Reviewerly/exHarmony huggingface dataset:
Distributed-Memory Sparse Kernels for Machine Learning

Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document clustering, and graph embedding. Frequently, the SDDMM output becomes the input sparse matrix for a sub-sequent SpMM operation. Existing work has focused on shared memory parallelization of these primitives. While there has been extensive analysis of communication-minimizing distributed 1.5D algorithms for SpMM, no such analysis exists for SDDMM or the back-to-back sequence of SDDMM and SpMM, termed FusedMM. We show that distributed memory 1.5D and 2.5D algorithms for SpMM can be converted to algorithms for SD-DMM with identical communication costs and input / output data layouts. Further, we give two communication-eliding strategies to reduce costs further for FusedMM kernels: either reusing the replication of an input dense matrix for the SDDMM and SpMM in sequence, or fusing the local SDDMM and SpMM kernels. We benchmark FusedMM algorithms on Cori, a Cray XC40 at LBNL, using Erdos-Renyi random matrices and large real-world sparse matrices. On 256 nodes with 68 cores each, 1.5D FusedMM algorithms using either communication eliding approach can save at least 30% of time spent exclusively in communication compared to executing a distributed-memory SpMM and SDDMM kernel in sequence. Our 2.5D communication-eliding algorithms save 21 % of communication time compared to the unoptimized sequence. On real-world matrices with hundreds of millions of edges, all of our algorithms exhibit at least a 10x speedup over the SpMM algorithm in PETSc. On these matrices, our communication-eliding techniques exhibit runtimes up to 1.6 times faster than an unoptimized sequence of SDDMM and SpMM. We embed and test the scaling of our algorithms in real-world applications, including collaborative filtering via alternating-least-squares and inference for attention-based graph neural networks.
Factors Impacting Defect Density in Software Development Projects

This study empirically examines the impact of technical and situational factors on the quality of software development. Defect density was used to measure the post-implementation quality of software projects. The non-parametric Kruskal-Wallis test and the parametric Welch T-test were used to test the differences in defect density for technological and situational factors related to these projects. Results suggest technological and situational factors significantly impact the quality of software. Empirical findings revealed the following factors result in lower defect density: (1) project enhancements versus new project development; (2) smaller projects versus larger projects; (3) using a development methodology; (4) using later generation programming languages; (5) developing projects with in-house teams; (6) using an iterative development methodology versus a waterfall development methodology; (7) using larger development teams versus smaller teams; (8) using CASE tools; and (9) developing projects on a standalone platform versus developing on a client\server multiplatform.
Developer’s Roadmap to Design Software Vulnerability Detection Model Using Different AI Approaches

Automatic software vulnerability detection has caught the eyes of researchers as because software vulnerabilities are exploited vehemently causing major cyber-attacks.Thus, designing a vulnerability detector is an inevitable approach to eliminate vulnerabilities.With the advances of Natural language processing in the application of interpreting source code as text, AI approaches based on Machine Learning, Deep Learning and Graph Neural Network have impactful research works.The key requirement for developing an AI based vulnerability detector model from a developer perspective is to identify which AI model to adopt, availability of labelled dataset, how to represent essential feature and tokenizing the extracted feature vectors, specification of vulnerability coverage with detection granularity.Most of the literature review work explores AI approaches based on either Machine Learning or Deep Learning model.The existing literature work either highlight only feature representation technique or identifying granularity level and dataset.A qualitative comparative analysis on ML, DL, GNN based model is presented in this work to get a complete picture on VDM thus addressing the challenges of a researcher to choose suitable architecture, feature representation and processing required for designing a VDM.This work focuses on putting together all the essential bits required for designing an automated software vulnerability detection model using any various AI approaches.
Detection of cancer from histopathology medical image data using ML with CNN ResNet-50 architecture

A critical task in the early stages of a medical career is detecting brain tumors or cancer. However, there are currently some techniques that can aid in early detection, but their accuracy is still inadequate. Therefore, research is underway within this field to improve the early detection process of brain tumors and cancer. We also made an effort to work on it. To determine whether an image is cancerous or not, we used a machine learning technique. For classification, we trained a convolutional neural network and used Architecture ResNet-50. We used two types of dataset for our work. One is the Breast cancer Wisconsin dataset which is publicly open for research purposes and another one is the Breast Histopathology images dataset (biopsy cell image) which is also an open-source dataset and taken from Kaggle. We got a maximum of 91.7% accuracy in our whole detection process. We can use MRI, CT scan, or any type of medical image in our implementation if we wish. This work is mainly focused on the detection process by training my network and getting the best results so that early detection of cancer can be possible.
NFT Student Teacher Incentive System (NFT-STIS)

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The Coming Battle of Digital Currencies

We model the dynamic global competition among national fiat currencies, cryptocurrencies, and Central Bank Digital Currencies (CBDCs) among multiple countries, whereby a country's fiscal strength and currency strength are mutually reinforcing. The rise of cryptocurrencies hurts stronger fiat currencies (e.g., USD), but can benefit weaker fiat currencies by reducing competition from stronger ones. Regulatory reserve requirements on stablecoins pegged to a fiat currency (e.g., USDT and USDC) can mitigate the adverse effects of cryptocurrencies on that currency. Countries strategically implement CBDCs in response to competition from emerging cryptocurrencies and other currencies. Our model reveals the following pecking order: Countries with strong but non-dominant currencies (e.g., China and India) have the highest incentives to launch CBDCs to gain technological first-mover advantage and potentially mitigate extant adverse dollarization; countries with the strongest currencies (e.g., the United States) are the next in line and benefit from developing CBDC early on to nip cryptocurrency growth in the bud and to counteract competitors' CBDCs; nations with the weakest currencies forgo implementing CBDCs and adopt cryptocurrencies instead. Overall, strong fiat currency competition and the emergence of cryptocurrencies spur financial innovation and digital currency development. Our findings help rationalize recent developments in currency and payment digitization, while providing timely insights into the global battle of currencies and the future of money.
Bounding open space risk with decoupling autoencoders in open set recognition

Abstract One-vs-Rest (OVR) classification aims to distinguish a single class of interest (COI) from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class is extended from the classes observed during training to unseen and possibly unrelated classes, a setting referred to as open set recognition (OSR). In this work, we propose a novel architecture, namely decoupling autoencoder (DAE), which provides a proven upper bound on the open space risk and minimizes open space risk via a dedicated training routine. Our method is benchmarked within three different scenarios, each isolating different aspects of OSR, namely plain classification, outlier detection, and dataset shift. The results conclusively show that DAE achieves robust performance across all three tasks. This level of cross-task robustness is not observed for any of the seven potent baselines from the OSR, OVR, outlier detection, and ensembling domain which, apart from ATA (Lübbering et al., From imbalanced classification to supervised outlier detection problems: adversarially trained auto encoders. In: Artificial neural networks and machine learning—ICANN 2020, 2020), tend to fail on either one of the tasks. Similar to DAE, ATA is based on autoencoders and facilitates the reconstruction error to predict the inlierness of a sample. However unlike DAE, it does not provide any uncertainty scores and therefore lacks rudimentary means of interpretation. Our adversarial robustness and local stability results further support DAE’s superiority in the OSR setting, emphasizing its applicability in safety-critical systems.
Quantized Sparse Training: A Unified Trainable Framework for Joint Pruning and Quantization in DNNs

Deep neural networks typically have extensive parameters and computational operations. Pruning and quantization techniques have been widely used to reduce the complexity of deep models. Both techniques can be jointly used for realizing significantly higher compression ratios. However, separate optimization processes and difficulties in choosing the hyperparameters limit the application of both the techniques simultaneously. In this study, we propose a novel compression framework, termed as quantized sparse training, that prunes and quantizes networks jointly in a unified training process. We integrate pruning and quantization into a gradient-based optimization process based on the straight-through estimator. Quantized sparse training enables us to simultaneously train, prune, and quantize a network from scratch. The empirical results validate the superiority of the proposed methodology over the recent state-of-the-art baselines with respect to both the model size and accuracy. Specifically, quantized sparse training achieves a 135 KB model size in the case of VGG16, without any accuracy degradation, which is 40% of the model size feasible based on the state-of-the-art pruning and quantization approach.
The early bird catches the worm: The role of regulatory uncertainty in early adoption of blockchain’s cryptocurrency by fintech ventures

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The r/wallstreetbets ‘war machine’: Explicating dynamics of consumer resistance and capture

Following the recent #GameStop ‘market disruption’ where r/wallstreetbets ‘rogue’ traders were able to momentarily topple billion dollar hedge funds, we employ Deleuze and Guattari’s ‘war machine’ concept in order to comment on the potential of consumer resistance when matched up against global financial markets. While most extant theory follows the Foucauldian tradition in asserting that consumer resistance is a reaction to power, we use Deleuze and Guattari’s immanent theorisation of desire to make the case for thinking of resistance as primary. Far from a hagiography of affective affirmation, our immanent perspective draws attention to how state and corporate forms are readily able to co-opt consumer resistance. Ultimately, we make the case for reorienting consumer resistance research away from seeking out ruptures and breaks in stable structures of power to asking a more difficult question: how can resistance be organised to avoid capture?
A Compressed Model-Agnostic Meta-Learning Model Based on Pruning for Disease Diagnosis

Meta-learning has been widely used in medical image analysis. However, it requires a large amount of storage space and computing resources to train and use neural networks, especially model-agnostic meta-learning (MAML) models, making networks difficult to deploy on embedded systems and low-power devices for smart healthcare. Aiming at this problem, we explore to compress a MAML model with pruning methods for disease diagnosis. First, for each task, we find unimportant and redundant connections in MAML for its classification, respectively. Next, we find common unimportant connections for most tasks with intersections. Finally, we prune the common unimportant connections of the initial network. We conduct some experiments to assess the proposed model by comparison with MAML on Omniglot dataset and MiniImagenet dataset. The results show that our method reduces 40% parameters of the raw models, without incurring accuracy loss, demonstrating the potential of the proposed method for disease diagnosis.
Assessment of the General Public’s Understanding of Rapidly Produced Earthquake Information Products ShakeMap and PAGER

Abstract Public engagement through outreach is a key mechanism for learning about science and to communicate societal impacts of government-funded science. However, outreach effectiveness could be limited if approaches are not evidenced based. Partnerships with cognitive scientists who study fundamental learning processes suggest helping people learn how earthquakes happen would improve understanding of hazards and motivation for preparedness. We used a seismology–cognitive science partnership to evaluate understanding by nongeoscientists of widely viewed U.S. Geological Survey (USGS) ShakeMap and Prompt Assessment of Global Earthquakes for Response (PAGER) products originally developed for technical use. After discussions with USGS staff, we constructed 13 free response questions probing understanding of these products, including the graph literacy scale (GLS) for comparison. Through Zoom interviews of 101 participants, we found poor performance (28% correct) on the PAGER and ShakeMap questions despite good performance (76% correct) on the GLS. When coding free responses, we identified an average of 12.4 misconceptions per participant, approximately 1 per question. Five misconceptions were observed in over half the participants, including how the reports are constructed and used in real time, treating reports using modeling or simulations as containing only direct observations, and difficulty interpreting probabilities. The performance and misconceptions led us to develop a revised PAGER with scatter plots to visualize fatality and damage probabilities among other simplifications. We used a multiple-choice survey to assess understanding using the original and new visualizations with another 100 participants. We found significant improvement: 49% correct when seeing the revised visualization first, compared with 36% when seeing the original visualization first. Finally, we surveyed 147 participants on whether they could distinguish expert-defined shaking and damage descriptors for three macroseismic terms (moderate, strong, and severe). Participants struggled, choosing greater consequences than intended for lower intensity terms. Overall, our findings suggest that the key aspects of these earthquake information products are poorly understood by nongeoscientists in aseismic regions, but adjustments can improve effectiveness.
Enhancing BDI Agents Using Fuzzy Logic for CPS and IoT Interoperability Using the JaCa Platform

Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the agent &amp; artifact approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty.
Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence

This paper introduces a Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence (FNTNMF-KLD) for extracting the nonlinear features of a matrix in standard NMF. This method uses a nonlinear transformation to act on the feature matrix for constructing a NMF model based on the objective function of Kullback-Leibler Divergence, and the Taylor series expansion and the Newton iteration formula of solving root are used to obtain the iterative update rules of the basis matrix and the feature matrix. Experimental results show that the proposed method obtains the nonlinear features of data matrix in a more efficient way. In object recognition and clustering tasks, better accuracy can be achieved over some typical NMF methods.
Clustered Bayesian classification for within-class separation

The Bayesian classification is one of the frequently used approaches in machine learning. This approach obtains probabilities based on attributes of classes using Bayes' theorem and makes predictions according to these probabilities. Bayesian classifiers employ densities such as Gaussian, kernel, multivariate Gaussian, and Copula densities when attributes consist of continuous variables. These densities partially produce rough density values. When the attributes of any of the classes are concentrated on more than one region, above mentioned densities are not inherently suitable. In order to overcome this problem, this study introduces a novel approach called Clustered Bayesian classification. The proposed method creates a new class variable by detecting the different concentrations within the class using the Gaussian Mixture Clustering method. It makes predictions by setting a model over the new class variable. Then, the probabilities of the original classes are calculated over the probabilities of the new classes. The proposed method is compared with 5 different Bayesian classifiers on 27 different data sets. As a result, it has been seen that Clustered Bayesian classification outperformed all Bayesian classifiers for different performance metrics.
Contextual relation embedding and interpretable triplet capsule for inductive relation prediction

Relation prediction is a task for knowledge graph completion which is targeted at predicting missing relationships between entities. Most previous works focus on learning latent representations of entities and relations, resulting in the limitation of transductive setting where the model is incapable of directly predicting relations for unseen entities. In order to predict relation inductively, recent works provide alternatives to operate on the subgraph surrounding a candidate triplet. However, they only exploit structural information while failing to take full advantage of textual information of relations. Moreover, they pay no attention to interpretability of the model as the inference process is not explainable. In this paper, we introduce an Interpretable Triplet Capsule Network (ITCN) for inductive relation prediction, which extracts textual information of relations by pre-trained BERT and provides interpretation by adapted Capsule Network. Unlike previous subgraph-based inductive models which ignore textual information associated with relations, ITCN leverages pre-trained BERT to learn prior knowledge carried in the semantic meaning of relations and produce Contextual Relation Embedding (CRE). Moreover, as Routing Mechanism of Capsule Network can reveal the relationship between inputted and outputted capsules, we construct triplets in knowledge graph into capsules and adapt the original Routing Mechanism into Sorted Multilayer Routing Mechanism (SMRM) to provide interpretation. Experimental results suggest that ITCN achieves state-of-the-art performance on three benchmark datasets, highlighting the benefit brought by the CRE and interpretability of the model.
A hybrid steady-state evolutionary algorithm using random swaps for Gaussian model-based clustering

A new hybrid evolutionary algorithm (EA) for Gaussian mixture model-based clustering is proposed. The EA is a steady-state method that, in each generation, selects two individuals from a population, creates two offspring using either mutation or crossover, and fine-tunes the offspring using the expectation maximization (EM) algorithm. The offspring compete with their parents for survival into the next generation. The approach proposed uses a random swap, which replaces a component mean with a randomly chosen feature vector as a mutation operator. In the crossover operator, a random component is copied from the source mixture into a destination mixture. Copying in crossover favors components of the source mixture located away from the components of the destination mixture. In computational experiments, the approach was compared to a multiple restarts EM algorithm, a random swap EM method, and a state-of-the-art hybrid evolutionary algorithm for Gaussian mixture model learning on one real and 29 synthetic datasets. The results indicate that, given the same computational budget, the proposed method usually learns mixtures with higher log-likelihoods than other benchmarks competing algorithms. The partitions of data obtained by the method correspond best to the original divisions of datasets into classes.
A whole new ball game: Fan perceptions of augmented reality enhanced sport broadcasts

For a large portion of its history, broadcasting has been stagnant when it comes to incorporating new and innovative technologies. However, due to declining viewership and consumer desire for customizable content, augmented reality (AR) graphics have begun to be incorporated into multiple broadcast products. The current study contributes to the literature by providing much needed answers to questions surrounding consumer perceptions and the implementation of AR in broadcasts. Guided by the Elaboration Likelihood Model and using sport broadcasts as the context, three NBA broadcasts were assessed: a mascot-mode with over the top AR graphics, a coach-mode with play-by-play AR graphics, and a traditional broadcast with no graphics. Following random assignment, participants in the current study were more likely to re-view (p < .05) and recommend via word of mouth (p < .05) the coach-mode AR than the mascot-mode AR. As formidable media providers like ESPN do not see AR as a fad, the current findings are noteworthy. Both AR enhancements under investigation represent substantial alterations to the core, traditional product. Thus, media providers are advised to introduce AR components gradually and systematically, so as not to overload the conservative viewer.