Reviewerly/exHarmony huggingface dataset:
Attention Guided Multiple Source and Target Domain Adaptation

Domain adaptation aims to alleviate the distribution discrepancy between source and target domains. Most conventional methods focus on one target domain setting adapted from one or multiple source domains while neglecting the multi-target domain setting. We argue that different target domains also have complementary information, which is very important for performance improvement. In this paper, we propose an Attention-guided Multiple source-and-target Domain Adaptation (AMDA) method to capture the context dependency information on transferable regions among multiple source and target domains. The innovation points of this paper are as follows: (1) We use numerous adversarial strategies to harvest sufficient information from multiple source and target domains, which extends the generalization and robustness of the feature pools. (2) We propose an intra-domain and inter-domain attention module to explore transferable context information. The proposed attention module can learn domain-invariant representations and reduce the negative transfer by focusing on transferable knowledge. Extensive experiments validate the effectiveness of our method with achieving state-of-the-art performance on several unsupervised domain adaptation datasets.
Online Topology Identification From Vector Autoregressive Time Series

Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multi-variate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes an alternative to the well-known, yet usually intractable, Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Their constant complexity per update also renders these algorithms appealing for big-data scenarios. Despite using data sequentially, both algorithms are shown to asymptotically attain the same average performance as a batch estimator which uses the entire data set at once. To this end, sublinear (static) regret bounds are established. Performance is also characterized in time-varying setups by means of dynamic regret analysis. Numerical results with real and synthetic data further support the merits of the proposed algorithms in static and dynamic scenarios.
A novel dimension reduction and dictionary learning framework for high-dimensional data classification

High-dimensional problem poses significant challenges for dictionary learning based classification architecture. Joint Dimension Reduction and Dictionary Learning (JDRDL) framework shows great potential for overcoming the challenges caused by high dimensionality. However, most of the existing JDRDL approaches do not consider the complex nonlinear relationships within high-dimensional data, which limits their classification performance. To overcome this problem, a novel joint dimension reduction and dictionary learning framework is proposed in this paper for high-dimensional data classification. Firstly, at dimension reduction stage, an autoencoder is employed to learn a nonlinear mapping that reduces dimensionality and preserves nonlinear structure of the high-dimensional data. Then, at dictionary learning stage, the locality constraint with label embedding, which takes the locality and label information into account together, is incorporated into the learning process to preserve desirable nonlinear local structure and enhance class discrimination. Moreover, the mapping function and dictionary are optimized simultaneously to enhance the performance. Encouraging experimental results on multiple benchmark datasets confirm that the proposed framework is effective and efficient for high-dimensional data classification.
Securing Emission Data of Smart Vehicles with Blockchain and Self-Sovereign Identities

Modern Internet of Things (IoT) networks including vehicle networks face an increased demand for security and access control with respect to privacy for sensitive data. Data manipulation and tampering of emissions values due to the economic incentives and environmental and health issues require a tamper-proof solution with the use of blockchain (BC) where the integrity of data is ensured. In this paper, we propose the integration of a public permissioned Self-Sovereign Identities (SSI) framework with a permissioned consortium BC based architecture. This innovatively supports the decentralization of the authentication and authorization processes to overcome the single point of failure problems and the use of SSI to assign identities to IoT devices. Additionally, it gives full control to the holders for their identities, whether they are humans, organizations or smart vehicles. With the practice of advanced zero-knowledge proof (ZKP) cryptographic techniques, the exposure of sensitive and private information is minimized to the absolute necessary and gains in performance and scalability are achieved. Furthermore, the way this ecosystem of technologies is combined guarantees a trusted environment for enabling and automating vehicles' emissions certification according to emissions standards and regulations. Detailed descriptions of the processes required to integrate Hyperledger Indy (HLI) SSIs to authenticate and authorize entities on a Hyperledger Fabric (HLF) network are being quoted.
Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning

In this paper, we propose a general framework termed centroid estimation with guaranteed efficiency (CEGE) for weakly supervised learning (WSL) with incomplete, inexact, and inaccurate supervision. The core of our framework is to devise an unbiased and statistically efficient risk estimator that is applicable to various weak supervision. Specifically, by decomposing the loss function (e.g., the squared loss and hinge loss) into a label-independent term and a label-dependent term, we discover that only the latter is influenced by the weak supervision and is related to the centroid of the entire dataset. Therefore, by constructing two auxiliary pseudo-labeled datasets with synthesized labels, we derive unbiased estimates of centroid based on the two auxiliary datasets, respectively. These two estimates are further linearly combined with a properly decided coefficient which makes the final combined estimate not only unbiased but also statistically efficient. This is better than some existing methods that only care about the unbiasedness of estimation but ignore the statistical efficiency. The good statistical efficiency of the derived estimator is guaranteed as we theoretically prove that it acquires the minimum variance when estimating the centroid. As a result, intensive experimental results on a large number of benchmark datasets demonstrate that our CEGE generally obtains better performance than the existing approaches related to typical WSL problems including semi-supervised learning, positive-unlabeled learning, multiple instance learning, and label noise learning.
Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health Data

Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.
On the Question of Pedagogical Digital Competence

Educational institutions develop professional training programs for teachers so they could bring technology into the classroom and take the quality of education to a new level. However, despite the measures being adopted, a number of researchers report unsatisfying results. One of the root causes of this situation seems to be the absence of clear understanding of what the notions of digital competence and pedagogical digital competence are supposed to include. This problem is tightly connected with the problem of creating a framework for the development of digital skills of an instructor. On one hand, there is a demand for universal models that would include a wide range of pedagogical digital skills. There is a demand for universal models, which would include the widest possible range of digital competences of a teacher. Among them there are such existing frameworks as DigiCompEdu, ICT CFT, TETCs, which cover numerous aspects of instructors’ work. Meanwhile, there is the lack of models that would structurize the professional practice of a teacher at the micro level of using a digital instrument. There is a necessity for the framework comprising a limited set of primary basic skills which would be universal enough to be applicable to operating any kind of technology. In the article, we propose a version of this type of a framework.
Walking and Teleportation in Wide-area Virtual Reality Experiences

Location-based or Out-of-Home Entertainment refers to experiences such as theme and amusement parks, laser tag and paintball arenas, roller and ice skating rinks, zoos and aquariums, or science centers and museums among many other family entertainment and cultural venues. More recently, location-based VR has emerged as a new category of out-of-home entertainment. These VR experiences can be likened to social entertainment options such as laser tag, where physical movement is an inherent part of the experience versus at-home VR experiences where physical movement often needs to be replaced by artificial locomotion techniques due to tracking space constraints. In this work, we present the first VR study to understand the impact of natural walking in a large physical space on presence and user preference. We compare it with teleportation in the same large space, since teleportation is the most commonly used locomotion technique for consumer, at-home VR. Our results show that walking was overwhelmingly preferred by the participants and teleportation leads to significantly higher self-reported simulator sickness. The data also shows a trend towards higher self-reported presence for natural walking.
Singularity, misspecification and the convergence rate of EM

A line of recent work has analyzed the behavior of the Expectation-Maximization (EM) algorithm in the well-specified setting, in which the population likelihood is locally strongly concave around its maximizing argument. Examples include suitably separated Gaussian mixture models and mixtures of linear regressions. We consider over-specified settings in which the number of fitted components is larger than the number of components in the true distribution. Such mis-specified settings can lead to singularity in the Fisher information matrix, and moreover, the maximum likelihood estimator based on $n$ i.i.d. samples in $d$ dimensions can have a nonstandard $\mathcal{O}((d/n)^{\frac{1}{4}})$ rate of convergence. Focusing on the simple setting of two-component mixtures fit to a $d$-dimensional Gaussian distribution, we study the behavior of the EM algorithm both when the mixture weights are different (unbalanced case), and are equal (balanced case). Our analysis reveals a sharp distinction between these two cases: in the former, the EM algorithm converges geometrically to a point at Euclidean distance of $\mathcal{O}((d/n)^{\frac{1}{2}})$ from the true parameter, whereas in the latter case, the convergence rate is exponentially slower, and the fixed point has a much lower $\mathcal{O}((d/n)^{\frac{1}{4}})$ accuracy. Analysis of this singular case requires the introduction of some novel techniques: in particular, we make use of a careful form of localization in the associated empirical process, and develop a recursive argument to progressively sharpen the statistical rate.
Fully integer-based quantization for mobile convolutional neural network inference

Deploying deep convolutional neural networks on mobile devices is challenging because of the conflict between their heavy computational overhead and the hardware’s restricted computing capacity. Network quantization is typically used to alleviate this problem. However, we found that a “datatype mismatch” issue in existing low bitwidth quantization approaches can generate severe instruction redundancy, dramatically reducing their running efficiency on mobile devices. We therefore propose a novel quantization approach which ensures that only integer-based arithmetic is needed during the inference stage of the quantized model. To this end, we improved the quantization function to compel the quantized value to follow a standard integer format. Then we presented to simultaneously quantize the batch normalization parameters by a logarithm-like method. By doing so, the quantized model can keep the advantage of low bitwidth representation, while preventing the occurrence of “datatype mismatch” issue and corresponding instruction redundancy. Comprehensive experiments show that our method can achieve comparable prediction accuracy to other state-of-the-art methods while reducing the run-time latency by a large margin. Our fully integer-based quantized Resnet-18 has 4-bit weights, 4-bit activations and only a 0.7% top-1 and 0.4% top-5 accuracy drop on the ImageNet dataset. The assembly language implementation of a series of building blockscan reach a maximum of 4.33× the speed of the original full-precision version on an ARMv8 CPU.
NAFM: Neural and Attentional Factorization Machine for Web API Recommendation

With the wide adoption of SOA (Service Oriented Architecture), a massive amount of innovative applications emerge on the Internet. One of the popular representations is Mashup composed of multiple Web APIs. Recommending desirable Web APIs to develop Mashup applications has attracted much attention. A dozen of service recommendation approaches are proposed by incorporating multi-dimensional features extracted from service repository into recommendation models. Among the existing works, factorization machine based models show better performance than traditional collaborative filtering techniques in accuracy. However, they either model factorized interactions with the same weight or neglect the non-linear and complex inherent structure of real-world data. In real-world applications, different predictor variables usually have different predictive power, and not all features contain useful signal for estimating the target. Moreover, higher-order feature interactions are usually underlain in real-world data. To address these drawbacks, this paper proposes a hybrid factorization machine model with a novel neural network architecture named NAFM by integrating deep neural network to capture the non-linear feature interactions and attention mechanism to capture the different importance of feature interactions. Comprehensive experiments on a real-world dataset show that the proposed approach outperforms the other state-of-the-art models for service recommendation.
Chunking and data compression in verbal short-term memory

Short-term verbal memory is improved when words can be chunked into larger units. Miller (1956) suggested that the capacity of verbal short-term memory is determined by the number of chunks that can be stored in memory, rather than by the number of items or the amount of information. But how does the improvement due to chunking come about, and is memory really determined by the number of chunks? One possibility is that chunking is a form of data compression. It allows more information to be stored in the available capacity. An alternative is that chunking operates primarily by redintegration. Chunks exist only in long-term memory, and enable the corresponding items in short-term memory to be reconstructed more reliably from a degraded trace. We review the data favoring each of these views and discuss the implications of treating chunking as data compression. Contrary to Miller, we suggest that memory capacity is primarily determined both by the amount of information that can be stored but also by the underlying representational vocabulary of the memory system. Given the limitations on the representations that can be stored in verbal short-term memory, chunking can sometimes allow the information capacity of short-term memory to be exploited more efficiently. (202 words).
Integrating Quantum Computing into Workflow Modeling and Execution

Quantum computing has the potential to significantly impact many application domains, as several quantum algorithms are promising to solve problems more efficiently than possible on classical computers. However, various complex pre- and post-processing tasks have to be performed when executing a quantum circuit, which require immense mathematical and technical knowledge. For example, calculations on today's quantum computers are noisy and require an error mitigation task after the execution. Hence, integrating classical applications with quantum circuits is a difficult challenge. In this paper, we introduce a modeling extension for imperative workflow languages to enable the integration of quantum computations and ease the orchestration of classical applications and quantum circuits. Further, we show how the extension can be mapped to native modeling constructs of extended workflow languages to retain the portability of the workflows. We validate the practical feasibility of our approach by applying our proposed extension to BPMN and introduce Quantum4BPMN.
Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.
Historical natural kinds and mineralogy: Systematizing contingency in the context of necessity

The advancement of science depends upon developing classification protocols that systematize natural objects and phenomena into "natural kinds"-categorizations that are conjectured to represent genuine divisions in nature by virtue of playing central roles in the articulation of successful scientific theories. In the physical sciences, theoretically powerful classification systems, such as the periodic table, are typically time independent. Similarly, the standard classification of mineral species by the International Mineralogical Association's Commission on New Minerals, Nomenclature, and Classification relies on idealized chemical composition and crystal structure, which are time-independent attributes selected on the basis of theoretical considerations from chemical theory and solid-state physics. However, when considering mineral kinds in the historical context of planetary evolution, a different, time-dependent classification scheme is warranted. We propose an "evolutionary" system of mineral classification based on recognition of the role played by minerals in the origin and development of planetary systems. Lacking a comprehensive theory of chemical evolution capable of explaining the time-dependent pattern of chemical complexification exhibited by our universe, we recommend a bootstrapping approach to mineral classification based on observations of geological field studies, astronomical observations, laboratory experiments, and analyses of natural samples and their environments. This approach holds the potential to elucidate underlying universal principles of cosmic chemical complexification.