Reviewerly/exHarmony huggingface dataset:
Image Based Person Re-Id Using GAN, DWT, and MLTP with ANN Classifier

The recognition of an individual from images/videos captured from different cameras in real-world surveillance scenarios is the Person's re-identification.We propose image-based person reidentification (Re-id) using GAN, DWT, and MLTP with ANN classifiers.The central server database contains four benchmarked face databases by considering six images per person.The generative adversarial network (GAN) is used to convert side-angled test images into frontal images for an effective person Re-id.The discrete wavelet transform (DWT) and modified local ternary pattern (MLTP) techniques are used on images of the central server database and generated frontal test images to extract initial features.The Final features are computed by using the convolution of the DWT-LL band and MLTP features.The artificial neural network (ANN) is used to categorize and recognize test images.The existing research based on HOG, Hu Invariant moments, CNN, and SVM is presented and published, having a recognition accuracy of 97%.However, our projected technique uses GAN, which improved recognition accuracy to 99% compared to existing techniques having low recognition accuracy.
Multi-Valued Neutrosophic Convolutional LSTM for Intrusion Detection

Cyber security is an essential area of study because of the positive effects that networks have on modern society.As it becomes simpler for cybercriminals to launch novel assaults, the scale and complexity of today's networks continue to grow.Therefore, it is crucial to create an efficient instruction detection system (IDS) capable of constantly monitoring network traffic for suspicious activity and issuing alerts as necessary.Several researchers have employed machine learning (ML) and deep learning (DL) techniques to create an effective IDS.Amongst, an effective IDS detection model convolutional neural network (CNN)-long short term memory (LSTM extracts temporal and geographical aspects of network traffic to lower the false alarm rate (FAR) and raise the detection rate (DR).However, the epistemic uncertainty in intrusion data affect the efficiency of CNN-LSTM model.This paper handle uncertainty information by proposing a multivalued neutrosophic convolutional LSTM (MVN-ConvLSTM) which extracts different deep features.MVN-ConvLSTM uses a neutrosophic set (NS) which considers truth (), indeterminacy (), and falsity () memberships of every features.First, the intrusion features are mapped to NS domain as six sets, positive degree of truth-membership ( ), positive degree of falsity membership ( ), Positive degree of indeterminate towards falsity and indeterminate towards truth membership ( ), negative degree of truth-membership ( ), negative degree of falsity membership ( ), and negative degree of indeterminate towards falsity and indeterminate towards truth membership ( ).Next, an MVN-ConvLSTM is generated with four parallel paths, two of which take input from and two from , and a suitable combination of these paths is used to produce the output.The weights of the neural network are updated in four different directions at once using a back propagation approach.The efficiency of MVN-ConvLSTM to handle uncertainty in intrusion dataset is proved by comparing conventional CNN models which provides lower FAR and high DR.Finally, the test results show that the MVN-ConvLSTM achieves accuracy value of 94.63%, 92.84% and 91.84% on three different datasets CIC-IDS2018, WSN-DS and UNSW-NB15.
Access Control to Robotic Systems Based on Biometric: The Generalized Model and its Practical Implementation

The paper presents a generalized system's structural scheme and components for monitoring and controlling access to robotic systems based on biometric data for decision-making.Mathematical decision-making models for choosing the best alternative based on fuzzy sets are proposed.The fuzzy analytical hierarchy process determined weights of 6 criteria (for k1 criterion weighting factor is equal to 2.2; for k2 criterion -1.8; for k3 criterion -0.6; for k4 criterion -0.6; for k5 criterion -0.6; for k6 criterion -0.2, that is, most weighty would be k1recognition accuracy) against which the best option was evaluated.Thus, during evaluation of alternative on six criteria it was obtained that t alternative X1 (face recognition in full-face), will have the key value of membership function of resulting fuzzy set alternatives with ideal value of integral criterion, equal to 0.53, and as result, implemented rational choice, taking into account given criteria.The difference is that we have introduced such criteria as anti-spoofing.The access control system for robotic systems works in real-time.It is implemented based on an algorithmic decision-making complex, which includes two-factor authentication to increase security: registration involves entering employee data from the keyboard (password, login, surname, first name, position) and physiological authentication (identification by face).Face detection is proposed to be implemented on several images: face, profile, face, and profile in the mask.A distinctive feature of our development is that often, a main requirement for "access control systems with facial identification" is the frontal location of the face relative to the camera; in our case, the image of the face at an angle was added.
Institutions governance is critical to economic success: Evidence from Tanzania’s economic growth

Economic performance is considered the fundamental cause of economic growth. Economic performance affects economic growth through the allocation of resources like physical and human capital. Unfortunately, there are few empirical studies showing institutions’ impact on Economic growth in Tanzania. This study aimed to examine the causal relationship between institutions and economic growth by focusing on the evidence from Tanzania Economic growth. More specifically, the study established the state or characteristics of key components of governance in economic production function of Tanzania. This is a time series study using econometric estimation to give empirical contents of the relationship between governance institutions and economic growth. Estimation techniques deployed to analyze the data were Regression approach within Ordinary Least Square (OLS), generalized Auto Regressive Distributed Lag (ARDL) and Error Correction Model (ECM) process. The results revealed that a causal relationship of institutions has a positive and significant impact on economic growth in Tanzania. The study recommends that components of governance since are critical to economic growth should be emphasized to the public institutions for raising better performance in the country. Therefore, the study concludes that institutional quality may lead to a sustainable increase in country’s income in the long run, and at the same time, success of any policy could be influenced by the soundness of institutions.
Sentiment Analysis on Twitter's Big Data Against the Covid- 19 Pandemic Using Machine Learning Algorithms

This paper analyzes users' reactions on Twitter to the COVID-19 pandemic, using machine learning and data mining algorithms to classify tweets according to economic and health fears.A large dataset of tweets is explored, extracted, transformed, loaded, cleansed, and analyzed.The proposed framework improves prediction quality with a proposed dictionary that is used to classify tweets.The study compares four supervised machine learning algorithms and finds that people discuss the pandemic's dangers from economic and health perspectives with equal frequency.The Naive Bayes algorithm achieves the highest percentage of correct predictions.
Federated Split Learning with Data and Label Privacy Preservation in Vehicular Networks

Federated learning (FL) is an emerging distributed learning paradigm widely used in vehicular networks, where vehicles are enabled to train the deep model for the server while keeping private data locally. However, the annotation of private data by vehicular users is very difficult since the high costs and professional needs, and one solution is that roadside infrastructures could provide label mapping to the data according to the geographical coordinates. In this scenario where vehicles and roadside infrastructures hold the data and labels, respectively, traditional FL is not applicable since it needs each participant to have both data and labels. In this paper, we propose a federated split learning (FSL) paradigm that split the deep model into two submodels which are trained separately in the vehicles and the roadside infrastructures. The vehicles and the roadside infrastructures exchange the intermediate data (i.e., smashed data and cut layer gradients) in training local submodels and upload the local gradients to the global server for aggregation into the global model. Specifically, we first adopt three types of privacy attacks to demonstrate that attackers could recover the private data and labels according to the shared intermediate data and uploaded local gradients. We then propose a differential privacy (DP)-based defense mechanism to defend the privacy attacks by perturbing the intermediate data. Furthermore, we design a contract-based incentive mechanism that encourages vehicles and roadside infrastructures to enhance training performance by adjusting their privacy strategies. The simulation results illustrated that the proposed defense mechanism can remarkably emasculate the performance of attacks and the proposed incentive mechanism is efficient in the FSL paradigm for vehicular networks.
Configured quantum reservoir computing for multi-task machine learning

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua’s circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence.
Gamelan Selonding as Part of an Essential Instrument in the Sustainment of the Sacred Art Activities of the Batur Indigenous People in Kintamani, Bali

Objective: The purpose of this study is to analyze the gamelan selonding, which has become an important part of the structure of the sacred arts of the people in the Batur customary environment, Bali. In general, gamelan selonding is not an essential instrument in Balinese sacred art activities. In contrast to the sacred art activities of the Batur indigenous people, gamelan selonding seems to be something essential.&#x0D; &#x0D; Method: Both of the problems have been resolved by using qualitative methods. All primary data was successfully collected through observation, FGD, and interviews with informants in Kintamani, Bali. Some of the data classified as secondary was successfully collected through literature studies on Balinese culture. All data were analysed qualitatively using practise theory, symbol theory, and reception theory. Practical theory with the help of symbol theory and reception theory is used in analysing the contribution of gamelan selonding to the sacred art activities of the Batur customary villager, while symbol theory and reception theory with the help of practical theory are used in analysing the form of gamelan selonding.&#x0D; &#x0D; Results: Gamelan selonding is an essential part of the continuation of the sacred arts of the Batur indigenous people due to the existence of religious benefits, social benefits, artistic benefits, and cultural capital. As an essential instrument, the gamelan selonding is often used by the indigenous people of Batur for artistic activities in the sacred area of Kintamani, Bali. The gamelan selonding is a type of traditional Balinese musical instrument with a seven-tone pelog barrel. The shape of this gamelan physically consists of two pelvises and a wide blade made of iron, which is placed on a pelawah wooden.&#x0D; &#x0D; Conclusion: The gamelan selonding holds significant importance among the sacred arts of the indigenous Batur community, since it encompasses various dimensions including religious, social, aesthetic, and cultural aspects. This gamelan ensemble under consideration comprises two resonating chambers and a broad metallic blade positioned above a hardwood resonator.
MEGA: Meta-Graph Augmented Pre-Training Model for Knowledge Graph Completion

Nowadays, a large number of Knowledge Graph Completion (KGC) methods have been proposed by using embedding based manners, to overcome the incompleteness problem faced with knowledge graph (KG). One important recent innovation in Natural Language Processing (NLP) domain is the employ of deep neural models that make the most of pre-training, culminating in BERT, the most popular example of this line of approaches today. Recently, a series of new KGC methods introducing a pre-trained language model, such as KG-BERT, have been developed and released compelling performance. However, previous pre-training based KGC methods usually train the model by using simple training task and only utilize one-hop relational signals in KG, which leads that they cannot model high-order semantic contexts and multi-hop complex relatedness. To overcome this problem, this article presents a novel pre-training framework for KGC task, which especially consists of both one-hop relation level task (low-order) and multi-hop meta-graph level task (high-order). Hence, the proposed method can capture not only the elaborate sub-graph structure but also the subtle semantic information on the given KG. The empirical results show the efficiency of the proposed method on the widely used real-world datasets.
Neuronal diversity can improve machine learning for physics and beyond

Abstract Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and physics-informed Hamiltonian neural networks learning Hénon–Heiles stellar orbits and the swing of a video recorded pendulum clock. Such learned diversity provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.
Response to: “The robust concept of mineral‐associated organic matter saturation: A letter to Begill et al. (2023)”

In this response to a letter to the editor, we provide evidence that the findings regarding a non-detectable limit of mineral-associated organic carbon as published in Begill et al. (2023) are robust. This is mainly done by showing that no methodological bias was present and that the main correlation was not driven by a few exceptional soils.
Wrap-type phase unwrapping utilizing arctangent operation as a differential amplifier through shifted phase maps

This paper introduces a novel phase unwrapping algorithm based on the investigation of the arctangent function. Acting as a differential amplifier associated with nonlinear response, the arctangent operation facilitates distinguishing various wrap types. Phase jumps from two shifted-wrapped maps aligned on the same grids are combined as a pair to function as differential mode inputs. This discerns wraps from continuous and discontinuous surfaces through distinct outputs (threshold phase values). Simulations and experiments validate the feasibility of the proposed wrap-type identification. A complete mathematical form is derived to explain the effects of shifted wraps due to various causes. This paper significantly contributes to the foundational understanding of the 2 π ambiguity problem in phase unwrapping.
Impact of the 3rd edition of bethesda system for reporting thyroid cytopathology on grey zone categories

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
Short-Long-Term Propagation-Based Video Inpainting

In this article, we propose a new method to inpaint videos with removed regions. Our method was developed based on combining both short-term propagation-based inpainting (STPI) and long-term propagation-based inpainting (LTPI) modules. The STPI module is designed to in-fill an image from a single frame with local reference information, while the LTPI module uses multiple STPI modules to inpaint the whole video, giving high temporal consistency and low complexity. With both of the proposed modules, the correlated spatiotemporal information of frames can be propagated throughout the video, offering reliable short-long-term source information for inpainting. The experimental results demonstrate that our proposed method provides better results when compared with the state of the art.
Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation

Visual object trackers based on Siamese networks perform well in visual object tracking (VOT); however, degradation of the tracking accuracy occurs when the target has fast motion, large-scale changes, and occlusion. In this study, in order to solve this problem and enhance the inference speed of the tracker, fast and accurate visual tracking with a group convolution and pixel-level correlation based on a Siamese network is proposed. The algorithm incorporates multi-layer feature information on the basis of Siamese networks. We designed a multi-scale feature aggregated channel attention block (MCA) and a global-to-local-information-fused spatial attention block (GSA), which enhance the feature extraction capability of the network. The use of a pixel-level mutual correlation operation in the network to match the search region with the template region refines the bounding box and reduces background interference. Comparing our work with the latest algorithms, the precision and success rates on the UAV123, OTB100, LaSOT, and GOT10K datasets were improved, and our tracker was able to run at 40FPS, with a better performance in complex scenes such as those with occlusion, illumination changes, and fast-motion situations.
Realizing tight-binding Hamiltonians using site-controlled coupled cavity arrays

Analog quantum simulators rely on programmable and scalable quantum devices to emulate Hamiltonians describing various physical phenomenon. Photonic coupled cavity arrays are a promising alternative platform for realizing such simulators, due to their potential for scalability, small size, and high-temperature operability. However, programmability and nonlinearity in photonic cavities remain outstanding challenges. Here, using a silicon photonic coupled cavity array made up of [Formula: see text] high quality factor ([Formula: see text] up to[Formula: see text]) resonators and equipped with specially designed thermo-optic island heaters for independent control of cavities, we demonstrate a programmable photonic cavity array in the telecom regime, implementing tight-binding Hamiltonians with access to the full eigenenergy spectrum. We report a [Formula: see text] reduction in the thermal crosstalk between neighboring sites of the cavity array compared to traditional heaters, and then present a control scheme to program the cavity array to a given tight-binding Hamiltonian. The ability to independently program high-Q photonic cavities, along with the compatibility of silicon photonics to high volume manufacturing opens new opportunities for scalable quantum simulation using telecom regime infrared photons.
A Dataset of 108 Novel Noun-Noun Compound Words with Active and Passive Interpretation

We created a dataset of 205 English novel noun-noun compounds (NNCs, e.g., “doctor charity”) by combining nouns with higher and lower agentivity (i.e., the probability of being an agent in a sentence). We collected active and passive interpretations of NNCs from a group of 58 English native speakers. We then measured interpretation time differences between NNCs with active and passive interpretations (i.e., 108 NNCs), using data obtained from a group of 68 English native speakers. Data were collected online using crowdsourcing platforms (SONA and Prolific). The datasets are available at osf.io/gvc2w/ and can be used to address questions about semantic and syntactic composition.
A qualitative and quantitative comparative study of different denoising and enhancement techniques for breast mammograms, ultrasound and magnetic resonance images

Breast cancer ranks as the second highest-diagnosed malignancy in women. Mammography, ultrasound, and magnetic resonance imaging (MRI) are the most frequently used imaging modalities to identify breast cancer. These imaging modalities produce a digital image that helps to highlight breast cancer-associated abnormalities like calcification, masses and lumps. However, these digital images are ordinarily low-contrast images and are affected by noise. Furthermore, the presence of noise limits radiologists&apos; ability to accurately interpret breast cancer. So, with pre-processing, the noise present in mammograms, Ultrasounds and MRIs is removed to predict breast cancer accurately. Over the years, various pre-processing techniques have been developed to overcome the noise and enhance the image. The pre-processing helps in denoising as well as the classification of breast cancer by providing a processed image. This paper offers a qualitative and quantitative comparative study of the most recent and efficient denoising and enhancement techniques for Mammography, Ultrasound and MRI imaging modalities. The quantitative assessments are provided in terms of full reference, no reference and human visual system-based image assessment metrics. The datasets from the mammographic image analysis society, ultrasound web and RIDER have been used for Mammography, Ultrasound and MRI, respectively.