Reviewerly/exHarmony huggingface dataset:
Speeding Up Speech Synthesis In Diffusion Models By Reducing Data Distribution Recovery Steps Via Content Transfer

Diffusion based vocoders have been criticised for being slow due to the many steps required during sampling. Moreover, the model's loss function that is popularly implemented is designed such that the target is the original input $x_0$ or error $\epsilon_0$. For early time steps of the reverse process, this results in large prediction errors, which can lead to speech distortions and increase the learning time. We propose a setup where the targets are the different outputs of forward process time steps with a goal to reduce the magnitude of prediction errors and reduce the training time. We use the different layers of a neural network (NN) to perform denoising by training them to learn to generate representations similar to the noised outputs in the forward process of the diffusion. The NN layers learn to progressively denoise the input in the reverse process until finally the final layer estimates the clean speech. To avoid 1:1 mapping between layers of the neural network and the forward process steps, we define a skip parameter $\tau>1$ such that an NN layer is trained to cumulatively remove the noise injected in the $\tau$ steps in the forward process. This significantly reduces the number of data distribution recovery steps and, consequently, the time to generate speech. We show through extensive evaluation that the proposed technique generates high-fidelity speech in competitive time that outperforms current state-of-the-art tools. The proposed technique is also able to generalize well to unseen speech.
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data

Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data. The time cost of communication is an essential bottleneck in federated learning, especially for training large-scale deep neural networks. Some communication-efficient federated learning methods, such as FedAvg and FedAdam, share the same learning rate across different clients. But they are not efficient when data is heterogeneous. To maximize the performance of optimization methods, the main challenge is how to adjust the learning rate without hurting the convergence. In this paper, we propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate based on local historical gradient squares and synchronized learning rates. Theoretical analysis shows that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients, which enables promising scalability in federated optimization. We also empirically compare our method with several communication-efficient federated optimization methods. Extensive experimental results on Computer Vision (CV) tasks and Natural Language Processing (NLP) task show the efficacy of our proposed FedLALR method and also coincides with our theoretical findings.
Do learned speech symbols follow Zipf's law?

In this study, we investigate whether speech symbols, learned through deep learning, follow Zipf's law, akin to natural language symbols. Zipf's law is an empirical law that delineates the frequency distribution of words, forming fundamentals for statistical analysis in natural language processing. Natural language symbols, which are invented by humans to symbolize speech content, are recognized to comply with this law. On the other hand, recent breakthroughs in spoken language processing have given rise to the development of learned speech symbols; these are data-driven symbolizations of speech content. Our objective is to ascertain whether these data-driven speech symbols follow Zipf's law, as the same as natural language symbols. Through our investigation, we aim to forge new ways for the statistical analysis of spoken language processing.
Simulating the flight gate assignment problem on a trapped ion quantum computer

We study the flight gate assignment problem on IonQ's Aria trapped ion quantum computer using the variational quantum eigensolver. Utilizing the conditional value at risk as an aggregation function, we demonstrate that current trapped ion quantum hardware is able to obtain good solutions for this combinatorial optimization problem with high probability. In particular, we run the full variational quantum eigensolver for small instances and we perform inference runs for larger systems, demonstrating that current and near-future quantum hardware is suitable for addressing combinatorial optimization problems.
Talking to Data Visualizations: Opportunities and Challenges

Speech is one of the interaction modalities that we increasingly come across in natural user interfaces. However, its use in collaborative scenarios has not yet been thoroughly investigated. In this reflection statement, we discuss the opportunities and challenges of integrating speech interaction in multimodal solutions for collaborative work with data visualizations. We discuss related findings from other research communities and how we could build upon their work to explore and make use of speech interaction for data visualizations in co-located, hybrid, and remote settings.
Photonic quantum metrology with variational quantum optical non-linearities

Photonic quantum metrology harnesses quantum states of light, such as NOON or Twin-Fock states, to measure unknown parameters beyond classical precision limits. Current protocols suffer from two severe limitations that preclude their scalability: the exponential decrease in fidelities (or probabilities) when generating states with large photon numbers due to gate errors, and the increased sensitivity of such states to noise. Here, we develop a deterministic protocol combining quantum optical non-linearities and variational quantum algorithms that provides a substantial improvement on both fronts. First, we show how the variational protocol can generate metrologically-relevant states with a small number of operations which does not significantly depend on photon-number, resulting in exponential improvements in fidelities when gate errors are considered. Second, we show that such states offer a better robustness to noise compared to other states in the literature. Since our protocol harnesses interactions already appearing in state-of-the-art setups, such as cavity QED, we expect that it will lead to more scalable photonic quantum metrology in the near future.
Grassroots Police Officers' Cyber-Terminology Knowledge and Its Impact on Cybercrime Investigations in Northeast Nigeria

This study assesses the familiarity of grassroots police officers with cyber terminologies and their impact on cybercrime investigations in northeastern police commands of Nigeria. The research employed a survey approach, incorporating a comprehensive review of relevant literature. Data was gathered using the Cybercrime Related Scales (QCRS), a questionnaire comprising twenty items. The survey was administered to police Command/Barracks across the Zone, encompassing three Police Barracks from each state, totaling eighteen Barracks, and involving a sample of 500 police officers. The questionnaire's reliability was confirmed at 0.87 and 0.91, respectively. A research question and hypothesis were formulated and tested at a significance level of 0.05. Data analysis involved Chi-square and correlation Statistical Methods through the Statistical Package for Social Sciences (SPSS) Software package. The null hypothesis was retained while the alternative was rejected. The researcher concluded that there is no significant divergence in the familiarity of cybercrime terminologies on crime investigations among police officers in the Northeast states of Nigeria Police commands and recommends that there is a need for training and re-training of police personnel to equip them with the knowledge of cyber terminologies among others.
Is the Computing Continuum Already Here?

The computing continuum, a novel paradigm that extends beyond the current silos of cloud and edge computing, can enable the seamless and dynamic deployment of applications across diverse infrastructures. By utilizing the cloud-native features and scalability of Kubernetes, this concept promotes deployment transparency, communication transparency, and resource availability transparency. Key features of this paradigm include intent-driven policies, a decentralized architecture, multi-ownership, and a fluid topology. Integral to the computing continuum are the building blocks of dynamic discovery and peering, hierarchical resource continuum, resource and service reflection, network continuum, and storage and data continuum. The implementation of these principles allows organizations to foster an efficient, dynamic, and seamless computing environment, thereby facilitating the deployment of complex distributed applications across varying infrastructures.
Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts

User-generated texts available on the web and social platforms are often long and semantically challenging, making them difficult to annotate. Obtaining human annotation becomes increasingly difficult as problem domains become more specialized. For example, many health NLP problems require domain experts to be a part of the annotation pipeline. Thus, it is crucial that we develop low-resource NLP solutions able to work with this set of limited-data problems. In this study, we employ Abstract Meaning Representation (AMR) graphs as a means to model low-resource Health NLP tasks sourced from various online health resources and communities. AMRs are well suited to model online health texts as they can represent multi-sentence inputs, abstract away from complex terminology, and model long-distance relationships between co-referring tokens. AMRs thus improve the ability of pre-trained language models to reason about high-complexity texts. Our experiments show that we can improve performance on 6 low-resource health NLP tasks by augmenting text embeddings with semantic graph embeddings. Our approach is task agnostic and easy to merge into any standard text classification pipeline. We experimentally validate that AMRs are useful in the modeling of complex texts by analyzing performance through the lens of two textual complexity measures: the Flesch Kincaid Reading Level and Syntactic Complexity. Our error analysis shows that AMR-infused language models perform better on complex texts and generally show less predictive variance in the presence of changing complexity.
Context is Environment

Two lines of work are taking the central stage in AI research. On the one hand, the community is making increasing efforts to build models that discard spurious correlations and generalize better in novel test environments. Unfortunately, the bitter lesson so far is that no proposal convincingly outperforms a simple empirical risk minimization baseline. On the other hand, large language models (LLMs) have erupted as algorithms able to learn in-context, generalizing on-the-fly to eclectic contextual circumstances that users enforce by means of prompting. In this paper, we argue that context is environment, and posit that in-context learning holds the key to better domain generalization. Via extensive theory and experiments, we show that paying attention to context$\unicode{x2013}\unicode{x2013}$unlabeled examples as they arrive$\unicode{x2013}\unicode{x2013}$allows our proposed In-Context Risk Minimization (ICRM) algorithm to zoom-in on the test environment risk minimizer, leading to significant out-of-distribution performance improvements. From all of this, two messages are worth taking home. Researchers in domain generalization should consider environment as context, and harness the adaptive power of in-context learning. Researchers in LLMs should consider context as environment, to better structure data towards generalization.
The role of causality in explainable artificial intelligence

Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning

Autonomous mobility tasks such as lastmile delivery require reasoning about operator indicated preferences over terrains on which the robot should navigate to ensure both robot safety and mission success. However, coping with out of distribution data from novel terrains or appearance changes due to lighting variations remains a fundamental problem in visual terrain adaptive navigation. Existing solutions either require labor intensive manual data recollection and labeling or use handcoded reward functions that may not align with operator preferences. In this work, we posit that operator preferences for visually novel terrains, which the robot should adhere to, can often be extrapolated from established terrain references within the inertial, proprioceptive, and tactile domain. Leveraging this insight, we introduce Preference extrApolation for Terrain awarE Robot Navigation, PATERN, a novel framework for extrapolating operator terrain preferences for visual navigation. PATERN learns to map inertial, proprioceptive, tactile measurements from the robots observations to a representation space and performs nearest neighbor search in this space to estimate operator preferences over novel terrains. Through physical robot experiments in outdoor environments, we assess PATERNs capability to extrapolate preferences and generalize to novel terrains and challenging lighting conditions. Compared to baseline approaches, our findings indicate that PATERN robustly generalizes to diverse terrains and varied lighting conditions, while navigating in a preference aligned manner.
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree

In explainable artificial intelligence (XAI) research, the predominant focus has been on interpreting models for experts and practitioners. Model agnostic and local explanation approaches are deemed interpretable and sufficient in many applications. However, in domains like healthcare, where end users are patients without AI or domain expertise, there is an urgent need for model explanations that are more comprehensible and instil trust in the model's operations. We hypothesise that generating model explanations that are narrative, patient-specific and global(holistic of the model) would enable better understandability and enable decision-making. We test this using a decision tree model to generate both local and global explanations for patients identified as having a high risk of coronary heart disease. These explanations are presented to non-expert users. We find a strong individual preference for a specific type of explanation. The majority of participants prefer global explanations, while a smaller group prefers local explanations. A task based evaluation of mental models of these participants provide valuable feedback to enhance narrative global explanations. This, in turn, guides the design of health informatics systems that are both trustworthy and actionable.
OptiRoute: A Heuristic-assisted Deep Reinforcement Learning Framework for UAV-UGV Collaborative Route Planning

Unmanned aerial vehicles (UAVs) are capable of surveying expansive areas, but their operational range is constrained by limited battery capacity. The deployment of mobile recharging stations using unmanned ground vehicles (UGVs) significantly extends the endurance and effectiveness of UAVs. However, optimizing the routes of both UAVs and UGVs, known as the UAV-UGV cooperative routing problem, poses substantial challenges, particularly with respect to the selection of recharging locations. Here in this paper, we leverage reinforcement learning (RL) for the purpose of identifying optimal recharging locations while employing constraint programming to determine cooperative routes for the UAV and UGV. Our proposed framework is then benchmarked against a baseline solution that employs Genetic Algorithms (GA) to select rendezvous points. Our findings reveal that RL surpasses GA in terms of reducing overall mission time, minimizing UAV-UGV idle time, and mitigating energy consumption for both the UAV and UGV. These results underscore the efficacy of incorporating heuristics to assist RL, a method we refer to as heuristics-assisted RL, in generating high-quality solutions for intricate routing problems.
Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.
HyperDE: An Adaptive Hyper-Heuristic for Global Optimization

In this paper, a novel global optimization approach in the form of an adaptive hyper-heuristic, namely HyperDE, is proposed. As the naming suggests, the method is based on the Differential Evolution (DE) heuristic, which is a well-established optimization approach inspired by the theory of evolution. Additionally, two other similar approaches are introduced for comparison and validation, HyperSSA and HyperBES, based on Sparrow Search Algorithm (SSA) and Bald Eagle Search (BES), respectively. The method consists of a genetic algorithm that is adopted as a high-level online learning mechanism, in order to adjust the hyper-parameters and facilitate the collaboration of a homogeneous set of low-level heuristics with the intent of maximizing the performance of the search for global optima. Comparison with the heuristics that the proposed methodologies are based on, along with other state-of-the-art methods, is favorable.
Robust Identification in the Limit from Incomplete Positive Data

Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine conditions under which learning algorithms for classes of formal languages are able to succeed when the data presentations are systematically incomplete; that is, when certain kinds of examples are systematically absent. One motivation comes from linguistics, where the phonotactic pattern of a language may be understood as the intersection of formal languages, each of which formalizes a distinct linguistic generalization. We examine under what conditions these generalizations can be learned when the only data available to a learner belongs to their intersection. In particular, we provide three formal definitions of robustness in the identification in the limit from positive data paradigm, and several theorems which describe the kinds of classes of formal languages which are, and are not, robustly learnable in the relevant sense. We relate these results to classes relevant to natural language phonology.
The Impact of State Merging on Predictive Accuracy in Probabilistic Tree Automata: Dietze’s Conjecture Revisited

Dietze’s conjecture concerns the problem of equipping a tree automaton M with weights to make it probabilistic, in such a way that the resulting automaton N predicts a given corpus $$\mathcal {C}$$ as accurately as possible. The conjecture states that the accuracy cannot increase if the states in M are merged with respect to an equivalence relation $$\sim $$ so that the result is a smaller automaton $$M^\sim $$ . Put differently, merging states can never improve predictions. This is under the assumption that both M and $$M^\sim $$ are bottom-up deterministic and accept every tree in $$\mathcal {C}$$ . We prove that the conjecture holds, using a construction that turns any probabilistic version $$N^\sim $$ of $$M^\sim $$ into a probabilistic version N of M, such that N assigns at least as great a weight to each tree in $$\mathcal {C}$$ as $$N^\sim $$ does.
Cross-domain mechanism for few-shot object detection on Urine Sediment Image

Deep learning object detection networks require a large amount of box annotation data for training, which is difficult to obtain in the medical image field. The few-shot object detection algorithm is significant for an unseen category, which can be identified and localized with a few labeled data. For medical image datasets, the image style and target features are incredibly different from the knowledge obtained from training on the original dataset. We propose a background suppression attention(BSA) and feature space fine-tuning module (FSF) for this cross-domain situation where there is a large gap between the source and target domains. The background suppression attention reduces the influence of background information in the training process. The feature space fine-tuning module adjusts the feature distribution of the interest features, which helps to make better predictions. Our approach improves detection performance by using only the information extracted from the model without maintaining additional information, which is convenient and can be easily plugged into other networks. We evaluate the detection performance in the in-domain situation and cross-domain situation. In-domain experiments on the VOC and COCO datasets and the cross-domain experiments on the VOC to medical image dataset UriSed2K show that our proposed method effectively improves the few-shot detection performance.