Reviewerly/exHarmony huggingface dataset:
A Survey on Explainable Anomaly Detection

In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterise each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.
Environmental Sustainability of Metaverse: Perspectives from Romanian Developers

The metaverse is currently in the process of development and has applications in various fields, ranging from the gaming industry to art, communication, education, and fashion. Researchers regard the metaverse as the future of the internet, and, in this context, the impact of this new universe’s development on the environment needs to be investigated to find viable solutions for its sustainability. We have discussed the economic and social sustainability consequences of the metaverse, and we have largely concentrated on the environmental effects. In conducting this research, we used a quantitative methodology mainly based on a structured questionnaire. We analyzed the impact of the metaverse on the environment from the perspective of professionals in the Romanian software development and programming industries. We selected this sample because the field is newly emerging and because they are experts on how the metaverse functions and evolves. We concluded that IT professionals believe that the new universe could have several negative impacts on the natural environment, such as increased power consumption or increased CO2. emissions and the negative impact can be mitigated by adopting clear regulations and sustainable policies at the international level. This study aims to contribute to the long-term sustainability of the metaverse ecosystem by facilitating a comprehensive understanding of its functioning and evolution, as well as by addressing potential negative impacts on the natural environment. Additionally, the study seeks to make a scholarly contribution to advancing a sustainable metaverse by fostering informed decision-making processes and encouraging responsible practices within the industry. This research might be useful for technology companies, academics, and policymakers.
Molecular Quantum Circuit Design: A Graph-Based Approach

Science is rich in abstract concepts that capture complex processes in astonishingly simple ways. A prominent example is the reduction of molecules to simple graphs. This work introduces a design principle for parametrized quantum circuits based on chemical graphs, providing a way forward in three major obstacles in quantum circuit design for molecular systems: Operator ordering, parameter initialization and initial state preparation. It allows physical interpretation of each individual component and provides an heuristic to qualitatively estimate the difficulty of preparing ground states for individual instances of molecules.
AdaPrompt: Adaptive Model Training for Prompt-based NLP

Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in community.The main idea is to bridge the gap between NLP downstream tasks and language modeling (LM), by mapping these tasks into natural language prompts, which are then filled by pretrained language models (PLMs).However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining.First, prompt information is not necessarily sufficiently present during LM pretraining.Second, task-specific data are not necessarily well represented during pretraining.We address these two issues by proposing AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs by making use of both task and prompt characteristics.In addition, we make use of knowledge in Natural Language Inference models for deriving adaptive verbalizers.Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings.In addition, in zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence

Multi-intent detection and slot filling joint model attracts more and more attention since it can handle multi-intent utterances, which is closer to complex real-world scenarios.Most existing joint models rely entirely on the training procedure to obtain the implicit correlation between intents and slots.However, they ignore the fact that leveraging the rich global knowledge in the corpus can determine the intuitive and explicit correlation between intents and slots.In this paper, we aim to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling.To be specific, an intent-slot cooccurrence graph is constructed based on the entire training corpus to globally discover correlation between intents and slots.Based on the global intent-slot co-occurrence, we propose a novel graph neural network to model the interaction between the two subtasks.Experimental results on two public multi-intent datasets demonstrate that our approach outperforms the state-of-the-art models.
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling

Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation.To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and externalinformation exploiting.First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG).Based on CMG, we perform structure refinement with the guidance of the graph information bottleneck principle, actively denoising the less-informative features.Next, we perform topic modeling over the input image and text, incorporating latent multimodal topic features to enrich the contexts.On the benchmark MRE dataset, our system outperforms the current best model significantly.With further in-depth analyses, we reveal the great potential of our method for the MRE task.Our codes are open at https://github.com/ChocoWu/MRE-ISE.
A fine-grained comparison of pragmatic language understanding in humans and language models

Pragmatics and non-literal language understanding are essential to human communication, and present a long-standing challenge for artificial language models.We perform a finegrained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials.We ask whether models (1) select pragmatic interpretations of speaker utterances, (2) make similar error patterns as humans, and (3) use similar linguistic cues as humans to solve the tasks.We find that the largest models achieve high accuracy and match human error patterns: within incorrect responses, models favor literal interpretations over heuristic-based distractors.We also find preliminary evidence that models and humans are sensitive to similar linguistic cues.Our results suggest that pragmatic behaviors can emerge in models without explicitly constructed representations of mental states.However, models tend to struggle with phenomena relying on social expectation violations.
Reasoning Implicit Sentiment with Chain-of-Thought Prompting

While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA) the opinion cues come in an implicit and obscure manner.Thus detecting implicit sentiment requires the common-sense and multi-hop reasoning ability to infer the latent intent of opinion.Inspired by the recent chain-of-thought (CoT) idea, in this work we introduce a Threehop Reasoning (THOR) CoT framework to mimic the human-like reasoning process for ISA.We design a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion, and finally the sentiment polarity.Our THOR+Flan-T5 (11B) pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup.More strikingly, THOR+GPT3 (175B) boosts the SoTA by over 50% F1 on zero-shot setting.Our code is open at https: //github.com/scofield7419/THOR-ISA.
Transforming Visual Scene Graphs to Image Captions

We propose to TransForm Scene Graphs into more descriptive Captions (TFSGC).In TF-SGC, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs.After embedding, different graph embeddings contain diverse specific knowledge for generating the words with different part-of-speech, e.g., object/attribute embedding is good for generating nouns/adjectives.Motivated by this, we design a Mixture-of-Expert (MOE)-based decoder, where each expert is built on MHA, for discriminating the graph embeddings to generate different kinds of words.Since both the encoder and decoder are built based on the MHA, as a result, we construct a simple and homogeneous encoder-decoder unlike the previous heterogeneous ones which usually apply Fully-Connected-based GNN and LSTM-based decoder.The homogeneous architecture enables us to unify the training configuration of the whole model instead of specifying different training strategies for diverse sub-networks as in the heterogeneous pipeline, which releases the training difficulty.Extensive experiments on the MS-COCO captioning benchmark validate the effectiveness of our TFSGC.The code is in: https://anonymous.4open.science/r/ACL23_TFSGC.
ProsocialDialog: A Prosocial Backbone for Conversational Agents

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them.To address this issue, we introduce PROSOCIALDIALOG, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms.Covering diverse unethical, problematic, biased, and toxic situations, PROSOCIALDIALOG contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-ofthumb, RoTs).Created via a human-AI collaborative framework, PROSOCIALDIALOG consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost.Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings.Additionally, Canary effectively guides off-the-shelf language models to generate significantly more prosocial responses.Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.
Exploring Representation-level Augmentation for Code Search

Code search, which aims at retrieving the most relevant code fragment for a given natural language query, is a common activity in software development practice.Recently, contrastive learning is widely used in code search research, where many data augmentation approaches for source code (e.g., semantic-preserving program transformation) are proposed to learn better representations.However, these augmentations are at the raw-data level, which requires additional code analysis in the preprocessing stage and additional training costs in the training stage.In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representationlevel augmentation that unifies existing methods.Then, we propose three new augmentation methods (linear extrapolation, binary interpolation, and Gaussian scaling) based on the general format.Furthermore, we theoretically analyze the advantages of the proposed augmentation methods over traditional contrastive learning methods on code search.We experimentally evaluate the proposed representationlevel augmentation methods with state-of-theart code search models on a large-scale public dataset consisting of six programming languages.The experimental results show that our approach can consistently boost the performance of the studied code search models.
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework

Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals.Despite the use of large-scale unlabeled data, the performance of unsupervised methods typically lags far behind that of the supervised counterparts in most downstream tasks.In this work, we propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data.Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data.Comprehensive experiments show that GenSE achieves an average correlation score of 85.19 on the STS datasets and consistent performance improvement on four domain adaptation tasks, significantly surpassing the state-of-the-art methods and convincingly corroborating its effectiveness and generalization ability.
Better Few-Shot Relation Extraction with Label Prompt Dropout

Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples.Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task.However, what is the best way to leverage such label information in the learning process is an important research question.Existing works largely assume such textual labels are always present during both learning and prediction.In this work, we argue that such approaches may not always lead to optimal results.Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process.Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task. 1
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) faces two specific challenges: 1) How to capture useful entity-related visual information; 2) How to alleviate the interference of visual noise.Previous works have gained progress by improving interacting mechanisms or seeking for better visual features.However, existing methods neglect the integrity of entity semantics and conduct cross-modal interaction at token-level, which cuts apart the semantics of entities and makes non-entity tokens easily interfered with by irrelevant visual noise.Thus in this paper, we propose an end-to-end heterogeneous Graph-based Entity-level Interacting model (GEI) for MNER.GEI first utilizes a span detection subtask to obtain entity representations, which serve as the bridge between two modalities.Then, the heterogeneous graph interacting network interacts entity with object nodes to capture entity-related visual information, and fuses it into only entity-associated tokens to rid non-entity tokens of the visual noise.Experiments on two widely used datasets demonstrate the effectiveness of our method.
Photovoltaic Power Forecast Using Deep Learning Techniques with Hyperparameters Based on Bayesian Optimization: A Case Study in the Galapagos Islands

Hydropower systems are the basis of electricity power generation in Ecuador. However, some isolated areas in the Amazon and Galapagos Islands are not connected to the National Interconnected System. Therefore, isolated generation systems based on renewable energy sources (RES) emerge as a solution to increase electricity coverage in these areas. An extraordinary case occurs in the Galapagos Islands due to their biodiversity in flora and fauna, where the primary energy source comes from fossil fuels despite their significant amount of solar resources. Therefore, RES use, especially photovoltaic (PV) and wind power, is essential to cover the required load demand without negatively affecting the islands’ biodiversity. In this regard, the design and installation planning of PV systems require perfect knowledge of the amount of energy available at a given location, where power forecasting plays a fundamental role. Therefore, this paper presents the design and comparison of different deep learning techniques: long-short-term memory (LSTM), LSTM Projected, Bidirectional LSTM, Gated Recurrent Units, Convolutional Neural Networks, and hybrid models to forecast photovoltaic power generation in the Galapagos Islands of Ecuador. The proposed approach uses an optimized hyperparameter-based Bayesian optimization algorithm to reduce the forecast error and training time. The results demonstrate the accurate performance of all the methods by achieving a low-error short-term prediction, an excellent correlation of over 99%, and minimizing the training time.
Decentralized Policy-Hidden Fine-Grained Redaction in Blockchain-Based IoT Systems

Currently, decentralized redactable blockchains have been widely applied in IoT systems for secure and controllable data management. Unfortunately, existing works ignore policy privacy (i.e., the content of users’ redaction policies), causing severe privacy leakage threats to users since users’ policies usually contain large amounts of private information (e.g., health conditions and geographical locations) and limiting the applications in IoT systems. To bridge this research gap, we propose PFRB, a policy-hidden fine-grained redactable blockchain in decentralized blockchain-based IoT systems. PFRB follows the decentralized settings and fine-grained chameleon hash-based redaction in existing redactable blockchains. In addition, PFRB hides users’ policies during policy matching such that apart from successful policy matching, users’ policy contents cannot be inferred and valid redactions cannot be executed. Some main technical challenges include determining how to hide policy contents and support policy matching. Inspired by Newton’s interpolation formula-based secret sharing, PFRB converts policy contents into polynomial parameters and utilizes multi-authority attribute-based encryption to further hide these parameters. Theoretical analysis proves the correctness and security against the chosen-plaintext attack. Extensive experiments on the FISCO blockchain platform and IoT devices show that PFRB achieves competitive efficiency over current redactable blockchains.
Adaptive Fuzzy Echo State Network Control of Fractional-Order Large-Scale Nonlinear Systems With Time-Varying Deferred Constraints

In the traditional constrained control of nonlinear systems, the controller design usually requires that the initial value of the system meets certain strict conditions, and generally only static constraints are considered. This article concentrates on the issue of adaptive fuzzy echo state network decentralized control for fractional-order (FO) large-scale nonlinear systems where strong interconnections and time-varying deferred constraints are considered. Combining the backstepping technique, an FO fuzzy echo state network is constructed to approximate unknown nonlinear functions and interconnected terms in each step, which greatly removes some additional assumptions on unknown functions but also has a higher degree of design freedom and stronger robustness. A shifting function and an error transformation scheme are introduced to handle the constraints against the unknown initial tracking condition; moreover, the constraint conditions are satisfied within a specified time even if which are violated initially by using an time-varying barrier Lyapunov function. Specially, an equivalent definition of the bivariate convex function is given, and an inequality is constructed, which can be used to analyze the stability of FO systems by constructing a bivariate Lyapunov function. According to the FO Lyapunov stability theorem, the proposed adaptive controller can ensure that all the signals involved remain bounded and the tracking error possesses a fast convergence. Finally, an example of the FO single-machine-infinite bus power system illustrates the effectiveness of the proposed control strategy.
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification

Recent works have shown that unstructured text (doc-uments) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source like Wikipedia and are limited to a single source of information. Large Language Models (LLM) trained on web-scale text show impressive abilities to repurpose their learned knowledge for a multitude of tasks. In this work, we provide a novel perspective on using an LLM to provide text supervision for a zero-shot image classification model. The LLM is provided with a few text descriptions from different annota-tors as examples. The LLM is conditioned on these exam-ples to generate multiple text descriptions for each class (re-ferred to as views). Our proposed model, I2MVFormer, learns multi-view semantic embeddings for zero-shot image classification with these class views. We show that each text view of a class provides complementary information allowing a model to learn a highly discriminative class embed-ding. Moreover, we show that I2MVFormer is better at consuming the multi-view text supervision from LLM compared to baseline models. I2MVFormer establishes a new state-of-the-art on three public benchmark datasets for zero-shot image classification with unsupervised semantic embeddings. Code available at https://github.com/ferjad/I2DFormer