Reviewerly/exHarmony huggingface dataset:
The Economics of Content Moderation: Theory and Experimental Evidence from Hate Speech on Twitter

The Economics of Content Moderation: Theory and Experimental Evidence from Hate Speech on Twitter Rafael Jimenez-Duran University of Chicago November 2022 New Working Paper Series No. #324 Abstract Social media platforms ban users and remove posts to moderate their content. This “speech policing" remains controversial because little is known about its consequences and the costs and benefits for different individuals. I conduct two pre-registered field experiments on Twitter to examine the effect of moderating hate speech on user behavior and welfare. Randomly reporting posts for violating the rules against hateful conduct increases the likelihood that Twitter removes them. Reporting does not affect the activity on the platform of the posts' authors or their likelihood of reposting hate, but it does increase the activity of those attacked by the posts. These results are consistent with a model in which content moderation is a quality decision for platforms that increases user engagement and hence advertising revenue. The second experiment shows that changing users' perceived content removal does not change their willingness to pause using social media, a measure of consumer surplus. My results imply that content moderation does not necessarily moderate users, but it can marginally increase advertising revenue. It can be consistent with both profit and welfare maximization as long as out-of-platform externalities are small.
Deep-Sentiment: An Effective Deep Sentiment Analysis Using a Decision-Based Recurrent Neural Network (D-RNN)

Sentiment analysis is a sub-domain in opinion mining that extracts sentiments from the users’ opinions from text messages. Opinions from E-commerce websites, blogs, online social media, etc., and these opinions are in the form of text, suggestions, and comments. This paper describes the new sentiment analysis model to predict sentiments effectively that can be used to improve product quality and sales. The proposed approach is an integrated model combining several techniques, such as the pre-trained model BERT-large-cased (BLC) for training the dataset. BLC model contains 24-layer, 1024-hidden, 16-heads, 340M parameters. Optimization algorithms can fine-tune pre-trained models, such as BERT, for sentiment analysis tasks. Fine-tuning involves training the pre-trained model on a specific sentiment analysis task to improve performance. Stochastic Gradient Descent (SGD) is the optimized algorithm that helps to analyze the sentiments effectively from the given datasets. The next step is the combination of pre-processing techniques such as Tokenization, Stop Word Removal, etc. The next step focused on Bag-of-Words (BoW) and word embedding techniques like Word2Vec used to extract the features from the datasets. The deep sentiment analysis (DSA) based classification is designed to classify the sentiments based on aspect and priority model to achieve better results. The proposed model combines Aspect and Priority-based Sentiment analysis with a Decision-based Recurrent Neural Network (D-RNN). The experiments are conducted using Twitter, Restaurant, and Laptop datasets available publicly on Kaggle—the proposed model’s performance is analyzed using a confusion matrix. The proposed approach addresses various challenges in analyzing the sentiment analysis. Python programming language with several libraries such as Keras, Pandas, and others extracts the sentiments from given datasets. The comparison between the existing and proposed models shows the effectiveness of the sentiment outputs.
Identifying and monitoring emerging blockchain technologies using patent analysis

The analysis of patent data is an important tool for identifying and monitoring trends in blockchain technology. Bibliometrics, citation analyses, supplemented with text mining, support patent data analyses. However, each of these provides specific information, so this study integrates them and provides more valuable information for monitoring blockchain technology as it emerges and identify changing trends in blockchain technology. Its results and methods are applicable to the work of expert research and development in blockchain technology.
A fast method of reionization parameter space exploration using GPR trained SCRIPT

ABSTRACT Efficient exploration of parameter spaces is crucial to extract physical information about the Epoch of Reionization from various observational probes. To this end, we propose a fast technique based on Gaussian process regression training applied to a semi-numerical photon-conserving reionization model, SCRIPT. Our approach takes advantage of the numerical convergence properties of SCRIPT and constructs a training set based on low-cost, coarse-resolution simulations. A likelihood emulator is then trained using this set to produce results in approximately two orders of magnitude less computational time than a full Markov Chain Monte Carlo (MCMC) run, while still generating reasonable 68 per cent and 95 per cent confidence contours. Furthermore, we conduct a forecasting study using simulated data to demonstrate the applicability of this technique. This method is particularly useful when full MCMC analysis is not feasible due to expensive likelihood computations.
Conserve the open media ecosystem! Legal and ethical considerations when using online repositories for <scp>AI</scp> training in ecological research

Ecological researchers who train Artificial Intelligence models using digital media have to be cognizant of legal and ethical implications when sourcing such content from online repositories. The way forward? Complying with Creative Commons licensing requirements, obtaining consent from media creators and adhering to FAIR data principles. Collective action from researchers, repositories, licence providers, and legislators is needed to conserve this complex open media ecosystem. This way, we can continue to develop innovative applications to address pressing ecological issues while maintaining the trust of content creators and respecting the legal and ethical framework of online media use.
Noise-reducing graph neural network with intent-target co-action for session-based recommendation

Session-based recommendation (SBR) originates from a real-world need to provide effective recommendation solutions for unlogged users. How to utilize short interaction sequences of anonymous users for practical recommendations has become a critical issue. Current research on SBR generally suffers from noisy terms interfering with prediction, difficulty in capturing long-term dependencies, and ineffective utilization of session-level information. Inspired by the outstanding learning ability of the sparse attention mechanism for distant information and reduced noise through sparse transformations, we propose an SBR model. We combine the graph neural network with sparse graph attention mechanisms to learn enhanced and denoised target information and long-term preferences. In addition, we propose a similar-intent collaboration module that can efficiently utilize session-level collaboration information. Combining the information, the model generates a reliable representation of the session. Our model is superior to the current SOTA model on three widely used datasets (Diginetica, Tmall, RetailRocket). Compared with the best baseline, we improved by 5.09%, 3.86%, and 5.45% with P@20 and by 12.63%, 22.78%, and 3.06% with MRR@20.
Self-Testing with Dishonest Parties and Device-Independent Entanglement Certification in Quantum Communication Networks

We consider the task of device-independent quantum state certification in a network where some of the nodes may collude and act dishonestly. We introduce the paradigm of self-testing with dishonest parties and provide a certification protocol for the Greenberger-Horne-Zeilinger state in this framework, together with robust statements about the fidelity of the shared state. We extend our results to the cluster scenario, where many subgroups of parties may collude. Our findings provide a new operational motivation for the strong definition of genuine multipartite nonlocality originally introduced by Svetlichny.
Making Changes in Webpages Discoverable: A Change-Text Search Interface for Web Archives

Webpages change over time, and web archives hold copies of historical versions of webpages. Users of web archives, such as journalists, want to find and view changes on webpages over time. However, the current search interfaces for web archives do not support this task. For the web archives that include a full-text search feature, multiple versions of the same webpage that match the search query are shown individually without enumerating changes, or are grouped together in a way that hides changes. We present a change text search engine that allows users to find changes in webpages. We describe the implementation of the search engine backend and frontend, including a tool that allows users to view the changes between two webpage versions in context as an animation. We evaluate the search engine with U.S. federal environmental webpages that changed between 2016 and 2020. The change text search results page can clearly show when terms and phrases were added or removed from webpages. The inverted index can also be queried to identify salient and frequently deleted terms in a corpus.
Deep supervised hashing for fast retrieval of radio image cubes

The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing algorithms have been shown to be efficient at image retrieval tasks in the fields of computer vision and multimedia. However, there are limited applications of these methodologies in the field of astronomy. In this work, we utilize deep hashing to rapidly search for similar images in a large database. The experiment uses a balanced dataset of 2708 samples consisting of four classes: Compact, FRI, FRII, and Bent. The performance of the method was evaluated using the mean average precision (mAP) metric where a precision of $88.5\%$ was achieved. The experimental results demonstrate the capability to search and retrieve similar radio images efficiently and at scale. The retrieval is based on the Hamming distance between the binary hash of the query image and those of the reference images in the database.
Poisoning Attack in Federated Learning Using Normalizing Flows

Federated learning can effectively solve the “isolated data island” problem but is vulnerable to poisoning attacks, mainly including backdoor attack and label-flipping attack. The backdoor attack needs to modify the input samples, which is impractical. The label-flipping attack is simple, and there is still room for improvement in the attack effect. Therefore, we propose a method to generate poisoned samples for the targeted attack using normalizing flows (SniperFlow) and apply it in the poisoning attacks in federated learning (NFPA). To the best of our knowledge, this is the first work that explores the use of normalizing flows in the poisoning Attack in federated learning. And the experiments prove that our method has outstanding attack performance, better than the label-flipping attack at the accuracy of the target task.
Yes but.. Can ChatGPT Identify Entities in Historical Documents?

Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents. For the last few months, the conversational agent ChatGPT has “prompted” a lot of interest in the scientific community and public due to its capacity of generating plausible-sounding answers. In this paper, we explore this ability by probing it in the named entity recognition and classification (NERC) task in primary sources (e.g., historical newspapers and classical commentaries) in a zero-shot manner and by comparing it with state-of-the-art LM-based systems. Our findings indicate several shortcomings in identifying entities in historical text that range from the consistency of entity annotation guidelines, entity complexity, and code-switching, to the specificity of prompting. Moreover, as expected, the inaccessibility of historical archives to the public (and thus on the Internet) also impacts its performance.
Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems

Abstract The continuous application of artificial intelligence (AI) technologies in online education has led to significant progress, especially in the field of Intelligent Tutoring Systems (ITS), online courses and learning management systems (LMS). An important research direction of the field is to provide students with customised learning trajectories via student modelling. Previous studies have shown that customisation of learning trajectories could effectively improve students’ learning experiences and outcomes. However, training an ITS that can customise students’ learning trajectories suffers from cold-start, time-consumption, human labour-intensity, and cost problems. One feasible approach is to simulate real students’ behaviour trajectories through algorithms, to generate data that could be used to train the ITS. Nonetheless, implementing high-accuracy student modelling methods that effectively address these issues remains an ongoing challenge. Traditional simulation methods, in particular, encounter difficulties in ensuring the quality and diversity of the generated data, thereby limiting their capacity to provide intelligent tutoring systems (ITS) with high-fidelity and diverse training data. We thus propose Sim-GAIL, a novel student modelling method based on generative adversarial imitation learning (GAIL). To the best of our knowledge, it is the first method using GAIL to address the challenge of lacking training data, resulting from the issues mentioned above. We analyse and compare the performance of Sim-GAIL with two traditional Reinforcement Learning-based and Imitation Learning-based methods using action distribution evaluation, cumulative reward evaluation, and offline-policy evaluation. The experiments demonstrate that our method outperforms traditional ones on most metrics. Moreover, we apply our method to a domain plagued by the cold-start problem, knowledge tracing (KT), and the results show that our novel method could effectively improve the KT model’s prediction accuracy in a cold-start scenario.
Focal surface projection: Extending projector depth of field using a phase‐only spatial light modulator

Abstract We present a focal surface projection to solve the narrow depth‐of‐field problem in projection mapping applications. We apply a phase‐only spatial light modulator to realize nonuniform focusing distances, whereby the projected contents appear focused on a surface with considerable depth variations. The feasibility of the proposed technique was validated through a physical experiment.
Cornflakes: Zero-Copy Serialization for Microsecond-Scale Networking

Data serialization is critical for many datacenter applications, but the memory copies required to move application data into packets are costly. Recent zero-copy APIs expose NIC scatter-gather capabilities, raising the possibility of offloading this data movement to the NIC. However, as the memory coordination required for scatter-gather adds bookkeeping overhead, scatter-gather is not always useful. We describe Cornflakes, a hybrid serialization library stack that uses scatter-gather for serialization when it improves performance and falls back to memory copies otherwise. We have implemented Cornflakes within a UDP and TCP networking stack, across Mellanox and Intel NICs. On a Twitter cache trace, Cornflakes achieves 15.4% higher throughput than prior software approaches on a custom key-value store and 8.8% higher throughput than Redis serialization within Redis.
RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks

In e-commerce, fake reviewers frequently post fake reviews to mislead consumers into making unwise shopping decisions, seriously affecting customers' benefits. Graph neural networks (GNNs) have been widely cultivated in fake reviewer detection in recent years, but they exhibit two main limitations. Firstly, most approaches model reviewer detection with a homogeneous graph, ignoring the interdependent clues among reviewers, reviews, and products, making them ineffective against stealthy fake reviewers. Secondly, few works noticed the camouflage behavior of fake reviewers, i.e., fake reviewers disguise themselves as authentic ones by establishing links with genuine reviewers, which extremely weakens the suspiciousness of fake reviewers and undermines detection performance. This paper proposes RHGNN, a robust fake reviewers detection framework based on Reinforced Heterogeneous Graph Neural Networks. Particularly, we first model fake reviewer detection with a heterogeneous graph consisting of reviews, reviewers, and products, to capture stealthy fraud clues by investigating their heterogeneous interactive relations. Subsequently, we present a reinforced neighbor selector (RNS) to sample and aggregate top−k informative neighbors while filtering out the camouflaged relationships. Finally, we propose a self-supervised heterogeneous graph embedding method based on mutual information to learn the discriminative features of fake reviewers. Experimental results show that RHGNN demonstrates promising and robust performance, especially in detecting well-disguised fake reviewers.
HEADSET: Human Emotion Awareness under Partial Occlusions Multimodal DataSET

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.
Toward Connecting Speech Acts and Search Actions in Conversational Search Tasks

Conversational search systems can improve user experience in digital libraries by facilitating a natural and intuitive way to interact with library content. However, most conversational search systems are limited to performing simple tasks and controlling smart devices. Therefore, there is a need for systems that can accurately understand the user's information requirements and perform the appropriate search activity. Prior research on intelligent systems suggested that it is possible to comprehend the functional aspect of discourse (search intent) by identifying the speech acts in user dialogues. In this work, we automatically identify the speech acts associated with spoken utterances and use them to predict the system-level search actions. First, we conducted a Wizard-of-Oz study to collect data from 75 search sessions. We performed thematic analysis to curate a gold standard dataset - containing 1,834 utterances and 509 system actions - of human-system interactions in three information-seeking scenarios. Next, we developed attention-based deep neural networks to understand natural language and predict speech acts. Then, the speech acts were fed to the model to predict the corresponding system-level search actions. We also annotated a second dataset to validate our results. For the two datasets, the best-performing classification model achieved maximum accuracy of 90.2% and 72.7% for speech act classification and 58.8% and 61.1 %, respectively, for search act classification.
Guidelines for instructional design of courses for the development of self-regulated learning for teachers

Self-regulated learning strategies are essential for learning, and to teach learners to use them, teachers must master them. The objective of this study was to identify training priorities for including these strategies in online courses for teachers, and to determine whether opinion influences the use of strategies. The design was non-experimental and cross-sectional, and involved administering the revised Cuestionario de Evaluación de las Estrategias de Aprendizaje de los Estudiantes Universitarios (CEVEAPEU questionnaire) to 285 teachers. The results allow us to differentiate between priority weaknesses, lower-priority weaknesses, and strengths in relation to metacognitive strategies (planning, objectives, self-evaluation, self-regulation, and context). To determine this, a frequency analysis was used, followed by a Mann-Whitney U (for 2-group variables) and Kruskal-Wallis (for 3 groups) test to determine whether opinion influences the use of strategies. Priority weaknesses were identified in the knowledge of objectives and evaluation criteria (planification); the ability to recognise when an exam has been passed (self-evaluation); modifying initial plans, dedicating more effort to difficult subjects, and learning new study techniques (self-regulation); and taking advantage of study time (context control). We concluded that opinions on self-regulated learning influence the use of strategies. Finally, work should be done on the recognition of the importance of self-regulated learning, time management strategies, self-evaluation techniques, flexibility, and self-control.
Wide field 3D optical profilometry using a diffraction Lloyd’s mirror interferometer

Interference fringe projection is used as a non-contact optical profilometry method for accurate 3D measurements. In interferometric fringe projection schemes, the maximum measurable size of the test object is limited by the optics of the interferometer. In this work, we report the application of a diffraction Lloyd's mirror interferometer (DLMI) as a wide-field sinusoidal fringe projection system for 3D shape measurement. The DLMI works on diffracted light and therefore generates interference fringes over a large area. This enables measurement of large objects using DLMI as compared to a conventional Lloyd's mirror interferometer. The performance of the proposed system is evaluated in terms of its stability and reproducibility of the results through measurement of the standard deviation in the phase values.