Reviewerly/exHarmony huggingface dataset:
Faster variational quantum algorithms with quantum kernel-based surrogate models

Abstract We present a new optimization strategy for small-to-intermediate scale variational quantum algorithms (VQAs) on noisy near-term quantum processors which uses a Gaussian process surrogate model equipped with a classically-evaluated quantum kernel. VQAs are typically optimized using gradient-based approaches however these are difficult to implement on current noisy devices, requiring large numbers of objective function evaluations. Our approach shifts this computational burden onto the classical optimizer component of these hybrid algorithms, greatly reducing the number of quantum circuit evaluations required from the quantum processor. We focus on the variational quantum eigensolver (VQE) algorithm and demonstrate numerically that these surrogate models are particularly well suited to the algorithm’s objective function. Next, we apply these models to both noiseless and noisy VQE simulations and show that they exhibit better performance than widely-used classical kernels in terms of final accuracy and convergence speed. Compared to the typically-used stochastic gradient-descent approach to VQAs, our quantum kernel-based approach is found to consistently achieve significantly higher accuracy while requiring less than an order of magnitude fewer quantum circuit executions. We analyze the performance of the quantum kernel-based models in terms of the kernels’ induced feature spaces and explicitly construct their feature maps. Finally, we describe a scheme for approximating the best-performing quantum kernel using a classically-efficient tensor network representation of its input state and so provide a pathway for scaling this strategy to larger systems.
Test Set Sizing Via Random Matrix Theory

Abstract This paper uses techniques from Random Matrix Theory to find the ideal training-testing data split for a simple linear regression with m data points, each an independent n-dimensional multivariate Gaussian. It defines ``ideal'' as satisfying the Integrity Metric , i.e. the empirical model error is the actual measurement noise, and thus fairly reflects the value or lack of same of the model. This paper is the first to solve for the training and test set sizes for any model in a way that is truly optimal. The number of data points in the training set is the root of a quartic polynomial Theorem 1 derives which depends only on m and n ; the covariance matrix of the multivariate Gaussian, the true model parameters, and the true measurement noise drop out of the calculations. The critical mathematical difficulties were realizing that the problems herein were discussed in the context of the Jacobi Ensemble, a probability distribution describing the eigenvalues of a known random matrix model, and evaluating a new integral in the style of Selberg and Aomoto. Mathematical results are supported with thorough computational evidence. This paper is a step towards automatic choices of training/test set sizes in machine learning.
How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongside text prompts during conversations. Given Bard's impressive track record in handling textual inputs, we explore its capabilities in understanding and interpreting visual data (images) conditioned by text questions. This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models, especially in addressing complex computer vision problems that demand accurate visual and language understanding. Specifically, in this study, we focus on 15 diverse task scenarios encompassing regular, camouflaged, medical, under-water and remote sensing data to comprehensively evaluate Bard's performance. Our primary finding indicates that Bard still struggles in these vision scenarios, highlighting the significant gap in vision-based understanding that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, leading to enhanced capabilities in comprehending and interpreting fine-grained visual data. Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand
Effects of Information Governance (IG) on Profitability in the Nigerian Banking Sector

Aims: The article provides an overview of the Nigerian banking industry, highlighting its regulation by the Central Bank of Nigeria (CBN) and the role of information governance (IG) in enhancing the industry's robustness. Despite being recognized as one of the top performers in Africa, there are inconsistencies in information governance, particularly in data management.&#x0D; Place of Study: The case study of Capital One showed that implementing appropriate governance policies could have either entirely mitigated the attack or reduced the time the hackers had unauthorized access, minimizing the attack's impact on the organization.&#x0D; Methodology: The study employed a mixed-methods approach to explore the feasibility of implementing an Information Governance Framework (IGF) in the financial sector. The quantitative data collected from Nigerian Deposit Insurance Corporation (NDIC), the regulator of banking industry in Nigeria revealed that implementing IG programs is crucial for profitability in the banking industry.&#x0D; Results: The study concludes that effective IG depends on formalized structures, accountability, privacy, ethics, transparency, monitoring, compliance, and suitability. Therefore, with 95% confidence, it can be stated that IG policies does not contribute to mitigating data breaches in the Nigerian banking industry and improving profitability. The study has shown that financial institutions can utilize information governance to mitigate data breaches and increase profits. While data breaches remain a real threat, banks can leverage IG policies to enhance profitability by safeguarding their assets.&#x0D; Conclusion: It is concluded that Nigerian banks must remain vigilant and address any changes within their business infrastructure to mitigate risks and protect sensitive data using appropriate IG policies and standards.
Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN

Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
Autonomous Justification for Enabling Explainable Decision Support in Human-Robot Teaming

Justification is an important facet of policy explanation, a process for describing the behavior of an autonomous system.In human-robot collaboration, an autonomous agent can attempt to justify distinctly important decisions by offering explanations as to why those decisions are right or reasonable, leveraging a snapshot of its internal reasoning to do so.Without sufficient insight into a robot's decision-making process, it becomes challenging for users to trust or comply with those important decisions, especially when they are viewed as confusing or contrary to the user's expectations (e.g., when decisions change as new information is introduced to the agent's decision-making process).In this work we characterize the benefits of justification within the context of decision-support during humanrobot teaming (i.e., agents giving recommendations to human teammates).We introduce a formal framework using value of information theory to strategically time justifications during periods of misaligned expectations for greater effect.We also characterize four different types of counterfactual justification derived from established explainable AI literature and evaluate them against each other in a human-subjects study involving a collaborative, partially observable search task.Based on our findings, we present takeaways on the effective use of different types of justifications in human-robot teaming scenarios, to improve user compliance and decision-making by strategically influencing human teammate thinking patterns.Finally, we present an augmented reality system incorporating these findings into a real-world decision-support system for human-robot teaming.
The Mediating Effect of Job Satisfaction on Technology Law Compliance: A Study of Employee Perceptions in the Digital Era

This study investigates the mediating effect of job satisfaction on technology law compliance and explores employee perceptions in the digital era. As technology continues to play a pivotal role in organizations, it is crucial to understand how job satisfaction influences compliance with technology-related laws and regulations. The paper reviews relevant literature on technology law compliance and job satisfaction, establishing a theoretical framework that positions job satisfaction as a potential mediator. A quantitative research approach is adopted, utilizing a questionnaire to gather data from employees. The collected data is analyzed through correlation and mediation analyses. The findings shed light on the relationship between job satisfaction, technology law compliance, and employee perceptions. The study contributes to the existing body of knowledge by providing insights into the role of job satisfaction in promoting compliance in the digital age. The implications of the findings for both theory and practice are discussed, along with the limitations of the study and recommendations for future research.
Counterfactual Explanations and Model Multiplicity: a Relational Verification View

We study the interplay between counterfactual explanations and model multiplicity in the context of neural network classifiers. We show that current explanation methods often produce counterfactuals whose validity is not preserved under model multiplicity. We then study the problem of generating counterfactuals that are guaranteed to be robust to model multiplicity, characterise its complexity and propose an approach to solve this problem using ideas from relational verification.
Explainable Clustering with CREAM

This paper proposes CREAM, a new explainable clustering technique based on decision tree induction, providing human-interpretable clusters by performing hypercubic approximations of the input feature space. CREAM may also be applied to data sets describing classification and regression tasks, given that the algorithm discriminates amongst input and output features. We also present OrCHiD, an automated tuning procedure to select the optimum CREAM parameter. Experiments demonstrating the effectiveness of CREAM in clustering, classification, and regression tasks are reported here, in comparison with other state-of-the-art techniques used as benchmarks.
Blockchain Segmentation: A Storage Optimization Technique for Large Data

The increasing size of blockchain technology has become a major obstacle to its decentralization and implementation in big data applications. This proposed system presents a solution, called block chain segmentation, which separates the block chain into chunks and permits each block to only hold a copy of a single segment. Our strategy solves the issue of a single failure point, where a threat actor could keep all duplicates of a segment and then depart the system, leading to the segment’s permanent loss. This study demonstrates theoretically that by limiting the number of blocks an adversary can store and distributing every segment among a number of blocks in the cloud, it is better suited for managing massive amounts of data because the storage demand is significantly lower than in conventional architectures. The main goal of this method is to construct a blockchain that can effectively handle big data by minimizing storage space requirements on large-scale datasets, while also ensuring enhanced security for user data, this system reduced 33% of storage space in the proposed experiment.
R2Net: Efficient and flexible diffeomorphic image registration using Lipschitz continuous residual networks

Classical diffeomorphic image registration methods, while being accurate, face the challenges of high computational costs. Deep learning based approaches provide a fast alternative to address these issues; however, most existing deep solutions either lose the good property of diffeomorphism or have limited flexibility to capture large deformations, under the assumption that deformations are driven by stationary velocity fields (SVFs). Also, the adopted squaring and scaling technique for integrating SVFs is time- and memory-consuming, hindering deep methods from handling large image volumes. In this paper, we present an unsupervised diffeomorphic image registration framework, which uses deep residual networks (ResNets) as numerical approximations of the underlying continuous diffeomorphic setting governed by ordinary differential equations, which is parameterized by either SVFs or time-varying (non-stationary) velocity fields. This flexible parameterization in our Residual Registration Network (R2Net) not only provides the model’s ability to capture large deformation but also reduces the time and memory cost when integrating velocity fields for deformation generation. Also, we introduce a Lipschitz continuity constraint into the ResNet block to help achieve diffeomorphic deformations. To enhance the ability of our model for handling images with large volume sizes, we employ a hierarchical extension with a multi-phase learning strategy to solve the image registration task in a coarse-to-fine fashion. We demonstrate our models on four 3D image registration tasks with a wide range of anatomies, including brain MRIs, cine cardiac MRIs, and lung CT scans. Compared to classical methods SyN and diffeomorphic VoxelMorph, our models achieve comparable or better registration accuracy with much smoother deformations. Our source code is available online at https://github.com/ankitajoshi15/R2Net.
Enhancing the Accuracy of an Image Classification Model Using Cross-Modality Transfer Learning

Applying deep learning (DL) algorithms for image classification tasks becomes more challenging with insufficient training data. Transfer learning (TL) has been proposed to address these problems. In theory, TL requires only a small amount of knowledge to be transferred to the target task, but traditional transfer learning often requires the presence of the same or similar features in the source and target domains. Cross-modality transfer learning (CMTL) solves this problem by learning knowledge in a source domain completely different from the target domain, often using a source domain with a large amount of data, which helps the model learn more features. Most existing research on CMTL has focused on image-to-image transfer. In this paper, the CMTL problem is formulated from the text domain to the image domain. Our study started by training two separately pre-trained models in the text and image domains to obtain the network structure. The knowledge of the two pre-trained models was transferred via CMTL to obtain a new hybrid model (combining the BERT and BEiT models). Next, GridSearchCV and 5-fold cross-validation were used to identify the most suitable combination of hyperparameters (batch size and learning rate) and optimizers (SGDM and ADAM) for our model. To evaluate their impact, 48 two-tuple hyperparameters and two well-known optimizers were used. The performance evaluation metrics were validation accuracy, F1-score, precision, and recall. The ablation study confirms that the hybrid model enhanced accuracy by 12.8% compared with the original BEiT model. In addition, the results show that these two hyperparameters can significantly impact model performance.
Exploring the impacts of using the artificial intelligence voice-enabled chatbots on customers interactions in the United Arab Emirates

&lt;p&gt;Over the past decade, chatbots have experienced a significant increase in&lt;br /&gt;popularity, especially since the outbreak of COVID-19. In the United Arab&lt;br /&gt;Emirates, most businesses have accelerated their digital transformation and&lt;br /&gt;are relying on chatbots as a primary way to interact with customers. However,&lt;br /&gt;many of these chatbots lack a voice input option for customers. This study&lt;br /&gt;investigates the benefits and challenges of incorporating artificial intelligence&lt;br /&gt;(AI) voice-enabled chatbots into United Arab Emirates (UAE) businesses and&lt;br /&gt;how this impacts customer experience. Qualitative research techniques were&lt;br /&gt;used to gather information, and the results demonstrate that implementing AI&lt;br /&gt;chatbots with voice input and sentiment analysis features can enhance the&lt;br /&gt;customer experience by making it more efficient and convenient.&lt;br /&gt;Additionally, the study found that AI chatbots can ultimately save businesses&lt;br /&gt;time and money, and while they may reduce the need for human agents, they&lt;br /&gt;will not replace them entirely. Finally, an implementation framework and&lt;br /&gt;suggestions are provided for businesses that are interested in adopting AI&lt;br /&gt;voice-enabled chatbots for customer interactions.&lt;/p&gt;
Omega-Completeness of the Logic of Here-and-There and Strong Equivalence of Logic Programs

Theory of strongly equivalent transformations is an essential part of the methodology of representing knowledge in answer set programming. Strong equivalence of two programs can be sometimes characterized as the possibility of deriving the rules of each program from the rules of the other in some deductive system. This paper describes a system with this property for the language mini-GRINGO. The key to the proof is an ω-completeness theorem for the many-sorted logic of here-and-there.
Developing Chatbots for Cyber Security: Assessing Threats through Sentiment Analysis on Social Media

In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustain-able strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research pro-vides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.
pNLP-Mixer: an Efficient all-MLP Architecture for Language

Large pre-trained language models based on transformer architecture have drastically changed the natural language processing (NLP) landscape.However, deploying those models for on-device applications in constrained devices such as smart watches is completely impractical due to their size and inference cost.As an alternative to transformer-based architectures, recent work on efficient NLP has shown that weight-efficient models can attain competitive performance for simple tasks, such as slot filling and intent classification, with model sizes in the order of the megabyte.This work introduces the pNLP-Mixer architecture, an embedding-free MLP-Mixer model for on-device NLP that achieves high weightefficiency thanks to a novel projection layer.We evaluate a pNLP-Mixer model of only one megabyte in size on two multi-lingual semantic parsing datasets, MTOP and multiATIS.Our quantized model achieves 99.4% and 97.8% the performance of mBERT on MTOP and multi-ATIS, while using 170x fewer parameters.Our model consistently beats the state-of-the-art of tiny models (pQRNN), which is twice as large, by a margin up to 7.8% on MTOP.
ChatGPT is not a good indigenous translator

This report investigates the continuous challenges of Machine Translation (MT) systems on indigenous and extremely low-resource language pairs.Despite the notable achievements of Large Language Models (LLMs) that excel in various tasks, their applicability to lowresource languages remains questionable.In this study, we leveraged the AmericasNLP competition to evaluate the translation performance of different systems for Spanish to 11 indigenous languages from South America.Our team, LTLAmsterdam, submitted a total of four systems including GPT-4, a bilingual model, finetuned M2M100, and a combination of finetuned M2M100 with kNN-MT.We found that even large language models like GPT-4 are not well-suited for extremely low-resource languages.Our results suggest that fine-tuning M2M100 models can offer significantly better performance for extremely low-resource translation.
LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models

Systems that support users in the automatic creation of visualizations must address several subtasks -understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications.In this work, we pose visualization generation as a multi-stage generation problem and argue that well-orchestrated pipelines based on large language models (LLMs) and image generation models (IGMs) are suitable to addressing these tasks.We present LIDA, a novel tool for generating grammar-agnostic visualizations and infographics.LIDA comprises of 4 modules -A SUMMARIZER that converts data into a rich but compact natural language summary, a GOAL EXPLORER that enumerates visualization goals given the data, a VISGENERATOR that generates, refines, executes and filters visualization code and an INFOGRAPHER module that yields data-faithful stylized graphics using IGMs.LIDA provides a python api, and a hybrid USER INTERFACE (direct manipulation and multilingual natural language) for interactive chart, infographics and data story generation.Code and demo are available at this urlhttps://microsoft.github.io/lida/