Reviewerly/exHarmony huggingface dataset:
Behavior-based Understanding of Elderly People with Dementia: A Hierarchical Classification of Daily Object Use

Providing individualized daily living care is quintessentially important to ensure the quality of life for elderly individuals, especially in nursing homes. Such care involves facilitating independent living, supporting social participation within nursing home settings, and preventing unintentional injuries such as falls. To effectively implement this, caregivers need to thoroughly understand the daily living activities of elderly people and to improve their living environments. The purpose of this study is to develop a system that can assist in planning residents' daily living care through automatically summarizing the daily activities in their rooms using depth cameras that respect privacy. The developed system consists of a function for extracting how elderly individuals use daily objects and another function for classifying behaviors based on the object use activities through hierarchical clustering. This system allows caregivers to understand the daily routines of the residents without predefining behaviors to be identified. To evaluate the effectiveness of the proposed method, the authors applied the method to analyzing 9 days’ worth of activities of an 87-year-old female resident in a nursing home. The experimental results demonstrate that the system was able to detect abnormal behaviors such as the repeated, unnatural use of drawers, without any predefined criteria for abnormal behaviors. The caregivers confirmed the utility of the system in summarizing daily behavior patterns and automatically detecting abnormal behaviors typically seen in elderly individuals with dementia.
Verification of the Effects of Exercise on the Body and Mind Using a Boxing Glove-Type Sensory Augmentation Device

The background of this study is the increased number of people lacking exercise owing to the rise in remote work since the outbreak of the new coronavirus infection. As exercise is strongly linked to mental health, a lack of physical activity leads to an increase in psychological stress among individuals. Currently, few devices are available that can accurately evaluate exercise movements and allow people to engage in casual workouts. Therefore, this study aimed to investigate the effects of boxing training on the body and mind by using a sensory augmentation device with a high amusement factor. The sensory augmentation device used in this study was designed for boxing, a high-intensity full-body exercise. Inertial sensors were placed on the wrist of the boxing glove to determine the type and power of the punching action. The device then produces sound, vibration, and light according to according to the force used to give the punching action a more spectacular appearance. We measured mental and physical states during amusement-oriented training using this boxing glove-type sensory enhancer and evaluated its effects. The results demonstrated that training with a sensory augmentation device led to heightened motor induction and increased positive emotions. This suggests that the production of sound, vibration, or light influences the reward circuits of the emotional system. Potential applications of this technology include exploring its effectiveness in various exercise movements to determine its impact on physical and mental well-being.
Bookwander: From Printed Fiction to Virtual Reality—Four Design Approaches for Enhanced VR Reading Experiences

This paper explores the transformation of traditional written fiction into an immersive virtual reality (VR) experience. By combining VR technology with specific design methods, the paper proposes creating an adaptable environment that prioritises the text over other perceptible stimuli. The paper introduces Bookwander, an experimental VR reading experience which incorporates four unique design approaches, namely, The Power of Text, Selective Imagery, Scenic Reading, and Playful Reading. Each approach contributes to immersion, interpretation, visualisation, and interaction in distinct ways. The preliminary results from a qualitative pilot study indicate that providing spatial-based content in a reactive environment to the text strikes a suitable balance between traditional reading and a VR experience. Participants felt immersed without compromising the reading experience, and the various visual representations did not hinder interpretation or imagination. The paper concludes with recommendations for further developments in immersive reading and future research directions in this field.
Annotated Logics and Application—An Overview

The author comments on the annotated logic, which was born in the late 80 s of the last century and was the main subject of investigation of his career. Abe is considered one of the leading pioneers in the applications of paraconsistent logic. The chapter closes with theoretical and application aspects.
Probabilistic Autoepistemic Equilibrium Logic

In this short note, we consider the definition of an Probabilistic Epistemic Logic (PE) and its non-monotonic extension, we call Probabilistic Autoepistemic Equilibrium Logic (PAEE). PE introduces a probabilistic modality that allows expressing lower bounds on conditional probability constructs. Regular (non-probabilistic) modal epistemic operators $$\textbf{K}\, $$ and $$\textbf{M}\, $$ can be defined as derived constructs in PE so that, in fact, for that modal epistemic fragment, PE collapses into modal logic KD45. The non-monotonic extension of PE follows the same steps as Equilibrium Logic [7], the main logical characterisation of Answer Set Programming [1], ASP. Equilibrium logic consists in a selection among the models of a theory under the intermediate logic of Here-and-There (HT) [5]. Similarly, we define the combination of PE with HT, we call PEHT, and then, define a model selection criterion that gives rise to the non-monotonic formalism of Autoepistemic PE. We end up showing that, if we consider again the modal epistemic fragment of the syntax, PAEE collapses to Epistemic Specifications [4], a well-known epistemic extension of ASP and we use a previous result to illustrate how the further addition of the excluded middle axiom, eventually produces Autoepistemic Logic [6] as a particular case.
Hybrid Parallel Ant Colony Optimization for Application to Quantum Computing to Solve Large-Scale Combinatorial Optimization Problems

Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. An algorithm referred to as Quantum Approximate Optimization Algorithm (QAOA) has been recently proposed to approximately solve hard problems using a protocol know as bang–bang. The technique is based on unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. The QAOA was explored in the context of several optimization problems such as the Max-Cut problem and the Traveling Salesman Problem (TSP). Due to the relatively small node-size solution capability of the available quantum computers and simulators, we developed a hybrid approach where sub-graphs of a TSP tour can be executed on a quantum computer, and the results from the quantum optimization are combined for a further optimization of the whole tour. Since the local optimization of a sub-graph is prone to becoming trapped in a local minimum, we overcame this problem by using a parallel Ant Colony Optimization (ACO) algorithm with periodic pheromone exchange between colonies. Our method exceeds existing approaches which have attempted partitioning a graph for small problems (less than 48 nodes) with sub-optimal results. We obtained optimum results for benchmarks with less than 150 nodes and results usually within 1% of the known optimal solution for benchmarks with around 2000 nodes.
A method to generate adversarial examples based on color variety of adjacent pixels

Deep neural networks have improved the performance of large-scale learning tasks such as image recognition and speech recognition. However, neural networks also have vulnerabilities. Adversarial examples are generated by adding perturbations to images to cause incorrect predictions of image classifiers. The well-known perturbation attack is JSMA, which is relatively fast to generate perturbation and requires only simple procedures and is widely used in cybersecurity, anomaly detection and intrusion detection. However, there are problems with the way to perturb pixels. JSMA’s perturbations are easily perceivable by the human eyes because JSAM adds large perturbations to pixels. Some previous methods to generate adversarial examples did not assume that adversarial examples are checked by human eyes and allow larger perturbation to be adding to a single pixel. However, in situations where a deep learning model causes significant damage if it misrecognizes an input, a visual check by a human is necessary. In such cases, adversarial examples should not only cause misclassification in the image classifier system but also require less perturbation to avoid human perception of the perturbation. We propose methods to improve the JSMA problems. Specifically, it adjusts the amount of perturbation by calculating the variance between the value of the pixel to be perturbed and its surrounding pixels. If a large perturbation is added to the area of an image with a large pixel value variation, the perturbation will be imperceptible. In such case, perceivability does not increase significantly with a slightly larger perturbation. In contrast, if the large perturbation is added to the area of an image with small pixel value variation, the perturbation will be more perceptible. In such case, perturbations must be small. In our previous study, we assumed thresholds to classify the perturbations into two classes, large perturbation and small perturbation. If the variance was larger than the threshold, a larger perturbation was added; if the variance was smaller than the threshold, a smaller perturbation was added, which achieved a reduction in the amount of perturbation. However, there were still rooms of improvements of the perturbation to reduce the perceptibility. In this study, we focused on that there were differences in the perception of perturbations depending on the color of the pixel. The amount of perturbation should vary from pixel to pixel, not a fixed amount. Not only the variance of the surrounding pixels but also the variance of a larger area is calculated. By using these ratios, the amount of perturbation is varied from pixel to pixel. Experimental results using cifar-10 showed that the proposed method reduced the amount of perturbation to pixels with a misclassification success rate comparable to that of JSMA and our past method. We also confirmed that the reduced perturbation made the perturbation less perceptible.
Computer-Aided Breast Cancer Classification Framework for Predictive, Preventive, and Personalized Medicine

In 3P medicine, a physician applies a more holistic approach to breast cancer treatment that focuses on prevention, prediction, and personalized treatment. There are many approaches to meet these criteria that, in the end, result in the creation of customized treatment plans which meet the patient’s individual needs. In this chapter, we describe a neural network approach to computer-aided automatic cancer classification using cytological slides to numerically grade breast cancer malignancies. The proposition of such a system is an answer to fast, repeatable, and reliable cancer detection that can lead to faster diagnosis. Early detection and effective treatment can reduce mortality by up to 30%. To establish a precise and faster diagnosis, a biopsy examination is often required. Here, we describe a computer-aided breast cancer classification framework that allows the estimation of a malignancy grade. Grading breast cancer malignancies is an essential step in breast cancer diagnosis and helps determine their prognosis and course of treatment. In the case of breast cancer, the determination of cancer malignancy influences the patient’s type of treatment and therefore it not only has a prognostic, but also a predictive value. The proposed framework can determine these factors from microscopic images of fine needle aspirates with high accuracy. Moreover, the results of this study have shown that machine learning methods are very effective in breast cancer prevention by providing a range of tools for early detection and diagnosis.
Simulating Quantum Circuits Using Efficient Tensor Network Contraction Algorithms with Subexponential Upper Bound

We derive a rigorous upper bound on the classical computation time of finite-ranged tensor network contractions in $d\ensuremath{\ge}2$ dimensions. Consequently, we show that quantum circuits of single-qubit and finite-ranged two-qubit gates can be classically simulated in subexponential time in the number of gates. Moreover, we present and implement an algorithm guaranteed to meet our bound and which finds contraction orders with vastly lower computational times in practice. In practically relevant cases this beats standard simulation schemes and, for certain quantum circuits, also a state-of-the-art method. Specifically, our algorithm leads to speedups of several orders of magnitude over naive contraction schemes for two-dimensional quantum circuits on as little as an $8\ifmmode\times\else\texttimes\fi{}8$ lattice. We obtain similarly efficient contraction schemes for Google's Sycamore-type quantum circuits, instantaneous quantum polynomial-time circuits, and nonhomogeneous ($2+1$)-dimensional random quantum circuits.
Time-delay signature suppression of chaotic signal in semiconductor laser based on optoelectronic hybrid feedback

An approach to generating chaotic signals with low time-delay signatures (TDSs) from a semiconductor laser (SCL) is proposed and demonstrated based on optoelectronic hybrid feedback. Through using a chirped fiber Bragg grating (CFBG) to provide distributed feedback, a chaotic signal with a low TDS is generated from the SCL. With the assistance of the nonlinear optoelectronic feedback provided by a microwave photonic link, the relaxation oscillation effect in the SCL is effectively suppressed, and the periodicity of the oscillation is greatly weakened. Hence, the TDS of the generated chaotic signal from the SCL is further suppressed, and the effective bandwidth is enlarged. Both simulation and experiment are carried out to verify the feasibility of the proposed scheme to suppress the TDS. In the experiment, a chaotic signal with a large effective bandwidth of 12.93 GHz, an extremely high permutation entropy (PE) of 0.9983, and a low TDS of 0.04, is generated by using a CFBG with a dispersion coefficient of 22.33 ps/nm. This TDS value is at the same level as that obtained by using the SCL-based scheme relying solely on distributed feedback in a CFBG with a dispersion coefficient of 2000 ps/nm.
Multi-robot geometric task-and-motion planning for collaborative manipulation tasks

Abstract We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous , monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. We focus on collaborative manipulation tasks where the robots have to adopt intelligent collaboration strategies to be successful and effective, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging MR-GTAMP domains and show that it outperforms two state-of-the-art baselines with respect to the planning time, the resulting plan length and the number of objects moved. We also show that our framework can be applied to underground mining operations where a robotic arm needs to coordinate with an autonomous roof bolter. We demonstrate plan execution in two roof-bolting scenarios both in simulation and on robots.
Tailoring Photon Statistics with an Atom-Based Two-Photon Interferometer

Controlling the photon statistics of light is paramount for quantum science and technologies. Recently, we demonstrated that transmitting resonant laser light past an ensemble of two-level emitters can result in a stream of single photons or excess photon pairs. This transformation is due to quantum interference between the transmitted laser light and the incoherently scattered photon pairs [Prasad et al., Nat. Photonics 14, 719 (2020)]. Here, using the dispersion of the atomic medium, we actively control the relative quantum phase between these two components. We thereby realize a tunable two-photon interferometer and observe interference fringes in the normalized photon coincidence rate. When tuning the relative phase, the coincidence rate varies periodically, giving rise to a continuous modification of the photon statistics from antibunching to bunching. Beyond the fundamental insight that there exists a tunable quantum phase between incoherent and coherent light that dictates the photon statistics, our results lend themselves to the development of novel quantum light sources.
A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5

The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance.
Detection of heavy metals using atomic absorption and emission spectroscopy in drinking water of Faisalabad, Pakistan: microbial safety and quality status assessment

Synthetic dyes from textile industries are designated as hazardous waste since they release heavy metals and dangerous contaminants in water that harm the environment and human health. The current study was aimed at investigating the heavy metals (Cd, Cu, Pb, Ni, Zn, Cr, and Mn) in drinking water in seven regions of Faisalabad, Pakistan, through atomic absorption spectroscopy (AAS) and optical emission spectroscopy (OES). The physicochemical quality of 86.36% of the water samples met the WHO standards in the targeted regions, while 13.64% failed to meet the criteria. The water quality index (WQI) of selected water samples was calculated by integrating 11 physicochemical parameters, and the results indicated fair quality, whereas Madina, Jinnah, and Lyallpur Town showed poor water quality with contamination of Mn > Ni > Pb. The order of contamination for heavy metals in groundwater was Ni > Cu > Zn > Cr > Pb > Mn > Cd. The total plate count (TPC) and total coliform count (TCC) were found within the permissible range of WHO guidelines. Hence, consuming groundwater in the research regions may not present major health risks due to ingested metal toxicity; however, levels of Mn, Ni, and Pb might pose risks.
Continuous-variable quantum tomography of high-amplitude states

Quantum state tomography is an essential component of modern quantum technology. In application to continuous-variable harmonic-oscillator systems, such as the electromagnetic field, existing tomography methods typically reconstruct the state in discrete bases, and are hence limited to states with relatively low amplitudes and energies. Here, we overcome this limitation by utilizing a feed-forward neural network to obtain the density matrix directly in the continuous position basis. An important benefit of our approach is the ability to choose specific regions in the phase space for detailed reconstruction. This results in a relatively slow scaling of the amount of resources required for the reconstruction with the state amplitude, and hence allows us to dramatically increase the range of amplitudes accessible with our method.
ANALISIS KORELASI ANTARA NILAI DEVIATION INDEX DAN ENTRANCE SURFACE AIR KERMA (ESAK) DALAM UPAYA OPTIMISASI DOSIS RADIASI PASIEN DI MANDAYA ROYAL HOSPITAL PURI

Chest radiograph examination is the most common diagnostic radiology procedure. Therefore, optimization is required regarding chest examination, both in terms of image quality and the radiation dose received by the patient. This study discusses the relationship between the deviation index and the patient's radiation dose produced. The deviation index is a parameter that can determine whether the resulting image is underexposed or overexposed. This study's varied parameter is the exposure index target (EI_T). The study observed 100 patients, with 50 patients having an EI_T of 400 and 50 having an EI_T of 250. Based on the research findings, varying the EI_T parameter resulted in an average deviation index value of 1.86 ± 0.16 (for EI_T 400) and 2.18 ± 0.133 (for EI_T 250).Meanwhile, the Entrance Surface Air Kerma (ESAK) value calculation was done manually as the Si-INTAN account is currently undergoing maintenance. The resulting ESAK values from the study were 0.318 mGy ± 0.027 (for EI_T 400) and 0.296 mGy ± 0.020 (for EI_T 250). Based on the research findings, it can be concluded that the deviation index value remains an effective tool for measuring the quality of images produced during the optimization process of patient radiation dose. Keywords: exposure index, deviation index, ESAK, DRL, Si-INTAN