Reviewerly/exHarmony huggingface dataset:
Testing of adaptive and context‐aware systems: approaches and challenges

Summary Adaptive systems (ASs) and context‐aware systems (CASs) are able to evaluate their own behaviour and to adapt it when the system fails to accomplish its goals or when better functionality or performance is possible. Ensuring the reliability of ASs and CASs is demanding because failures might have undesirable consequences. Testing ASs and CASs effectively is not trivial because of the inherent characteristics of these systems. The literature lacks a comprehensive review that provides a broad picture of the area; current reviews are outdated and incomplete. The objectives of this study are characterizing the state of the art in AS and CAS testing and discussing approaches, challenges, observed trends, and research limitations and directions. We performed a systematic literature review (SLR) and a thematic analysis of studies, reporting up‐to‐date, refined and extended results when compared with existing reviews. Based on 102 selected studies, we (i) characterized testing approaches by grouping techniques for ASs and CASs; (ii) updated and refined a characterization of testing challenges for ASs and CASs; and (iii) analysed and discussed research trends and implications for AS and CAS testing. There are recurring research concerns regarding AS and CAS testing. Examples are the generation of test cases and built‐in tests. Moreover, we also identified recurring testing challenges such as context monitoring and runtime decisions. Moreover, there are some trends such as model‐based testing and hybrid techniques and some little investigated issues like uncertainty and prediction of changes. All in all, our results may provide guidance for developers and researchers with respect to the practice and the future research on AS and CAS testing.
A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements

This article proposes a Gaussian filtering method to approximate the single-target updates and normalizing constants for multitarget tracking with nonlinear, non-Gaussian measurements, and a state-dependent probability of detection. The Gaussian approximation is based on the posterior linearization technique, which seeks the optimal affine approximation of the nonlinearities in a mean square error sense. The normalizing constant is approximated using sigma-points based on the posterior. The proposed approach is implemented in a Poisson multi-Bernoulli mixture filter and compared against standard methods to approximate single-target posteriors and normalizing constants in two range-bearings tracking scenarios.
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training

The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is more communication efficient than vanilla Parallel momentum SGD that incurs global average across all computing nodes. On the other hand, the large-batch training has been demonstrated critical to achieve runtime speedup. This motivates us to investigate how DmSGD performs in the large-batch scenario.In this work, we find the momentum term can amplify the inconsistency bias in DmSGD. Such bias becomes more evident as batch-size grows large and hence results in severe performance degradation. We next propose DecentLaM, a novel decentralized large-batch momentum SGD to remove the momentum-incurred bias. The convergence rate for both strongly convex and non-convex scenarios is established. Our theoretical results justify the superiority of DecentLaM to DmSGD especially in the large-batch scenario. Experimental results on a a variety of computer vision tasks and models show that DecentLaM promises both efficient and high-quality training.
A Reduced Feature Representation Scheme for Offline Handwritten Character Recognition

Recognizing handwritten characters (HCR) is a significant and challenging area of study in the field of computer vision and pattern recognition. Here, a sliding window-based platform for effective visualization of handwritten character images is designed. Three specific feature extraction strategies are suggested to recognize the characters. Those are an angular movement of shape-dependent characteristics, center-to-boundary of the text line, and center-to-boundary of text’s edge feature. The feature selection method is implemented after producing the features to reduce the dimension of the feature matrix. So that, the implementation time can be reduced, somehow. The accuracy from the reduced feature set has also been computed to assess the effectiveness. The feature selection is performed using an extra tree classifier. The characters in this study are recognized using the random forest classifier. The suggested method achieves quite better accuracy with the limited feature set.
News Text Classification and Recommendation Technology Based on Wide &amp; Deep-Bert Model

With the rapid development of Internet technology, news information on various social platforms is growing wildly, generating large amounts of data. Since short text information such as news headlines, short messages, and newsletters has a small number of words and limited content, it is often difficult to extract effective information. The sparse feature information causes difficulties in text classification. If the news system cannot efficiently and accurately realize news classification and users preference recommendation, it will inevitably affect the experience and frequency of platform users. This paper mainly studies the application of deep learning in the field of text classification and users' personal recommendation. It uses multiple English text data sets to learn text features. Based on the Wide&Deep model, combined with the improved BERT pretraining model, the Wide&Deep-BERT model is designed. In addition, the corresponding news text classification and recommendation technology process is proposed, and the Tensorflow deep learning framework is used to experimentally verify the technology, which proves the effectiveness and practicability of the design technology.
Blind identification of convolutional codes based on deep learning

Blind identification of channel codes is becoming increasingly important in signal interception and intelligent communication systems. However, most existing channel codes recognition algorithms extract features manually, which makes them highly demanding in real-world application. Thus, efficiently identifying channel codes is difficult using present technologies. This paper presents a deep residual network-based deep learning (DL) approach on the blind identification of convolutional code parameters for a given soft-decision sequence. The proposed method can blindly identify the convolutional codes without the need for the prior information about its coding parameters, and it achieves over 88% of recognition accuracy for 17 forms of convolutional codes when SNR exceeds or equals zero. Furthermore, we investigate factors affecting the accuracy of channel codes recognition including input length, model depth and data type. A comparison of the recognition accuracy between the proposed algorithm, log-likelihood ratio (LLR)-based traditional blind identification algorithm, and DL-based algorithm are then made. Experiment results show that deep residual network-based approaches could provide significant improvements over the traditional algorithm or existing DL-based algorithms in the blind identification of convolutional codes.
Image Captioning using Artificial Intelligence

Abstract In modern science there is a rapid development of artificial intelligence, image processing has gradually fascinated and inspired the attention of many researchers in the field of artificial intelligence and has become an interesting and demanding task. The main idea of Image caption is to automatically generate natural language descriptions according to the information observed in an image, this is an important portion of scene understanding, which combines all the knowledge and information available of computer vision and natural language processing. The use of image caption is broad and noteworthy, for example, the understanding of human-computer collaboration. This paper reviews the related methods and focuses on the attention mechanism, which plays a vital role in computer vision and is broadly used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper proposes some open challenges in the image caption task.
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS

The recent progress of deep convolutional neural networks has enabled great success in single image super-resolution (SISR) and many other vision tasks. Their performances are also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this paper, we expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. We use a hierarchical search strategy to find the best connection with local and global features. In this process, we define a complexity-based penalty for solving image super-resolution, which can be considered a multi-objective problem. Experiments show that our DeCoNASNet outperforms the state-of-the-art lightweight super-resolution networks designed by handcraft methods and existing NAS-based design.
Manifold Optimization-Assisted Gaussian Variational Approximation

Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference, especially in high-dimensional and large data settings. To control the computational cost, while being able to capture the correlations among the variables, the low rank plus diagonal structure was introduced in the previous literature for the Gaussian covariance matrix. For a specific Bayesian learning task, the uniqueness of the solution is usually ensured by imposing stringent constraints on the parameterized covariance matrix, which could break down during the optimization process. In this article, we consider two special covariance structures by applying the Stiefel manifold and Grassmann manifold constraints, to address the optimization difficulty in such factorization architectures. To speed up the updating process with minimum hyperparameter-tuning efforts, we design two new schemes of Riemannian stochastic gradient descent methods and compare them with other existing methods of optimizing on manifolds. In addition to fixing the identification issue, results from both simulation and empirical experiments prove the ability of the proposed methods of obtaining competitive accuracy and comparable converge speed in both high-dimensional and large-scale learning tasks. Supplementary materials for this article are available online.
Investigating the Statistical Assumptions of Naïve Bayes Classifiers

This paper investigates the impact of the probability distribution of a Naive Bayes classifier and the statistical distribution of the underlying feature data on the classifier's performance. Typical Naive Bayes performance assumptions lack quantitative and rigorous evidence in the common literature creating risk in rote application of Naive Bayes. This study investigates these performance assumptions to quantify where they are true, and the risk of maintaining those assumptions when utilizing Naive Bayes classifiers. Naive Bayes classifiers' exceptionally fast training times, performance, ease to implement, and minimal required resources often make them candidates for early classification trials, especially in Natural Language Processing tasks such as sentiment analysis. It is frequently assumed that the performance of a Naive Bayes classifier is heavily reliant with the distribution of the underlying data. This assumption is noted both in standard documentation and academic research and has largely been accepted as truth with little verification. This paper outlines an experiment that tests this assumption with real world sentiment analysis data. Naive Bayes classifiers were tested against non-Gaussian data, non-Gaussian feature weighted data, Gaussian-like data, and synthetically generated Gaussian data to observe the relationship between classifier performance and data distribution. Initial findings suggested that while this assumption is partially true, there may be additional factors heavily related with Naive Bayes performance that are not strictly related to a feature's distribution.
Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks

Despite the success of deep neural networks (DNN) in many applications, their ability to model uncertainty is still significantly limited. For example, in safety-critical applications such as autonomous driving, it is crucial to obtain a prediction that reflects different types of uncertainty to address life-threatening situations appropriately. In such cases, it is essential to be aware of the risk (i.e., aleatoric uncertainty) and the reliability (i.e., epistemic uncertainty) that comes with a prediction. We present AE-DNN, a model allowing the separation of aleatoric and epistemic uncertainty while maintaining a proper generalization capability. AE-DNN is based on deterministic DNN, which can determine the respective uncertainty measures in a single forward pass. In analyses with synthetic and image data, we show that our method improves the modeling of epistemic uncertainty while providing an intuitively understandable separation of risk and reliability.
A Gibbs Sampler for a Class of Random Convex Polytopes

We present a Gibbs sampler for the Dempster–Shafer (DS) approach to statistical inference for categorical distributions. The DS framework extends the Bayesian approach, allows in particular the use of partial prior information, and yields three-valued uncertainty assessments representing probabilities “for,” “against,” and “don’t know” about formal assertions of interest. The proposed algorithm targets the distribution of a class of random convex polytopes which encapsulate the DS inference. The sampler relies on an equivalence between the iterative constraints of the vertex configuration and the nonnegativity of cycles in a fully connected directed graph. Illustrations include the testing of independence in 2 × 2 contingency tables and parameter estimation of the linkage model.
Code Similarity Detection Based on Siamese Network

At present, with the continuous expansion of software scale, problems such as plagiarism, clone and reuse in software code become increasingly prominent, and the study of code similarity plays an important role. The existing studies have problems such as inaccurate representation of code semantic information and insufficient acquisition of word vector feature information. To solve the above problems, this paper proposes a code similarity calculation model based on a deep learning framework. This method firstly expresses the source code semantics. Secondly, it uses the Siamese network to extract semantic feature information. Finally, it utilizes the cosine distance to calculate the similarity of feature vector in high-dimensional space. Experiments have proved that our method has better performance in terms of precision, recall and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> compared with the baseline method. For this reason, our method can effectively obtain code semantic information and improve the performance of code similarity measurement.
MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Extraction

With the improvement of data volume, computing power and algorithms, deep learning has achieved rapid development and showing excellent performance. Recently, many deep learning models are proposed to solve the problems in different areas. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior, which makes it applicable to tasks such as handwriting recognition or speech recognition. However, the RNN relies heavily on the automatic learning ability to update parameters that concentrate on the data flow but seldom considers the feature extraction capability of the gate mechanism. In this paper, we propose a novel architecture to build the forget gate which is generated by multiple bases. Instead of using the traditional single-layer fully-connected network, we use a Multiple Attention (MA) based network to generate the forget gate which refines the optimization space of gate function and improve the granularity of the recurrent neural network to approximate the map in the ground truth. Due to the benefit of MA structure on the gate mechanism, the proposed MA-LSTM model achieves better feature extraction capability than other known models.
Video scene detection based on link prediction using graph convolution network

With the development of the Internet, multimedia data grows by an exponential level. The demand for video organization, summarization and retrieval has been increasing where scene detection plays an essential role. Existing shot clustering algorithms for scene detection usually treat temporal shot sequence as unconstrained data. The graph based scene detection methods can locate the scene boundaries by taking the temporal relation among shots into account, while most of them only rely on low-level features to determine whether the connected shot pairs are similar or not. The optimized algorithms considering temporal sequence of shots or combining multi-modal features will bring parameter trouble and computational burden. In this paper, we propose a novel temporal clustering method based on graph convolution network and the link transitivity of shot nodes, without involving complicated steps and prior parameter setting such as the number of clusters. In particular, the graph convolution network is used to predict the link possibility of node pairs that are close in temporal sequence. The shots are then clustered into scene segments by merging all possible links. Experimental results on BBC and OVSD datasets show that our approach is more robust and effective than the comparison methods in terms of F1-score.
Unlinkable Collaborative Learning Transactions: Privacy-Awareness in Decentralized Approaches

Smart contracts (SCs) and collaborative learning (CL) are disclosed publicly, in which most transactions and activities that occur by the parties can be bared in real-time.Both are strengthened in a decentralized manner.CL allows numerous clients to collectively build deep learning models privately by aggregating the gradient values from clients' devices, yet it lacks the incentive mechanism for the contributing clients.On the other hand, the merits of SCs can be a plausible solution as an incentive mechanism in the CL system because self-executing contracts with immutable data records are resistant to failure.The clients can claim the rewards by stating their contribution arbitrarily in the SCs and tendering a proof transaction function.Nevertheless, directly adopting SCs in the CL system could breach clients' privacy because the transactions are exposed openly.The observer can infer the properties of the clients' resources.Therefore, we designed schemes that can overcome observers' ability to link clients' information with their associated devices during training.In essence, our schemes are unbiased.We also provide a secure incentive mechanism for the parties in the CL system by obscuring the information values.Finally, the numerical results indicate that the proposed schemes satisfy the design goals.
FedMONN: Meta Operation Neural Network for Secure Federated Aggregation

Federated learning enables collaborative machine learning among multiple independent participants while preserving data privacy of each participant through model aggregation during training. However, model aggregation still faces potential risks that are associated with indirect leakage, such as parameters. In this paper, we first propose an algorithm called Meta Operation Neural Network (MONN) to perform basic arithmetic operations on encrypted data and generate operation results in a plaintext way. MONN is actually a general neural network composed of an encoder and a meta operation decoder, where both the encryption and meta decryption are lossless. MONN can be applied to federated learning for secure model aggregation. In this way, data privacy can be well preserved not only from malicious attackers but also untrustworthy servers. Experimental results reveal the following three key properties: 1) The proposed MONN based federated aggregation method (denoted as FedMONN) can reach satisfactory performance comparable with non-federated counterparts; 2) FedMONN is more secure than the classic federated averaging and one-time pad aggregation, even the server is not a trustable third party; 3) FedMONN is much efficient than the federated aggregation based on the Paillier homomorphic encryption technique.
Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics

Abstract A robot’s ability to provide explanatory descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing the desired transparency in decision making is challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning methods. As a step towards addressing this challenge, our architecture combines the complementary strengths of non-monotonic logical reasoning with incomplete commonsense domain knowledge, deep learning, and inductive learning. During reasoning and learning, the architecture enables a robot to provide on-demand explanations of its decisions, the evolution of associated beliefs, and the outcomes of hypothetical actions, in the form of relational descriptions of relevant domain objects, attributes, and actions. The architecture’s capabilities are illustrated and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects. Experimental results indicate the ability to reliably acquire and merge new information about the domain in the form of constraints, preconditions, and effects of actions, and to provide accurate explanations in the presence of noisy sensing and actuation.
Flow-Based Situation-Aware Approach for eHealth Data Processing

A major challenge for remote patient monitoring (RPM) systems is processing data collected from a large number of healthcare devices and developing suitable algorithms and approaches to react accordingly to a wide spectrum of situations of interest, which must be properly detected. Flow-based programming, whose operation is based on state changes, is a promising approach to overcome this challenge, facilitating the personalization of data processing according to patients' health conditions. However, this approach is not suited to the detection of complex contextual situations, thereby hindering its adoption in RPM systems for data processing. In this regard, we propose an approach that combines situation awareness and flow-based programming to widen the capability of RPM systems to handle many-sided scenarios, which requires the monitoring of complex patient healthcare conditions. An evaluation of the proposed approach was conducted to demonstrate the flexibility of the solution for processing heterogeneous health information.