Reviewerly/exHarmony huggingface dataset:
Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Abstract A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Human Centricity and User Interaction Application Areas &gt; Internet Commercial, Legal, and Ethical Issues &gt; Ethical Considerations Commercial, Legal, and Ethical Issues &gt; Security and Privacy
Multi-scenario analysis on hydrogen production development using PESTEL and FCM models

Governments efforts to reduce future emissions and change the trajectory of fossil fuel consumption must include widespread adoption of the Hydrogen (H2) promise. However, from a long-term viewpoint, inherent uncertainties drive the strategic vision of H2 development, usage and establishment. The future trends created by a variety of exogenous and endogenous elements, including Political, Economic, Social, Technological, Environmental, and Legal factors (PESTEL) are significantly influenced by these unpredictable volatilities as they come into play. The objective of this study is to recognize, define, and analyze significant PESTEL aspects influencing the dynamics of H2 energy in an unpredictably changing environment. Fuzzy Inference systems (FIS) and Fuzzy Cognitive Maps (FCM) methodologies were used for the case scenario of Greece to analyze how interrelated criteria from the previous categories interact dynamically and behave in a holistic approach. With the help of diverse quadruple helix stakeholders, a survey and three workshops were conducted to establish a participatory modeling framework with multi-level scenario analysis phases for the Greek use case. Initially, an FCM model was determined as an amalgamation of qualitative and quantitative valued concepts. This model was augmented by four more concepts related to Greek H2 production plants projecting future trends of this industry. We run several scenarios (including best and worst case) to analyze the influence of politics and regional economy to the growth of H2 production industry. The scenario of energy crisis depicts a limited positive trend of the hydrogen production. While the economy grows, and the crisis diminishes results show a stronger turn to the hydrogen production. Societal factors affect the process in all phases and scenarios. Results span from deterioration trends to slight improvements depending on the starting values of key criteria.
A survey on Evolutionary Reinforcement Learning algorithms

Reinforcement Learning (RL) has proven to be highly effective in various real-world applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been utilized as an alternative to RL algorithms. Recently, Evolutionary Reinforcement Learning algorithms (ERLs) have emerged as a promising solution that combines the advantages of both RL and EA. This paper presents a comprehensive survey that encompasses a majority of the studies in this exciting research area. We classify these ERLs according to the EA used in their frameworks and analyze the strengths and limitations of various EA components and combination schemes. Additionally, we conduct several experiments to evaluate the performance of some representative ERLs. By categorizing the different approaches and assessing their effectiveness, the paper can assist researchers and practitioners in selecting the most suitable method for their particular application.
Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models

Real-world graphs such as social networks, communication networks, and rating networks are constantly evolving over time. Many deep learning architectures have been developed to learn effective node representations using both graph structure and dynamics. While being crucial for practical applications, the robustness of these representation learners for dynamic graphs in the presence of adversarial attacks is highly understudied. In this work, we design a novel adversarial attack on discrete-time dynamic graph models where we desire to perturb the input graph sequence in a manner that preserves the temporal dynamics of the graph while dropping the performance of representation learners. To this end, we motivate a novel Temporal Dynamics-Aware Perturbation (TDAP) constraint, which ensures that perturbations introduced at each time step are restricted to only a small fraction of the number of changes in the graph since the previous time step. We present a theoretically-motivated Projected Gradient Descent approach for dynamic graphs to find effective perturbations under the TDAP constraint. Experiments on two tasks - dynamic link prediction and node classification, show that our approach is up to 4x more effective than the baseline methods for attacking these models. We extend our approach to a more practical online setting where graphs become available in real-time and show up to 5x superior performance over baselines We also show that our approach successfully evades state-of-the-art neural approaches for anomaly detection, thereby promoting the need to study robustness as a part of representation-learning approaches for dynamic graphs.
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments. The study shows that pFL methods with partial model-sharing can significantly boost robustness against backdoor attacks. In contrast, pFL methods with full model-sharing do not show robustness. To analyze the reasons for varying robustness performances, we provide comprehensive ablation studies on different pFL methods. Based on our findings, we further propose a lightweight defense method, Simple-Tuning, which empirically improves defense performance against backdoor attacks. We believe that our work could provide both guidance for pFL application in terms of its robustness and offer valuable insights to design more robust FL methods in the future. We open-source our code to establish the first benchmark for black-box backdoor attacks in pFL: https://github.com/alibaba/FederatedScope/tree/backdoor-bench.
One Embedder, Any Task: Instruction-Finetuned Text Embeddings

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions).Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training.We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss.We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation.INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets.Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.Our model, code, and data are available at https: //instructor-embedding.github.io.
Bernice: A Multilingual Pre-trained Encoder for Twitter

The language of Twitter differs significantly from that of other domains commonly included in large language model training.While tweets are typically multilingual and contain informal language, including emoji and hashtags, most pre-trained language models for Twitter are either monolingual, adapted from other domains rather than trained exclusively on Twitter, or are trained on a limited amount of in-domain Twitter data.We introduce Bernice, the first multilingual RoBERTa language model trained from scratch on 2.5 billion tweets with a custom tweet-focused tokenizer.We evaluate on a variety of monolingual and multilingual Twitter benchmarks, finding that our model consistently exceeds or matches the performance of a variety of models adapted to social media data as well as strong multilingual baselines, despite being trained on less data overall.We posit that it is more efficient compute-and data-wise to train completely on in-domain data with a specialized domain-specific tokenizer.
In-Context Learning for Few-Shot Dialogue State Tracking

Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero and few shot learning could greatly benefit dialogue state tracking (DST).In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates.To better leverage a tabular domain description in the LM prompt, we reformulate DST into a text-to-SQL problem.We also propose a novel approach to retrieve annotated dialogues as exemplars.Empirical results on MultiWOZ show that our method IC-DST substantially outperforms previous fine-tuned state-of-the-art models in fewshot settings.In addition, we test IC-DST in zero-shot settings, in which the model only takes a fixed task instruction as input, finding that it outperforms previous zero-shot methods by a large margin. 1
“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset

As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presuppose which types of biases models can exhibit. In this work, we present a new, more inclusive bias measurement dataset, HolisticBias, which includes nearly 600 descriptor terms across 13 different demographic axes. HolisticBias was assembled in a participatory process including experts and community members with lived experience of these terms. These descriptors combine with a set of bias measurement templates to produce over 450,000 unique sentence prompts, which we use to explore, identify, and reduce novel forms of bias in several generative models. We demonstrate that HolisticBias is effective at measuring previously undetectable biases in token likelihoods from language models, as well as in an offensiveness classifier. We will invite additions and amendments to the dataset, which we hope will serve as a basis for more easy-to-use and standardized methods for evaluating bias in NLP models.
An Investigation of the Effectiveness of Deepfake Models and Tools

With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.
Machine Learning Application for Evidence Image Enhancement

Taking into account the uses of ML in the field of vision, many practical vision systems' first processing stages include enhancing or reconstructing images. The goal of these tools is to enhance the quality of photos and give accurate data for making decisions based on appearance. In this research study, the authors examine three distinct types of neural networks: convolutional networks, residual networks, and generative countermeasure networks. There is a proposal for a model structure of a scalable supplementary generation network as part of a network that enhances evidence images as a generative countermeasure. The authors present the objective loss function definition, as well as the periodic consistency loss and the periodic perceptual consistency loss analysis. An in-depth solution framework for picture layering is offered once the problem's core aspects are explained. This approach implements multitasking with the help of adaptive feature learning, this provides a strong theoretical guarantee.
The corona virus search optimizer for solving global and engineering optimization problems

In this paper, a new and effective meta-heuristic method named corona-virus search optimizer (CVSO) is proposed inspired based on the movement and search of the corona-virus among different societies, prevalence, and death of individuals. The CVSO has local and global search suitable balance due to the use of evolutionary strategies as well as appropriate social learning. This algorithm has desirable optimization power, easy implementation and requires only a control parameter. The CVSO algorithm is implemented on 30 modern and standard test functions of CEC2014 and also 10 test functions of CEC2019. The performance and capability of CVSO is compared with new and well-known meta-heuristics including particle swarm optimization (PSO), artificial bee colony (ABC) tree growth algorithm (TGA), and Lion optimization algorithm (LOA), bat algorithm (BA) and harris hawks optimization (HHO). The results showed that the CVSO can be more effective than other algorithms to solve the test functions and also various complex real-world engineering optimization problems in terms of optimization accuracy and convergence rate. Also, the superiority of CVSO is demonstrated in the designing of a hybrid energy system by achieving a lower cost of M$ 2.7492 compared to TGA, LOA, ABC, PSO, BA, and HHO algorithms, which achieve costs of M$ 2.7878, M$ 2.7556, M$ 2.8521, M$ 2.9503, M$ 3.0942, and M$ 2.9614, respectively. In addition, due to the optimal balance between local and global search and strength, it is able to avoid getting stuck in locally optimal solutions while obtaining global optimal solutions with a higher convergence rate than other algorithms. The source code of CVSO is publicly available at https://github.com/Irajfaraji/CVSO.
Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis

Intelligent fault diagnosis has attracted intensive efforts in machine predictive maintenance. However, the structural information from multi-sensor signals has not been fully investigated. In this study, a novel temporal–spatial graph neural network with an attention-aware module (A-TSGNN) is proposed to accomplish multi-source information fusion. First, the graph structure naturally organizes the diverse sensors. The graph convolution model realizes the feature representation in the spatial dimension. Then, time-dependent learning is applied in the temporal dimension, and a temporal–spatial learning framework is built. An additional attention module is designed to learn the flexible weights and model the importance of individual sensors and their correlations. Experiments on a wind turbine dataset achieves an accuracy of 0.9669 and an F1-score of 0.9649. For the gearbox dataset, the values are 0.9927 and 0.9920, respectively. The overall macro-average area under the curve metrics reach a perfect score of 1.00 for both datasets, indicating exceptional performance. The adaptive attention mechanism is also discussed to verify the superiority of the A-TSGNN. Furthermore, comparisons with the single-sensor scheme and other fusion models demonstrate the stable performance of the proposed method. The A-TSGNN provides a potential model for comprehensively utilizing multi-sensor data, showing a promising prospect.
A practical wearable fall detection system based on tiny convolutional neural networks

Falls are a major public health problem in a rapidly aging society due to their high prevalence and severe consequences among the older population. Therefore, automatic fall detection is of primary importance to enable timely medical intervention for older fall victims. The existing fall detection methods suffer from either poor robustness or high computational cost, limiting their practical applications in wearable fall detection systems. In this study, a tiny convolutional neural network (TinyCNN) with two-stage efficient feature extraction was proposed and evaluated on two large-scale public fall datasets (KFall and SisFall) collected from wearable inertial sensors. Cross-validation results showed that TinyCNN achieved both mean sensitivities and specificities over 99 % on the KFall, and over 98 % on the SisFall. While benefiting from the effectiveness of TinyCNN, we also addressed its black-box effect using class activation map (CAM). Furthermore, by employing quantization techniques, TinyCNN achieved a low latency of 0.037 s on an ultra-low-power microcontroller (Arduino Nano BLE 33 Sense), enabling real-time performance without sacrificing the accuracy. Compared with existing state-of-the-art fall detection algorithms, the newly proposed TinyCNN yielded a more balanced performance in terms of accuracy and practicality. Inspired by these promising results, we further developed a practical wearable prototype system based on this embeddable TinyCNN and an accompanying mobile App for automatic fall detection and immediate alarm. This AI-enabled wearable system has great potential for real-life fall detection applications among older individuals.
ChatGPT: Its Applications and Limitations

ChatGPT is a state-of-the-art language model that has gained widespread attention due to its ability to generate human-like responses to natural language inputs. The model has been trained on a massive corpus of text data, enabling it to provide quick and accurate responses to a wide range of user queries. However, while ChatGPT has shown impressive capabilities in many areas, it also has its limitations. One of the main challenges with the model is its tendency to produce biased or inappropriate responses based on the input it has been trained on. Additionally, the model’s reliance on statistical patterns in text data means that it may struggle with more nuanced or complex language tasks. Despite these limitations, ChatGPT represents a significant step forward in the development of conversational AI, and its potential applications are vast, from chatbots and customer service to content creation and education. As the technology continues to evolve, we will likely see even more sophisticated language models emerge, with even greater capabilities and fewer limitations.
Artificial Intelligence Enabled Sustainable Life Cycle System Using Vedic Scripture and Quantum Computing

The proposed approach builds a sustainable life cycle system that uses artificial intelligence by fusing the ideas of Vedic scripture with the capabilities of quantum computing. The system makes use of both cutting-edge quantum computing technology and old Vedic wisdom to address the sustainability issues that contemporary civilization is currently facing. Based on the sustainability principles deriving from Vedic text, the system will use AI algorithms to analyze and optimize the full life cycle of items, from raw materials through disposal. AI algorithms will run more quickly and efficiently with the help of quantum computing, enabling quicker analysis and better decision-making. The system has enormous potential to advance sustainable growth and improve both human and environmental well-being.