Reviewerly/exHarmony huggingface dataset:
A Survey of Central Bank Digital Currency Adoption in African countries

The paper presents a survey of central bank digital currency (CBDC) adoption in African countries. Secondary data based on desk research were used to conduct the survey. Data for each African country were collected from publicly available information about each country’s interest and efforts in issuing a central bank digital currency. The survey shows that 70 per cent of African countries have not shown any interest in central bank digital currency. The West African region has the highest number of countries that have not shown any interest in central bank digital currency. Only 4 African countries have a robust payment system infrastructure that can support central bank digital currency. Only 14 African countries have officially indicated interest in central bank digital currency. Only 13 African countries have announced that they are studying central bank digital currency to determine whether they will pursue central bank digital currency as a short-term or long-term goal. Only 4 African countries have reached the pilot test stage of issuing a central bank digital currency. Finally, only one African country has formally issued a central bank digital currency. The policy implication of the findings is that there is low interest in central bank digital currency in the African continent. The low interest in central bank digital currency in African is attributed to the strong preference for cash payments, lack of a robust payment system, low use of digital payments, central banks’ focus on other priorities, fear of failure, lack of government interest in digital currency and concerns about CBDC privacy risk and security threats. These factors can slowdown the level of development and economic inclusion in African countries. There is need to accelerate the issuance of CBDC in African countries.
Solar Energy Modeling and Assessment in Texas

This paper presents solar energy modeling and assessment in Texas, USA. The study is conducted based on the records from the Zero Energy Lab, which established and began renewable production monitoring in 2015, and data from NASA. It includes the assessment of the localized climatic information, availability of solar energy resources, and solar power productivity analysis. Characteristics of PV panels and the entire power conversion system that are used in renewable energy building are extracted and modeled. An accurate data-based behavioral model of the PV system is created which offers a highly accurate real-world result. Based on the model, the effectiveness of photovoltaic energy usage in Texas is analyzed and the model can apply to any solar power prediction.
Predictive Auto-scaler for Kubernetes Cloud

One of the basic requirements with adapting to cloud technology is to find an optimal resource allocation based on the dynamic workload. The default functioning of Kubernetes Horizontal Pod Auto-scaling in cloud is scaling of its pods only when the threshold of the cluster/application is crossed in order to adapt to increasing workload. Rather we want to deploy a proactive provisioning framework based on machine learning based predictions. We have demonstrated a novel deep learning framework based on a transformer in the area of dynamic workload predictions and showed how to apply the results to a custom auto-scaler in cloud. Our Framework builds time-series predictive models in machine learning such as ARIMA, LSTM, Bi-LSTM and transformer models. The dynamic scaling framework applies machine learning algorithms and presents recommendations to make proactive and smart decisions. Though the transformer model has been used in NLP and Vision applications mostly, we showed that the transformer based model can produce the most effective results in cloud workload predictions.
Formal Methods in Unmanned Aerial Vehicle Swarm Control for Wildfire Detection and Monitoring

Drones or unmanned aerial swarms play a significant role in surveillance, suppression, observation and tactical planning. These tools are used to assist in fighting forest wildfire threats by expediting the detecting, containing, and extinguishing fire process. This study proposed an initial specification for a UAV swarm, modelling the swarm with its operator, leader, and followers and verifying the model using PRISM. This study aims to model a continuous stream of rotary drones towards a suspected fire area for detection and constant monitoring and mapping of the fire. This approach enables low-cost solutions to smaller local users and provides continuous real-time telemetry of the fire.
Academic Information Storage and Verification Using Blockchain Technologies

In general, academic verification of an employee is done manually takes a lot of time, cost and less trustworthy. The trustworthiness of the certificate is dependent on the employee. Due to financial benefits and other advantages, fake certificates are easily created by fraudulent members. Detecting a fraudulent certificate is a hard process. Nowadays, blockchain technology provides wonderful benefits to all sectors for maintaining documents in an immutable manner. Once the data is stored in a blockchain, no one can change or delete that data. Hence, blockchain technology is used to store and verify academic information is highly scalable and guaranteeing the privacy of users’ data. In a proposed work, the blockchain network is created between the educational institutions and employers for efficient information sharing and verification. For unique identification of the certificates QRcode is assigned to each academic certificate. By using SHA256 algorithm the hash value of the QRcode is calculated and has been stored in a block in a distributed ledger. The employer calculates the hash value of the certificate which is submitted by the employee and compared this hash value to the hash value of the certificate stored in the block. If both are equal, the employer identifies it’s a valid certificate or else rejected it’s invalid certificate. Thus, trustworthy verification like confidentiality, authentication, authorization, ownership and privacy is achieved through blockchain technology. Likewise, the certificate verification latency is reduced and throughput is increased in this approach.
Ranking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysis

AbstractAbstractAs an essential information resource, online reviews play an important role in consumers' decision-making processes. To solve the product ranking problem through online reviews, two important issues are involved: sentiment analysis (SA) for online reviews and product ranking based on multi-criteria decision-making (MCDM) methods. However, merely a few studies have considered the impact of SA accuracy, which can significantly affect the final decision-making process. This paper proposes a novel data-driven method for ranking products through online reviews based on interval type-2 fuzzy sets (IT2FSs) and SA. In this method, after acquiring online reviews, the explicit and implicit attributes are extracted from the website itself and the latent Dirichlet allocation (LDA) model, respectively. Thereafter, a deep learning model is adopted to identify the five sentiment intensities of online reviews, based on which the SA results are represented as IT2FSs by considering the classification effect. After type-reduction for IT2FSs, the ranking order is obtained based on the exponential TODIM (ExpTODIM) method. Furthermore, a case study on ranking travel products from Trip.com Group through online reviews is provided to illustrate the effectiveness and applicability of the proposed method.Keywords: Product rankingonline reviewssentiment analysisinterval type-2 fuzzy setsExpTODIM Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFunding The work is supported by the National Natural Science Foundation of China (NSFC) under Project 72071151.
Language independent optimization of text readability formulas with deep reinforcement learning

Abstract Readability formulas are used to assess the level of difficulty of a text. These language dependent formulas are introduced with pre-defined parameters. Deep reinforcement learning models can be used for parameter optimization. In this article we argue that an Actor-Critic based model can be used to optimize the parameters in the readability formulas. Furthermore, a selection model is proposed for selecting the most suitable formula to assess the readability of the input text. English and Persian data sets are used for both training and testing. The experimental results of the parameter optimization model show that, on average, the F-score of the model for English increases from 24.7% in the baseline to 38.8%, and for Persian from 23.5% to 47.7%. The proposed algorithm selection model further improves the parameter optimization model to 65.5% based on F-score for both English and Persian.
A Cognitive Simulation Model of a Regional Higher Education System

We propose and investigate a difference Stackelberg game with sustainable development requirements based on a cognitive model of a regional higher education system. In this model, the federal administration acts as the Leader, whereas higher education institutions (HEIs) act as Followers. The model is identified on an example of the Rostov region of the Russian Federation. The results of computer simulation with some analytical solution elements are presented. The influence of sustainable development conditions on the choice of control strategies is described. Several recommendations for the upper-level control subject and HEIs are formulated.
Blockchain-Assisted Reputation Mechanism for Distributed Cloud Storage

Distributed cloud storage (DCS) has been undergoing fast development to provide customers with multiple storage resources in a mix of locations and environments that best meet the service and performance requirements. Trust is widely regarded as one of the top obstacles to the adoption and growth of DCS. Nonetheless, the existing works did not take the storage resource capacity of cloud service providers (CSPs) into the reputation evaluation, and most rely on a centralized reputation management framework. We bridge this gap by proposing a blockchain-assisted reputation mechanism for DCS. First, a blockchain-assisted DCS model is proposed to ensure the traceability and tamper proof of reputation-related data. Second, we propose a service credibility quantification method based on rating screening to mitigate the impact of outliers. Third, we design a stochastic process to characterize the storage resource change to quantify CSPs survival probability. Finally, we derive a reputation calculation algorithm based on the above two metrics that protects the authenticity of CSPs' reputations in the presence of attacks. The security analysis and simulation results show that the proposed mechanism is reliable and effective in promoting the service success rate and improving the efficiency and security of the service of DCS.
Lattice-Based Threshold Signcryption for Blockchain Oracle Data Transmission

Threshold signature can solve the problems of high cost and network congestion during the execution of smart contracts. However, the information transmitted in public links is vulnerable to eavesdropping, tampering and other network attacks, it is essential to ensure the confidentiality and integrity during data transmission. However, traditional threshold signcryption is not suitable for blockchain setting and cannot withstand the quantum computing attacks. In view of these reasons, one lattice-based threshold signcryption for blockchain oracle data transmission (BCODT-LTSC) is devised in this article. BCODT-LTSC satisfies the existential unforgeability, confidentiality and threshold characteristic; it has very low computation cost and is suitable for the application in blockchain scenario.
Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images

In this paper, a novel rotation and scale invariant approach for texture classification based on Gabor filters has been proposed. These filters are designed to capture the visual content of the images based on their impulse responses which are sensitive to rotation and scaling in the images. The filter responses are rearranged according to the filter exhibiting the response having largest amplitude, followed by the calculation of patterns after binarizing the responses based on a particular threshold. This threshold is obtained as the average energy of Gabor filter responses at a particular pixel. The binary patterns are converted to decimal numbers, the histograms of which are used as texture features. The proposed features are used to classify the images from two famous texture datasets: Brodatz, CUReT and UMD texture albums. Experiments show that the proposed feature extraction method performs really well when compared with several other state-of-the-art methods considered in this paper and is more robust to noise.
Seismic Random Noise Suppression Model Based on Downsampling and Superresolution

Seismic random noise suppression presents two main challenges: achieving thorough noise suppression while simultaneously ensuring complete restoration of effective signal content. However, due to the complexity of random noise, existing denoising methods often only achieve an awkward balance between removing random noise and restoring effective signals. In this paper, we propose a novel random noise suppression model based on downsampling and super-resolution. By decoupling the denoising and signal restoration processes, our method reduces the difficulty of addressing these two challenges and mitigates the likelihood of suboptimal results. On the one hand, the high fitting-capacity Downsampling network uses non-linear transformations to separate random noise and effective signals while purifying the high-order features of effective signals. On the other hand, the Super-resolution network expands the low-dimensional seismic signal content containing the high-order features of the signal to restore the signal structure. Moreover, we propose a new adversarial loss by introducing the gradient between the generated data and the real data, which enhances the perceptual quality of the super-resolution results and recovers the content of effective signals better. Because both subnetworks are not affected by signal/noise features during processing, the model exhibits strong fitting and generalization abilities. The experimental evaluation on four different types of seismic data demonstrates the superiority of our method in suppressing random noise and restoring the content of effective signals.
Soft Sensing of NOx Emissions From Thermal Power Units Based on Adaptive GMM Two-Step Clustering Algorithm and Ensemble Learning

The accurate measurement of NOx concentration is the basis of accurate ammonia injection in selective catalytic reduction (SCR) system of thermal power unit. Excessive or too little ammonia injection will cause ammonia escape or exceeding the standard of NOx emission, and cause environmental pollution. There exist a large measurement lag and errors during the purging process in the measurement of NOx concentration in the SCR system of thermal power units. Therefore, to enable the intelligent control of the SCR system, it is necessary to carry out soft sensing of NOx emissions. This paper proposes a soft sensing algorithm for NOx emissions of thermal power units based on an adaptive Gaussian Mixture Model (GMM) two-step clustering algorithm and ensemble learning. Adaptive GMM two-step clustering (AGTSC) algorithm is used to softly divide the historical data into working conditions. Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM) is also adopted to construct individual learners for each working condition. Here we use the parameter regression algorithm to train the combiner based on the output of the individual learner and the membership signal of the working condition as the input of the combiner. The clustering algorithm is tested with three datasets. The results show that this algorithm addresses the shortcomings of GMM including not being applicable to the clustering of non-convex datasets and manual selection of the number of clusters. The soft sensing algorithm is verified by using the historical data of the SCR system of 1000 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MW</i> thermal power units. The results show that the proposed method has higher accuracy and stronger generalization ability than the conventional method, hence providing an effective method for soft sensing of NOx emissions of thermal power units.
SAFELearning: Secure Aggregation in Federated Learning With Backdoor Detectability

For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure aggregation</i> . However, secure aggregation makes model poisoning attacks such as backdooring more convenient given that existing anomaly detection methods mostly require access to plaintext local models. This paper proposes a new federated learning technique SAFE-Learning to support backdoor detection for secure aggregation. We achieve this through two new primitives - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oblivious random grouping (ORG)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial parameter disclosure (PPD)</i> . ORG partitions participants into one-time random subgroups with group configurations oblivious to participants; PPD allows secure partial disclosure of aggregated subgroup models for anomaly detection without leaking individual model privacy. ORG is based on our construction of several new primitives including tree-based random subgroup generation, oblivious secure aggregation, and randomized Diffie-Hellman key exchange. ORG can thwart colluding attackers from knowing each other’s group membership assignment with non-negligible advantage than random guess. Backdoor attacks are detected based on statistical distributions of the subgroup aggregated parameters of the learning iterations. SAFELearning can significantly reduce backdoor model accuracy without jeopardizing the main task accuracy under common backdoor strategies. Extensive experiments show SAFELearning is robust against malicious and faulty participants, whilst being more efficient than the state-of-art secure aggregation protocol in terms of both communication and computation costs.
Semi-supervised nuclei segmentation based on multi-edge features fusion attention network

The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.
Creative Futuring for More-Than-Human Worlds: Exhibitions as Sites to Ponder Environmental Governance

Abstract The article presents the Smart Urban Governance for More-than-Human Future(s) anthology, comprising six speculative creative works. It draws on techniques of futuring as a methodology to explore how creative practice as an act of futuring and exhibitions as sites to ponder environmenta governance can empower more-than-human futures. Reporting on participatory observations and semistructured interviews with the exhibition audience the article posits that creative futuring can empower futures by developing awareness that environmental governance can facilitate nonhuman agencies that conserve and repair the ecological world.
The Bayesian Mutation Sampler Explains Distributions of Causal Judgments

One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in 'mutation sampling' when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.
The Character of Eco-feedback Systems for Energy Communities

Energy communities are emerging around Europe with a vision to transition the traditional centralised energy system to one where individual households can collaborate to become energy producers. Although the vision of energy communities is still in a formative stage, it seems members of such organisations will need digital technology to manage and organise renewable energy together. The design of eco-feedback designs appears as a digital means to create awareness of this need. Yet, until recently, eco-feedback has been designed to target the individual householder. The research presented in this paper aims to identify design characteristics of eco-feedback systems aimed at collaborative energy communities. We explore two different concept designs that are evaluated in a focus group session with prospective energy community members to establish design characteristics. The findings show that participants not only identified characteristics related to different eco-feedback design characters but also identified a set of properties related to the collaborative and social nature of the community.