Reviewerly/exHarmony huggingface dataset:
CONSTRUCTING GAMBLING REALITY: A CRITICAL MULTIMODAL ANALYSIS ON ONLINE GAMBLING ADVERTISEMENTS

&lt;span&gt;This study aims to reveal the ideology contained in online gambling advertisements that appear on social media. The data for this qualitative descriptive research was three online gambling advertisements that were taken randomly on Instagram social media at the hashtag #judionline on September 18, 2022. The analysis process was carried out by analyzing the signs on the ads using a triangular semiotic theory framework by Charles S. Pierce. The findings showed that the three online gambling advertisements that appear on Instagram have similarities and differences in the use of the signs to construct the reality presented on online gambling advertisements. The continuity between the signs in online gambling advertisements creates an impression that online gambling is a fun, profitable and promising activity for the audience. The researcher suggests that readers who find online gambling advertisements on social media should be more careful and critical in reading the information raised by advertisements so that they can avoid things that can cause losses.&lt;/span&gt;
A Rule-Based Unstructured Information Extraction Model for Announcements of Listed Companies' Stock Increase or Decrease

With the development and application of Internet technology, a large amount of text information has become an important source of information for people [1]. However, the diversity of information sources and the complexity of expression make it one of the hotspots in current natural language processing research to extract valuable information efficiently and accurately from a large amount of text. In view of this problem, this paper takes the extraction of stock increase or decrease information as an example to study how to automatically extract unstructured information from the announcements of listed companies to help investors better obtain information and make wise investment decisions. This paper proposes a rule-based unstructured information extraction model called RUIE that combines web scraping technology and NLP technology to extract features from HTML documents, automatically identifying and extracting information related to stock increase or decrease. The model has strong practicality and promotion value and has broad application prospects in the field of investors and related research.
UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters

Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated. Consequently, the model fitting becomes less important. In this work, we focus on the concept of unimodality and propose a flexible cluster definition called locally unimodal cluster. A locally unimodal cluster extends for as long as unimodality is locally preserved across pairs of subclusters of the data. Then, we propose the UniForCE method for locally unimodal clustering. The method starts with an initial overclustering of the data and relies on the unimodality graph that connects subclusters forming unimodal pairs. Such pairs are identified using an appropriate statistical test. UniForCE identifies maximal locally unimodal clusters by computing a spanning forest in the unimodality graph. Experimental results on both real and synthetic datasets illustrate that the proposed methodology is particularly flexible and robust in discovering regular and highly complex cluster shapes. Most importantly, it automatically provides an adequate estimation of the number of clusters.
Uncertainty-based Fairness Measures

Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been addressed by the ML community with various measures of fairness that depend on the prediction outcomes of the ML models, either at the group level or the individual level. These fairness measures are limited in that they utilize point predictions, neglecting their variances, or uncertainties, making them susceptible to noise, missingness and shifts in data. In this paper, we first show that an ML model may appear to be fair with existing point-based fairness measures but biased against a demographic group in terms of prediction uncertainties. Then, we introduce new fairness measures based on different types of uncertainties, namely, aleatoric uncertainty and epistemic uncertainty. We demonstrate on many datasets that (i) our uncertainty-based measures are complementary to existing measures of fairness, and (ii) they provide more insights about the underlying issues leading to bias.
On the existence of MRD self-dual codes

In this paper, we investigate the existence of self-dual MRD codes $C\subset L^n$, where $L/F$ is an arbitrary field extension of degree $m\geq n$. We then apply our results to the case of finite fields, and prove that if $m=n$ and $F=\mathbb{F}_q$, a self-dual MRD code exists if and only if $q\equiv n\equiv 3 \ [4].$
Implicit Affordance Acquisition via Causal Action-Effect Modeling in the Video Domain

Affordance knowledge is a fundamental aspect of commonsense knowledge. Recent findings indicate that world knowledge emerges through large-scale self-supervised pretraining, motivating our exploration of acquiring affordance knowledge from the visual domain. To this end, we augment an existing instructional video resource to create the new Causal Action-Effect (CAE) dataset and design two novel pretraining tasks -- Masked Action Modeling (MAM) and Masked Effect Modeling (MEM) -- promoting the acquisition of two affordance properties in models: behavior and entity equivalence, respectively. We empirically demonstrate the effectiveness of our proposed methods in learning affordance properties. Furthermore, we show that a model pretrained on both tasks outperforms a strong image-based visual-linguistic foundation model (FLAVA) as well as pure linguistic models on a zero-shot physical reasoning probing task.
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing

Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models, yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbone of the diffusion model, we delve into the role of skip connection in U-Net and reveal that hierarchical features aggregating long-distance information across encoder and decoder make a significant impact on the content and quality of image generation. Based on the observation, we propose an efficient generative tuning framework, dubbed SCEdit, which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner. Furthermore, the proposed framework allows for straightforward extension to controllable image synthesis by injecting different conditions with Controllable SC-Tuner, simplifying and unifying the network design for multi-condition inputs. Our SCEdit substantially reduces training parameters, memory usage, and computational expense due to its lightweight tuners, with backward propagation only passing to the decoder blocks. Extensive experiments conducted on text-to-image generation and controllable image synthesis tasks demonstrate the superiority of our method in terms of efficiency and performance. Project page: \url{https://scedit.github.io/}
Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure

We present counting reward automata-a finite state machine variant capable of modelling any reward function expressible as a formal language. Unlike previous approaches, which are limited to the expression of tasks as regular languages, our framework allows for tasks described by unrestricted grammars. We prove that an agent equipped with such an abstract machine is able to solve a larger set of tasks than those utilising current approaches. We show that this increase in expressive power does not come at the cost of increased automaton complexity. A selection of learning algorithms are presented which exploit automaton structure to improve sample efficiency. We show that the state machines required in our formulation can be specified from natural language task descriptions using large language models. Empirical results demonstrate that our method outperforms competing approaches in terms of sample efficiency, automaton complexity, and task completion.
Learning Temporal Properties is NP-hard

We investigate the complexity of LTL learning, which consists in deciding given a finite set of positive ultimately periodic words, a finite set of negative ultimately periodic words, and a bound B given in unary, if there is an LTL-formula of size less than or equal to B that all positive words satisfy and that all negative violate. We prove that this decision problem is NP-hard. We then use this result to show that CTL learning is also NP-hard. CTL learning is similar to LTL learning except that words are replaced by finite Kripke structures and we look for the existence of CTL formulae.
DeRDaVa: Deletion-Robust Data Valuation for Machine Learning

Data valuation is concerned with determining a fair valuation of data from data sources to compensate them or to identify training examples that are the most or least useful for predictions. With the rising interest in personal data ownership and data protection regulations, model owners will likely have to fulfil more data deletion requests. This raises issues that have not been addressed by existing works: Are the data valuation scores still fair with deletions? Must the scores be expensively recomputed? The answer is no. To avoid recomputations, we propose using our data valuation framework DeRDaVa upfront for valuing each data source's contribution to preserving robust model performance after anticipated data deletions. DeRDaVa can be efficiently approximated and will assign higher values to data that are more useful or less likely to be deleted. We further generalize DeRDaVa to Risk-DeRDaVa to cater to risk-averse/seeking model owners who are concerned with the worst/best-cases model utility. We also empirically demonstrate the practicality of our solutions.
Make It Make Sense! Understanding and Facilitating Sensemaking in Computational Notebooks

Reusing and making sense of other scientists' computational notebooks. However, making sense of existing notebooks is a struggle, as these reference notebooks are often exploratory, have messy structures, include multiple alternatives, and have little explanation. To help mitigate these issues, we developed a catalog of cognitive tasks associated with the sensemaking process. Utilizing this catalog, we introduce Porpoise: an interactive overlay on computational notebooks. Porpoise integrates computational notebook features with digital design, grouping cells into labeled sections that can be expanded, collapsed, or annotated for improved sensemaking. We investigated data scientists' needs with unfamiliar computational notebooks and investigated the impact of Porpoise adaptations on their comprehension process. Our counterbalanced study with 24 data scientists found Porpoise enhanced code comprehension, making the experience more akin to reading a book, with one participant describing it as It's really like reading a book.
Towards Cognitive Load Assessment Using Electrooculography Measures

Cognitive load assessment is crucial for understanding human performance in various domains. This study investigates the impact of different task conditions and time constraints on cognitive load using multiple measures, including subjective evaluations, performance metrics, and physiological eye-tracking data. Fifteen participants completed a series of primary and secondary tasks with different time limits. The NASA-TLX questionnaire, reaction time, inverse efficiency score, and eye-related features (blink, saccade, and fixation frequency) were utilized to assess cognitive load. The study results show significant differences in the level of cognitive load required for different tasks and when under time constraints. The study also found that there was a positive correlation (r = 0.331, p = 0.014) between how often participants blinked their eyes and the level of cognitive load required but a negative correlation (r = -0.290, p = 0.032) between how often participants made quick eye movements (saccades) and the level of cognitive load required. Additionally, the analysis revealed a significant negative correlation (r = -0.347, p = 0.009) and (r = -0.370, p = 0.005) between fixation and saccade frequencies under time constraints.
GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis

We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video. Building upon the efficiency of Gaussian splatting, our approach extends the representation to accommodate dynamic elements via a deformable set of Gaussians residing in a canonical space, and a time-dependent deformation field defined by a multi-layer perceptron (MLP). Moreover, under the assumption that most natural scenes have large regions that remain static, we allow the MLP to focus its representational power by additionally including a static Gaussian point cloud. The concatenated dynamic and static point clouds form the input for the Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Our method achieves results that are comparable to state-of-the-art dynamic neural radiance field methods while allowing much faster optimization and rendering. Project website: https://lynl7130.github.io/gaufre/index.html
Hypergraph Transformer for Semi-Supervised Classification

Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, delivering remarkable performance across various tasks, e.g., hypergraph node classification. However, these models struggle to capture global structural information due to their reliance on local message passing. To address this challenge, we propose a novel hypergraph learning framework, HyperGraph Transformer (HyperGT). HyperGT uses a Transformer-based neural network architecture to effectively consider global correlations among all nodes and hyperedges. To incorporate local structural information, HyperGT has two distinct designs: i) a positional encoding based on the hypergraph incidence matrix, offering valuable insights into node-node and hyperedge-hyperedge interactions; and ii) a hypergraph structure regularization in the loss function, capturing connectivities between nodes and hyperedges. Through these designs, HyperGT achieves comprehensive hypergraph representation learning by effectively incorporating global interactions while preserving local connectivity patterns. Extensive experiments conducted on real-world hypergraph node classification tasks showcase that HyperGT consistently outperforms existing methods, establishing new state-of-the-art benchmarks. Ablation studies affirm the effectiveness of the individual designs of our model.
zk-DASTARK: A quantum-resistant, data authentication and zero-knowledge proof scheme for protecting data feed to smart contracts

With the emergence of blockchain and smart contracts, traditional digital applications 1&#x0D; such as identity management, supply chain management, banking and finance etc. are expected 2&#x0D; to be transformed into Decentralized Applications (DApps). Blockchain interoperability is a new 3&#x0D; and exciting aspect of blockchain technology that is quickly gaining popularity in many industries. 4&#x0D; However, the widespread use of blockchains has not yet been achieved because DApps running on the 5&#x0D; blockchain using smart contracts require access to authentic off-chain data. Users are more conscious 6&#x0D; of their personal data privacy and consequently are more reluctant to share their data, posing a 7&#x0D; challenge in the adoption of DApps. To solve this challenge, we present a novel quantum-resistant, 8&#x0D; data authentication and zero-knowledge proof scheme named zk-DASTARK. Based on our proposed 9&#x0D; scheme, we present a novel framework which can be used to feed authenticated off-chain data to 10&#x0D; DApps without compromising user data privacy. Our proposed framework is quite efficient against 11&#x0D; well-known off-chain data authentication frameworks. The proposed framework is implemented on 12&#x0D; state-of-the-art quantum-resistant IOTA Blockchain and is highly efficient as it takes approximately 13&#x0D; 60 ms to generate a proof and approximately 10 ms to verify a proof.
Language as a Moderating Factor in Cyber-Safety Awareness Among School Learners in South Africa: Systematic Literature Review (SLR)

The internet has become an integral part of everyone’s daily activities, both professionally and socially. Cyberattacks and risks have increased due to internet usage. Schoolchildren are targeted since they lack awareness about cyberattacks or internet safety. It is important to educate learners on how to stay safe while using internet platforms. This article identified socio-demographic and operational factors affecting cyber-safety awareness in South African schools. This study examined how teachers implement the school's language policy and use language in instruction. Language is the main factor affecting school cyber-safety knowledge Methodologically, the PRISMA guideline for Systematic literature review (SLR) search was used. SLR is a comprehensive method of examining literature that identifies, evaluates, and synthesises all relevant research. SLRs outperform narrative or scoping reviews and are the gold standard for knowledge synthesis. The keywords were searched using standardized search strategies from 6 databases which includes Elsevier, Scopus, ScienceDirect, IEEE Xplore, ACM, Springer, and Web of Science. From the databases, 118 articles published between 2014 to 2023 were retrieved and 47 articles were included for this study. The findings reveal that schools apply language policies differently depending on their preferences. This study also shows gaps in knowledge of cyber-safety and language learning and instruction's mediating role, suggesting further research in several areas. It is evident that language does play a significant role in raising cyber-safety awareness in South Africa.
PENINGKATAN PENGETAHUAN SISWA SD TENTANG MANFAAT BUAH DAN SAYUR DENGAN MENGGUNAKAN MEDIA LEAFLET

Children in Indonesia have a habit of consuming very low fruits and vegetables. Result of Riskesdas in 2018 shows that overall the population of age &gt; 5 years lacked 95,5% of the fruit and vegetables. The aim of this research is to determine the effect of Leaflet media education on increasing knowledge about the benefits of consuming fruit and vegetables in primary school children. This study was a Pre-Experimental Design With One Group Pretest-Posttest Design Carried Out at the Inpres Sudiang Makassar. The samle size in this study was 97 people taken by the total sampling. Variables in the study were knowledge of primary school student before being given an intervention in the form of education through leaflet media and knowledge after intervention. The effect of variables using questionnaires was tested by using the wilcoxon test. The results showed that in SD Inpres Sudiang Makassar, the age was generally 60.8%, the class was generally 27.8%, and the gender was generally 25.8%. The father's occupation was generally private employee (29.9%), while the mother's occupation was generally a housewife (77.3%). The level of knowledge before education was generally good (85.6%), while the level of knowledge after education was generally good (100%). Statistical analysis showed a difference in nutritional knowledge before and after the provision of media leaflet education (p = 0.000). It is recommended for further research, it is expected to be able to develop educational media in accordance with the development of technology so thah it supports interest and reading interest and reading interesr for respondents.
Aspect-Level Sentiment Analysis Based on Knowledge Graph and Recurrent Attention Network

In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a bidirectional long short-term memory network (Bi-LSTM) with a knowledge graph's synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. This data is then analyzed using a multi-layer gated recurrent unit (GRU) to pinpoint sentiment characteristics related to specific aspect terms. Tests on three widely available datasets demonstrate this method's superior performance in sentiment classification.
Deep Metric Learning for Computer Vision: A Brief Overview

Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised deep-learning applications, these methods tend to be less adequate when there is large intra-class variance and low inter-class variance in input data distribution. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples by learning a representation function that maps these data samples into a representative embedding space. It leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminative embedding space even for distributions having low inter-class and high intra-class variances. In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches.
Quantum Factoring Algorithm using Grover Search

We present a quantum algorithm for factoring products of prime numbers which exploits Grover search to factor any $n$-bit biprime using $2n-5$ qubits or less. The algorithm doesn't depend on any properties of the number to be factored, has guaranteed convergence, and doesn't require complex classical pre or post-processing. Large scale simulations confirm a success probability asymptotically reaching 100% for $>800$ random biprimes with $5\leq n\leq 35$ (corresponding to $5 - 65$ qubits) with the largest being $30398263859 = 7393\times 4111763$. We also present a variant of the algorithm based on digital adiabatic quantum computing and show that Grover based factorization requires quadratically fewer iteration steps in most cases.