching accuracy of stereo matching algorithms in specular regions of images and propose a multi-attention-based stereo matching algorithm called MANet. The proposed algorithm embeds a multi-spectral attention module into the residual feature-extraction network of the PSMNet algorithm. It utilizes different 2D discrete cosine transforms to extract frequency-specific feature information, providing rich and effective features for cost computation in matching. The pyramid pooling module incorporates a coordinated attention mechanism, which not only maintains long-range dependencies with directional awareness but also captures more positional information during the pooling process, thereby enhancing the network's representational capacity. The MANet algorithm was evaluated on three major benchmark datasets, namely, SceneFlow, KITTI2015, and KITTI2012, and compared with relevant algorithms. Experimental results demonstrated that the MANet algorithm achieved higher accuracy in predicting disparities and exhibited stronger robustness against specular reflections, enabling more accurate disparity prediction in specular regions.
Power/Area-Efficient ECC Processor Implementation for Resource-Constrained Devices

The use of resource-constrained devices is rising nowadays, and these devices mostly operate with sensitive data. Consequently, security is a key issue for these devices. In this paper, we propose a compact ECC (elliptic curve cryptography) architecture for resource-constrained devices based on López–Dahab (LD) projective point arithmetic operations on GF(2m). To achieve an efficient area-power hardware ECC implementation, an efficient digit-serial multiplier is developed. The proposed multiplier is built on a Bivariate Polynomial Basis representation and a modified Radix-n Interleaved Multiplication (mRnIM) method (for area and power complexities reduction). Furthermore, the LD-Montgomery point multiplication algorithm is adjusted for accurate scheduling in the compact ECC architecture to eliminate data reliance and improve signal management. Meanwhile, the area complexity is reduced by reuse of resources, and clock gating and asynchronous counter are exploited to reduce the power consumption. Finally, the proposed compact ECC architecture is implemented over GF(2m) (m = 163, 233, 283, 409, and 571) on Xilinx FPGAs’ (Field-Programmable Gate Array) Virtex 5, Virtex 6, and Virtex 7, showing that the efficiency of this design outperforms to date when compared to reported works individually. It utilizes less area and consumes low power. The FPGA results clearly demonstrate that the proposed ECC architecture is appropriate for constraint-resources devices.
Application of Data Particle Geometrical Divide Algorithms in the Process of Radar Signal Recognition

The process of recognising and classifying radar signals and their radiation sources is currently a key element of operational activities in the electromagnetic environment. Systems of this type, called ELINT class systems, are passive solutions that detect, process, and analyse radio-electronic signals, providing distinctive information on the identified emission source in the final stage of data processing. The data processing in the mentioned types of systems is a very sophisticated issue and is based on advanced machine learning algorithms, artificial neural networks, fractal analysis, intra-pulse analysis, unintentional out-of-band emission analysis, and hybrids of these methods. Currently, there is no optimal method that would allow for the unambiguous identification of particular copies of the same type of radar emission source. This article constitutes an attempt to analyse radar signals generated by six radars of the same type under comparable measurement conditions for all six cases. The concept of the SEI module for the ELINT system was proposed in this paper. The main aim was to perform an advanced analysis, the purpose of which was to identify particular copies of those radars. Pioneering in this research is the application of the author's algorithm for the data particle geometrical divide, which at the moment has no reference in international publication reports. The research revealed that applying the data particle geometrical divide algorithms to the SEI process concerning six copies of the same radar type allows for almost three times better accuracy than a random labelling strategy within approximately one second.
Network Activity and Ethereum Gas Prices

This article explores the extent to which network activity can explain changes in Ethereum transaction fees. Such fees are referred to as “gas prices” within the Ethereum blockchain, and are important inputs not only for executing transactions, but also for the deployment of smart contracts within the network. Using a bootstrapped quantile regression model, it can be shown that network activity, such as the sizes of blocks or the number of transactions and contracts, can have a heterogeneous relationship with gas prices across periods of low and high gas price changes. Of all the network activity variables examined herein, the number of intraday transactions within Ethereum’s blockchain is most consistent in explaining gas fees across the full distribution of gas fee changes. From a statistical perspective, the bootstrapped quantile regression approach demonstrates that linear modeling techniques may yield but a partial view of the rich dynamics found in the full range of gas price changes’ conditional distribution. This is an important finding given that Ethereum’s blockchain has undergone fundamental economic and technological regime changes, such as the recent implementation of the Ethereum Improvement Proposal (EIP) 1559, which aims to provide an algorithmic updating rule to estimate Ethereum’s “base fee”.
OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems

This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO's performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications.
Optimizing Stage Construction and Level Balancing of Match-3 Puzzle Game with PPO Algorithm Machine Learning

Although the global market has witnessed a proliferation of diverse match-3 puzzle games, achieving success in this competitive market remains challenging. The crucial factors that determine the success of match-3 puzzle games are the creation of numerous engaging stages and precise level balancing. The purpose of this study is to propose a match-3 puzzle game system that aims at identifying the most effective algorithm for training artificial intelligence agents in stage construction and level balancing verification. To validate the systems’ usefulness, this paper conducted experiments with the Proximal Policy Optimization (PPO) algorithm and obtained cumulative reward and entropy value graphs. Consequently, it has been confirmed that the system can be employed to compare learning outcomes for each algorithm and identify the optimal algorithm suitable for match-3 puzzle games. The use of machine learning technology in match-3 puzzle games holds the promise of revolutionizing game development and leading to the creation of more captivating and rewarding gaming experiences for players.
Remarks on fixed point assertions in digital topology, 6

This paper continues a series discussing flaws in published assertions concerning fixed points in digital metric spaces.
Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.
OptiDJS+: A Next-Generation Enhanced Dynamic Johnson Sequencing Algorithm for Efficient Resource Scheduling in Distributed Overloading within Cloud Computing Environment

The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments.