Reviewerly/exHarmony huggingface dataset:
Learning Sparse Codes with Entropy-Based ELBOs

Standard probabilistic sparse coding assumes a Laplace prior, a linear mapping from latents to observables, and Gaussian observable distributions. We here derive a solely entropy-based learning objective for the parameters of standard sparse coding. The novel variational objective has the following features: (A) unlike MAP approximations, it uses non-trivial posterior approximations for probabilistic inference; (B) unlike for previous non-trivial approximations, the novel objective is fully analytical; and (C) the objective allows for a novel principled form of annealing. The objective is derived by first showing that the standard ELBO objective converges to a sum of entropies, which matches similar recent results for generative models with Gaussian priors. The conditions under which the ELBO becomes equal to entropies are then shown to have analytical solutions, which leads to the fully analytical objective. Numerical experiments are used to demonstrate the feasibility of learning with such entropy-based ELBOs. We investigate different posterior approximations including Gaussians with correlated latents and deep amortized approximations. Furthermore, we numerically investigate entropy-based annealing which results in improved learning. Our main contributions are theoretical, however, and they are twofold: (1) for non-trivial posterior approximations, we provide the (to the knowledge of the authors) first analytical ELBO objective for standard probabilistic sparse coding; and (2) we provide the first demonstration on how a recently shown convergence of the ELBO to entropy sums can be used for learning.
On the Illegality and Regulation of Algorithmic Price Discrimination in China&amp;#39;s Digital Economy

The legal issue of algorithmic price discrimination sparked by the in-depth use of big data and algorithm techniques has emerged as a significant concern in the development of China&amp;#39;s digital economy. Although Chinese law has implemented many regulations on the collection and protection of personal information, data security and governance, as well as on price discrimination, instances of algorithmic price discrimination have arisen in judicial practice. The legal issue surrounding algorithmic price discrimination has not yet been fully resolved. Legal studies in China on this issue mainly uses &amp;quot;big data killing&amp;quot; or &amp;quot;algorithmic price discrimination&amp;quot; to define it. Regarding the legal classification and regulation of algorithmic price discrimination: the Anti-Monopoly Law&amp;#39;s regulatory measures are limited from a competition law standpoint. Instead, Anti-Unfair Competition Law provides a more appropriate framework. As for civil law, the question of whether the algorithmic price discrimination qualifies as a civil tort still requires discussion; nevertheless, it satisfies all the constitutive elements of fraud in Chinese civil law.
FairXAI - A Taxonomy and Framework for Fairness and Explainability Synergy in Machine Learning

&lt;p&gt;Explainable Artificial Intelligence (XAI) and Fair Learning have made significant strides in various application domains, including criminal recidivism predictions, healthcare settings, toxic comment detection, automatic speech detection, recommendation systems, and image segmentation. However, these two fields have largely evolved independently. Recent studies have demonstrated that incorporating explanations into decision-making processes enhances the transparency and trustworthiness of AI systems. In light of this, our objective is to conduct a systematic review of FairXAI, which explores the interplay between fairness and explainability frameworks. To commence, we propose a taxonomy of FairXAI that utilizes XAI as a means to both mitigate and evaluate bias. This taxonomy will serve as a base for machine learning researchers operating in diverse domains. Additionally, we will undertake an extensive review of existing articles, taking into account factors such as the purpose of the interaction, target audience, and domain and context. Moreover, we outline an interaction framework for FairXAI considering various fairness perceptions and propose a FairXAI wheel that encompasses four core properties that must be verified and evaluated. This will serve as a practical tool for researchers and practitioners, ensuring the fairness and transparency of their AI systems. Furthermore, we will identify challenges and conflicts in the interactions between fairness and explainability, which could potentially pave the way for enhancing the responsibility of AI systems. As the inaugural review of its kind, we hope that this survey will inspire scholars to address these challenges by scrutinizing current research in their respective domains. &lt;/p&gt;
EnLem: An Ensemble Learning-based Model for Detecting Phishing Websites

&lt;p&gt;In this paper, there is a novel model based on ensemble learning for predicting phishing websites. The overall design is a combination of three individual machine-learning models with the help of the uni-variate feature selection model for detecting phishing and non-phishing URLs. The proposed model can minimize the error rate and provide improved accuracy with a short execution time. As compared with other traditional models, this model has higher performance metrics for detecting phishing websites compared with traditional machine learning and deep learning models. &lt;/p&gt;
INVESTIGATING THE USE OF BAHASA INDONESIA (INDONESIAN) IN EFL CLASSROOM

Using the first language in teaching English has always been controversial in teaching and learning English. In Indonesia, Bahasa Indonesia is also used in many English classes. This research aims to investigate the teacher and student purposes of using Bahasa Indonesia in EFL classrooms. This research used a qualitative design with interviews and observation instruments as the data collection techniques. Participants in this research were English teachers and students at one of the senior high schools in Majalengka. The findings show that there are many purposes for teachers and students to use of Indonesian in the classroom. As long as the lecturer or teacher uses L1 correctly it can support the teaching process.
Online sextortion: Characteristics of offences from a decade of community reporting

Online sextortion is an organised form of blackmail which can have a serious financial and traumatic impact on its victims. Responding to a dearth of evidence about this crime, this study analyses patterns within a large dataset of over 23,000 anonymous victim reports, collected via an online support community. Using common responses within these reports, this study identifies the most typical patterns of offending, including the profile assumed by offenders, the platforms through which the offence is initiated and enabled, payment methods and amounts demanded, and the national origins of most offences. Analysis shows that the mix of social media and dating platforms being used to approach and communicate with victims is changing over time, but the tactics employed by offenders are remarkably standardised. Payment demands involved in the crime were previously centralised in a few key service providers, but are increasingly diversifying. The variety of platforms involved in online sextortion points towards enforcement and safeguarding challenges, motivating an analysis of common risk factors that can inform the design of broadly-applicable countermeasures.
Parties, political finance, and governance in Africa: extracting money and shaping states in Benin and Ghana <b>Parties, political finance, and governance in Africa: extracting money and shaping states in Benin and Ghana</b> , by Rachel Sigman, Cambridge, Cambridge University Press, 2023, 310 pp., index, $110(paperback), ISBN 978-1-009-26283-5

"Parties, political finance, and governance in Africa: extracting money and shaping states in Benin and Ghana." Democratization, ahead-of-print(ahead-of-print), pp. 1–2
Prompt Enhance API Recommendation: Visualize the User’s Real Intention behind this Query

Abstract Developers frequently rely on APIs in their daily programming tasks, as APIs have become an indispensable tool for program development. However, with a vast number of open-source libraries available, selecting the appropriate API quickly can be a common challenge for programmers. Previous research on API recommendation primarily focused on designing better approaches to interpret user input. However, in practical applications, it is often difficult for users, especially novice programmers, to express their real intentions due to the limitations of language expression and programming capabilities.To address this issue, this paper introduces PTAPI, an approach that visualizes the user's real intentions based on their query to enhance recommendation performance. Firstly, PTAPI identifies the prompt template from Stack Overflow (SO) posts based on the user's input. Secondly, the obtained prompt template is combined with the user's input to generate a new question. Finally, the newly generated question leverages dual information sources from SO posts and API official documentation to provide recommendations.To evaluate the effectiveness of PTAPI, we conducted experiments at both the class-level and method-level. The experimental results demonstrate the effectiveness of the proposed approach, with a significant improvement in the success rate.
Unsupervised multi-source domain adaptation for person re-identification via sample weighting

The aim of unsupervised domain adaptation (UDA) in person re-identification (re-ID) is to develop a model that can identify the same individual across different cameras in the target domain, using labeled data from the source domain and unlabeled data from the target domain. However, existing UDA person re-ID methods typically assume a single source domain and a single target domain, and seldom consider the scenario of multiple source domains and a single target domain. In the latter scenario, differences in sample size between domains can lead to biased training of the model. To address this, we propose an unsupervised multi-source domain adaptation person re-ID method via sample weighting. Our approach utilizes multiple source domains to leverage valuable label information and balances the inter-domain sample imbalance through sample weighting. We also employ an adversarial learning method to align the domains. The experimental results, conducted on four datasets, demonstrate the effectiveness of our proposed method.
A Diamond-Based Autonomous Commodity Money for BRICS Countries and Global Public Use

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GeoKG'2022 Workshop Report: The 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs

The topic of knowledge graphs (KGs) has recently attracted extensive attention in both industry and academia. Knowledge graphs are a new paradigm for representing, retrieving, integrating, and reasoning data from highly heterogeneous and multimodal sources. For example, international conferences, such as the Knowledge Graph Conference, have emerged in the past years, not to mention the increasing number of specialized workshops on KGs co-located with major computer science conferences, including Knowledge Graph Workshop at KDD 2021, Workshop for Deep Learning in Knowledge Graphs at ISWC 2021, Workshop on Knowledge Graph Construction at ESWC 2021, to name but a few. Also, the number of KGs-related papers accepted at these major conferences, including SIGSPATIAL, is rapidly increasing. Meanwhile, we also witness the increasing popularity of knowledge graph technologies in geography, geoinformatics, and GIScience domains. There are growing numbers of knowledge graph-related manuscripts accepted to the top geospatial-related venues, such as the International Journal of Geographical Information Science, Transactions in GIS, the International Journal of Applied Earth Observation and Geoinformation, and so on. Transactions in GIS also held two special issues about knowledge graphs: 1) Symbolic and Subsymbolic GeoAI: Geospatial Knowledge Graphs and Spatially Explicit Machine Learning [4], and 2) Knowledge-based GIS (K-GIS): Theories, Techniques and Applications. In addition, government agencies, industries, and non-governmental organizations (NGOs) are also lifting resources on topics of exploring KGs to build up interdisciplinary science and applications. One example is NSF's Accelerate Convergence Program, which promotes the idea of building an Open Knowledge Network to harness the data revolution. Funded by this program, the KnowWhereGraph is presently among the largest geo-enabled knowledge graphs, which integrates 28 different data layers at the intersection between humans and their environment [2].
Construction and Application of 'Mechanical Manufacturing Technology' Course Resource Library

'Mechanical Manufacturing Technology' course is a compulsory course for mechanical design and manufacturing and its automation, mechanical and electronic engineering and other related majors. It takes mechanical manufacturing as the main line. The course content integrates a number of theoretical and practical knowledge, involving the processing and assembly of new materials, new processes and new equipment, the detection of mechanical parts, the types and selection of tools, and the selection of fixtures. The course is closely related to engineering practice and has certain challenges for teachers. In teaching, it is necessary to constantly update the course content to meet the needs of society and the times. Based on the teaching law and course outline of application-oriented undergraduate education, this paper proposes to establish a course resource library of mechanical design and manufacturing, so as to promote the deep integration of information technology and education and teaching, integrate existing course resources, take the actual work tasks of enterprises as the guide, carry out course development and design according to the project-based teaching mode, provide teachers and students with various multimedia teaching resources, cultivate students ' autonomous learning ability, and comprehensively improve the teaching quality.
PELATIHAN TIPS DAN TRIK MICROSOFT POWER POINT DI SALAM PATUK GUNUNGKIDUL

ABSTRACT&#x0D; The residents of the Salam RT 15 hamlet community in particular include residents of rural communities who are far from the district and provincial cities. Varied levels of education make people really need additional knowledge for insight in following current developments that are increasingly advanced with information technology.&#x0D; Microsoft Powerpoint is an application for presentations that is really needed by the residents of the Salam Rt 15 hamlet community in particular. The purpose of this activity is to increase people's knowledge and skills in using the Microsoft Powerpoint application&#x0D; The target of this activity is the management and residents of RT 15 in Padukuhan Salam, Gunungkidul. The method used is by conducting training and mentoring on the use of the Microsoft Powerpoint application. The result of this dedication is that there is an increase in knowledge and skills in using the Microsoft Powerpoint application, namely from the results of the pretest compared to the results of the posttest that were carried out showing that 7 people were declared good during the pretest, then after the posttest there were 15 people, 16 people were declared lacking during the pretest, so after the posttest they stayed to 11 people.
Proactively incremental-learning QAOA

Solving optimization problems with high performance is the target of existing works of Quantum Approximate Optimization Algorithm (QAOA). With this intention, we propose an advanced QAOA based on incremental learning, where the training trajectory is proactively segmented into incremental phases. Taking the MaxCut problem as our example, we randomly select a small subgraph from the whole graph and train the quantum circuit to get optimized parameters for the MaxCut of the subgraph in the first phase. Then in each subsequent incremental phase, a portion of the remaining nodes and edges are added to the current subgraph, and the circuit is retrained to get new optimized parameters. The above operation is repeated until the MaxCut problem on the whole graph is solved. The key point is that the optimized parameters of the previous phase will be reused in the initial parameters of the current phase. Numerous simulation experiments show our method has superior performance on Approximation Ratio (AR) and training time compared to prevalent works of QAOA. Specifically, the AR is higher than standard QAOA by 13.17% on weighted random graphs.
Complex Organ Mask Guided Radiology Report Generation

The goal of automatic report generation is to generate a clinically accurate and coherent phrase from a single given X-ray image, which could alleviate the workload of traditional radiology reporting. However, in a real-world scenario, radiologists frequently face the challenge of producing extensive reports derived from numerous medical images, thereby medical report generation from multi-image perspective is needed. In this paper, we propose the Complex Organ Mask Guided (termed as COMG) report generation model, which incorporates masks from multiple organs (e.g., bones, lungs, heart, and mediastinum), to provide more detailed information and guide the model's attention to these crucial body regions. Specifically, we leverage prior knowledge of the disease corresponding to each organ in the fusion process to enhance the disease identification phase during the report generation process. Additionally, cosine similarity loss is introduced as target function to ensure the convergence of cross-modal consistency and facilitate model optimization.Experimental results on two public datasets show that COMG achieves a 11.4% and 9.7% improvement in terms of BLEU@4 scores over the SOTA model KiUT on IU-Xray and MIMIC, respectively. The code is publicly available at https://github.com/GaryGuTC/COMG_model.
Zero-shot Bilingual App Reviews Mining with Large Language Models

App reviews from app stores are crucial for improving software requirements. A large number of valuable reviews are continually being posted, describing software problems and expected features. Effectively utilizing user reviews necessitates the extraction of relevant information, as well as their subsequent summarization. Due to the substantial volume of user reviews, manual analysis is arduous. Various approaches based on natural language processing (NLP) have been proposed for automatic user review mining. However, the majority of them requires a manually crafted dataset to train their models, which limits their usage in real-world scenarios. In this work, we propose Mini-BAR, a tool that integrates large language models (LLMs) to perform zero-shot mining of user reviews in both English and French. Specifically, Mini-BAR is designed to (i) classify the user reviews, (ii) cluster similar reviews together, (iii) generate an abstractive summary for each cluster and (iv) rank the user review clusters. To evaluate the performance of Mini-BAR, we created a dataset containing 6,000 English and 6,000 French annotated user reviews and conducted extensive experiments. Preliminary results demonstrate the effectiveness and efficiency of Mini-BAR in requirement engineering by analyzing bilingual app reviews. (Replication package containing the code, dataset, and experiment setups on https://github.com/Jl-wei/mini-bar )
Bus Factor Explorer

Bus factor (BF) is a metric that tracks knowledge distribution in a project. It is the minimal number of engineers that have to leave for a project to stall. Despite the fact that there are several algorithms for calculating the bus factor, only a few tools allow easy calculation of bus factor and convenient analysis of results for projects hosted on Git-based providers. We introduce Bus Factor Explorer, a web application that provides an interface and an API to compute, export, and explore the Bus Factor metric via treemap visualization, simulation mode, and chart editor. It supports repositories hosted on GitHub and enables functionality to search repositories in the interface and process many repositories at the same time. Our tool allows users to identify the files and subsystems at risk of stalling in the event of developer turnover by analyzing the VCS history. The application and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/bus-factor-explorer. The demonstration video can be found on YouTube: https://youtu.be/uIoV79N14z8
CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space

Deep neural networks (DNNs) have demonstrated their outperformance in various software systems, but also exhibit misbehavior and even result in irreversible disasters. Therefore, it is crucial to identify the misbehavior of DNN-based software and improve DNNs' quality. Test input prioritization is one of the most appealing ways to guarantee DNNs' quality, which prioritizes test inputs so that more bug-revealing inputs can be identified earlier with limited time and manual labeling efforts. However, the existing prioritization methods are still limited from three aspects: certifiability, effectiveness, and generalizability. To overcome the challenges, we propose CertPri, a test input prioritization technique designed based on a movement cost perspective of test inputs in DNNs' feature space. CertPri differs from previous works in three key aspects: (1) certifiable - it provides a formal robustness guarantee for the movement cost; (2) effective - it leverages formally guaranteed movement costs to identify malicious bug-revealing inputs; and (3) generic - it can be applied to various tasks, data, models, and scenarios. Extensive evaluations across 2 tasks (i.e., classification and regression), 6 data forms, 4 model structures, and 2 scenarios (i.e., white-box and black-box) demonstrate CertPri's superior performance. For instance, it significantly improves 53.97 % prioritization effectiveness on average compared with baselines. Its robustness and generalizability are 1.41~2.00 times and 1.33~3.39 times that of baselines on average, respectively. The code of CertPri is open-sourced at https://github.com/haibinzheng/CertPri.
An Empirical Study on Low- and High-Level Explanations of Deep Learning Misbehaviours

Background: Most quality assessment approaches for Deep Learning (DL) focus on finding misbehaviour-inducing inputs. However, it is difficult to clearly understand the causes of misbehaviours, due to the DL software opaqueness. Recent research proposed different techniques to explain DL misbehaviours, producing input explanations either at a “low level” (raw input elements) or at a “high level” (input features). Aims: We aim to compare the similarity between different explanations and assess to what extent they are understandable. Method: We have conducted an empirical study involving 3 state-of-the-art techniques for DL explanation in 13 configurations, applied to 2 different DL tasks. We have also collected answers from 48 questionnaires submitted to SE experts. Results: Low- and high-level techniques provide dissimilar explanations for the same inputs. However, experts deemed none of the explanations as useful in 28% of the cases. Conclusion: Despite the complementarity of existing explanations, further research is needed to produce better explanations.