Reviewerly/exHarmony huggingface dataset:
Memory-Aware Active Learning in Mobile Sensing Systems

We propose a novel active learning framework for activity recognition using wearable sensors. Our work is unique in that it takes limitations of the oracle into account when selecting sensor data for annotation by the oracle. Our approach is inspired by human-beings' limited capacity to respond to prompts on their mobile device. This capacity constraint is manifested not only in the number of queries that a person can respond to in a given time-frame but also in the time lag between the query issuance and the oracle response. We introduce the notion of mindful active learning and propose a computational framework, called EMMA, to maximize the active learning performance taking informativeness of sensor data, query budget, and human memory into account. We formulate this optimization problem, propose an approach to model memory retention, discuss the complexity of the problem, and propose a greedy heuristic to solve the optimization problem. Additionally, we design an approach to perform mindful active learning in batch where multiple sensor observations are selected simultaneously for querying the oracle. We demonstrate the effectiveness of our approach using three publicly available activity datasets and by simulating oracles with various memory strengths. We show that the activity recognition accuracy ranges from 21% to 97% depending on memory strength, query budget, and difficulty of the machine learning task. Our results also indicate that EMMA achieves an accuracy level that is, on average, 13.5% higher than the case when only informativeness of the sensor data is considered for active learning. Moreover, we show that the performance of our approach is at most 20% less than the experimental upper-bound and up to 80% higher than the experimental lower-bound. To evaluate the performance of EMMA for batch active learning, we design two instantiations of EMMA to perform active learning in batch mode. We show that these algorithms improve the algorithm training time at the cost of a reduced accuracy in performance. Another finding in our work is that integrating clustering into the process of selecting sensor observations for batch active learning improves the activity learning performance by 11.1% on average, mainly due to reducing the redundancy among the selected sensor observations. We observe that mindful active learning is most beneficial when the query budget is small and/or the oracle's memory is weak. This observation emphasizes advantages of utilizing mindful active learning strategies in mobile health settings that involve interaction with older adults and other populations with cognitive impairments.
Maximizing Plan Legibility in Stochastic Environments

Legible behavior allows an observing agent to infer the intention of an observed agent. Producing legible behavior is crucial for successful multi-agent interaction in many domains. We introduce techniques for legible planning in stochastic environments. Maximizing legibility, however, presents a complex trade-off between maximizing the underlying rewards. Hence, we propose a method to balance the trade-off. In our experiments, we demonstrate that maximizing legibility results in unambiguous behaviors.
Learning Hierarchical Teaching Policies for Cooperative Agents

Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teammates has demonstrated that action advising accelerates team-wide learning. However, the prior work has simplified the learning of advising policies by using simple function approximations and only considered advising with primitive (low-level) actions, limiting the scalability of learning and teaching to complex domains. This paper introduces a novel learning-to-teach framework, called hierarchical multiagent teaching (HMAT), that improves scalability to complex environments by using the deep representation for student policies and by advising with more expressive extended action sequences over multiple levels of temporal abstraction. Our empirical evaluations demonstrate that HMAT improves team-wide learning progress in large, complex domains where previous approaches fail. HMAT also learns teaching policies that can effectively transfer knowledge to different teammates with knowledge of different tasks, even when the teammates have heterogeneous action spaces.
Image Analysis Based on Nonnegative/Binary Matrix Factorization

Using nonnegative/binary matrix factorization (NBMF), a matrix can be decomposed into a nonnegative matrix and a binary matrix. Our analysis of facial images, based on NBMF and using the Fujitsu Digital Annealer, leads to successful image reconstruction and image classification. The NBMF algorithm converges in fewer iterations than those required for the convergence of nonnegative matrix factorization (NMF), although both techniques perform comparably in image classification.
A Novel Visual Sensor Stabilization Platform for Robotic Sharks Based on Improved LADRC and Digital Image Algorithm

Autonomous underwater missions require the construction of a stable visual sensing system. However, acquiring continuous steady image sequences is a very challenging task for bionic robotic fish due to their tight internal space and the inherent periodic disturbance caused by the tail beating. To solve this problem, this paper proposes a modified stabilization strategy that combines mechanical devices and digital image techniques to enhance the visual sensor stability and resist periodic disturbance. More specifically, an improved window function-based linear active disturbance rejection control (LADRC) was utilized for mechanical stabilization. Furthermore, a rapid algorithm with inertial measurement units (IMUs) was implemented for digital stabilization. The experiments regarding mechanical stabilization, digital stabilization, and target recognition on the experimental platform for simulating fishlike oscillations demonstrated the effectiveness of the proposed methods. The success of these experiments provides valuable insight into the construction of underwater visual sensing systems and also establishes a solid foundation for the visual applications for robotic fish in dynamic aquatic environments.
Macro-to-micro quantum mapping and the emergence of nonlinearity

As a universal theory of physics, quantum mechanics must assign states to every level of description of a system -- from a full microscopic description, all the way up to an effective macroscopic characterization -- and also to describe the interconnections among them. Assuming that we only have a coarse-grained access to a physical system, here we show how to assign to it a microscopic description that abides by all macroscopic constraints. In order to do that, we employ general coarse-graining maps, allowing our approach to be used even when the split between system and environment is not obvious. As a by-product, we show how effective nonlinear dynamics can emerge from the linear quantum evolution, and we readily apply it to a state discrimination task.
Towards Outlier Sensor Detection in Ambient Intelligent Platforms—A Low-Complexity Statistical Approach

Sensor networks in real-world environments, such as smart cities or ambient intelligent platforms, provide applications with large and heterogeneous sets of data streams. Outliers-observations that do not conform to an expected behavior-has then turned into a crucial task to establish and maintain secure and reliable databases in this kind of platforms. However, the procedures to obtain accurate models for erratic observations have to operate with low complexity in terms of storage and computational time, in order to attend the limited processing and storage capabilities of the sensor nodes in these environments. In this work, we analyze three binary classifiers based on three statistical prediction models-ARIMA (Auto-Regressive Integrated Moving Average), GAM (Generalized Additive Model), and LOESS (LOcal RegrESSion)-for outlier detection with low memory consumption and computational time rates. As a result, we provide (1) the best classifier and settings to detect outliers, based on the ARIMA model, and (2) two real-world classified datasets as ground truths for future research.
Testing a quantum error-correcting code on various platforms

Quantum error correction plays an important role in fault-tolerant quantum information processing. It is usually difficult to experimentally realize quantum error correction, as it requires multiple qubits and quantum gates with high fidelity. Here we propose a simple quantum error-correcting code for the detected amplitude damping channel. The code requires only two qubits. We implement the encoding, the channel, and the recovery on an optical platform, the IBM Q System, and a nuclear magnetic resonance system. For all of these systems, the error correction advantage appears when the damping rate exceeds some threshold. We compare the features of these quantum information processing systems used and demonstrate the advantage of quantum error correction on current quantum computing platforms.
Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Background Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a different perspective on the performance, they remain to be overall measures for the whole sample, discounting the uniqueness of each case or patient. Intuitively, we know that all cases are not equal, but the present evaluative approaches do not take case difficulty into account. Objective A more case-based, comprehensive approach is warranted to assess supervised ML outcomes and forms the rationale for this study. This study aims to demonstrate how the item response theory (IRT) can be used to stratify the data based on how difficult each case is to classify, independent of the outcome measure of interest (eg, accuracy). This stratification allows the evaluation of ML classifiers to take the form of a distribution rather than a single scalar value. Methods Two large, public intensive care unit data sets, Medical Information Mart for Intensive Care III and electronic intensive care unit, were used to showcase this method in predicting mortality. For each data set, a balanced sample (n=8078 and n=21,940, respectively) and an imbalanced sample (n=12,117 and n=32,910, respectively) were drawn. A 2-parameter logistic model was used to provide scores for each case. Several ML algorithms were used in the demonstration to classify cases based on their health-related features: logistic regression, linear discriminant analysis, K-nearest neighbors, decision tree, naive Bayes, and a neural network. Generalized linear mixed model analyses were used to assess the effects of case difficulty strata, ML algorithm, and the interaction between them in predicting accuracy. Results The results showed significant effects (P&lt;.001) for case difficulty strata, ML algorithm, and their interaction in predicting accuracy and illustrated that all classifiers performed better with easier-to-classify cases and that overall the neural network performed best. Significant interactions suggest that cases that fall in the most arduous strata should be handled by logistic regression, linear discriminant analysis, decision tree, or neural network but not by naive Bayes or K-nearest neighbors. Conventional metrics for ML classification have been reported for methodological comparison. Conclusions This demonstration shows that using the IRT is a viable method for understanding the data that are provided to ML algorithms, independent of outcome measures, and highlights how well classifiers differentiate cases of varying difficulty. This method explains which features are indicative of healthy states and why. It enables end users to tailor the classifier that is appropriate to the difficulty level of the patient for personalized medicine.
Conservative Stochastic Optimization With Expectation Constraints

This paper considers stochastic convex optimization problems where the objective and constraint functions involve expectations with respect to the data indices or environmental variables, in addition to deterministic convex constraints on the domain of the variables. Although the setting is generic and arises in different machine learning applications, online and efficient approaches for solving such problems have not been widely studied. Since the underlying data distribution is unknown a priori, a closed-form solution is generally not available, and classical deterministic optimization paradigms are not applicable. State-of-the-art approaches, such as those using the saddle point framework, can ensure that the optimality gap as well as the constraint violation decay as $\O\left(T^{-\frac{1}{2}}\right)$ where $T$ is the number of stochastic gradients. The domain constraints are assumed simple and handled via projection at every iteration. In this work, we propose a novel conservative stochastic optimization algorithm (CSOA) that achieves zero constraint violation and $\O\left(T^{-\frac{1}{2}}\right)$ optimality gap. Further, the projection operation (for scenarios when calculating projection is expensive) in the proposed algorithm can be avoided by considering the conditional gradient or Frank-Wolfe (FW) variant of the algorithm. The state-of-the-art stochastic FW variants achieve an optimality gap of $\O\left(T^{-\frac{1}{3}}\right)$ after $T$ iterations, though these algorithms have not been applied to problems with functional expectation constraints. In this work, we propose the FW-CSOA algorithm that is not only projection-free but also achieves zero constraint violation with $\O\left(T^{-\frac{1}{4}}\right)$ decay of the optimality gap. The efficacy of the proposed algorithms is tested on two relevant problems: fair classification and structured matrix completion.
Producer Liability for AI-Based Technologies in the European Union

The manufacturer&amp;#39;s liability for defective products has remained almost unmodified since 1985 when Directive 85/374/EEC (=PLD) was enacted. Perhaps new technology based on artificial intelligence (=AI) could bring about a turning point in the regulation if concepts such as &amp;quot;product&amp;quot; and &amp;quot;defect&amp;quot; or aspects such as &amp;quot;grounds of liability&amp;quot;, the so-called &amp;quot;development risks defense&amp;quot;, and the &amp;quot;solidarity&amp;quot; are reconsidered. The Group of Experts on Liability and New Technologies (=NTF), in its &amp;ldquo;Liability for AI and other emerging digital technologies&amp;rdquo; Report, recommends, inter alia, the regulation of two different civil liability regimes: strict liability and fault-based liability. Thus, it will be necessary to determine precisely the cases to which these regimes apply and how to deal with &amp;ldquo;uncertain causation&amp;rdquo;. The alleviation of the victim&amp;rsquo;s burden of proof should be considered. From the various documents being published, it appears that the producer&amp;rsquo;s strict liability will remain as the main liability rule, but it will be combined -as the NFT suggests- in the case of the breach of a duty of care with a fault-based liability rule. This approach leaves some open questions, i.e., how to properly combine both grounds of liability in the domain of products that cause damages. In my view, the liability regime suggested by the NTF is far more complicated that the regime which distinguishes three types of defects that are often stressed: the defect of design, the defect of manufacturing, and the defect of information.
Extended Multi WLS Method for Lossless Image Coding

In this paper, the most efficient (from data compaction point of view) and current image lossless coding method is presented. Being computationally complex, the algorithm is still more time efficient than its main competitors. The presented cascaded method is based on the Weighted Least Square (WLS) technique, with many improvements introduced, e.g., its main stage is followed by a two-step NLMS predictor ended with Context-Dependent Constant Component Removing. The prediction error is coded by a highly efficient binary context arithmetic coder. The performance of the new algorithm is compared to that of other coders for a set of widely used benchmark images.
A new adaptive XOR, hashing and encryption-based authentication protocol for secure transmission of the medical data in Internet of Things (IoT)

Internet of Things (IoT) plays a prominent role in health-care of patients, which assist the physicians and patients through the assistance in effective decision-making and additionally, in the medical field, IoT plays a significant role in real-time monitoring of the patients. Even though the data provided by the IoT devices ensure the effective decision-making, the data is susceptible to the network attacks. Thus, the paper proposes an authentication protocol for enabling the secure data transmission in IoT based on three functions, such as encryption function, hashing function, and adaptive XOR function. The proposed authentication protocol is named as, Adaptive XOR, hashing and Encryption Key Exchange (AXHE) protocol, which is the combination of the functions, such as encryption function, hashing function, and adaptive XOR function. The protocol ensures the security in the communication through two successive phases, such as registration and authentication of the user, where the user name, password, public keys, private keys, and security factor are employed. The authentication is progressed as seven levels and whenever the security factor matches, the user is authenticated and the communication continues. The analysis of the proposed AXHE is performed using 50 and 100 nodes in the presence of DOS and black hole attacks, which acquires the detection rate, throughput, and detection delay of 0.3859, 0.32, and 6.535 s, respectively.
‘Write’ but not ‘spell’ Chinese characters with a BCI-controlled robot

Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words. However, different from spelling English words in one-dimension sequences, Chinese characters are often written in a two-dimensional structure. Previous studies had never investigated how to use BCI to ‘write’ but not ‘spell’ Chinese characters. This study developed an innovative BCI-controlled robot for writing Chinese characters. The BCI system contained 108 commands displayed in a 9*12 array. A pixel-based writing method was proposed to map the starting point and ending point of each stroke of Chinese characters to the array. Connecting the starting and ending points for each stroke can make up any Chinese character. The large command set was encoded by the hybrid P300 and SSVEP features efficiently, in which each output needed only 1s of EEG data. The task-related component analysis was used to decode the combined features. Five subjects participated in this study and achieved an average accuracy of 87.23% and a maximal accuracy of 100%. The corresponding information transfer rate was 56.85 bits/min and 71.10 bits/min, respectively. The BCI-controlled robotic arm could write a Chinese character ‘福’ with 16 strokes within 5.7 seconds for the best subject. The demo video can be found at https://www.youtube.com/watch?v=A1w-e2dBGl0. The study results demonstrated that the proposed BCI-controlled robot is efficient for writing ideogram (e.g. Chinese characters) and phonogram (e.g. English letter), leading to broad prospects for real-world applications of BCIs.
Encoding scheme using quantum dots for single logical qubit information onto four-photon decoherence-free states

We designed an encoding scheme, using quantum dots (QDs), for single logical qubit information by encoding quantum information onto four-photon decoherence-free states to acquire immunity against collective decoherence. The designed scheme comprised of QDs, confined in single-sided cavities (QD-cavity systems), used for arbitrary quantum information, encoded onto four-photon decoherence-free states (logical qubits). For our scheme, which can generate the four-photon decoherence-free states, and can encode quantum information onto logical qubits, high efficiency and reliable performance of the interaction between the photons and QD-cavity systems is essential. Thus, through our analysis of the performance of QD-cavity systems under vacuum noise and sideband leakage, we demonstrate that the encoding scheme for single logical qubit information could be feasibly implemented.
Quantum-classical algorithms for skewed linear systems with an optimized Hadamard test

The authors propose two hybrid quantum-classical algorithms for finding approximate solutions to heavily skewed systems of linear equations for overdetermined and underdetermined cases. The algorithms, which have polylogarithmic dependence on the larger dimension and polynomial dependence in other natural quantities, could potentially be used on noisy intermediate scaled quantum devices.
Sketch-a-Segmenter: Sketch-Based Photo Segmenter Generation

Given pixel-level annotated data, traditional photo segmentation techniques have achieved promising results. However, these photo segmentation models can only identify objects in categories for which data annotation and training have been carried out. This limitation has inspired recent work on few-shot and zero-shot learning for image segmentation. In this paper, we show the value of sketch for photo segmentation, in particular as a transferable representation to describe a concept to be segmented. We show, for the first time, that it is possible to generate a photo-segmentation model of a novel category using just a single sketch and furthermore exploit the unique fine-grained characteristics of sketch to produce more detailed segmentation. More specifically, we propose a sketch-based photo segmentation method that takes sketch as input and synthesizes the weights required for a neural network to segment the corresponding region of a given photo. Our framework can be applied at both the category-level and the instance-level, and fine-grained input sketches provide more accurate segmentation in the latter. This framework generalizes across categories via sketch and thus provides an alternative to zero-shot learning when segmenting a photo from a category without annotated training data. To investigate the instance-level relationship across sketch and photo, we create the SketchySeg dataset which contains segmentation annotations for photos corresponding to paired sketches in the Sketchy Dataset.
Robust path-following control design of heavy vehicles based on multiobjective evolutionary optimization

The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods which demand prior knowledge of the system dynamics. In this case, the control system designer depends on the quality of the uncertain model to obtain an optimal control performance. This work proposes a robust recursive controller designed via multiobjective optimization to overcome these shortcomings. Furthermore, a local search approach for multiobjective optimization problems is presented. The proposed method applies to any multiobjective evolutionary algorithm already established in the literature. The results presented show that this combination of model-based controller and machine learning improves the effectiveness of the system in terms of robustness, stability and smoothness.