Reviewerly/exHarmony huggingface dataset:
CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the significant and undoubted progress of the field in addressing the issue of catastrophic forgetting, benchmarking different continual learning approaches is a difficult task by itself. In fact, given the proliferation of different settings, training and evaluation protocols, metrics and nomenclature, it is often tricky to properly characterize a continual learning algorithm, relate it to other solutions and gauge its real-world applicability. The first Continual Learning in Computer Vision challenge held at CVPR in 2020 has been one of the first opportunities to evaluate different continual learning algorithms on a common hardware with a large set of shared evaluation metrics and 3 different settings based on the realistic CORe50 video benchmark. In this paper, we report the main results of the competition, which counted more than 79 teams registered and 11 finalists. We also summarize the winning approaches, current challenges and future research directions.
Short-term forecasting of global solar irradiance in tropical environments with incomplete data

Electricity access is a common issue around the world. Many countries that face this problem are located in the tropical region and solar energy might be an alternative to mitigate this limitation. Accurate mechanisms for forecasting solar irradiance and insolation provide important information regarding the potential for generating solar energy. Furthermore, this data is relevant for the planning of renewable energy projects, and energy policy formulation. This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation that only requires solar irradiance historical data for training. Our approach includes a data imputation stage to handle missing data. In the prediction stage, we consider four data-driven approaches: Autoregressive Integrated Moving Average, Single Layer Feed Forward Network, Multiple Layer Feed Forward Network, and Long Short-Term Memory. The experiments are performed in a real-world dataset collected by 12 Automatic Weather Stations located in Nariño - Colombia. Our results show that the neural network-based models outperform Autoregressive Integrated Moving Average in most cases, and that Long Short-Term Memory exhibits better performance in cloudy environments (where more randomness is expected).
Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images

Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by the presence of excess immature lymphocytes., Even though automation in ALL prognosis is essential for cancer diagnosis, it remains a challenge due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands that experienced pathologists read cell images carefully, which is arduous, time-consuming, and often hampered by interobserver variation. This article has automated the ALL recognition task by employing deep Convolutional Neural Networks (CNNs). The weighted ensemble of deep CNNs is explored to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates’ corresponding metrics, such as F1-score, area under the curve (AUC), and kappa values. Various data augmentations and pre-processing are incorporated to achieve a better generalization of the network. Our proposed model was trained and evaluated utilizing the C-NMC-2019 ALL dataset. The proposed weighted ensemble model has outputted a weighted F1-score of 89.7%, a balanced accuracy of 88.3%, and an AUC of 0.948 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately exhibit coarse and scatter learned areas in most cases. Since the proposed ensemble yields a better result for the aimed task, it can support clinical decisions to detect ALL patients in an early stage.
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes

We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yielding explanations in the form of “this looks like that.” However, while previous methods use spatially rigid prototypes, we address this shortcoming by proposing spatially flexible prototypes. Each prototype is made up of several prototypical parts that adaptively change their relative spatial positions depending on the input image. Consequently, a Deformable ProtoPNet can explicitly capture pose variations and context, improving both model accuracy and the richness of explanations provided. Compared to other case-based interpretable models using prototypes, our approach achieves state-of-the-art accuracy and gives an explanation with greater context. The code is available at https://github.com/jdonnelly36IDeformable-ProtoPNet.
Eunomia: Anonymous and Secure Vehicular Digital Forensics Based on Blockchain

Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak evidence. Therefore, protecting privacy and evidence accountability while guaranteeing access control and traceability in VDF is no easy task. To address the above-mentioned issues, we propose Eunomia: an anonymous and secure VDF scheme based on blockchain. It preserves privacy with decentralized anonymous credentials without trusted third parties. Vehicular data and evidence are uploaded by data providers to the blockchain and stored in distributed data storage. Each investigation is modeled as a finite state machine with state transitions being executed by smart contracts. Eunomia achieves fine-grained evidence access control via ciphertext-policy attribute-based encryption and Bulletproofs. A user must hold specific attributes and a temporary-and-unexpired token/warrant to retrieve data from the blockchain. Finally, a secret key is embedded into data to trace the traitor if any evidence breach happens. We use a formal analysis to demonstrate the strong privacy and security properties of Eunomia. Moreover, we build a prototype in a WiFi-based Ethereum test network to evaluate its performance.
Does aggression lead to hate? Detecting and reasoning offensive traits in hinglish code-mixed texts

Aggression is a prominent trait of human beings that can affect social harmony in a negative way. The hate mongers misuse the freedom of speech in social media platforms to flood with their venomous comments in many forms. Identifying different traits of online offense is thus inevitable and the need of the hour. Existing studies usually handle one or two offense traits at a time, mainly due to the lack of a combined annotated dataset and a scientific study that provides insights into the relationship among the traits. In this paper, we study the relationship among five offense traits – aggression, hate, sarcasm, humor, and stance in Hinglish (Hindi-English) social media code-mixed texts. We employ various state-of-the-art deep learning systems at different morphological granularities for the classification across five offense traits. Our evaluation of the unified framework suggests ∼90% performance across all major traits. Furthermore, we propose a novel notion of causal importance score to quantify the effect of different abusive keywords and the overall context on the offensiveness of the texts.
Secured Insurance Framework Using Blockchain and Smart Contract

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy.
GRAF

Microservice is an architectural style that has been widely adopted in various latency-sensitive applications. Similar to the monolith, autoscaling has attracted the attention of operators for managing resource utilization of microservices. However, it is still challenging to optimize resources in terms of latency service-level-objective (SLO) without human intervention. In this paper, we present GRAF, a graph neural network-based proactive resource allocation framework for minimizing total CPU resources while satisfying latency SLO. GRAF leverages front-end workload, distributed tracing data, and machine learning approaches to (a) observe/estimate impact of traffic change (b) find optimal resource combinations (c) make proactive resource allocation. Experiments using various open-source benchmarks demonstrate that GRAF successfully targets latency SLO while saving up to 19% of total CPU resources compared to the fine-tuned autoscaler. Moreover, GRAF handles traffic surge with 36% fewer resources while achieving up to 2.6x faster tail latency convergence compared to the Kubernetes autoscaler.
Developing Turkish sentiment analysis models using machine learning and e-commerce data

With the increment of Internet usage, there has been a significant increase in the access and interaction of users in social media, blogs, forums, and criticism sites recently. With social media, access to a large amount of data on various products, services, social and political events is provided. Important feedback about products and services can be obtained as a result of analyzing such data. This study aims to determine the sentiments expressed via texts on social media using machine learning methods. As a result of initial research, it is determined that the best case in which texts and emotions match was the product reviews and ratings used on e-commerce websites. Reviews on different products along with review scores from an e-commerce website have been converted into a table to be used in the machine learning-based sentiment analysis models. Reviews have been classified into three groups as positive, negative, and neutral using the review scores. Considering this claim, Turkish sentiment analysis models were developed using support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and k-nearest neighbors (KNN). Cross-validation results on independent test data taken from the same e-commerce website show that the SVM-based and RF-based sentiment analysis models outperform the other models. In more detail, there is no strict order between SVM-based and RF-based prediction models, but the results of the SVM-based and RF-based models, in general, are the highest or, in the worst case, similar if we compare them with the scores obtained by using the DT-based, LR-based, and KNN-based models. It can be concluded that SVM and RF are viable methods that can be used to classify product reviews into three groups as positive, negative, and neutral within acceptable error rates.
An intelligent system for modeling and evaluation of domain ontologies for Crystallography as a prospective domain with a focus on their retrieval

Ontological design is a better approach to depict the chosen theory and terms and the related concepts. It is necessary as some theories and terms are quite troublesome for understanding and their relationship within the selected region or field. In this study, Crystallography is chosen as the main field and different approaches were used to build the relationship between the concepts present in it. Ontological phraseology designing is formulated where various concepts within the specific fields are put on and the relationship is coupled among various terms. To execute the designed model, various classes and subclasses within the chosen subject are portrayed using OWL. Furthermore, to represent the network of the chosen subject, the displayed ontological model has been analyzed by utilizing a subjective and measurable approach. A different criterion is studied for subjective examination, and a measurable analysis was performed. The designed model shows a reuse proportion of 87%. The social spider is used for ontology retrieval and the average precision and average accuracy are 93.89% and 95.89%, respectively. An overall F-measure of 97.81% has been achieved for modeling of domain ontologies based on human expert judgment for Crystallography domain.
Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

Abstract Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that may be inefficient to simulate classically but could provide superior performance in some tasks. Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology there may be a similar adoption of these quantum techniques. In this work a quantum support vector machine (QSVM) is implemented for signal-background classification. We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. For this same dataset the best classical method tested, a classical Support Vector Machine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of 0.793. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As further improvements to the error rates and availability of quantum computers materialise, they could form a new approach for data analysis in high energy physics.
Bitcoin investments and climate change: A financial and carbon intensity perspective

Climate-related criticism of Bitcoin is primarily based on the network's absolute carbon emissions, without considering its market value. Taking a relative emission perspective and utilizing the mean–variance portfolio optimization framework, we study the financial and carbon-related implications of Bitcoin investments. The results of our in-sample analysis show that adding Bitcoin to a diversified equity portfolio can both enhance the risk–return relationship of the portfolio and reduce the portfolio's aggregate carbon emissions. This finding persists under various assumptions regarding Bitcoin prices, carbon emission estimates, and carbon prices.
The Development of Extended Reality in Education: Inspiration from the Research Literature

Education is an important driving force for sustainable social development. Emerging technologies such as extended reality (XR), including augmented reality (AR), virtual reality (VR) and mixed reality (MR), have been widely used. Recently, a large number of theoretical and empirical studies on the use of XR in the field of education for sustainable development have emerged. This paper uses bibliometric analysis to analyze the publication and citation trends of articles, prolific authors, institutions and countries, influential works, current topics, emerging trends, and knowledge structure to explore the overall productivity and XR research trends in the field of education for the period 1991–2021. Future development directions are also considered. On the basis of bibliometric analysis, this paper puts forward suggestions for the application of XR in the field of education for sustainable development.
Fitness peak clustering based dynamic multi-swarm particle swarm optimization with enhanced learning strategy

Particle Swarm Optimization (PSO) is a well-known swarm intelligence algorithm and its performance primarily depends on the tradeoff between exploration and exploitation. In order to well balance the exploration and exploitation, this paper presents a fitness peak clustering based dynamic multi-swarm Particle Swarm Optimization (FPCMSPSO) with enhanced learning strategy. In the presented FPCMSPSO, first, FPC-based partitioning method is utilized to divide the initialized population into several sub-swarms so as to avoid crossover evolution caused by random partitioning. These sub-swarms evolve independently based on comprehensive learning strategy and along with further evolution they would merge into a global swarm according to their own stagnancy information. Second, an enhanced learning strategy is exploited to some particles, and their velocities are updated based on learning exemplars alternately generated by comprehensive learning or dimensional learning strategies according to their stagnancy information. Extensive experimental results demonstrate that the solution accuracy, convergence speed and stability of FPCMSPSO are remarkably improved due to the usage of above strategies. The comparative results of FPCMSPSO with other existing PSO variants on various optimization problems show that FPCMSPSO statistically outperforms other PSO variants with significant difference.
A robust auto encoder-gated recurrent unit (AE-GRU) based deep learning approach for short term solar power forecasting

The increasing presence of solar power plants shows its potency as one of the key renewable energy resource to fulfill energy needs of the community. This increasing presence can be effectively utilized by using efficient prediction of solar power plant output for stable grid operation. The availability of processors with high computational capability and access to baseline data provide edge to deep learning models for prediction of solar power plants. In this work, sequence to sequence auto-encoder (AE) and Gated Recurrent unit (GRU) based hybrid deep learning approach is developed, which further advances other recent works offered in solar power forecasting. The AE extracts the internal relationship of input solar data, minimizes the reconstruction error and taps the important feature correlation. Furthermore, GRU exploits the time dependencies of the data. For comprehensive evaluation, different deep learning models and their hybrid forms are analyzed for different prediction durations i.e. 24 h, 48 h, and 15 days prediction. The analysis compares models on three major performance indices, i.e., mean square error, mean absolute error, and R2errors with the same hyperparameters for this time series prediction. The outcome ascertains that the proposed AE-GRU based deep learning model performs better than other deep learning models.
Dataset of Factors affecting online cheating by accounting students: The relevance of social factors and the fraud triangle model factors

In this paper, the authors present survey data concerning to understand the motivations underlying the cheating behavior while university exams are held online during the Covid-19 pandemic. In pursuing its aims, this study uses an integrated theoretical framework that includes the social capital theory and the fraud triangle theory. Through the use of a previously tested questionnaire, this study gathers data concerning the students cheating behavior from 213 respondents across a group of Jordanian universities. The findings of this study show that pressures, opportunities, rationalization, social norms, and social trust are all factors that affect the behavioral intention to cheat, which ultimately lead accounting students to commit cheating while taking exams online. This research provides several practical contributions to the educators who are seeking to minimize the chances for dishonest students to cheat in online exams. Future studies can refer to the study and its findings when it comes to educational equity and policy making regarding distance education.
Intelligent reading recognition method of a pointer meter based on deep learning in a real environment

Abstract Accurate reading of a pointer meter is a crucial task in complex environments, such as substations, the military and aerospace. Current recognition algorithms are mainly used to identify the same type and a non-tilt meter, which has limited application in a real environment. In this paper, we propose a novel end-to-end intelligent reading method for a pointer meter based on deep learning, which locates the meter and extracts the pointer simultaneously without any prior information. In particular, the pointer is directly and precisely extracted using the designed semi-pointer detection method without any handcrafted features designed in advance, which avoids accumulated error caused by preprocessing. Based on the extracted panel object, including the semi-pointer, panel center and scale characters, the indicated value of the pointer is obtained by a local angle method, which achieves better performance than the traditional angle method by referring to the neighboring scale lines of the pointer. Experimental results demonstrate that the method is faster and more effective than some common methods. It is worth noting that this study has the advantage of being able to recognize pointer meters under complex conditions such as tilt, rotation, blur and illumination. This is acceptable for the actual application requirements in a real environment with a recognition accuracy of 99.20% and an average reference error of 0.34%.