Reviewerly/exHarmony huggingface dataset:
Image classification with a fully connected opto-electronic neural network

Optical approaches have made great strides enabling high-speed, scalable computing necessary for modern deep learning and AI applications. In this study, we introduce a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. The system is designed to be real-time and parallelized, utilizing arrays of light emitters and detectors connected with independent analog electronics. We experimentally demonstrate the operation of our system and compare its performance to a single-layer analog through simulations.
Feature and texture distillation via neural network training

Encoded-diffraction hybrid systems—optical encoding and simple electronic decoding—offers feature distillation via model training. Additionally, the most faithfully reconstructed images are not the ones that are best classified. We parametrize our results with singular value decomposition (SVD) entropy, a proxy for image complexity.
Computation and implementation of large scalable Spiking Neural Network

Photonic neural networks are a highly sought-after area of research due to their potential for high-performance complex computing. Unlike artificial neural networks, which use simple nonlinear maps, biological neurons transmit information and perform computations through spikes that depend on spike time and/or rate. Through comprehensive studies and experiments, a strong foundation has been laid for the development of photonic neural networks. We have recently developed a large-scale spiking neural network, which serves as a proof-of-concept experiment for novel bio-inspired learning concepts. This breakthrough is significant because it demonstrates the potential of using photonic neural networks for advanced computing and highlights the importance of incorporating biological principles into artificial intelligence research.
Broadband Biphoton Generation and Polarization Splitting in a Monolithic AlGaAs Chip

Integrated quantum photonics is a key tool towards large scale quantum technologies. In this work we present an AlGaAs-based photonic circuit for the on-chip generation of broadband orthogonally polarized photons and the deterministic separation of the photons into separate spatial modes, facilitating their further use in protocols. We demonstrate that 85% of the pairs are deterministically separated by the chip over a full 60 nm bandwidth and we assess the chip operation in the quantum regime via a Hong-Ou-Mandel experiment displaying a raw visibility of 75.5% over the same full bandwidth.
Spatially entangled states of light in nonlinear waveguide arrays - INVITED

We demonstrate a nonlinear AlGaAs photonic chip generating biphotons with nonclassical spatial correlations. Photon pairs are generated by parametric down conversion in a waveguide array and simultaneously spread through quantum walks along the various waveguides. This concept implements a compact and versatile source of spatially entangled states, operating at room temperature and telecom wavelength, that could serve as a workbench for simulating condensed matter problems on-chip.
Hankel Transform

For solving boundary value problems formulated in cylindrical coordinate system, Hankel transforms are employed. A formal derivation of Hankel transform is given below.
Non-local property of single-photon states: Illustration with spontaneously emitted photon from a Hydrogen atom

We discuss a new proof of the non-locality of single-photons using a concrete physical observable such as the local energy density. As an illustration of this non-locality, we compute the mean value of the local energy observable for the spontaneous emission from a Hydrogen atom. We find that, in the subspace of single-photon states, the mean value of the local energy density observable is non-zero everywhere and decreases as a power of the distance.
Occurrence and Impact of Heavy Metals on Groundwater Sources: A Case Study of Two Communities in Nasarawa State, Nigeria

Heavy metals are major contaminants in groundwater, and they have potentials for toxicity even at low concentrations with health hazards. This led to the determination of heavy metal concentrations and evaluation of HMPI and MI of groundwater sources in Keffi and Karu to ascertain the suitability for domestic usage. Heavy metals, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn, were determined using AAS. The results obtained showed that Cd, Cr and Pb had mean concentrations higher than the SON recommended limit. Statistical analyses at p &lt; 0.05 showed that there were no significant differences between heavy metal contents in water from boreholes and wells in Keffi and Karu. HMPI were 77.33 and 92.08 for borehole and hand dug well water respectively in Keffi and 105.27 and 127.41 for borehole and hand dug well water respectively in Karu. The values from Keffi are lower than the critical value of 100, while those of Karu are slightly higher. MI for borehole and hand dug well waters was 0.564 and 0.606 respectively in Keffi and for Karu, the values were 0.634 and 0.723 respectively and are all less than 1. These suggest that the water sources are not affected with heavy metal pollution when compared to the water quality classification scheme adopted.
The Fusion of Feature Extraction Applications and Blurring Techniques for Classifying Irish Sign Language

Sign language plays a crucial role in the non-verbal communication of a person with disabilities such as deafness and dumbness. The role of hand gestures for disabled people and its common use is undeniable. Therefore, image-based hand gestures were the most potential method to recognize sign language. This research attempted to combine feature extraction applications and blurring techniques for recognition. Not only the level of accuracy but also processing time was studied and analyzed when different feature-based extraction approaches. These methods include Principal Component Analysis (PCA) and Discrete Wavelet Transformation (DWT). Following this, the comparison between the three classification models named Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), and Ensemble method was performed in this article. As a result, both feature extraction approaches with a particular blurring technique were reported with a performance of about 99%. In addition, the effectiveness of different blurring and image-cropping techniques was surveyed. These results were the first selection to find the most optimal methods for future work.
Principles of correctional and developmental work of a teacher-psychologist with children and adolescents with mild mental retardation.

Abstract: this article addresses the issue of psychological support for children and adolescents with mild mental retardation on the basis of a general education organization, the need to maintain structure in interaction with this age category.
Classification in Multi-Label Datasets

Multi-label datasets contain several classes, where each class can have multiple values. They appear in several domains such as music categorization into emotions and directed marketing. In this chapter, we are interested in the most popular task of Data Mining, which is the classification, more precisely classification in multi-label datasets. To do this, we will present the different methods used to extract knowledge from these datasets. These methods are divided into two categories: problem transformation methods and algorithm adaptation ones. The methods of the first category transform multi-label classification problem into one or more single classification problems. While the methods of the second category extend a specific learning algorithm, in order to handle multi-label datasets directly. Also, we will present the different evaluation measures used to evaluate the quality of extracted knowledge.
Information security failures identified and measured – ISO/IEC 27001:2013 controls ranked based on GDPR penalty case analysis

This paper identifies the failures and impacts of information security, as well as the most effective controls to mitigate information security risks in organizations.Root cause analysis was conducted on all year 2020 GDPR penalty cases (n = 81) based on misconduct as defined in GDPR article 32: "security of processing."ISO/IEC 27,001 controls were used as failure identifiers in the analysis.As a result, this study presents both the most frequent and most expensive information security failures and correspondingly ranks and presents the correlation of the controls observed in the analysis.From a theoretical perspective, our study contributes by bridging the gap between regulation and information security and introduces a statistical method to analyze the GDPR penalty cases, and provides previously unreported findings about information security failures and their respective solutions.From a practical perspective, the results of our study are useful for organizations which aspire to manage information security more effectively in order to prevent the most typical and expensive information security failures.Organizations, as well as auditors implementing and assuring the ISO 27001, may use our results as a guideline whereby controls should be applied and verified first in sequential order based on their impact and interdependence
A hierarchical multi-agent allocation-action learning framework for multi-subtask games

Abstract Great progress has been made in the domain of multi-agent reinforcement learning in recent years. Most work concentrates on solving a single task by learning the cooperative behaviors of agents. However, many real-world problems are normally composed of a set of subtasks in which the execution order follows a certain procedure. Cooperative behaviors should be learned on the premise that agents are first allocated to those subtasks. In this paper, we propose a hierarchical framework for learning the dynamic allocation of agents among subtasks, as well as cooperative behaviors. We design networks corresponding to agents and subnetworks, respectively, which constitute the whole hierarchical network. For the upper layer, a novel allocation learning mechanism is devised to map an agent network to a subtask network. Each agent network could only be assigned to only one subtask network at each time step. For the lower layer, an action learning module is designed to compute appropriate actions for each agent with the allocation result. The agent networks together with the subtask networks are updated by a common reward obtained from the environment. To evaluate the effectiveness of our framework, we conduct experiments in two challenging environments, i.e., Google Research Football and SAVETHECITY. Empirical results show that our framework achieves much better performance than other recent methods.
Analyzing the Impact of Augmentation Techniques on Deep Learning Models for Deceptive Review Detection: A Comparative Study

Deep Learning has brought forth captivating applications, and among them, Natural Language Processing (NLP) stands out. This study delves into the role of the data augmentation training strategy in advancing NLP. Data augmentation involves the creation of synthetic training data through transformations, and it is a well-explored research area across various machine learning domains. Apart from enhancing a model's generalization capabilities, data augmentation addresses a wide range of challenges, such as limited training data, regularization of the learning objective, and privacy protection by limiting data usage. The objective of this study is to investigate how data augmentation improves model accuracy and precise predictions, specifically using deep learning-based models. Furthermore, the study conducts a comparative analysis between deep learning models without data augmentation and those with data augmentation.
Multi-level control for multiple mobile robot systems

Abstract This paper contributes with a multi-level, hierarchical control system for a fleet of mobile robots sharing a common 2D motion space. The system consists of three levels, with the top level being a supervisor based on a discrete representation of the Multiple Mobile Robot System (MMRS), in which robot motion processes are seen as sequences of stages. The supervisor controls centrally the changes of their stages by robots, ensuring their collision-, and deadlock-free concurrent movement. The intermediate control level supervises locally the execution of robot motion on individual stages in a manner consistent with the decisions of the top level. The lowest level, robot control, is responsible for motion execution as determined by the local supervisor. We capitalize on some earlier results concerning the supervisory control of MMRS and propose a common framework for three supervisory control models. Then we propose relevant solutions for the local supervisors, in particular, a DES-based robot-motion-mode control and application of the Artificial Potential Field model for ensuring collision-free motion of two robots sharing a space sector. Next we assume simple robot control and subject the system to simulation experiments aimed at comparing the impact of the different solutions on the performance of MMRS.
3D Adversarial Augmentations for Robust Out-of-Domain Predictions

Abstract Since real-world training datasets cannot properly sample the long tail of the underlying data distribution, corner cases and rare out-of-domain samples can severely hinder the performance of state-of-the-art models. This problem becomes even more severe for dense tasks, such as 3D semantic segmentation, where points of non-standard objects can be confidently associated to the wrong class. In this work, we focus on improving the generalization to out-of-domain data. We achieve this by augmenting the training set with adversarial examples. First, we learn a set of vectors that deform the objects in an adversarial fashion. To prevent the adversarial examples from being too far from the existing data distribution, we preserve their plausibility through a series of constraints, ensuring sensor-awareness and shapes smoothness. Then, we perform adversarial augmentation by applying the learned sample-independent vectors to the available objects when training a model. We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation. Despite training on a standard single dataset, our approach substantially improves the robustness and generalization of both 3D object detection and 3D semantic segmentation methods to out-of-domain data.
Evaluation of Spaciousness in Virtually Simulated Isolated and Confined Environments

This study delves into the intricate interplay between interior architectural elements and the perception of spaciousness within isolated, confined and extreme environments (ICE). Building on prior literature and research, we focused on four key interior components: interior furnishings, illumination levels, surface texture, and ceiling height. These elements are subjected to rigorous testing within virtual environments, employing a head-mounted display. Additionally, the participant's assessment of spaciousness is examined through the prism of familiarity, excitement, and fascination in their aesthetic judgments. The study's outcomes illuminate that participants with design training and a sense of familiarity wield substantial influence over the perception of spaciousness, particularly in environments characterized by over-furnishing, low illuminance, or decreased texture density. The study further observes that changes in lighting and texture have a discernible impact on participants' relative error in size estimation, with heightened illumination and texture density yielding more precise length measurements. Consequently, the study underscores the pivotal role of interior architectural elements in shaping the perception of spaciousness within ICE environments.
The impact of transformational leadership on enhancing organizational trust: Moderating role of empowerment

Worker empowerment is one of the main objectives of any organization. The purpose of the current study is to examine the impact of transformational leadership on organizational trust (trust in coworkers, trust in organization management, and trust in supervisors). In addition, it examines whether workers’ empowerment moderates the mentioned impact. An analytical descriptive approach was adopted, and a survey collected relevant data. A stratified random sample included 294 department managers, branch managers, and heads of departments who work in Jordanian telecommunications companies (Zain, Orange, and Umniah). The study employed SPSS and AMOS 21 for data analysis and hypotheses testing. The paper revealed a significant impact of transformational leadership on organizational trust, except for the ideal influence and inspirational motivation dimensions. Furthermore, workers’ empowerment significantly moderates the impact of transformational leadership on organizational trust at Jordan’s telecommunications companies (Zain, Orange, and Umniah). The study recommends improving transformational leadership methods in telecom businesses as one of the most critical administrative leadership techniques. It is vital to ensure the continuation of telecom business leaders in these companies to foster organizational trust between company workers and administrative leaders. Additionally, future studies should find new funding sources, identify practical ways to put strategies into practice for intellectual stimulation and arousal in these companies, and conduct more studies on transformational leadership, organizational trust, and empowerment.
Adopting blockchain-based traceability in the fruit supply chain in a developing economy: facilitators and barriers

Purpose Blockchain-based traceability technology (BTT) is an emerging digital technology that claims to have the potential to fulfil the demand for traceability to safeguard fruit safety. Drawing on the technological, organisational and environmental (TOE) framework, this study aims to investigate the perceived facilitators and barriers that influence the behavioural intentions of multiple stakeholders in the Vietnamese fruit supply chain (i.e., farmers, trading enterprises and consumers) to adopt BTT. Design/methodology/approach This study utilised a qualitative approach of semi-structured in-depth interviews with 60 stakeholders in the Vietnamese fruit supply chain to achieve the research objectives. NVivo 12 was employed to analyse the collected data using content and thematic analysis. Findings The findings identify several perceived facilitators that motivate BTT adoption. These include trust, transparency, business performance, the formation of alliances, consumer awareness of food safety and ethical agricultural practices, fruit branding and the pivotal role of farmers' cooperatives. Meanwhile, the perceived barriers to BTT adoption include a lack of digital literacy amongst the stakeholders, poor organisational culture, the high cost of traceability-enabled products and data privacy and security governance. Practical implications This study suggests that technology awareness and perceived facilitators and barriers should be incorporated into the design and deployment of blockchain-based traceability technology in the agri-food supply chain in developing countries. Originality/value To the best of the authors' knowledge, this study is the first qualitative paper that attempts to fill the research gap of understanding the perceived facilitators and barriers that influence the intentions of multiple stakeholders in the fruit supply chain to adopt BTT in the context of a developing economy.
Exploring the impact of using Chat-GPT on student learning outcomes in technology learning: The comprehensive experiment

This experimental study investigates the impact of integrating Chat GPT (Generative Pre-trained Transformer) on student learning outcomes in technology education at Universitas Muhammadiyah Muara Bungo. The research involves an experimental group using Chat GPT and a control group with conventional methods. Data from 31 participants in each group were collected, assessing learning outcomes through final test scores. Analyzing the results with a t-test, the experimental group displayed significantly higher achievements than the control group, highlighting the positive effect of incorporating GPT Chat into educational technology. The study illuminates the potential of AI-powered chatbots like Chat GPT to enhance student learning outcomes. Further exploration is required to gauge its adaptability across diverse educational contexts for more enhanced learning results. T-test results, conducted at a 95% confidence level with α 0.05, and degrees of freedom dk = n1 + n2 - 2 = 60, showed tcount of 5.424 against ttable of 2.000, firmly establishing tcount &gt; ttable (5.424 &gt; 2.000). Consequently, the null hypothesis (H0) proposing no significant impact of Chat GPT utilization is refuted. Conversely, the alternative hypothesis (H1), signifying a significant influence from Chat GPT usage, is upheld, affirming its substantial role in students' technological education.
Inferring Attention Shifts for Salient Instance Ranking

Abstract The human visual system has limited capacity in simultaneously processing multiple visual inputs. Consequently, humans rely on shifting their attention from one location to another. When viewing an image of complex scenes, psychology studies and behavioural observations show that humans prioritise and sequentially shift attention among multiple visual stimuli. In this paper, we propose to predict the saliency rank of multiple objects by inferring human attention shift. We first construct a new large-scale salient object ranking dataset, with the saliency rank of objects defined by the order that an observer attends to these objects via attention shift. We then propose a new deep learning-based model to leverage both bottom-up and top-down attention mechanisms for saliency rank prediction. Our model includes three novel modules: Spatial Mask Module (SMM), Selective Attention Module (SAM) and Salient Instance Edge Module (SIEM). SMM integrates bottom-up and semantic object properties to enhance contextual object features, from which SAM learns the dependencies between object features and image features for saliency reasoning. SIEM is designed to improve segmentation of salient objects, which helps further improve their rank predictions. Experimental results show that our proposed network achieves state-of-the-art performances on the salient object ranking task across multiple datasets. Code and data are available at https://github.com/SirisAvishek/Attention_Shift_Ranks .