Reviewerly/exHarmony huggingface dataset:
Deceptive Reviews Detection in E-Commerce Websites Using Machine Learning

Online reviews influence the buying decision of customers. Thus, false reviews can hamper sales of a product because a potential buyer would not buy it upon seeing negative reviews. Detection of these spam or fake reviews are necessary for the good of the users. A number of methods were used in the past for spam review detection; some used methods like manual labelling of a small dataset and then using it as base to check authenticity of other reviews, but this is not considered a viable option considering the amount of manual work required. This problem occurs due to the scarcity of clearly labelled datasets for spam/not spam reviews. The problem seems synonymous to the problem of detecting fake news on social media sites but it is not because there is no ground truth for fact checking. A very important fact about spam reviews is that they bear similarity and are full of adjectives because a spammer writes reviews mainly with the intent of either defaming a product or rating it higher than the actuality. Review rating and verified purchase are features that play a vital impact in detection of the fakeness of a review. So here we try to categorize customer reviews into fake and not fake on the basis of review-centric feature so that a reviewer may know whether a review they are reading is a potential spam. Various review-centric features include the rating of the review, product with which the review is related and the authenticity of the review.
CN-Motifs Perceptive Graph Neural Networks

Graph neural networks (GNNs) have been the dominant approaches for graph representation learning. However, most GNNs are applied to homophily graphs and perform poorly on heterophily graphs. Meanwhile, these GNNs fail to directly capture long-range dependencies and complex interactions between 1-hop neighbors when generating node representations by iteratively aggregating directly connected neighbors. In addition, structural patterns, such as motifs which have been established as building blocks for graph structure, contain rich topological and semantical information and are worth studying further. In this paper, we introduce the common-neighbors based motifs, which we called CN-motifs, to generalize and enrich the definition of structural patterns. We group the 1-hop neighbors and construct a high-order graph according to CN-motifs, and propose CN-motifs Perceptive Graph Neural Networks (CNMPGNN), a novel framework which can effectively resolve problems mentioned above. Notably, by making full use of structural patterns, our model achieves the state-of-the-art results on several homophily and heterophily datasets.
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

Contrastive self-supervised learning (CSL) leverages unlabeled data to train models that provide instance-discriminative visual representations uniformly scattered in the feature space. In deployment, the common practice is to directly fine-tune models with the cross-entropy loss, which however may not be an optimal strategy. Although cross-entropy tends to separate inter-class features, the resulted models still have limited capability of reducing intra-class feature scattering that inherits from pre-training, and thus may suffer unsatisfactory performance on downstream tasks. In this paper, we investigate whether applying contrastive learning to fine-tuning would bring further benefits, and analytically find that optimizing the supervised contrastive loss benefits both class-discriminative representation learning and model optimization during fine-tuning. Inspired by these findings, we propose Contrast-regularized tuning (Core-tuning), a novel approach for fine-tuning contrastive self-supervised visual models. Instead of simply adding the contrastive loss to the objective of fine-tuning, Core-tuning also generates hard sample pairs for more effective contrastive learning through a novel feature mixup strategy, as well as improves the generalizability of the model by smoothing the decision boundary via mixed samples. Extensive experiments on image classification and semantic segmentation verify the effectiveness of Core-tuning.
Attitudes towards robots as transformational agents in tourism and hospitality

Robots are poised to radically transform the way industries provide services to customers. Tourism and hospitality companies adopt robots to attract guests, decrease costs, fill in job positions, and for other reasons. For the introduction of robots to be successful, they need not only to be a financially feasible investment for companies, but they also need to be accepted by the customers and the employees. This chapter adopts a demand-side perspective and presents some of the findings of the first global survey on attitudes towards robots in tourism, based on a sample of 1,273 respondents from 103 countries and territories. Through cluster analysis, this chapter identifies the characteristics of different identifiable groups of respondents (robophobes and robophiles) based on their attitudes towards robots. The data show that females are more likely to be robophobes than males. Furthermore, the chapter analyses how robophiles and robophobes perceive the robot application in various tourism/hospitality tasks (check-in/out, food preparation and delivery, cleaning, entertainment, etc.) and their willingness to pay for robot-delivered services. The findings show that robophobes have a significantly more pessimistic view upon robot delivery of services, regardless of the service asked about and that robophobes request a price discount for such services. Managerial implications, limitations, and future research directions are discussed as well with regard to customer resistance to the robot revolution in tourism and hospitality.
Entity-Based Knowledge Conflicts in Question Answering

Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over-reliance on memorized information (the cause of hallucinations), and uncover important factors that exacerbate this behaviour. Lastly, we propose a simple method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4% - 7%. Our findings demonstrate the importance for practitioners to evaluate model tendency to hallucinate rather than read, and show that our mitigation strategy encourages generalization to evolving information (i.e. time-dependent queries). To encourage these practices, we have released our framework for generating knowledge conflicts.
ReAct: Out-of-distribution Detection With Rectified Activations

Out-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks. One of the primary challenges is that models often produce highly confident predictions on OOD data, which undermines the driving principle in OOD detection that the model should only be confident about in-distribution samples. In this work, we propose ReAct--a simple and effective technique for reducing model overconfidence on OOD data. Our method is motivated by novel analysis on internal activations of neural networks, which displays highly distinctive signature patterns for OOD distributions. Our method can generalize effectively to different network architectures and different OOD detection scores. We empirically demonstrate that ReAct achieves competitive detection performance on a comprehensive suite of benchmark datasets, and give theoretical explication for our method's efficacy. On the ImageNet benchmark, ReAct reduces the false positive rate (FPR95) by 25.05% compared to the previous best method.
Influence of Different Representation of Requirements on Idea Generation: An Experimental Study

Abstract The objective of this research is to understand how different representations of requirements influence idea generation in terms of quantity, addressment, sketch detail, novelty, and variety of conceptual sketches. Requirements are statements of need, desires, and wishes of the stakeholders that are used by engineers to frame the problem. Essentially, requirements are the raison d’etre for any engineering project. As the requirements document provides constraints and criteria for a design, it defines and determines the success of a project. While there is research studying the effect of requirements on the conceptual sketch, little study has focused one the impact of different requirement representations on solution development. An experimental study was conducted with 52 fourth year mechanical engineering undergraduate students. Two design problems were formulated with three different representations: a problem statement with embedded requirements, a problem statement and a traditional requirement list, and a problem statement with contextualized scrum stories. Each student was provided each design problems with two different representations of requirements. It was found that the use of contextualized scrum story representations significantly affected the conceptual sketch in the novelty of solution fragments and addressment of requirements, while no significant change in variety, sketch detail, and quantity was seen. Also, the contextualized representation positively affected all metrics but the sketch quantity. Finally, it was found that quantity is not directly related to the number of requirements addressed in the sketches.
Tractable Regularization of Probabilistic Circuits

Probabilistic Circuits (PCs) are a promising avenue for probabilistic modeling. They combine advantages of probabilistic graphical models (PGMs) with those of neural networks (NNs). Crucially, however, they are tractable probabilistic models, supporting efficient and exact computation of many probabilistic inference queries, such as marginals and MAP. Further, since PCs are structured computation graphs, they can take advantage of deep-learning-style parameter updates, which greatly improves their scalability. However, this innovation also makes PCs prone to overfitting, which has been observed in many standard benchmarks. Despite the existence of abundant regularization techniques for both PGMs and NNs, they are not effective enough when applied to PCs. Instead, we re-think regularization for PCs and propose two intuitive techniques, data softening and entropy regularization, that both take advantage of PCs' tractability and still have an efficient implementation as a computation graph. Specifically, data softening provides a principled way to add uncertainty in datasets in closed form, which implicitly regularizes PC parameters. To learn parameters from a softened dataset, PCs only need linear time by virtue of their tractability. In entropy regularization, the exact entropy of the distribution encoded by a PC can be regularized directly, which is again infeasible for most other density estimation models. We show that both methods consistently improve the generalization performance of a wide variety of PCs. Moreover, when paired with a simple PC structure, we achieved state-of-the-art results on 10 out of 20 standard discrete density estimation benchmarks.
Editing Factual Knowledge in Language Models.

The factual knowledge acquired during pre-training and stored in the parameters of Language Models (LMs) can be useful in downstream tasks (e.g., question answering or textual inference). However, some facts can be incorrectly induced or become obsolete over time. We present KnowledgeEditor, a method which can be used to edit this knowledge and, thus, fix ‘bugs’ or unexpected predictions without the need for expensive re-training or fine-tuning. Besides being computationally efficient, KnowledgeEditordoes not require any modifications in LM pre-training (e.g., the use of meta-learning). In our approach, we train a hyper-network with constrained optimization to modify a fact without affecting the rest of the knowledge; the trained hyper-network is then used to predict the weight update at test time. We show KnowledgeEditor’s efficacy with two popular architectures and knowledge-intensive tasks: i) a BERT model fine-tuned for fact-checking, and ii) a sequence-to-sequence BART model for question answering. With our method, changing a prediction on the specific wording of a query tends to result in a consistent change in predictions also for its paraphrases. We show that this can be further encouraged by exploiting (e.g., automatically-generated) paraphrases during training. Interestingly, our hyper-network can be regarded as a ‘probe’ revealing which components need to be changed to manipulate factual knowledge; our analysis shows that the updates tend to be concentrated on a small subset of components. Source code available at https://github.com/nicola-decao/KnowledgeEditor
SkillCrest: A Skill Assessment System Using Deep Knowledge Tracing and User Feedback Sentiment Analysis

Skill Development on individuals holds an important role in the progression of a developing country. With the advancement of Artificial Intelligence, tutoring systems are becoming more automated. Especially during these days of the COVID-19 pandemic, there has been a sudden rise in demand for online tutoring services. Our work helps the user to track their skill using the Deep Knowledge Tracing System. It eases the assessment of a student with accurate results giving emphasis only on the skill development factor. This paper proposes a skill assessment system which adopts a hybrid model that consists of deep knowledge tracing and user feedback sentiment analysis. DKT is an algorithm that uses the Recurrent Neural Networks to track the improvement of each individual on a particular skill. The project also delivers the findings on a user level, by the creation of a website named “SkillCrest”. This website shows the primary information on skill development and helps the tutors to identify which skill a student lacks using DKT. This feature can also be used for self-assessment of a student. We also incorporated a sentiment analysis component in our website to analyze the feedback given by each individual. The implementation of sentiment analysis is proposed to be done using the Recurrent Neural Networks. This result is used further to retrain the model.
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers

We introduce SelfExplain, a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label. Experiments across five text-classification datasets show that SelfExplain facilitates interpretability without sacrificing performance. Most importantly, explanations from SelfExplain show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines.
A decay-based account of learning and adaptation in complex skills.

How do humans adapt to parametric changes in a task without having to learn a new skill from scratch? Many studies of memory and sensorimotor adaptation have proposed theories that incorporate a decay on prior events, which leads the agent to eventually forget old experiences. This study investigates if a similar decay mechanism can account for human adaptation in complex skills that require the simultaneous integration of cognitive, motor, and perceptual processes. In 2 experiments, subjects learned to play a novel racing video game while adapting to parametric changes in the physics of the game's controls. Human learning and performance were modeled using the ACT-R cognitive architecture, which has been used successfully to model learning and fluency across a wide range of skills in prior research. Anderson et al. (2019) introduced the Controller module, a new component of the architecture that learns the setting of control parameters for actions and allows the agent to execute the rapid and precise actions that are necessary for good performance on complex tasks. Model simulations support including a moderate time-based decay on the weight of the experiences that the Controller uses. This is implemented in the Controller module by discounting the influence of older observations which helps the agent to focus on recent experiences that better reflect the current relationship between different settings of a control parameter and the rate of payoff from using that setting. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML

Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evaluate hyperparameter configurations. As a result, a series of recent related works have focused on the direction of transfer learning for quickly fine-tuning hyperparameters on a dataset. Unfortunately, the community does not have a common large-scale benchmark for comparing HPO algorithms. Instead, the de facto practice consists of empirical protocols on arbitrary small-scale meta-datasets that vary inconsistently across publications, making reproducibility a challenge. To resolve this major bottleneck and enable a fair and fast comparison of black-box HPO methods on a level playing field, we propose HPO-B, a new large-scale benchmark in the form of a collection of meta-datasets. Our benchmark is assembled and preprocessed from the OpenML repository and consists of 176 search spaces (algorithms) evaluated sparsely on 196 datasets with a total of 6.4 million hyperparameter evaluations. For ensuring reproducibility on our benchmark, we detail explicit experimental protocols, splits, and evaluation measures for comparing methods for both non-transfer, as well as, transfer learning HPO.
The Comparative Analysis of Illustrations and Questions of Elementary School Experimental Observation Textbooks :Focused on 2009 and 2015 Revised Curriculum

This study compared and analyzed the types and roles of illustrations, and the s in the questions in the experimental observation textbooks for 3rd, 4th, 5th, and 6th graders of the 2009 and 2015 revised curriculum. This study provide basic data that can effectively utilize the textbooks of the 2015 revised curriculum by presenting the changes, trends of the textbooks according to the revision of the curriculum and suggestions to help develop the experimental observation textbooks of the revised curriculum in the future. The results of comparing and analyzing experimental observation textbooks for 3rd, 4th, 5th, and 6th graders of the 2009 revised curriculum and 2015 revised curriculum are as follows. First, the proportion of photographs in the types of illustrations is the highest at 63.6% in the 2009 revised curriculum and 58.5% in the 2015 revised curriculum, which is to give students a more realistic scientific experience through high objectivity and clarity. Second, the proportion of learning theorem in the roles of illustrations is the highest at 40.9% in the 2009 revised curriculum and 67.8% in the 2015 revised curriculum because experimental observation textbooks have the quality of auxiliary textbooks. Third, In terms of the science process skills in the questions, the proportion of the basic science process skills is the highest at 74.2% in the 2009 revised curriculum and 79.5% in the 2015 revised curriculum. Among the individual items of the science process skills, the proportion of observations is the highest at 32.2% in the 2009 revised curriculum and 34.4% in the 2015 revised curriculum.
HQA‐Trans: An end‐to‐end high‐quality‐awareness image translation framework for unsupervised cross‐domain pedestrian detection

Unsupervised cross-domain pedestrian detection has attracted attention in recent years. Although some works adopted unsupervised image translation frameworks to generate an intermediate domain to narrow the gap between source and target domains, the images in the intermediate domain tend to be distorted due to the instability of the generation network. In this work, we propose a new framework to improve the image quality of the generated intermediate domain via an end-to-end translation framework. First, an image quality assessment index is adopted and adjusted appropriately. The part that controls the image quality is kept, and the part that adversely affects the domain style translation is discarded. Secondly, the adjusted image quality assessment index is integrated into the unsupervised image translation framework, where a new loss with the index's weight is proposed. An end-to-end high-quality-awareness image translation framework is constructed to generate a high-quality intermediate domain directly through this process. Finally, the intermediate domain with high-quality images is applied for cross-domain pedestrian detection. Experimental results on benchmark datasets show that the proposed framework can effectively improve unsupervised cross-domain pedestrian detection performance. Compared with some state-of-the-art works, the proposed framework can also achieve superior performance under miss rate metrics.
Creating Virtual Reality Teaching Modules for Low-Cost Headsets

Abstract In the past few years, remote learning has been on a trend of steady growth and it is projected to remain on that course in the years to come. Additionally, the global COVID-19 pandemic forced a shift to remote learning which accelerated the existing trend to remote education. Unfortunately, learners find remote classes less engaging than traditional face-to-face classes. One technology that has shown great potential to improve students’ engagement, both in face-to-face classes and remote classes, is Virtual Reality (VR). Nevertheless, while educators are no longer limited to expensive, high-tech, and high-fidelity VR hardware thanks to the introduction of low-cost, low fidelity headset, like the Google Cardboard, educators are still limited in getting relevant content and find it difficult to create their own VR teaching modules. With the objective to address these limitations, this work introduces a new process to create VR content that is easy, rapid, and affordable for educators to adopt and implement into their curriculum. The results indicate great potential for low-cost VR in remote learning as the sample of students in this study reported that they enjoyed the ‘first-hand experience’ of touring places that were inaccessible to them due to the pandemic. However, the findings also show a strong need to address usability issues such as blurriness and dizziness.