Reviewerly/exHarmony huggingface dataset:
Lightning: A Reconfigurable Photonic-Electronic SmartNIC for Fast and Energy-Efficient Inference

The massive growth of machine learning-based applications and the end of Moore's law have created a pressing need to redesign computing platforms. We propose Lightning, the first reconfigurable photonic-electronic smartNIC to serve real-time deep neural network inference requests. Lightning uses a fast datapath to feed traffic from the NIC into the photonic domain without creating digital packet processing and data movement bottlenecks. To do so, Lightning leverages a novel reconfigurable count-action abstraction that keeps track of the required computation operations of each inference packet. Our count-action abstraction decouples the compute control plane from the data plane by counting the number of operations in each task and triggers the execution of the next task(s) without interrupting the dataflow. We evaluate Lightning's performance using four platforms: a prototype, chip synthesis, emulations, and simulations. Our prototype demonstrates the feasibility of performing 8-bit photonic multiply-accumulate operations with 99.25% accuracy. To the best of our knowledge, our prototype is the highest-frequency photonic computing system, capable of serving real-time inference queries at 4.055 GHz end-to-end. Our simulations with large DNN models show that compared to Nvidia A100 GPU, A100X DPU, and Brainwave smartNIC, Lightning accelerates the average inference serve time by 337×, 329×, and 42×, while consuming 352×, 419×, and 54× less energy, respectively.
ClickINC: In-network Computing as a Service in Heterogeneous Programmable Data-center Networks

In-Network Computing (INC) has found many applications for performance boosts or cost reduction. However, given heterogeneous devices, diverse applications, and multi-path network typologies, it is cumbersome and error-prone for application developers to effectively utilize the available network resources and gain predictable benefits without impeding normal network functions. Previous work is oriented to network operators more than application developers. We develop ClickINC to streamline the INC programming and deployment using a unified and automated workflow. Click-INC provides INC developers a modular programming abstractions, without concerning to the states of the devices and the network topology. We describe the ClickINC framework, model, language, workflow, and corresponding algorithms. Experiments on both an emulator and a prototype system demonstrate its feasibility and benefits.
Sammy: smoothing video traffic to be a friendly internet neighbor

On-demand streaming video traffic is managed by an adaptive bi-trate (ABR) algorithm whose job is to optimize quality of experience (QoE) for a single video session. ABR algorithms leave the question of sharing network resources up to transport-layer algorithms. We observe that as the internet gets faster relative to video streaming rates, this delegation of responsibility gives video traffic a burstier on-off traffic pattern. In this paper, we show we can substantially smooth video traffic to improve its interactions with the rest of the internet, while maintaining the same or better QoE for streaming video. We smooth video traffic with two design principles: application-informed pacing, which allows ABR algorithms to set an upper limit on packet-by-packet throughput, and by designing ABR algorithms that work with pacing. We propose a joint ABR and rate-control scheme, called Sammy, which selects both video quality and pacing rates. We implement our scheme and evaluate it at a large video streaming service. Our approach smooths video, making it a more friendly neighbor to other internet applications. One surprising result is that being friendlier requires no compromise for the video traffic: in large scale, production experiments, Sammy improves video QoE over an existing, extensively tested and tuned production ABR algorithm.
A hybrid machine learning and natural language processing model for early detection of acute coronary syndrome

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity. Predicting the associated risks of patients with chest pain using electronic health record data can help identify those needing more tailored care. This study proposes the development of a reliable prediction framework to serve as a diagnostic support tool for preventing misdiagnoses among patients with clinical concerns for ACS. Data were collected from an urban, demographically diverse hospital in Detroit, Michigan, for patients presenting to the emergency department (ED) with primary chief complaints of chest pain from January 2017 to August 2020. This study incorporated term frequency-inverse document frequency features from free-text summaries, which contain anecdotal symptom descriptions and are among the first data points provided upon entering the ED. The analysis included 16,096 patients with clinical concerns for ACS and trained three machine learning models, logistic regression, AdaBoost, and linear discriminant analysis, across different data processing stages to predict patients with ACS from non-ACS etiology. The AdaBoost model outperformed the other two models with an accuracy of 94% and an F1-score of 0.943 in predicting ACS on the testing data. This study identified key independent factors from patient demographics, comorbidities, and clinical narrative data that predicted ACS in patients. The prediction framework can serve as a decision-support tool to classify ACS and inform physicians about better ACS risk factors.
Constraint Programming Approach to Coverage-Path Planning for Autonomous Multi-UAV Infrastructure Inspection

This article presents a constraint modeling approach to global coverage-path planning for linear-infrastructure inspection using multiple autonomous UAVs. The problem is mathematically formulated as a variant of the Min–Max K-Chinese Postman Problem (MM K-CPP) with multi-weight edges. A high-level constraint programming language is used to model the problem, which enables model execution with different third-party solvers. The optimal solutions are obtained in a reasonable time for most of the tested instances and different numbers of vehicles involved in the inspection. For some graphs with multi-weight edges, a time limit is applied, as the problem is NP-hard and the computation time increases exponentially. Despite that, the final total inspection cost proved to be lower when compared with the solution obtained for the unrestricted MM K-CPP with single-weight edges. This model can be applied to plan coverage paths for linear-infrastructure inspection, resulting in a minimal total inspection time for relatively simple graphs that resemble real transmission networks. For more extensive graphs, it is possible to obtain valid solutions in a reasonable time, but optimality cannot be guaranteed. For future improvements, further optimization could be considered, or different models could be developed, possibly involving artificial neural networks.
Development of interactive powerpoint learning media based on information and communication technologies to improve student learning outcomes

Research conducted by researchers on the development of interactive PowerPoint learning media based on Information And Communication Technologies is based on conceptual and theoretical understanding using thematic learning that is felt to be not fully understood by students, this has resulted in decreased student learning outcomes. To solve problems in the learning process, learning media is needed. The purpose of this research is to implement the development of interactive PowerPoint media using ICT for fifth-grade students of SDN 1 Gamping. This media was developed in the form of several slides which are equipped with buttons that are inserted by hyperlinks in the learning menu so that it will make it easier for the user to go to the slide after the specified slide. This study used the ADDIE research model as a development model. The research findings showed an average material validity score of 80% (appropriate), an average language validity score of 92% (very feasible), and an average media validity score of 88% (very feasible). Calculation of media effectiveness based on n-gain obtained a score of 0.59 in the medium category. Responses to ICT-based interactive PowerPoint learning media were obtained from small-scale tests with an average score of 80% while field-scale tests obtained an average value of 86%, overall Student responses related to media can be said to be positive. So that the analysis data obtained states that ICT-based interactive PowerPoint learning media is effective in improving student learning outcomes.
Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion

Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. Furthermore, we introduce a novel approach for action recognition that combines skeleton-based information and multiple feature fusion. By extracting spatial geometric and temporal characteristics from actions, we employ a sliding window algorithm to integrate these features. Experimental results show the effectiveness of our approach, demonstrating its ability to accurately recognize and classify various human actions. Additionally, we address the evaluation of traditional fitness exercises, specifically focusing on the BaDunJin movements. We propose a multimodal information-based assessment method that combines pose detection and keypoint analysis. Label sequences are obtained through a pose detector and each frame’s keypoint coordinates are represented as pose vectors. Leveraging multimodal information, including label sequences and pose vectors, we explore action similarity and perform quantitative evaluations to help exercisers assess the quality of their exercise performance.
Automatic Face Recognition System Using Deep Convolutional Mixer Architecture and AdaBoost Classifier

In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban security. This is a difficult task due to face occlusion, which makes it hard to capture effective features. Existing work focuses on improving performance while ignoring issues like a small dataset, high computational complexity, and a lack of lightweight and efficient feature descriptors. In this paper, face recognition (FR) using a Convolutional mixer (AFR-Conv) algorithm is developed to handle face occlusion problems. A novel AFR-Conv architecture is designed by assigning priority-based weight to the different face patches along with residual connections and an AdaBoost classifier for automatically recognizing human faces. The AFR-Conv also leverages the strengths of pre-trained CNNs by extracting features using ResNet-50, Inception-v3, and DenseNet-161. The AdaBoost classifier combines these features’ weighted votes to predict labels for testing images. To develop this system, we use the data augmentation method to enhance the number of datasets using human face images. The AFR-Conv method is then used to extract robust features from images. Finally, to recognize human identity, an AdaBoost classifier is utilized. For the training and evaluation of the AFR-Conv model, a set of face images is collected from online data sources. The experimental results of the AFR-Conv approach are presented in terms of precision (PR), recall (RE), detection accuracy (DA), and F1-score metrics. Particularly, the proposed approach attains 95.5% PR, 97.6% RE, 97.5% DA, and 98.5% of F1-score on 8500 face images. The experimental results show that our proposed scheme outperforms advanced methods for face classification.
Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs

Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.
FinTech Cybersecurity Challenges and Regulations: Bahrain Case Study

Winds of change are blowing across the financial systems, with services and advancements in Financial Technology (FinTech) influencing all aspects of the financial sector and generating a continual stream of innovations. Despite benefits offered by FinTech, it creates new challenges that endanger financial institutes’ stability and integrity. As cyber-attacks increasingly threaten the FinTech industry, cybersecurity can be considered as one of the main challenges that need to be addressed to properly manage risks associated with integrating FinTech services in people’s day-to-day life. This Systematic Literature Review (SLR) highlights the cybersecurity challenges that FinTech industry faces and discusses existing measures that can effectively manage FinTech cybersecurity risks. An analysis of the existing literature and regulations is carried out to identify comparable components that exist across some internationally well-known cybersecurity standards and frameworks. Considering Bahrain as a case study, the paper explores key elements and factors that were not addressed adequately while implementing such standards. Research findings indicate that creating a cybersecurity framework for FinTech could be advantageous and offers a new perspective on the topic by demonstrating a natural extension of the existing knowledge. The findings offer useful suggestions for Bahrain’s financial regulators to get better acquainted with these aspects. It lays the foundation to develop a cybersecurity framework for FinTech specifically for Bahrain, and it endeavors to raise the level of cybersecurity and a trusted electronic environment for both the customers and service providers in Bahrain.
Metaverse in education: A systematic literature review

This article presents a summary of the prior studies on the use of the metaverse in the sector of education using a systematic literature review. The bibliometric analysis of this study is used to analyse published works in order to pinpoint the leading experts in the field, important subtopics, and potential research prospects. We also identify the most significant articles as well as patterns and clusters of related subjects. Our main findings showed that the three phrases “education,” “application,” and “metaverse” occurred most frequently and were connected to one another. The analysis part shows that concepts such as “challenge,” “teaching,” and “knowledge” have not been thoroughly researched. This research also emphasises the significance of appropriate learning settings, class layouts, the development of didactic strategies, and teacher preparation programmes. Finally, the results also present recommendations for future research in this field and provides an in-depth look at the potential uses of metaverses in education.
Automated Optimization-Based Deep Learning Models for Image Classification Tasks

Applying deep learning models requires design and optimization when solving multifaceted artificial intelligence tasks. Optimization relies on human expertise and is achieved only with great exertion. The current literature concentrates on automating design; optimization needs more attention. Similarly, most existing optimization libraries focus on other machine learning tasks rather than image classification. For this reason, an automated optimization scheme of deep learning models for image classification tasks is proposed in this paper. A sequential-model-based optimization algorithm was used to implement the proposed method. Four deep learning models, a transformer-based model, and standard datasets for image classification challenges were employed in the experiments. Through empirical evaluations, this paper demonstrates that the proposed scheme improves the performance of deep learning models. Specifically, for a Virtual Geometry Group (VGG-16), accuracy was heightened from 0.937 to 0.983, signifying a 73% relative error rate drop within an hour of automated optimization. Similarly, training-related parameter values are proposed to improve the performance of deep learning models. The scheme can be extended to automate the optimization of transformer-based models. The insights from this study may assist efforts to provide full access to the building and optimization of DL models, even for amateurs.
Does digital payment induce economic growth in emerging economies? The mediating role of institutional quality, consumption expenditure, and bank credit

ABSTRACTThis paper analyzes the direct and interactive effect of digital payments through institutional quality, consumption expenditure, and bank credit on economic growth for 25-member countries of the Committee on Payments and Market Infrastructures (CPMI) for 2012–2020. Using the Fixed Effect with Driscoll-Kraay Panel Corrected Estimators, it finds that a rise in digital payments positively impacts economic growth. The interaction effect of digital payments with other variables does not promote economic growth. To address differences among the selected countries and to have a robust analysis, the paper classified the selected countries into quantiles based on the GDP per capita level using the Bootstrapped Panel-Quantile Regression (BPQR) method. The BPQR confirms the positive relationship among digital payments, institutional quality, and consumption expenditure with economic growth, whereas credit impacts it negatively. Control variables, such as inflation, exchange rate, health, and unemployment, behave as per economic theories related to economic growth.KEYWORDS: Digital paymenteconomic growthinstitutional qualityconsumption expenditurebank creditSubject classification codes:: G21O43E21E51 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is part of the project work funded under an institute initiation grant by IISER Bhopal for the First Author [grant number IISERB/R&D/2022-23/142].
Improving task generalization via unified schema prompt

Task generalization has been a long-standing challenge in Natural Language Processing (NLP). Recent research attempts to improve the task generalization ability of pre-trained language models by mapping NLP tasks into human-readable prompted forms. However, these approaches require laborious and inflexible manual collection of prompts, and different prompts on the same downstream task may receive unstable performance. We propose Unified Schema Prompt, a flexible and extensible prompting method, which automatically customizes the learnable prompts for each task according to the task input schema. It models the shared knowledge between tasks, while keeping the characteristics of different task schema, and thus enhances task generalization ability. The schema prompt takes the explicit data structure of each task to formulate prompts so that little human effort is involved. To test the task generalization ability of schema prompt at scale, we conduct schema prompt-based multitask pre-training on a wide variety of general NLP tasks. The framework achieves strong zero-shot and few-shot generalization performance on 16 unseen downstream tasks from 8 task types (e.g., QA, NLI, etc.). Furthermore, comprehensive analyses demonstrate the effectiveness of each component in the schema prompt, its flexibility in task compositionality, and its ability to improve performance under a full-data fine-tuning setting.
Multi-level alignment for few-shot temporal action localization

Temporal action localization (TAL), which aims to localize actions in long untrimmed videos, requires a large number of annotated training data. However, it is expensive to obtain segment-level annotations for large-scale datasets. To overcome this challenge, a new few-shot learning method is proposed that localizes temporal actions for unseen classes with only a few training samples. In this study, a new multi-level encoder cosine-similarity alignment module is adopted that exploits the alignment of visual information at each temporal location. The proposed method arranges the video snippets that contain similar foreground action instances, and it captures the intra-class variations more implicitly. In addition, it incorporates cosine similarity in Transformer encoder layers that supports the self-attention mechanism. This emphasizes more on refined features at the higher encoder layers. Towards this objective, an episodic-based training scheme is adopted to learn the alignment of similar video snippets with a few training examples. At the test time, the learned context information is then adapted to novel classes. Experimental results show that the proposed method outperforms the state-of-the-art methods for few-shot temporal action localization with single and multiple action instances on the ActivityNet-1.3 dataset and achieves competitive results on the THUMOS-14 and HACS datasets.