Reviewerly/exHarmony huggingface dataset:
Indonesian Presidential Election Sentiment: Dataset of Response Public Before 2024.

Indonesia is one of the countries that is currently entering the political year for the election of President, Regional Heads, and Members of the Legislative in 2024. This has become a hot topic on social media, especially about the Presidential Election. Twitter is one of the platforms with the largest users in Indonesia. It is interesting to see the alignment of Twitter users towards presidential candidates who already have a carrying party, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan based on a sentiment analysis approach. User feedback data about Indonesian Presidential candidates are obtained from the Twitter platform using Twitter API with Python programming language. The data obtained was 30,000 data with each candidate as many as 10,000 data. Data is pulled in April 2023 with specific keywords. The time for data withdrawal is chosen based on the announcement of Presidential Candidates carried by political parties before the schedule for determining or campaigning for Presidential candidates. Current data can potentially be used again as a comparison of analysis of presidential candidates on campaign time spans and after campaigns or actual calculation results. The data that can be accessed is in CSV format and has gone through several stages such as labelling using Language experts, removing spam Tweets & empty cells and preprocessing.
A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation

Uncertain weather scenarios have an impact on the output of solar farms and therefore affect the security of the grid. It is advantageous for power system operators to forecast solar energy to balance the load generation and for optimal power scheduling. The most promising deep-learning techniques to combine weather variables with precise measurements of solar irradiance are not widely discussed. To close this research gap and produce better prediction results, this article aims to formulate and compare two distinctive deep learning algorithms for using time series forecasting approaches to predict solar irradiance. For multivariate data, the forecasting technique Bi-Directional Long Short-Term Memory (BiLSTM), and BiLSTM-GRU (Gated Recurrent Unit) Dropout, are examined in this study. The output results from the proposed model are compared with other benchmark models based on performance error measurements, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Mean Absolute Percentage Error (MAPE). It was found that the proposed hybrid method, BiLSTM-GRU with dropout outperformed the other methods in terms of solar irradiance predicting accuracy. The analysis presented the best RMSE of 1.55 and MAE of 1.13 for BiLSTM and RMSE of 1.40 and MAE of 0.91 for BiLSTM-GRU architecture using hyperparameter tuning. The comparison results show that the prediction accuracy is improved by tuning the hyperparameters.
DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection

In self-driving cars, adverse weather (e.g., fog, rain, snow, and cloud) or occlusion scenarios result in domain shift being unavoidable in object detection. Researchers have recently proposed Domain Adaptive Object Detection (DAOD), i.e., aligning the source and target domains at the image and instance levels distribution by utilizing the Unsupervised Domain Adaptation (UDA) method. However, the semantic correlation information is ignored leading to the effect of aligning not good, and low detection accuracy of objects in adverse weather or occlusion scenarios. Here, we propose a Dual Semantic Correlation Alignment (DSCA) method for DAOD to address the problem. The core idea behind DSCA is to make full use of semantic correlation information including context correlation semantic information and class correlation semantic information to align object semantic information in source and target domains, which supplement and enhance the missing information for target domains. It consists of a two-level semantic alignment: (1) context correlation semantic alignment is developed to obtain the context correlation semantic information of the object to align context semantic information at the image level; (2) class correlation semantic alignment is proposed to obtain the class correlation semantic information of the object to align class semantic information at the instance level. The two-level semantic alignment can effectively decrease negative transfer and complete object information to improve the detection accuracy of objects in different domains. Experiments on four challenging benchmarks show that our proposed DSCA method outperforms state-of-the-art DAOD methods.
Using Cryogenic CMOS Control Electronics to Enable a Two-Qubit Cross-Resonance Gate

CMOS chips comprised of a low-power processor and arbitrary waveform generator operating at 4 K are developed and tested, enabling two-qubit randomized benchmarking and highlighting the promise and challenges of cryogenic control.
4-DOF Visual Servoing of a Robotic Flexible Endoscope With a Predefined-Time Convergent and Noise-Immune Adaptive Neural Network

Endoscopes provide views inside the patient's body during minimally invasive surgery. Although various robotic endoscopes have been developed to reduce surgeons' workload in manual endoscope operations, autonomous endoscope manipulation remains challenging due to the misorientation effect and different disturbances. In this work, we developed an intelligent endoscope system to steer the surgical view automatically. To keep the target (i.e., the tip of an instrument) at the center of the camera view with a suitable size and orientation, an image moment-based 4-degree-of-freedom (DOF) visual servoing method is implemented. We propose an error learning-based sliding mode controller to realize fast and smooth error convergence. It is specially constructed to improve convergence rate without causing undesirable system chattering. Moreover, the kinematic modeling of the endoscope results in a quadratic programming problem, which is solved by a novel adaptive noise-immune zeroing neural network accelerated to predefined-time convergence by a newly constructed activation function. The experiments show that the proposed control strategy guarantees a superior convergence rate and robustness compared with existing methods. Lab tests show the application potential of the proposed endoscope system in clinical practice.
The construction and use of cognitive maps in model-based control.

When making decisions, we sometimes rely on habit and at other times plan toward goals. Planning requires the construction and use of an internal representation of the environment, a cognitive map. How are these maps constructed, and how do they guide goal-directed decisions? We coupled a sequential decision-making task with a behavioral representational similarity analysis approach to examine how relationships between choice options change when people build a cognitive map of the task structure. We found that participants who encoded stronger higher-order relationships among choice options showed increased planning and better performance. These higher-order relationships were more strongly encoded among objects encountered in high-reward contexts, indicating a role for motivation during cognitive map construction. In contrast, lower-order relationships such as simple visual co-occurrence of objects did not predict goal-directed planning. These results show that the construction of cognitive maps is an active process, with motivation dictating the degree to which higher-order relationships are encoded and used for planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Design of buoy observation network over the Arctic Ocean

In the Arctic, near-surface temperature affects the surface energy balance and the evolution of planetary boundary layer. Particularly in summer, due to sea-ice extent variations, the uncertainties of simulated near-surface temperature in the Arctic Ocean increase. To reduce these uncertainties, high-quality near-surface temperature observations are necessary; however, in situ observations are difficult to operate in the Arctic. Although buoys can monitor various atmospheric variables in the Arctic over longer periods compared to those of other types of in situ monitoring, the number of buoy near-surface temperature observations in the Arctic has decreased since 2018. Near-surface temperature observations by buoys need to be added and the optimal buoy observation network need to be designed to reduce the uncertainties associated with estimating near-surface temperature over the Arctic Ocean in summer. In this study, buoy observation networks to monitor near-surface temperature were designed to minimize the 24-h forecast error variances of 2-m temperature in the Arctic. To design the optimal buoy observation network, an objective algorithm for the observation network design based on an ensemble sensitivity was used. The reductions of the forecast error variances of 2-m temperature were estimated quantitatively using the ensemble sensitivity. To calculate the ensemble sensitivity, ensemble members of the initial condition and response function (i.e. forecast aspect of interest) were composed of deterministic analyses and 24-h forecast errors of 2-m temperature, respectively, which were simulated by the Global Forecast System (GFS) during July, August, September, and October from 2016 to 2019. By using long-term GFS simulations, the buoy observation network was designed in a climatological perspective. The determined optimal buoy locations were consistent with regions where both the analysis variances and forecast error variances of 2-m temperature were large. Assuming that 2-m temperature buoy observations were assimilated to ensemble members from GFS simulations at the 10 optimal buoy locations, the analysis variances of 2-m temperature were reduced by 60.48% and the 24-h forecast error variances of the temperature were reduced by 2.83% compared to the respective variances before assimilating buoy observations. Thus, after efficiently using the limited buoy resources (i.e., 10 buoys) to design the observation network, the uncertainties of the 2-m temperature simulated by the numerical weather prediction (NWP) model were greatly reduced in the Arctic. The optimal buoy observation network can contribute to reducing uncertainties associated with the surface energy balance, planetary boundary layer, and sea-ice properties simulated by NWP models.
Multivariate Data Explanation by Jumping Emerging Patterns Visualization

Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.
Are there independent effects of constraint and predictability on eye movements during reading?

Evidence of processing costs for unexpected words presented in place of a more expected completion remains elusive in the eye-movement literature. The current study investigated whether such prediction error costs depend on the source of constraint violation provided by the prior context. Participants' eye movements were recorded as they read predictable words and unpredictable alternatives that were either semantically related or unrelated in three-sentence passages. The passages differed in whether the source of constraint originated solely from the global context provided by the first two semantically rich sentences of the passage, from the local context provided by the final sentence of the passage, from both the global and local context, or from none of the three sentences of the passage. The results revealed the expected processing advantage for predictable completions in any constraining context, although the relative contributions of the different sources of constraint varied across the time course of word processing. Unpredictable completions, however, did not yield any processing costs when the context constrained toward a different word, instead producing immediate processing benefits in the presence of any constraining context. Moreover, the initial processing of related unpredictable completions was enhanced further by the provision of a supportive global context. Predictability effects therefore do not appear to be determined by cloze probability alone but also by the nature of the prior contextual constraint especially when they encourage the construction of higher-level discourse representations. The implications of these findings for understanding existing theoretical models of predictive processing are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Hydrological Process Modelling using RBNN - A Neural Network Computing Technique

Severe erosion in the watersheds under Damodar Valley Corporations (DVC), Hazaribagh, Jharkhand, India has been taking place for a long time. Several soil and water conservation measures are being adopted by the Soil Conservation Department in Damodar Valley. For effective planning of soil conservation programmes, hydrologic models have been used, but with several limitations. In the present study, Nagwa watershed under DVC was selected for simulating monthly surface runoff and sediment yield using radial basis neural network (RBNN) model, which requires lesser data. Different sets of input data were employed and it was observed that only average monthly rainfall was sufficient to estimate surface runoff, while average monthly rainfall and average monthly discharge were needed for estimating sediment yield. The RBNN model was trained for the years 1991-2000 and validated for the period 2001-2007. Results indicate that coefficient of determination, R2, Nash-Sutcliffe simulation efficiency, NSE and root mean square error, RMSE (m3.s-1) values for RBNN model were 0.93, 0.92 and 1.25 during training, and 0.73, 0.73 and 0.77 during validation period, respectively. The model performed quite well for simulation of sediment yield with R2, NSE and RMSE values of 0.92, 0.92 and 1822 during training period; and 0.80, 0.70 and 2288 during validation period, respectively. It could be stated that RBNN model based on simple inputs couldbe a powerful tool for simulation of monthly surface runoff and sediment yield.
CFGN: A Lightweight Context Feature Guided Network for Image Super-Resolution

Convolutional neural networks (CNNs) have recently been widely and successfully applied in the image computer vision community, and obtained great advances in single image super-resolution (SR). However, most of the existing SR methods focus on designing networks with deeper or wider structures for better performance and suffer from the problem of heavy computational costs. To address this problem, we propose a novel Context Feature Guided Network (CFGN), which is an efficient and effective lightweight SR method. Specifically, To capture semantic features effectively, we propose a novel block, called context feature guided convolution (CFGC), to capture more discriminative features while enlarging the receptive field. Moreover, we design a novel context feature guided group (CFGG) to exploit the multi-scale context information. Extensive experiments demonstrate the effectiveness of our method with a good trade-off between performance and computational efficiency, compared with the proposed method over the state-of-the-art lightweight SR methods.
TD-MDB: A Truth Discovery Based Multi-Dimensional Bidding Strategy for Federated Learning in Industrial IoT Systems

As Industrial Internet of Things (IIoT) systems have superior distributed characteristics, Federated Learning (FL) has gradually become a mainstream distributed computing paradigm in recent years, promoting its intelligent development. A key factor of FL is the model accuracy, which relies on the truth of the participating Industrial Edge Device (IED)’s parameter. However, some untrusted IEDs can contaminate models by fabricating false FL parameters, thereby deceiving rewards, known as Model Poison Attack (MPA). In this paper, a Truth Discovery based Multi-Dimensional Bidding (TD-MDB) strategy is proposed to obtain truthful parameters at low cost for FL. In TD-MDB strategy, Parameter Server (PS) will randomly assign the lightweight trust evaluation model to test the trust of each IED according to the accuracy of IED’s calculation. Then, in order to resist MPA and inference attack, we use an improved clustering algorithm to divide the trusted IEDs into different clusters based on their different trust and stability. Finally, a device selection strategy is proposed to select cost-effective IEDs for model training based on Asynchronous Advantage Actor-Critic (A3C) algorithm. In TD-MDB strategy, the IEDs that provide more resources, high trust and low bid will be selected to train the model and maximize the real revenue of the system with high accuracy. We finally carry out extensive evaluations, where results demonstrate that the TD-MDB strategy can effectively provide efficient offloading strategy, resist MPA and ensure that truth computing for FL, which is better than the state-of-the-art strategies in model accuracy, system revenue and other performance indicators.
Cryptanalysis and Improvement of a Blockchain-Based Certificateless Signature for IIoT Devices

With the rapid development of the Industrial Internet of Things (IIoT), security and privacy issues have become increasingly prominent when the data from IIoT devices is shared across public networks. Very recently, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">al.</i> presented a blockchain-based certificateless signature (CLS) scheme for IIoT devices in order to achieve secure and lightweight communication (IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2021.3084753). Wang et al. claimed that their CLS scheme is secure under the assumption of the elliptic curve discrete logarithm problem. Unfortunately, by providing two attack methods to analyze the security of Wang et al.'s CLS scheme, we find that it is insecure against universal forgery attacks. Without knowing any information about the target user's private key, the two categories of attackers can successfully forge the valid signature of any message. Hence, Wang et al.'s CLS scheme fails to achieve the claimed security goals. To fix the security flaws, we propose an enhanced blockchain-based and pairing-free CLS scheme, and prove its security in the random oracle model. Furthermore, the analysis results demonstrate that our revised scheme has higher security while keeping the performance of the original scheme.
Preliminary Analysis of Simple Novelty Search

Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: 1) spread out to cover the space while trying to remain as efficiently packed as possible, and 2) metrics inspired by k Nearest Neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage suggest several ways to improve the search, and we demonstrate one simple improvement-generate some new points directly from the archive rather than the parent population.
Exploring reservoir computing: Implementation via double stochastic nanowire networks

Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.