Reviewerly/exHarmony huggingface dataset:
An advantage based policy transfer algorithm for reinforcement learning with metrics of transferability

Reinforcement learning (RL) can enable sequential decision-making in complex and high-dimensional environments if the acquisition of a new state-action pair is efficient, i.e., when interaction with the environment is inexpensive. However, there are a myriad of real-world applications in which a high number of interactions are infeasible. In these environments, transfer RL algorithms, which can be used for the transfer of knowledge from one or multiple source environments to a target environment, have been shown to increase learning speed and improve initial and asymptotic performance. However, most existing transfer RL algorithms are on-policy and sample inefficient, and often require heuristic choices in algorithm design. This paper proposes an off-policy Advantage-based Policy Transfer algorithm, APT-RL, for fixed domain environments. Its novelty is in using the popular notion of ``advantage'' as a regularizer, to weigh the knowledge that should be transferred from the source, relative to new knowledge learned in the target, removing the need for heuristic choices. Further, we propose a new transfer performance metric to evaluate the performance of our algorithm and unify existing transfer RL frameworks. Finally, we present a scalable, theoretically-backed task similarity measurement algorithm to illustrate the alignments between our proposed transferability metric and similarities between source and target environments. Numerical experiments on three continuous control benchmark tasks demonstrate that APT-RL outperforms existing transfer RL algorithms on most tasks, and is $10\%$ to $75\%$ more sample efficient than learning from scratch.
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model

Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore more emergent abilities in multimodality. Visual language models (VLMs), such as LLaVA, Flamingo, or GPT-4, have demonstrated impressive performance on various visio-linguistic tasks. Consequently, there are enormous applications of large models that could be potentially used on social media platforms. Despite that, there is a lack of related work on detecting or correcting hateful memes with VLMs. In this work, we study the ability of VLMs on hateful meme detection and hateful meme correction tasks with zero-shot prompting. From our empirical experiments, we show the effectiveness of the pretrained LLaVA model and discuss its strengths and weaknesses in these tasks.
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models

Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps. Yet, complex logical reasoning is still cumbersome for language models. Based on the dual process theory in cognitive science, we are the first to unravel the cognitive reasoning abilities of language models. Our framework employs an iterative methodology to construct a Cognitive Tree (CogTree). The root node of this tree represents the initial query, while the leaf nodes consist of straightforward questions that can be answered directly. This construction involves two main components: the implicit extraction module (referred to as the intuitive system) and the explicit reasoning module (referred to as the reflective system). The intuitive system rapidly generates multiple responses by utilizing in-context examples, while the reflective system scores these responses using comparative learning. The scores guide the intuitive system in its subsequent generation step. Our experimental results on two popular and challenging reasoning tasks indicate that it is possible to achieve a performance level comparable to that of GPT-3.5 (with 175B parameters), using a significantly smaller language model that contains fewer parameters (<=7B) than 5% of GPT-3.5.
Towards General-Purpose Speech Abilities for Large Language Models Using Unpaired Data

In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of LLM capabilities, without using any carefully curated paired data. The proposed model can utilize audio prompts as a replacement for text and sustain a conversation. Such a model also has extended cross-modal capabilities such as being able to perform speech question answering, speech translation, and audio summarization amongst many other closed and open-domain tasks. This is unlike prior approaches in speech, in which LLMs are extended to handle audio for a limited number of pre-designated tasks. Experiments show that our end-to-end approach is on par with or outperforms a cascaded system (speech recognizer + LLM) in terms of modeling the response to a prompt. Furthermore, unlike a cascade, our approach shows the ability to interchange text and audio modalities and utilize the prior context in a conversation to provide better results.
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives

Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over convolution neural networks in fundamental computer vision tasks. However, the scientific community has not fully grasped the inner workings of vision transformers, nor the basis for their decision-making, which underscores the importance of explainability methods. Understanding how these models arrive at their decisions not only improves their performance but also builds trust in AI systems. This study explores different explainability methods proposed for visual transformers and presents a taxonomy for organizing them according to their motivations, structures, and application scenarios. In addition, it provides a comprehensive review of evaluation criteria that can be used for comparing explanation results, as well as explainability tools and frameworks. Finally, the paper highlights essential but unexplored aspects that can enhance the explainability of visual transformers, and promising research directions are suggested for future investment.
Centralised or Decentralised? Data Analysis of Transaction Network of Hedera Hashgraph

An important virtue of distributed ledger technologies is their acclaimed higher level of decentralisation compared to traditional financial systems. Empirical literature, however, suggests that many systems tend towards centralisation as well. This study expands the current literature by offering a first-time, data-driven analysis of the degree of decentralisation of the platform Hedera Hashgraph, a public permissioned distributed ledger technology, employing data directly fetched from a network node. The results show a considerably higher amount of released supply compared to the release schedule and a growing number of daily active accounts. Also, Hedera Hashgraph exhibits a high centralisation of wealth and a shrinking core that acts as an intermediary in transactions for the rest of the network. However, the Nakamoto index and Theil index point to recent progress towards a more decentralised network.
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks.
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations. However, several recent studies have revealed that the default settings of FL may leak private training data under privacy attacks. Thus, it is still unclear whether and to what extent such privacy risks of FL exist in the medical domain, and if so, "how to mitigate such risks?". In this paper, first, we propose a holistic framework for Medical data Privacy risk analysis and mitigation in Federated Learning (MedPFL) to analyze privacy risks and develop effective mitigation strategies in FL for protecting private medical data. Second, we demonstrate the substantial privacy risks of using FL to process medical images, where adversaries can easily perform privacy attacks to reconstruct private medical images accurately. Third, we show that the defense approach of adding random noises may not always work effectively to protect medical images against privacy attacks in FL, which poses unique and pressing challenges associated with medical data for privacy protection.
Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions. However, the existing works have mostly overlooked the constraints of mobile devices, which may not have sufficient capabilities to implement resource-demanding semantic encoder/decoder based on deep learning. To address this issue, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver to assist multiuser text transmissions. Specifically, the SemRelay decodes semantic information from a base station and forwards it to the users using conventional bit transmission, hence effectively improving text transmission efficiency. To study the multiuser resource allocation, we formulate an optimization problem to maximize the multiuser weighted sum-rate by jointly designing the SemRelay transmit power allocation and system bandwidth allocation. Although this problem is non-convex and hence challenging to solve, we propose an efficient algorithm to obtain its high-quality suboptimal solution by using the block coordinate descent method. Last, numerical results show the effectiveness of the proposed algorithm as well as superior performance of the proposed SemRelay over the conventional decode-and-forward (DF) relay, especially in small bandwidth region.
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning

Federated Learning (FL) has emerged as a promising Machine Learning paradigm, enabling multiple users to collaboratively train a shared model while preserving their local data. To minimize computing and communication costs associated with parameter transfer, it is common practice in FL to select a subset of clients in each training round. This selection must consider both system and static heterogeneity. Therefore, we propose FLASH-RL, a framework that utilizes Double Deep QLearning (DDQL) to address both system and static heterogeneity in FL. FLASH-RL introduces a new reputation-based utility function to evaluate client contributions based on their current and past performances. Additionally, an adapted DDQL algorithm is proposed to expedite the learning process. Experimental results on MNIST and CIFAR-10 datasets have shown FLASH-RL's effectiveness in achieving a balanced trade-off between model performance and end-to-end latency against existing solutions. Indeed, FLASH-RL reduces latency by up to 24.83% compared to FedAVG and 24.67% compared to FAVOR. It also reduces the training rounds by up to 60.44% compared to FedAVG and +76% compared to FAVOR. In fall detection using the MobiAct dataset, FLASH-RL outperforms FedAVG by up to 2.82% in model's performance and reduces latency by up to 34.75%. Additionally, FLASH-RL achieves the target performance faster, with up to a 45.32% reduction in training rounds compared to FedAVG.
Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method

The automatic identification and analysis of pronunciation errors, known as Mispronunciation Detection and Diagnosis (MDD) plays a crucial role in Computer Aided Pronunciation Learning (CAPL) tools such as Second-Language (L2) learning or speech therapy applications. Existing MDD methods relying on analysing phonemes can only detect categorical errors of phonemes that have an adequate amount of training data to be modelled. With the unpredictable nature of the pronunciation errors of non-native or disordered speakers and the scarcity of training datasets, it is unfeasible to model all types of mispronunciations. Moreover, phoneme-level MDD approaches have a limited ability to provide detailed diagnostic information about the error made. In this paper, we propose a low-level MDD approach based on the detection of speech attribute features. Speech attribute features break down phoneme production into elementary components that are directly related to the articulatory system leading to more formative feedback to the learner. We further propose a multi-label variant of the Connectionist Temporal Classification (CTC) approach to jointly model the non-mutually exclusive speech attributes using a single model. The pre-trained wav2vec2 model was employed as a core model for the speech attribute detector. The proposed method was applied to L2 speech corpora collected from English learners from different native languages. The proposed speech attribute MDD method was further compared to the traditional phoneme-level MDD and achieved a significantly lower False Acceptance Rate (FAR), False Rejection Rate (FRR), and Diagnostic Error Rate (DER) over all speech attributes compared to the phoneme-level equivalent.
Improved Task Scheduling for Virtual Machines in the Cloud based on the Gravitational Search Algorithm

The rapid and convenient provision of the available computing resources is a crucial requirement in modern cloud computing environments. However, if only the execution time is taken into account when the resources are scheduled, it could lead to imbalanced workloads as well as to significant under-utilisation of the involved Virtual Machines (VMs). In the present work a novel task scheduling scheme is introduced, which is based on the proper adaptation of a modern and quite effective evolutionary optimization method, the Gravitational Search Algorithm (GSA). The proposed scheme aims at optimizing the entire scheduling procedure, in terms of both the tasks execution time and the system (VMs) resource utilisation. Moreover, the fitness function was properly selected considering both the above factors in an appropriately weighted function in order to obtain better results for large inputs. Sufficient simulation experiments show the efficiency of the proposed scheme, as well as its excellence over related approaches of the bibliography, with similar objectives.
Ball-AR: Fostering Playful Co-Located Interaction Through Environment-centric Physical Activity with AR

We present Ball-AR, an augmented reality (AR) game where two players in the same physical space attempt to hit each other with virtual dodgeballs overlaid on the physical world. Researchers have studied AR's potential for fostering co-located interaction and physical activity; however, they have not investigated the impacts of physical activity and physical environment on user experiences and interaction. We created an AR dodgeball game centered around encouraging physical activity and harnessing the physical environment. We then evaluated the game with five dyads to analyze the impacts of these design choices on the quality of gameplay and interaction between players. We found that physical activity and the shared physical space created memorable experiences and interactions among participants, although participants desired a more augmented and immersive experience.
Creating a Discipline-specific Commons for Infectious Disease Epidemiology

Objective: To create a commons for infectious disease (ID) epidemiology in which epidemiologists, public health officers, data producers, and software developers can not only share data and software, but receive assistance in improving their interoperability. Materials and Methods: We represented 586 datasets, 54 software, and 24 data formats in OWL 2 and then used logical queries to infer potentially interoperable combinations of software and datasets, as well as statistics about the FAIRness of the collection. We represented the objects in DATS 2.2 and a software metadata schema of our own design. We used these representations as the basis for the Content, Search, FAIR-o-meter, and Workflow pages that constitute the MIDAS Digital Commons. Results: Interoperability was limited by lack of standardization of input and output formats of software. When formats existed, they were human-readable specifications (22/24; 92%); only 3 formats (13%) had machine-readable specifications. Nevertheless, logical search of a triple store based on named data formats was able to identify scores of potentially interoperable combinations of software and datasets. Discussion: We improved the findability and availability of a sample of software and datasets and developed metrics for assessing interoperability. The barriers to interoperability included poor documentation of software input/output formats and little attention to standardization of most types of data in this field. Conclusion: Centralizing and formalizing the representation of digital objects within a commons promotes FAIRness, enables its measurement over time and the identification of potentially interoperable combinations of data and software.
SELF-EXPLAIN: Teaching Large Language Models to Reason Complex Questions by Themselves

Large language models (LLMs) can generate intermediate reasoning steps. To elicit the reliable reasoning, the common practice is to employ few-shot chain-of-thought prompting, where several in-context demonstrations for reasoning are prepended to the question. However, such chain-of-thought examples are expensive to craft, especially for professional domains, and can have high variance depending on human annotators. Therefore, this work investigates whether LLMs can teach themselves to reason without human-crafted demonstrations. We propose SELF-EXPLAIN to generate CoT examples by LLMs inspired by "encoding specificity" in human memory retrieval. We find using self-explanations makes LLMs more confident, more calibrated and less biased when answering complex questions. Moreover, we find prompting with self-explanations can even significantly outperform using human-crafted CoTs on several complex question answering dataset.
Context Consistency between Training and Testing in Simultaneous Machine Translation

Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context. However, there is a counterintuitive phenomenon about the context usage between training and testing: e.g., the wait-k testing model consistently trained with wait-k is much worse than that model inconsistently trained with wait-k' (k' is not equal to k) in terms of translation quality. To this end, we first investigate the underlying reasons behind this phenomenon and uncover the following two factors: 1) the limited correlation between translation quality and training (cross-entropy) loss; 2) exposure bias between training and testing. Based on both reasons, we then propose an effective training approach called context consistency training accordingly, which makes consistent the context usage between training and testing by optimizing translation quality and latency as bi-objectives and exposing the predictions to the model during the training. The experiments on three language pairs demonstrate our intuition: our system encouraging context consistency outperforms that existing systems with context inconsistency for the first time, with the help of our context consistency training approach.
Collaborative Goal Tracking of Multiple Mobile Robots Based on Geometric Graph Neural Network

Multi-robot systems are widely used in spatially distributed tasks, and their collaborative path planning is of great significance for working efficiency. Currently, different multi-robot collaborative path planning methods have been proposed, but how to process the sensory information of neighboring robots at different locations from a local perception perspective in real environment to make better decisions is still a major difficulty. To address this problem, this paper proposes a multi-robot collaborative path planning method based on geometric graph neural network (GeoGNN). GeoGNN introduces the relative position information of neighboring robots into each interaction layer of the graph neural network to better integrate neighbor sensing information. An expert data generation method is designed for the robot to advance in a single step, by which expert data are generated in ROS to train the network. Experimental results show that the accuracy of the proposed method is improved by about 5% compared to the model based only on CNN on the expert data set. In ROS simulation environment path planning test, the success rate is improved by about 4% compared to CNN and flowtime increase is reduced about 8%, which outperforms other graph neural network models.
Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions

Language model (LM) prompting--a popular paradigm for solving NLP tasks--has been shown to be susceptible to miscalibration and brittleness to slight prompt variations, caused by its discriminative prompting approach, i.e., predicting the label given the input. To address these issues, we propose Gen-Z--a generative prompting framework for zero-shot text classification. GEN-Z is generative, as it measures the LM likelihood of input text, conditioned on natural language descriptions of labels. The framework is multivariate, as label descriptions allow us to seamlessly integrate additional contextual information about the labels to improve task performance. On various standard classification benchmarks, with six open-source LM families, we show that zero-shot classification with simple contextualization of the data source of the evaluation set consistently outperforms both zero-shot and few-shot baselines while improving robustness to prompt variations. Further, our approach enables personalizing classification in a zero-shot manner by incorporating author, subject, or reader information in the label descriptions.