Reviewerly/exHarmony huggingface dataset:
GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients' evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD).
Sequential recommendation: A study on transformers, nearest neighbors and sampled metrics

Sequential recommendation problems have received increased research interest in recent years. In such scenarios, the task is to suggest items to users to consume next, given their past interaction history, e.g., the next movie to watch or the next item to place in the shopping cart. A number of machine learning models were proposed recently for the task of sequential recommendation, with the latest ones based on deep learning techniques, in particular on Transformers. Given the often surprisingly competitive performance of simpler nearest-neighbor methods for the related problem of session-based recommendation, we investigate the use of nearest-neighbor methods for sequential recommendation problems. Our analysis on four datasets shows that nearest-neighbor methods achieve comparable or better performance than the recent Transformer-based bert4rec method on two of them. However, the deep learning method outperforms the simple methods for the two larger datasets, confirming previous hypotheses that neural methods work best when more data is available. As a further result of our experiments, we found additional evidence that sampled metrics must be used with care, as they may not be predictive of an algorithm ranking that would be observed with the non-sampled, full evaluation.
Unsupervised domain adaptation via softmax-based prototype construction and adaptation

To mitigate the distribution difference between the source and target domains, there have been many unsupervised domain adaptation methods to achieve class-level alignment by aligning the prototypes of two domains. Since the labels of the target domain are unobserved, the target prototypes are constructed based on pseudo-labels. However, the inaccuracy of pseudo-labels may lead to biased prototypes, which introduce noise into the distribution alignment. Moreover, a shared feature extractor or two separate feature extractors are used to extract domain-invariant features. But the former is limited by the large domain gap and the latter increases the network parameters. To this end, we propose a Softmax-Based Prototype construction and Adaptation (SBPA) method, which constructs prototypes based on the softmax output of the classifier instead of ground-truth labels or pseudo-labels. SBPA performs domain-level alignment through adversarial training, and class-level alignment by aligning prototypes of the same class. In addition, SBPA contains a residual block to explicitly model the difference between the source and target domain features extracted by a shared feature extractor. We evaluate our method on four widely used datasets, and the results show that our method outperforms recent domain adaptation methods, especially on DomainNet, the hardest domain adaptation dataset by far.
BalanceFL: Addressing Class Imbalance in Long-Tail Federated Learning

Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of different nodes without ex-posing the raw data. However, a critical challenge faced by the current federated learning algorithms in real-world applications is the long-tailed data distribution, i.e., in both local and global views, the numbers of classes are often highly imbalanced. This would lead to poor model accuracy on some rare but vital classes, e.g., those related to safety in health and autonomous driving applications. In this paper, we propose BalanceFL, a federated learning frame-work that can robustly learn both common and rare classes from a long-tailed real-world dataset, addressing both the global and local data imbalance at the same time. Specifically, instead of letting nodes upload a class-drifted model trained on imbalanced private data, we design a novel local update scheme that rectifies the class imbalance, forcing the local model to behave as if it were trained on ideal uniform distributed data. To evaluate the performance of BalanceFL, we first adapt two public datasets to the long-tailed federated learning setting, and then collect a real-life IMU dataset for action recognition, which includes more than 10,000 data sam-ples and naturally exhibits the global long tail effect and the local imbalance. On all of these three datasets, BalanceFL outperforms state-of-the-art federated learning approaches by a large margin.
MNIST Handwritten Digit Recognition using Machine Learning

Identification of the MNIST database that will be in handwritten digit can be recognized by the machine. It can be recognized that the human handwritten form into the machine language. Here we are using some machine learning algorithm that is Convolutional Neural Network (CNN). MNIST database consist of nine digits that is 0 to 9. It can be used to change over manually written digits into machine lucid arrangements. The primary target of this work is to guarantee successful and dependable methodologies for acknowledgment of written by hand digits and make banking tasks more straightforward and mistake free. The MNIST database will be identified by the machine very accurately. Here we are finding the handwritten digit accuracy.
Metasurface on integrated photonic platform: from mode converters to machine learning

Abstract Integrated photonic circuits are created as a stable and small form factor analogue of fiber-based optical systems, from wavelength-division multiplication transceivers to more recent mode-division multiplexing components. Silicon nanowire waveguides guide the light in a way that single and few mode fibers define the direction of signal flow. Beyond communication tasks, on-chip cascaded interferometers and photonic meshes are also sought for optical computing and advanced signal processing technology. Here we review an alternative way of defining the light flow in the integrated photonic platform, using arrays of subwavelength meta-atoms or metalines for guiding the diffraction and interference of light. The integrated metasurface system mimics free-space optics, where on-chip analogues of basic optical components are developed with foundry compatible geometry, such as low-loss lens, spatial-light modulator, and other wavefront shapers. We discuss the role of metasurface in integrated photonic signal processing systems, introduce the design principles of such metasurface systems for low loss compact mode conversion, mathematical operation, diffractive optical systems for hyperspectral imaging, and tuning schemes of metasurface systems. Then we perceive reconfigurability schemes for metasurface framework, toward optical neural networks and analog photonic accelerators.
Essaying the design, development and validation processes of a new digital literacy scale

Purpose The emergence of new digital technologies in the 21st century has propelled individuals to use technology in their daily lives. Individuals now require a specific set of skills, competencies and knowledge to use these devices efficiently, intelligently yet with responsibility. The purpose of this research was to design, develop and validate a digital literacy scale (DLS) using the exploratory factor analysis (EFA). Design/methodology/approach A quantitative research methodology was used for this study, whereby a unipolar 5 point Likert scale self-report questionnaire was designed to measure the digital skills of the participants. Digital literacy was divided into an exhaustive and broad list of six modules-media literacy, information literacy, communication literacy, technology literacy, visual literacy and computer literacy. The questionnaire was administered to a sample of the 2,755 Year 12 and Year 13 students of randomly selected 33 secondary school students in Fiji. Sample adequacy of the sample was tested using the Kaiser-Meyer-Olkin test. Findings The EFA results prove the existence of the six modules of digital literacy that are being utilized in this research to design the DLS. The factor loadings or the correlation coefficients range from 0.3 to 0.8; therefore, all the 60 items were kept to design the DLS. The Cronbach Alpha value of 0.90 confirmed the internal consistency and reliability of the newly designed DLS. Research limitations/implications The research limitation of the study was the sample size. A broader sample would have made the results better. Research implications are; there is no scale in the south Pacific to measure digital literacy. The newly designed and developed scale is the first one that can be used to measure the digital literacy of the south Pacific populace and bridge the gap of the digital divide. Practical implications The developed and validated scale can be used to measure the digital literacy of individuals both in the south Pacific and beyond. The scale covers important aspects of the much needed 21st century skills. Once digital literacy is measured, relevant stakeholders can develop instruments to improve the digital literacy of individuals. Social implications The DLS can prove to be an important tool for society. In the 21st century people are required to have digital literacy to survive and thrive. Thus this tool can be used to measure digital literacy and further improve the digital literacy of the people. Originality/value The framework has been adopted from the work of Covello (2010) however, the definitions of digital literacy and other literacies are modified to meet the requirements of the 21st century. The tool is unique in its nature in the south Pacific and beyond. Looking at the digital literacy tools in literature, this tool covers all the aspects of digital literacy in the 21st century. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0532 .
Multi-attention concept-cognitive learning model: A perspective from conceptual clustering

Concept-cognitive learning (CCL), as a cognitive process, is an emerging field of simulating the human brain to learn concepts in the formal context. Simultaneously, attention is a core property of all perceptual and cognitive operations. Nevertheless, no current existing CCL models and conceptual clustering methods consider the impact of attention. In light of these observations, in this article, we present a novel concept learning method, called the multi-attention concept-cognitive learning model (MA-CLM), to address the issue by exploiting graph attention and the graph structure of the concept space. This model is deployed toward the goal of conceptual cognitive more reasonable: generate pseudo-concept with higher expected utility while taking into consideration making classification tasks more efficient. Specifically, a conceptual attention space is learned for each decision class via attribute attention. Furthermore, a new concept clustering and concept generation method based on graph attention was proposed based on the conceptual attention space. Comparative studies with S2CLα over a total of nine UCI data sets validate the effectiveness and efficiency of concept clustering based on graph attention in concept-cognitive learning. In addition, we also performed a comparative evaluation of MA-CLM against several classical classification algorithms to demonstrate the excellent properties in classification tasks. Finally, the model is validated by concept generation on the handwritten numeral dataset MNIST.
Using quantum amplitude amplification in genetic algorithms

The selection mechanism of genetic algorithms can play a key role in leading the optimization process towards suitable solutions of a given problem, as their application can affect the trade-off between exploration and exploitation. However, some selection operators suffer from an excessive exploitation that may lead to the loss of population diversity and to a premature convergence, and others present an excessive exploration that may cause genetic algorithms to produce poor results near to random. As a consequence, there is a strong need to rethink how a mating pool is created and introduce alternative approaches to genetic selection. In this paper, a quantum algorithm, named Quantum Genetic Sampling (QGS), has been designed to replace selection operators so as to provide an increased population diversity in genetic evolution and reduce the possibility that the optimization process converge to low quality solutions. The proposed approach is based on two sequential steps. In the first step, QGS models a problem's search space through a quantum state, where each quantum basis state is related to a possible solution of the problem. In the second step, QGS uses quantum amplitude amplification to properly adjust the probability of measuring an individual and placing it in the mating pool according to its fitness value. Unlike conventional selection operators, the quantum mechanical nature of the proposed approach allows for a non-zero probability of introducing new genetic material into the mating pool. Experiments based on well-known benchmark functions show the suitability of the proposed quantum operator for embedding in genetic algorithms, both in terms of accuracy and population diversity.
A fast spectral clustering technique using MST based proximity graph for diversified datasets

Spectral clustering is a popular unsupervised learning technique used for exploratory analysis of complex datasets. In spectral clustering, the efficient construction of a sparse similarity graph is one of the primary concerns because it influences both the clustering performance as well as the computational time. The sparser the similarity graph, the faster the computation of the eigenvectors, thus improving the overall clustering time. In this paper, we propose a parameter-free method to construct a sparse proximity graph based on the intrinsic structural information of the data points. The proposed method first divides the data into several partitions and then determines the pairs of neighboring partitions using the representative points of each partition. We propose a method called inter-partition MST (IPM) to capture the local proximity information of a neighboring pair. The size of the proposed graph is O(N), where N is the number of data points. For clustering, we apply multi-way spectral clustering on the proposed sparse similarity graph. Experimental analyses demonstrate that in terms of cluster quality, computational time and sparsity of the graph, the proposed algorithm outperforms the other spectral clustering methods.
Pushing Serverless to the Edge with WebAssembly Runtimes

Serverless computing has become a popular part of the cloud computing model, thanks to abstracting away infrastructure management and enabling developers to write functions that auto-scale in a polyglot environment, while only paying for the used compute time. While this model is ideal for handling unpredictable and bursty workloads, cold-start latencies of hundreds of milliseconds or more still hinder its support for latency-critical IoT services, and may cancel the latency benefits that come with proximity, when serverless functions are deployed at the edge. Moreover, CPU power and memory limitations which often characterize edge hosts drive latencies even higher. The root of the problem lies in the de facto runtime environments for serverless functions, namely container technologies such as Docker. A radical approach is thus to replace them with a more light-weight alternative. For this purpose, we examine WebAssembly's suitability for use as a serverless container runtime, with a focus on edge computing settings, and present the design and implementation of a WebAssembly-based runtime environment for serverless edge computing. WOW, our prototype for WebAssembly execution in Apache QpenWhisk, reduces cold-start latency by up to 99.5%, can improve on memory consumption by more than 5×, and increases function execution throughput by up to 4.2× on low-end edge computing equipment compared to the standard Docker-based container runtime for various serverless workloads.
Towards Spoofing Resistant Next Generation IoT Networks

The potential vulnerability to wireless spoofing attacks is still a critical concern for Next Generation Internet of Things (NGIoT) networks which may result in catastrophic consequences in mission&#x2013;critical applications. Conventional solutions may impose additional signal processing, protocol, and latency overheads which are inappropriate for NGIoT networks designed to provide high&#x2013;speed and low&#x2013;latency connections for a large number of resource&#x2013;constrained IoT devices. In this paper, we utilize the uniqueness of beam pattern features in mmWave&#x2013;enabled devices and propose a scalable security mechanism for the detection of wireless spoofing attacks in NGIoT networks. This uniqueness is proven to exist due to the non&#x2013;ideal manufacturing of antenna arrays used in mmWave&#x2013;enabled devices. In our approach, when legitimate mmWave&#x2013;enabled IoT devices enrol into the network, their unique beam features are learned by a learning model developed at the network server. Then, during data transmission, network base stations (gNBs)/Access Points (APs) measure the beam features from the received RF signals and send them to the network server for the detection of anomalies. We develop our learning model based on Deep Autoencoders (DAEs) that are an effective tool for anomaly detection. Fortunately, the beam feature extraction can be performed using the beam searching mechanism that is already provided in mmWave standards (5G&#x2013;NR and IEEE 802.11ad). Thus, feature extraction does not introduce any signal processing overheads to the system. Moreover, the proposed mechanism imposes zero computation/communication overhead to the resource&#x2014;constrained IoT nodes. In our experiments, we reached 98.6&#x0025; accuracy in the detection of illegitimate devices which confirms the effectiveness of the proposed approach.
Geogenic contaminants and groundwater quality around Lake Victoria goldfields in northwestern Tanzania

Geogenic contamination of groundwater is frequently associated with gold mining activities and related to drinking water quality problems worldwide. In Tanzania, elevated levels of trace elements (TEs) have been reported in drinking water sources within the Lake Victoria Basin, posing a serious health risk to communities. The present study aims to assess the groundwater quality with a focus on the concentration levels of geogenic contaminants in groundwater around the Lake Victoria goldfields in Geita and Mara districts. The water samples were collected from community drinking water sources and were analysed for physiochemical parameters (pH, EC, Eh), major ions, and trace elements. The analysed major ions included Na+, K+, Ca2+, Mg2+, SO42-, HCO3- and Cl- whereas the trace elements were As, Al, Li, Ba, B, Ti, V, U, Zr, Sr, Si, Mn Mo, Fe, Ni, Zn, Cr, Pb, Cd, and V. The present study revealed that the concentration levels of the major ions were mostly within the World Health Organization (WHO) drinking water standards in the following order of their relative abundance; for cations, Ca2+∼Na+>Mg2+>K+ and for anions was HCO3- > SO42- > NO3-, Cl- > PO43-. Statistical and geochemical modelling software such as 'R Studio', IBM SPSS, geochemical workbench, visual MINTEQ were used to understand the groundwater chemistry and evaluate its suitability for drinking purpose. The concentration of As in groundwater sources varies between below detection limit (bdl) and 300 μg/L, with highest levels in streams followed by shallow wells and boreholes. In approximately 48% of the analysed samples, As concentration exceeded the WHO drinking water guideline and Tanzania Bureau of Standards (TBS) guideline for drinking water value of 10 μg/L. The concentration of the analyzed TEs and mean values of physicochemical parameters were below the guideline limits based on WHO and TBS standards. The Canadian Council of Ministries of the Environment Water Quality Index (CCME WQI) shows that the overall water quality is acceptable with minimum threats of deviation from natural conditions. We recommend further geochemical exploration and the periodic risk assessment of groundwater in mining areas where high levels of As were recorded.
Training in virtual reality enables learning of a complex sports movement

Abstract Despite the increased use in sports, it is still unclear to what extent VR training tools can be applied for motor learning of complex movements. Previous VR studies primarily relate to realize performances rather than learning motor skills. Therefore, the current study compared VR with video training realizing the acquisition of karate technique, the Soto Uke moving forward in Zenkutsu Dachi, without being accompanied by a trainer or partner. Further analyses showed whether a less lavished forearm compared to a whole-body visualization in VR is necessary to acquire movements’ basics sufficiently. Four groups were tested: 2 groups conducted VR training (VR-WB: whole-body visualization, and VR-FA having only visualized the forearms), the third group passed through a video-based learning method (VB), and the control group (C) had no intervention. In consultation with karate experts, a scoring system was developed to determine the movements’ quality divided, into upper- and lower body performance and the fist pose. The three-way ANOVA with repeated measurements, including the between-subject factor group [VR-WB, VR-FA, VB, C] and the within-subject factors time [pre, post, retention] and body regions [upper body, lower body, fist pose], shows that all groups improved significantly (except for C) with the similar course after four training sessions in all body regions. Accordingly, VR training seems to be as effective as video training, and the transfer from VR-adapted skills into the natural environment was equally sufficient, although presenting different body visualization types. Further suggestions are made related to the features of future VR training simulations.