Reviewerly/exHarmony huggingface dataset:
Learning First-Order Representations for Planning from Black Box States: New Results

Recently Bonet and Geffner have shown that first-order representations for planning domains can be learned from the structure of the state space without any prior knowledge about the action schemas or domain predicates. For this, the learning problem is formulated as the search for a simplest first-order domain description D that along with information about instances I_i (number of objects and initial state) determine state space graphs G(P_i) that match the observed state graphs G_i where P_i = (D, I_i). The search is cast and solved approximately by means of a SAT solver that is called over a large family of propositional theories that differ just in the parameters encoding the possible number of action schemas and domain predicates, their arities, and the number of objects. In this work, we push the limits of these learners by moving to an answer set programming (ASP) encoding using the CLINGO system. The new encodings are more transparent and concise, extending the range of possible models while facilitating their exploration. We show that the domains introduced by Bonet and Geffner can be solved more efficiently in the new approach, often optimally, and furthermore, that the approach can be easily extended to handle partial information about the state graphs as well as noise that prevents some states from being distinguished.
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric

Measuring automatic speech recognition (ASR) system quality is critical for creating user-satisfying voice-driven applications. Word Error Rate (WER) has been traditionally used to evaluate ASR system quality; however, it sometimes correlates poorly with user perception/judgement of transcription quality. This is because WER weighs every word equally and does not consider semantic correctness which has a higher impact on user perception. In this work, we propose evaluating ASR output hypotheses quality with SemDist that can measure semantic correctness by using the distance between the semantic vectors of the reference and hypothesis extracted from a pre-trained language model. Our experimental results of 71K and 36K user annotated ASR output quality show that SemDist achieves higher correlation with user perception than WER. We also show that SemDist has higher correlation with downstream Natural Language Understanding (NLU) tasks than WER.
Empirical Policy Optimization for <i>n</i>-Player Markov Games

In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with the environment. In multiplayer Markov games (MGs), however, the interaction is nonstationary due to the behaviors of other players, so the agent has no fixed optimization objective. The challenge becomes finding equilibrium policies for all players. In this research, we treat the evolution of player policies as a dynamical process and propose a novel learning scheme for Nash equilibrium. The core is to evolve one’s policy according to not just its current in-game performance, but an aggregation of its performance over history. We show that for a variety of MGs, players in our learning scheme will provably converge to a point that is an approximation to Nash equilibrium. Combined with neural networks, we develop an empirical policy optimization algorithm, which is implemented in a reinforcement-learning framework and runs in a distributed way, with each player optimizing its policy based on own observations. We use two numerical examples to validate the convergence property on small-scale MGs, and a pong example to show the potential on large games.
Automated Disaster News Collection Classification and Geoparsing

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Head-free, Human Gaze-driven Assistive Robotic System for Reaching and Grasping

Patients with limb dysfunction have limited mobility, which prevents them from performing daily activities. We have developed an assistive robot system with an intuitive head free gaze interface. The system consists of multiple modules, including 3D gaze estimation, head free coordinate transformation, intention recognition, and robot trajectory planning. The robotic assistive system obtains clues from the user’s gaze to decode their intentions and implement actions. This allows the user only needs to look at the objects to make the robot system reach, grasp, and bring them to the user. The 3D gaze estimation is evaluated with 5 subjects, showing an overall accuracy of 5.53±1.2 cm. The integrated system’s experimental results show that the success rate is 96% in the implementation of automatic trajectory planning, and the success rate is 92% in the implementation of fixation-based trajectory planning. Finally, the results and work required to improve the system are discussed.
Hierarchical neural topic modeling with manifold regularization

Topic models have been widely used for learning the latent explainable representation of documents, but most of the existing approaches discover topics in a flat structure. In this study, we propose an effective hierarchical neural topic model with strong interpretability. Unlike the previous neural topic models, we explicitly model the dependency between layers of a network, and then combine latent variables of different layers to reconstruct documents. Utilizing this network structure, our model can extract a tree-shaped topic hierarchy with low redundancy and good explainability by exploiting dependency matrices. Furthermore, we introduce manifold regularization into the proposed method to improve the robustness of topic modeling. Experiments on real-world datasets validate that our model outperforms other topic models in several widely used metrics with much fewer computation costs.
Internet service providers as law enforcers and adjudicators. A public role of private actors

Private actors have become increasingly involved in the law enforcement process in recent years, taking up more proactive roles and being increasingly engaged in choices between conflicting rights and freedoms. The development and spread of information and communication technology (ICT) created a set of conditions in which the participation of private actors (service providers in this case) appears to be a necessity. These conditions include, for example, a lack of physical borders for ICT technologies, the speed and width of the spread of information on the Internet, as well as the growth of technological behemoths. The resulting reaction can be seen in various sectors, such as combatting illicit content online or gathering digital evidence. While executing these roles they may be compelled – de jure or de facto – to make value judgments which traditionally belong to the public authorities. At the same time the legal framework is either lacking or it does not fully cover the consequences of this fundamental paradigm shift, to the detriment of the authorities, private actors and persons concerned. The objective of this article is to examine the most important features of these developments and analyse resulting key legal problems. The author demonstrates that the legal landscape of cooperation between law enforcement and service providers must be rethought and offers a direction for this reflection.
Molecular dynamics on quantum annealers

Abstract In this work we demonstrate a practical prospect of using quantum annealers for simulation of molecular dynamics. A methodology developed for this goal, dubbed Quantum Differential Equations (QDE), is applied to propagate classical trajectories for the vibration of the hydrogen molecule in several regimes: nearly harmonic, highly anharmonic, and dissociative motion. The results obtained using the D-Wave 2000Q quantum annealer are all consistent and quickly converge to the analytical reference solution. Several alternative strategies for such calculations are explored and it was found that the most accurate results and the best efficiency are obtained by combining the quantum annealer with classical post-processing (greedy algorithm). Importantly, the QDE framework developed here is entirely general and can be applied to solve any system of first-order ordinary nonlinear differential equations using a quantum annealer.
XR Quality Index: Evaluating RAN Transmission Quality for XR services over 5G and Beyond

Recently, eXtended Reality (XR) has gained significant attention for providing immersive user experience in various applications with the help of 5G new radio (NR) network. To ensure the quality of experience (QoE) in XR services, wireless transmission plays an important role. In this paper, we propose a novel performance metric that can reflect the impact of network transmission on XR services, naming it as XR Quality Index (XQI). Specifically, fine-grained and coarse-grained XQI models are provided with detailed calculation procedures. The aim of XQI is to produce a final score that can reflect the impact of network transmission on QoE, and then could be used for network optimization. Simulation results verify the effects of XQI in evaluating the quality of XR videos.
Online stereo vision measurement based on correction of sensor structural parameters

Vibration can easily affect the structure of long baseline binocular vision sensors, resulting in changes in the external parameters of the binocular calibration model and the failure of measurement method. This paper presents an online stereo vision measurement based on correction of sensor structural parameters. The flexible structure model based on calibration model and iterative gradient descent nonlinear optimization model based on 3D redundant information are established. The optimal estimation of external parameters and object position measurement are realized according to multi-information constraints. Experiments show that this method can effectively solve the measurement failure caused by vibration in stereo vision measurement.
Taming the Merge Operator

Abstract Calculi with disjoint intersection types support a symmetric merge operator with subtyping. The merge operator generalizes record concatenation to any type, enabling expressive forms of object composition, and simple solutions to hard modularity problems. Unfortunately, recent calculi with disjoint intersection types and the merge operator lack a (direct) operational semantics with expected properties such as determinism and subject reduction , and only account for terminating programs . This paper proposes a type-directed operational semantics (TDOS) for calculi with intersection types and a merge operator. We study two variants of calculi in the literature. The first calculus, called λ i , is a variant of a calculus presented by Oliveira et al. (2016) and closely related to another calculus by Dunfield (2014). Although Dunfield proposes a direct small-step semantics for her calculus, her semantics lacks both determinism and subject reduction. Using our TDOS, we obtain a direct semantics for λ i that has both properties. The second calculus, called λ i + , employs the well-known subtyping relation of Barendregt, Coppo and Dezani-Ciancaglini (BCD). Therefore, λ i + extends the more basic subtyping relation of λ i , and also adds support for record types and nested composition (which enables recursive composition of merged components). To fully obtain determinism, both λ i and λ i + employ a disjointness restriction proposed in the original λ i calculus. As an added benefit the TDOS approach deals with recursion in a straightforward way, unlike previous calculi with disjoint intersection types where recursion is problematic. We relate the static and dynamic semantics of λ i to the original version of the calculus and the calculus by Dunfield. Furthermore, for λ i + , we show a novel formulation of BCD subtyping, which is algorithmic, has a very simple proof of transitivity and allows for the modular addition of distributivity rules (i.e. without affecting other rules of subtyping). All results have been fully formalized in the Coq theorem prover.
A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.
Remarks on topological spaces on $ {\mathbb Z}^n $ which are related to the Khalimsky $ n $-dimensional space

&lt;abstract&gt;&lt;p&gt;The present paper intensively studies various properties of certain topologies on the set of integers $ {\mathbb Z} $ (resp. $ {\mathbb Z}^n $) which are either homeomorphic or not homeomorphic to the typical Khalimsky line topology (resp. $ n $-dimensional Khalimsky topology). This finding plays a crucial role in addressing some problems which remain open in the field of digital topology.&lt;/p&gt;&lt;/abstract&gt;
Scanflow: A multi-graph framework for Machine Learning workflow management, supervision, and debugging

Machine Learning (ML) is more than just training models, the whole workflow must be considered. Once deployed, a ML model needs to be watched and constantly supervised and debugged to guarantee its validity and robustness in unexpected situations. Debugging in ML aims to identify (and address) the model weaknesses in not trivial contexts. Several techniques have been proposed to identify different types of model weaknesses, such as bias in classification, model decay, adversarial attacks, etc., yet there is not a generic framework that allows them to work in a collaborative, modular, portable, iterative way and, more importantly, flexible enough to allow both human- and machine-driven techniques. In this paper, we propose a novel containerized directed graph framework to support and accelerate end-to-end ML workflow management, supervision, and debugging. The framework allows defining and deploying ML workflows in containers, tracking their metadata, checking their behavior in production, and improving the models by using both learned and human-provided knowledge. We demonstrate these capabilities by integrating in the framework two hybrid systems to detect data drift distribution which identify the samples that are far from the latent space of the original distribution, ask for human intervention, and whether retrain the model or wrap it with a filter to remove the noise of corrupted data at inference time. We test these systems on MNIST-C, CIFAR-10-C, and FashionMNIST-C datasets, obtaining promising accuracy results with the help of human involvement.
Assessment of heavy metal pollution in agricultural soil around a gold mining area in Yitong County, China, based on satellite hyperspectral imagery

Soil is one of the essential natural resources that is at risk from heavy metal pollution. The traditional sampling method for soil heavy metal monitoring and assessment cannot meet the requirements for large-scale areas. The purpose of this study was to estimate the soil heavy metal concentrations based on Gaofen 5 (GF5) satellite hyperspectral imagery for the assessment of the heavy metal pollution in the study area and to analyze the scale effect under different resolutions. A total of 96 topsoil samples were collected in this work, and these samples were analyzed for the arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) contents. To solve the problem of the insignificant features caused by the complex imaging conditions of spaceborne hyperspectral satellite imagery, the binary weight symbiotic organisms search algorithm (BWSOS) was developed. After feature selection based on the BWSOS method, the heavy metal contents are inverted by the use of support vector machine regression. The experimental results show that the BWSOS feature selection method shows a good performance, with the Rp2 values for As, Cd, Cr, Cu, Ni, Pb, and Zn being 0.67, 0.68, 0.73, 0.71, 0.66, 0.65, and 0.71, respectively. Based on the estimated heavy metal concentration maps, the geoaccumulation index (Igeo), the pollution index, and the potential ecological risk index were calculated to assess the heavy metal pollution status in the study area. The results showed that only As contamination is present at a significant level, but with a low level of potential risk for the whole study area. A comparison with the results obtained using HyMap airborne hyperspectral imagery showed that the GF5 satellite hyperspectral imagery can obtain consistent results for heavy metal pollution assessment. The airborne hyperspectral imagery can provide more fine details, whereas the spaceborne hyperspectral imagery is more suitable for large-scale pollution assessment at a low cost.
Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices

In order to improve the accuracy of the evaluation results of multiperception intelligent wearable devices, the mathematical statistical characteristics based on speech, behavior, environment, and physical signs are proposed; first, the PCA feature compression algorithm was used to reduce the dimension of these features, and the differences among different training samples were compared and analyzed; then, three weak classifiers are designed using the logistic regression algorithm, and finally, a strong classifier with higher prediction accuracy is designed according to the boosting decision fusion method and ensemble learning idea. The results showed that the accuracy of the logistic regression model trained with the feature data of voice PCA was 0.964, but the recall rate and crossover results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on the boosting method and integrated learning are 0.969, and the prediction accuracy of K-folds cross-validation is also as high as 0.956; the superposition fusion results of three weak classifiers achieve a better classification effect.
Visual Robotic Perception System with Incremental Learning for Child–Robot Interaction Scenarios

This paper proposes a novel lightweight visual perception system with Incremental Learning (IL), tailored to child–robot interaction scenarios. Specifically, this encompasses both an action and emotion recognition module, with the former wrapped around an IL system, allowing novel actions to be easily added. This IL system enables the tutor aspiring to use robotic agents in interaction scenarios to further customize the system according to children’s needs. We perform extensive evaluations of the developed modules, achieving state-of-the-art results on both the children’s action BabyRobot dataset and the children’s emotion EmoReact dataset. Finally, we demonstrate the robustness and effectiveness of the IL system for action recognition by conducting a thorough experimental analysis for various conditions and parameters.
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection

The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes, and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase and feature integration phase. First, A perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments and analysis on eight popular benchmarks verify that our framework performs favorably against 9 state-of-art methods.
Fully-Succinct Publicly Verifiable Delegation from Constant-Size Assumptions

We construct a publicly verifiable, non-interactive delegation scheme for any polynomial size arithmetic circuit with proof-size and verification complexity comparable to those of pairing based zk-SNARKS. Concretely, the proof consists of O(1) group elements and verification requires O(1) pairings and n group exponentiations, where n is the size of the input. While known SNARK-based constructions rely on non-falsifiable assumptions, our construction can be proven sound under any constant size (\(k\ge 2\)) k-Matrix Diffie-Hellman (k-MDDH) assumption. However, the size of the reference string as well as the prover’s complexity are quadratic in the size of the circuit. This result demonstrates that we can construct delegation from very simple and well-understood assumptions. We consider this work a first step towards achieving practical delegation from standard, falsifiable assumptions.Our main technical contributions are first, the introduction and construction of what we call “no-signaling, somewhere statistically binding commitment schemes”. These commitments are extractable for any small part \(\mathbf {x}_S\) of an opening \(\mathbf {x}\), where \(S\subseteq [n]\) is of size at most K. Here n is the dimension of \(\mathbf {x}\) and \(\mathbf {x}_S=(x_i)_{i\in S}\). Importantly, for any \(S'\subseteq S\), extracting \(\mathbf {x}_{S'}\) can be done independently of \(S\setminus S'\). Second, we use these commitments to construct more efficient “quasi-arguments” with no-signaling extraction, introduced by Paneth and Rothblum (TCC 17). These arguments allow extracting parts of the witness of a statement and checking it against some local constraints without revealing which part is checked. We construct pairing-based quasi arguments for linear and quadratic constraints and combine them with the low-depth delegation result of González et al. (Asiacrypt 19) to construct the final delegation scheme.