Reviewerly/exHarmony huggingface dataset:
Digital Marketing Based on Machine Learning

The scan hole assessment revealed that some care was given to the behavior of marketers in relation to ML devices and their selection and use to aid critical and operational management. Examination introduced spotlights the choice and reception of ML-driven scientific devices by three particular gatherings: showcasing offices, media organizations, and promoters. The discoveries feature are as - the significant job of canny explanatory instruments in the creation and arrangement of showcasing methodologies, the absence of information about developing innovations, for example, ML and man-made consciousness, the likely use of ML devices in advertising, and; 4) the low degree of reception and using ML-driven scientific devices in promoting the board. A system comprising empowering agents as well as a procedure map was created to assist associations with recognizing the chances and effectively executing ventures that are arranged toward the sending and appropriation of the scientific ML instruments in advanced promoting.
Mutation Tree Reconstruction of Tumor Cells on FPGAs Using a Bit-Level Matrix Representation

Reconstructing the mutation history of a cancer cell in the form of a phylogenetic tree from noisy genome sequencing data requires a likelihood model and a search or optimization strategy. The "Single Cell Inference of Tumor Evolution" (SCITE) software performs such a search via Monte Carlo Markov Chains that explore and evaluate different random update operations on a current candidate tree. In this work, we present an FPGA accelerator design (denoted as "bit-based SCITE" (bbSCITE)) that implements all required operations with deterministic latency as bit-operations on a matrix representation. The largest design updates and counts 36100 set bits in each clock cycle and thus outperforms the original single-threaded software reference by up to 80 ×.
CSA Based Radix-4 Gemmini Systolic Array for Machine Learning Applications

Systolic arrays are becoming the backbone of machine learning accelerators due to high computational parallelism and data re-usability. This paper presents a novel fully factored systolic array architecture: it extracts out the booth encoding logic which is common across rows and carry propagate adder which is common across columns of the array. It results in significant reduction in area, power and delay. We have demonstrated the proposed scheme in open source Gemmini systolic array. The proposed systolic array fully integrates into the Gemmini accelerator without effecting it's functionality. The proposed systolic array architecture achieves up to 54%, 8.9% and 42.5% reduction in area, delay and power respectively, as compared to baseline Gemmini systolic array. The ADP and PDP of the proposed SA improves by 58.2% and 47.6% respectively.
Fractional Kalman Type Filters for Estimating the State, Parameters, and Order of Fractional Dynamical System in Problems of Navigation Information Processing

The statement and solution of problem of estimating the state, parameters, and fractional order of fractional dynamical systems are given. Extended and unscented fractional Kalman filters are presented. Illustrative examples of using the extended and unscented fractional order Kalman filters are given.
AI-assisted Cyber Security Exercise Content Generation: Modeling a Cyber Conflict

A cyber conflict can be defined as a cyberattack or a series of attacks that target the critical functions of a country. Such attacks can potentially wreak havoc on government and civilian infrastructure and disrupt critical systems, resulting in damage to the state and even loss of life. National bodies are usually expected to run cyber crisis exercises to prevent such attacks and prepare for their impact. Developing risk scenarios that are both relevant and up to date with the current threat landscape is a critical element in the success of any cyber exercise, especially a cyber conflict scenario.Our work explores the results of applying machine learning to unstructured information sources to generate structured cyber exercise content in preparation for or during a destructive cyber conflict. We collected a dataset of publicly available cyber security articles and used them to assess future threats and as a skeleton for new exercise scenarios. We utilize named-entity recognition to structure the information based on a novel ontology. With the help of graph comparison methodologies, we match the generated scenarios to known threat actors’ tactics, techniques, and procedures and enrich the final scenario accordingly, with the help of synthetic text generators following our novel artificial-intelligence-assisted cyber exercise framework (AiCEF).Our framework has been evaluated on its efficiency and speed and can produce structured cyber exercise scenarios in real time, provided with incident descriptions in raw text format or a set of keywords. By deep diving into a pool of pre-tagged incidents, AiCEF can build exercise content from scratch, assisting inexperienced exercise planners in generating a scenario quicker and achieving a level of quality similar to an experienced planner or subject matter expert.We have assessed our methodology for relevance and preparedness by applying it to a real cyber conflict use case to model two categories of crisis management exercise scenarios: pre-conflict and post-conflict initiation. Thus, we assess whether the generated scenarios match the attack trends and the news feeds that were not used in training the AiCEF and prove that we can provide targeted and customized awareness of upcoming incidents.
Exploring ChatGPT’s impact on post-secondary education: A qualitative study

As Chat Generative Pre-trained Transformer (ChatGPT) gains traction, its impact on post-secondary education is increasingly being debated. This qualitative study explores the perception of students and faculty members at a research university in Canada regarding ChatGPT’s use in a post-secondary setting, focusing on how it could be incorporated and what ways instructors can respond to this technology. We present the summary of a discussion that took place in a two-hour focus group session with 40 participants from the computer science and engineering departments, and highlight issues surrounding plagiarism, assessment methods, and the appropriate use of ChatGPT. Findings suggest that students are likely to use ChatGPT, but there is a need for specific guidelines, more classroom assessments, and mandatory reporting of ChatGPT use. The study contributes to the emergent research on ChatGPT in higher education and emphasizes the importance of proactively addressing challenges and opportunities associated with ChatGPT adoption and use.
FL-PSO: A Federated Learning approach with Particle Swarm Optimization for Brain Stroke Prediction

Healthcare is one of the significant application areas of Cyber-Physical Systems, wherein massive amounts of sensors and other physical entities are interconnected to each other. Diagnosing and predicting diseases at an early stage is crucial for any healthcare application and machine-learning approaches are widely explored for the same. However, the conventional machine learning approaches can lead to the leakage of sensitive information pertaining to patients. In this study, our primary objective is to develop a machine learning based framework for early brain stroke prediction. Federated Learning (FL) is included in the framework to preserve the privacy of the patient’s data which is used as the basis for brain stroke prediction. The hyperparameters of FL are further optimized using Particle Swarm Optimization (PSO) to yield predictions with enhanced accuracy without compromising with data privacy. The experimental research showed that the suggested FL-PSO framework outperformed its competitors in terms of metrics like accuracy, validating the superiority of the framework.
Securing Serverless Workflows on the Cloud Edge Continuum

Serverless Computing is an emergent solution that helps deploy applications in the Cloud and sometimes on the Edge, reducing the integration time and the maintenance cost of the data centers. The lack of a standard for functions and the impossibility of connecting them together in complex workflows is currently holding back the growth of Function-as-a-Service (FaaS) use. In this scenario, OpenWolf tries to overcome these issues by implementing a solution to spread functions over the Cloud-Edge Continuum and connecting them using a standardized Domain-Specific Language (DSL) to describe a serverless based workflow. In this work, we aim to enhance the OpenWolf project, solving many security threats the engine suffers, like the authenticated and authorized execution of workflows and the injection of malicious functions inside a workflow. We will validate this new version of OpenWolf in a Smart City surveillance scenario, providing validation and performance tests.
Edge Computing Solutions Supporting Voice Recognition Services for Speakers with Dysarthria

In the framework of Automatic Speech Recognition (ASR), the synergism between edge computing and artificial intelligence has led to the development of intelligent objects that process and respond to human speech. This acts as a key enabler for multiple application scenarios, such as smart home automation, where the user’s voice is an interface for interacting with appliances and computer systems. However, for millions of speakers with dysarthria worldwide, such a voice interaction is impossible because nowadays ASR technologies are not robust to their atypical speech commands. So these people, who also live with severe motor disabilities, are unable to benefit from many voice assistant services that might support their everyday life. To cope with the above challenges, this paper proposes a deep learning approach to isolated word recognition in the presence of dysarthria conditions, along with the deployment of customized ASR models on machine learning powered edge computing nodes. In this way, we work toward a low-cost, portable solution with the potential to operate next to the user with a disability, e.g., in a wheelchair or beside a bed, in an always active mode. Finally, experiments show the goodness (in terms of word error rate) of our speech recognition solution in comparison with other studies on isolated word recognition for impaired speech.
Information-Theoretically Secure Multi-Party Linear Regression and Logistic Regression

When the data used in linear regression and logistic regression come from multiple participants, due to the requirements for data privacy, data holders need to complete calculation with other participants without exposing any information about their private data. In this paper, we present new protocols for privacy-preserving linear regression and logistic regression training based on Shamir’s secret sharing scheme. Our protocols can protect users from semi-honest and malicious adversaries with information-theoretic security. Additionally, the number of participants in our protocols can be flexibly modified to suit a variety of real-world application circumstances. We conduct experiments in settings with varying numbers of participants, and the results demonstrate that our protocols can successfully carry out the task of training for linear regression and logistic regression while providing the advantages of high security and flexibility.
ParaDiS: a Parallel and Distributed framework for Significant pattern mining

Mining patterns having a high association with a class label is a supervised data mining technique, frequently used in many applications. As we test many patterns using statistical tests to find all interesting patterns, a certain association is likely achieved by chance. The state-of-the-art TopKWY algorithm mines the top-k interesting patterns while controlling the family-wise-error rate (FWER) in the result set. TopKWY is a sequential algorithm that internally uses compute-intensive closed pattern mining. Moreover, it tests several patterns against thousands of permuted class labels to control FWER. To the best of our knowledge, no parallel/distributed implementation exists to address the scalability challenges faced by TopKWY. The tree formed by the explored patterns in TopKWY is inherently irregular and the search strategy used for exploration, namely, the best-first search is non-trivial to emulate in a distributed setup. This paper designs and implements ParaDiS, a novel parallel and distributed framework for mining the top-k statistically significant patterns. We compare its performance with the sequential TopKWY algorithm for real-world datasets and observe a significant reduction in execution time. We further show that our framework achieves good speedup, minimal communication overhead, and faster pruning of non-promising branches by efficient sharing of significance threshold.
Following all the rules: Intuitionistic completeness for generalized proof-theoretic validity

Abstract Prawitz conjectured that the proof-theoretically valid logic is intuitionistic logic. Recent work on proof-theoretic validity has disproven this. In fact, it has been shown that proof-theoretic validity is not even closed under substitution. In this paper, we make a minor modification to the definition of proof-theoretic validity found in Prawitz’s 1973paper ‘Towards a foundation of a general proof theory’ and refined by Schroeder-Heister in ‘Validity concepts in proof-theoretic semantics’ (2006). We will call the new notion generalized proof-theoretic validity and show that the logic of generalized proof-theoretic validity is intuitionistic logic.
Sea View Extension for Semantic Segmentation in Cityscapes

Semantic segmentation in computer vision is a challenging area of research, aiming to accurately segment and categorize objects and regions within an image. One widely used dataset for this task is Cityscapes, which contains a variety of city-related object classes such as cars, pedestrians, bicycles, and buildings. However, the Cityscapes dataset does not include any aquatic view classes, which limits its potential for applications in coastal and marine environments. This paper presents a novel approach to extend the Cityscapes dataset with aquatic classes to address this limitation. Our proposed method involves the implementation of two state-of-the-art neural network models, one based on the Cityscapes dataset and the other on a common aquatic dataset. We then selectively extract the aquatic segmen-tation results from the corresponding model according to the aquatic label. We further generate a mask image for the sea class and merge it precisely with the resulting image from the Cityscapes-based model. Our method is evaluated by comparing the performance of the original Cityscapes-based model with the extended Cityscapes-based model on a set of test images that contain aquatic views. The results show that our approach can maintain the original model’s high segmentation accuracy for all views except for aquatic areas while preserving the relevant parts of the marine model in terms of accuracy and area coverage. Additionally, our approach does not require retraining, thus saving computational resources and time.
SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval

In dense retrieval, prior work has largely improved retrieval effectiveness using multi-vector dense representations, exemplified by ColBERT. In sparse retrieval, more recent work, such as SPLADE, demonstrated that one can also learn sparse lexical representations to achieve comparable effectiveness while enjoying better interpretability. In this work, we combine the strengths of both the sparse and dense representations for first-stage retrieval. Specifically, we propose SparseEmbed - a novel retrieval model that learns sparse lexical representations with contextual embeddings. Compared with SPLADE, our model leverages the contextual embeddings to improve model expressiveness. Compared with ColBERT, our sparse representations are trained end-to-end to optimize both efficiency and effectiveness.
RewardTLG: Learning to Temporally Language Grounding from Flexible Reward

Given a textual sentence provided by a user, the Temporal Language Grounding (TLG) task is defined as the process of finding a semantically relevant video moment or clip from an untrimmed video. In recent years, localization-based TLG methods have been explored, which adopt reinforcement learning to locate a clip from the video. However, these methods are not stable enough due to the stochastic exploration mechanism of reinforcement learning, which is sensitive to the reward. Therefore, providing a more flexible and reasonable reward has become a focus of attention for both academia and industry.
TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks

The problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which measures the trustworthiness on edge signs for high-order relationships inferred by balance theory and corrects incorrect embedding propagation based on the trustworthiness. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.
SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction

Low-resource relation extraction (LRE) aims to extract potential relations from limited labeled corpus to handle the problem of scarcity of human annotations. Previous works mainly consist of two categories of methods: (1) Self-training methods, which improve themselves through the models' predictions, thus suffering from confirmation bias when the predictions are wrong. (2) Self-ensembling methods, which learn task-agnostic representations, therefore, generally do not work well for specific tasks. In our work, we propose a novel LRE architecture named SelfLRE, which leverages two complementary modules, one module uses self-training to obtain pseudo-labels for unlabeled data, and the other module uses self-ensembling learning to obtain the task-agnostic representations, and leverages the existing pseudo-labels to refine the better task-specific representations on unlabeled data. The two models are jointly trained through multi-task learning to iteratively improve the effect of LRE task. Experiments on three public datasets show that SelfLRE achieves 1.81% performance gain over the SOTA baseline. Source code is available at: https://github.com/THU-BPM/SelfLRE.
Uncertainty Quantification for Text Classification

This full-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and epistemic uncertainty for text classification models. Then, we describe several state-of-the-art approaches to uncertainty quantification and analyze their scalability to big text data: Virtual Ensemble in GBDT, Bayesian Deep Learning (including Deep Ensemble, Monte-Carlo Dropout, Bayes by Backprop, and their generalization Epistemic Neural Networks), Evidential Deep Learning (including Prior Networks and Posterior Networks), as well as Distance Awareness (including Spectral-normalized Neural Gaussian Process and Deep Deterministic Uncertainty). Next, we talk about the latest advances in uncertainty quantification for pre-trained language models (including asking language models to express their uncertainty, interpreting uncertainties of text classifiers built on large-scale language models, uncertainty estimation in text generation, calibration of language models, and calibration for in-context learning). After that, we discuss typical application scenarios of uncertainty quantification in text classification (including in-domain calibration, cross-domain robustness, and novel class detection). Finally, we list popular performance metrics for the evaluation of uncertainty quantification effectiveness in text classification. Practical hands-on examples/exercises are provided to the attendees for them to experiment with different uncertainty quantification methods on a few real-world text classification datasets such as CLINC150.
Complex Item Set Recommendation

In this tutorial, we aim to shed light on the task of recommending a set of multiple items at once. In this scenario, historical interaction data between users and items could also be in the form of a sequence of interactions with sets of items. Complex sets of items being recommended together occur in different and diverse domains, such as grocery shopping with so-called baskets and fashion set recommendation with a focus on outfits rather than individual clothing items. We describe the current landscape of research and expose our participants to real-world examples of item set recommendation. We further provide our audience with hands-on experience via a notebook session. Finally, we describe open challenges and call for further research in the area, which we hope will inspire both early stage and more experienced researchers.
Personalized Stock Recommendation with Investors' Attention and Contextual Information

The personalized stock recommendation is a task to recommend suitable stocks for each investor. The personalized recommendations are valuable, especially in investment decision making as the objective of building a portfolio varies by each retail investor. In this paper, we propose a Personalized Stock Recommendation with Investors' Attention and Contextual Information (PSRIC). PSRIC aims to incorporate investors' financial decision-making process into a stock recommendation, and it consists of an investor modeling module and a context module. The investor modeling module models the investor's attention toward various stock information. The context module incorporates stock dynamics and investor profiles. The result shows that the proposed model outperforms the baseline models and verifies the usefulness of both modules in ablation studies.
Analysis of Quantum Key Distribution Protocols

Essential role of Internet-based communication is the security of information. Classical Cryptography is based on mathematical models and computational complexity to compute the secret key. It does not provide enough security because finding a fast method to calculate the secret key will compromise the security of the system. Quantum Key Distribution is one of the most promising methods which provide unconditional security. Quantum Key Distribution (QKD) uses a quantum property in exchanging information called photons, as carriers of information in security systems, and QKD cannot separate from protocols BB84, B92 and BBM92. In this paper, we are testing the probability error comparison of BB84 protocol, B92 protocol, and BBM92 using QuVis simulator software. The results show that the B92 protocol is more relevant in detecting eavesdropping in lower error rates than the other two protocols.