Reviewerly/exHarmony huggingface dataset:
Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed

Blockchain technology, with its decentralization characteristics, immutability, and traceability, is well-suited for facilitating secure storage, sharing, and management of data in decentralized Internet of Things (IoT) applications. Despite the increasing development of blockchain platforms, there is still no comprehensive approach for adopting blockchain technology in IoT systems. This is due to the blockchain's limited capability to process substantial transaction requests from a massive number of IoT devices. Hyperledger Fabric (HLF) is a popular open-source permissioned blockchain platform hosted by the Linux Foundation. This article reports a comprehensive empirical study that measures HLF's performance and identifies potential performance bottlenecks to better meet the requirements of blockchain-based IoT applications. The study considers the implementation of HLF on distributed large-scale IoT systems. First, a model for monitoring the performance of the HLF platform is presented. It addresses the overhead challenges while delivering more details on system performance and better scalability. Then, the proposed framework is implemented to evaluate the impact of varying network workloads on the performance of the blockchain platform in a large-scale distributed environment. In particular, the performance of the HLF is evaluated in terms of throughput, latency, network size, scalability, and the number of peers serviceable by the platform. The obtained experimental results indicate that the proposed framework can provide detailed real-time performance evaluation of blockchain systems for large-scale IoT applications.
Customer Gaze Estimation in Retail Using Deep Learning

At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the novel concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using only image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data and monocular depth estimation. The second stage presents a novel three-attention mechanism to estimate the gaze in the wild from field-of-view, depth range, and object channel attentions. The third stage generates the gaze saliency heatmap from the output attention map of the second stage. We train and evaluate the proposed model on the benchmark GOO-Real dataset and compare the results with baseline models. Further, we adapt our model to real-retail environments by introducing a novel Retail Gaze dataset. Extensive experiments demonstrate that our approach significantly improves remote gaze target estimation performance on GOO-Real and Retail Gaze datasets.
Prompt-free and Efficient Few-shot Learning with Language Models

Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.In this work, we propose PERFECT, a simple and efficient method for few-shot fine-tuning of PLMs without relying on any such handcrafting, which is highly effective given as few as 32 data points.PERFECT makes two key design choices: First, we show that manually engineered task prompts can be replaced with task-specific adapters that enable sample-efficient fine-tuning and reduce memory and storage costs by roughly factors of 5 and 100, respectively.Second, instead of using handcrafted verbalizers, we learn new multi-token label embeddings during fine-tuning, which are not tied to the model vocabulary and which allow us to avoid complex auto-regressive decoding.These embeddings are not only learnable from limited data but also enable nearly 100x faster training and inference.Experiments on a wide range of few shot NLP tasks demonstrate that PERFECT, while being simple and efficient, also outperforms existing state-of-theart few-shot learning methods.Our code is publicly available at https://github.com/facebookresearch/perfect.git.
Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs

UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved Moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.
Students' evaluation and learning experience on the utilization of Digital Interactive Math Comics (DIMaC) mobile app

Mobile phones are prevalent worldwide, and today's learners utilize this technology for instructional purposes. This educational design research aims to evaluate the developed interactive mobile application. This instructional material contributes to today's 21st century or digital native learners' needs to engage students in the teaching-learning process. The app is aligned with mathematics instruction, balancing visuals and Math content. Employing the Instructional Material Development for Non-print Materials instrument shows that the developed mobile app is acceptable for the Grade 11 students. Students reported a positive experience and noted an increased interest in Mathematics when using the app. Further research is needed to explore the integration of this app in the mathematics classroom.
BERT Learns to Teach: Knowledge Distillation with Meta Learning

We present Knowledge Distillation with Meta Learning (MetaDistil), a simple yet effective alternative to traditional knowledge distillation (KD) methods where the teacher model is fixed during training.We show the teacher network can learn to better transfer knowledge to the student network (i.e., learning to teach) with the feedback from the performance of the distilled student network in a meta learning framework.Moreover, we introduce a pilot update mechanism to improve the alignment between the inner-learner and meta-learner in meta learning algorithms that focus on an improved inner-learner.Experiments on various benchmarks show that MetaDistil can yield significant improvements compared with traditional KD algorithms and is less sensitive to the choice of different student capacity and hyperparameters, facilitating the use of KD on different tasks and models.
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion

Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts.However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability.In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton's quaternion space.We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can further capture time-evolved relations by theory.Empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.
Fine-Grained Controllable Text Generation Using Non-Residual Prompting

The introduction of immensely large causal language models (CLMs) has rejuvenated the interest in open-ended text generation.However, controlling the generative process for these Transformer-based models is at large an unsolved problem.Earlier work has explored either plug-and-play decoding strategies or more powerful but blunt approaches such as prompting.There hence currently exists a trade-off between fine-grained control and the capability for more expressive highlevel instructions.To alleviate this trade-off, we propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps.We propose a resourceefficient method for converting a pre-trained CLM into this architecture and demonstrate its potential in various experiments, including the novel task of contextualized word inclusion.Our method provides strong results in multiple experimental settings, proving itself to be both expressive and versatile. 1
The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail

Researchers in NLP often frame and discuss research results in ways that serve to deemphasize the field's successes, often in response to the field's widespread hype.Though wellmeaning, this has yielded many misleading or false claims about the limits of our best technology.This is a problem, and it may be more serious than it looks: It harms our credibility in ways that can make it harder to mitigate present-day harms, like those involving biased systems for content moderation or resume screening.It also limits our ability to prepare for the potentially enormous impacts of more distant future advances.This paper urges researchers to be careful about these claims and suggests some research directions and communication strategies that will make it easier to avoid or rebut them.
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types.We introduce the task setting of Zero-Shot Relation Triplet Extraction (Ze-roRTE) to encourage further research in lowresource relation extraction methods.Given an input sentence, each extracted triplet consists of the head entity, relation label, and tail entity where the relation label is not seen at the training stage.To solve ZeroRTE, we propose to synthesize relation examples by prompting language models to generate structured texts.Concretely, we unify language model prompts and structured text approaches to design a structured prompt template for generating synthetic relation samples when conditioning on relation label prompts (RelationPrompt).To overcome the limitation for extracting multiple relation triplets in a sentence, we design a novel Triplet Search Decoding method.Experiments on FewRel and Wiki-ZSL datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot relation classification.Our code and data are available at github.com/declare-lab/RelationPrompt.
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization

Charts are commonly used for exploring data and communicating insights.Generating natural language summaries from charts can be very helpful for people in inferring key insights that would otherwise require a lot of cognitive and perceptual efforts.We present Chart-totext, a large-scale benchmark with two datasets and a total of 44,096 charts covering a wide range of topics and chart types.We explain the dataset construction process and analyze the datasets.We also introduce a number of stateof-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.Our analysis with automatic and human evaluation shows that while our best models usually generate fluent summaries and yield reasonable BLEU scores, they also suffer from hallucinations and factual errors as well as difficulties in correctly explaining complex patterns and trends in charts.
Aspect-based Sentiment Analysis with Opinion Tree Generation

Existing studies usually extract these sentiment elements by decomposing the complex structure prediction task into multiple subtasks. Despite their effectiveness, these methods ignore the semantic structure in ABSA problems and require extensive task-specific designs. In this study, we introduce an Opinion Tree Generation task, which aims to jointly detect all sentiment elements in a tree. We believe that the opinion tree can reveal a more comprehensive and complete aspect-level sentiment structure. Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation. On one hand, a pre-trained model with large-scale unlabeled data is important for the tree generation model. On the other hand, the syntax and semantic features are very effective for forming the opinion tree structure. Extensive experiments show the superiority of our proposed method. The results also validate the tree structure is effective to generate sentimental elements.
Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence.The application of association rule mining in healthcare data has been widespread to this point in time.Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques.When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones.This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist).Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information.However, revealing this information puts it at risk of attack.Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining.In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected.It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database.Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).
Circulatory System Based Optimization (CSBO): an expert multilevel biologically inspired meta-heuristic algorithm

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body’s blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at http://www.optim-app.com/projects/csbo.
IoT Equipped Intelligent Distributed Framework for Smart Healthcare Systems

The fundamental aim of the healthcare sector is to incorporate different technologies to observe and keep a track of the various clinical parameters of the patients in day to day life. Distant patient observation applications are becoming popular as economical healthcare services are facilitated by these apps. The process of data management gathered through these applications also require due attention. Although cloud facilitated healthcare applications cater a variety of solutions to store patients record and deliver the required data as per need of all the stakeholders but are affected by security issues, more response time and affecting the continues availability of the system. To overcome these challenges, an intelligent IoT based distributed framework to deploy remote healthcare services is proposed in this chapter. In the proposed model, various entities of the system are interconnected using IoT and Distributed Database Management Systems is used to cater secure and fast data availability to the patients and health care workers. The concept of Blockchain is used to ensure the security of the patient medical records. The proposed model will comprise of intelligent analysis of the clinical records fetched from Distributed Database Management Systems secured with Blockchain. Proposed model is tested with true clinical data and results are discussed in detail.
Standardizing AI

The EU Artificial Intelligence Act proposal is based on a risk-oriented approach. While AI systems that pose an unacceptable risk will be banned, high-risk AI systems will be subject to strict obligations before they can be put on the market. Most of the provisions deal with high-risk systems, setting out obligations on providers, users and other participants across the AI value chain. At the heart of the proposal is the notion of co-regulation through standardization based on the New Legislative Framework. Accordingly, this chapter provides a critical analysis of the proposal, with particular focus on how the co-regulation, standardization and certification system envisaged contributes to European governance of AI and addresses the manifold ethical and legal concerns of (high-risk) AI systems.
CommunityClick

Local governments still depend on traditional town halls for community consultation, despite problems such as a lack of inclusive participation for attendees and difficulty for civic organizers to capture attendees' feedback in reports. Building on a formative study with 66 town hall attendees and 20 organizers, we designed and developed CommunityClick, a communitysourcing system that captures attendees' feedback in an inclusive manner and enables organizers to author more comprehensive reports. During the meeting, in addition to recording meeting audio to capture vocal attendees' feedback, we modify iClickers to give voice to reticent attendees by allowing them to provide real-time feedback beyond a binary signal. This information then automatically feeds into a meeting transcript augmented with attendees' feedback and organizers' tags. The augmented transcript along with a feedback-weighted summary of the transcript generated from text analysis methods is incorporated into an interactive authoring tool for organizers to write reports. From a field experiment at a town hall meeting, we demonstrate how CommunityClick can improve inclusivity by providing multiple avenues for attendees to share opinions. Additionally, interviews with eight expert organizers demonstrate CommunityClick's utility in creating more comprehensive and accurate reports to inform critical civic decision-making. We discuss the possibility of integrating CommunityClick with town hall meetings in the future as well as expanding to other domains.
BeleniosVS: Secrecy and Verifiability Against a Corrupted Voting Device

Electronic voting systems aim at two conflicting properties, namely privacy and verifiability, while trying to minimise the trust assumptions on the various voting components.Most existing voting systems either assume trust in the voting device or in the voting server.We propose a novel remote voting scheme BeleniosVS that achieves both privacy and verifiability against a dishonest voting server as well as a dishonest voting device.In particular, a voter does not leak her vote to her voting device and she can check that her ballot on the bulletin board does correspond to her intended vote.More specifically, we assume two elections authorities: the voting server and a registrar that acts only during the setup.Then BeleniosVS guarantees both privacy and verifiability against a dishonest voting device, provided that not both election authorities are corrupted.Additionally, our scheme guarantees receipt-freeness against an external adversary.We provide a formal proof of privacy, receipt-freeness, and verifiability using the tool ProVerif, covering a hundred cases of threat scenarios.Proving verifiability required to develop a set of sufficient conditions, that can be handled by ProVerif.This contribution is of independent interest.
Impact of the Metaverse on the Digital Future: People’s Perspective

The Metaverse has been attracting significant attention from around the globe in recent years. Although the metaverse is in its infancy, it has the potential to bring about a massive transformation. Many companies are already developing the hardware and software that will enable virtual interactions to become more widespread. As the idea of the metaverse grows, where people can not only communicate with others but also work in virtual environments, researchers are seriously considering how the metaverse might influence peoples daily lives. This study aimed to examine the perceptions of people towards the metaverse and propose the metaverse as a concept for everyday life based on a review of bibliometric studies on metaverse-related publications. An online survey was conducted on different social media platforms to get an overview of the views of people. A total of 220 people took part in the survey. Participants gave their opinions about the Metaverse and how it would benefit them. This study employs bibliometric data to describe the metaverse as a concept for everyday life from the perspective of experts and survey respondents. This study concludes that the metaverse is a virtual world loaded with possibilities. It can provide a more realistic experience than reality.