Reviewerly/exHarmony huggingface dataset:
Fuzzy Rule Interpolation With $K$-Neighbors for TSK Models

When a fuzzy system is presented with an incomplete (or sparse) rule base, fuzzy rule interpolation (FRI) offers a useful mechanism to infer conclusions for unmatched observations. However, most existing FRI methodologies are established for Mamdani inference models, but not for Takagi–Sugeno–Kang (TSK) ones. This article presents a novel approach for computing interpolated outcomes with TSK models, using only a small number of neighboring rules to an unmatched observation. Compared with existing methods, the new approach helps improve the computational efficiency of the overall interpolative reasoning process, while minimizing the adverse impact on accuracy induced by firing those rules of low similarities with the new observation. For problems that involve a rule base of a large size, where closest neighboring rules may be rather alike to one another, a rule-clustering-based method is introduced. It derives an interpolated conclusion by first clustering rules into different groups with a clustering algorithm and then, by utilizing only those rules that are each selected from one of a given, small number of closest rule clusters. Systematic experimental examinations are carried out to verify the efficacy of the introduced techniques, in comparison with state-of-the-art methods, over a range of benchmark regression problems, while employing different clustering algorithms (which also shows the flexibility in ways of implementing the novel approach).
Web Blog Content Curation Using Fuzzy-Related Capsule Network-Based Auto Encoder

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.
Random noise suppression and super-resolution reconstruction algorithm of seismic profile based on GAN

Abstract In this paper, we propose a random noise suppression and super-resolution reconstruction algorithm for seismic profiles based on Generative Adversarial Networks, in anticipation of reducing the influence of random noise and low resolution on seismic profiles. Firstly, the algorithm used the residual learning strategy to construct a de-noising subnet to accurate separate the interference noise on the basis of protecting the effective signal. Furthermore, it iterated the back-projection unit to complete the reconstruction of the high-resolution seismic sections image, while responsed sampling error to enhance the super-resolution performance of the algorithm. For seismic data characteristics, designed the discriminator to be a fully convolutional neural network, used a larger convolution kernels to extract data features and continuously strengthened the supervision of the generator performance optimization during the training process. The results on the synthetic data and the actual data indicated that the algorithm could improve the quality of seismic cross-section, make ideal signal-to-noise ratio and further improve the resolution of the reconstructed cross-sectional image. Besides, the observations of geological structures such as fractures were also clearer.
Privacy-preserving association rule mining based on electronic medical system

Privacy protection during collaborative distributed association rule mining is an important research, which has been widely used in market prediction, medical research and other fields. In medical research, Domadiya et al. (Sadhana 43(8):127, 2018) focused on mining association rules from horizontally distributed healthcare data to diagnose heart disease. They claimed they proposed a more effective privacy-preserving distributed association rule mining (PPDARM) scheme. However, a serious security scrutiny of the scheme is performed, and we find it vulnerable to protect the support of the itemsets from any electronic health record (EHR) system, which is the most important parameter Domadiya et al. tried to protect. In this paper, we first present the cryptanalysis of the PPDARM scheme proposed by Domadiya et al. as well as some revised performance analyses. Then a new PPDARM scheme with less interactions is proposed to avert the shortcomings of Domadiya et al., using the homomorphic properties of the distributed Paillier cryptosystem to accomplish the cooperative computation. Our scheme allows the directed authority (miner) to obtain the final results rather than all cooperative EHR systems, in case of semi-honest but pseudo EHR systems. Moreover, security analysis and performance evaluation demonstrate our proposal is efficient and feasible.
VAGA: Towards Accurate and Interpretable Outlier Detection Based on Variational Auto-Encoder and Genetic Algorithm for High-Dimensional Data

The curse of dimensionality in high-dimensional data makes it difficult to capture the abnormality of data points in full data space. To deal with this problem, we propose an outlier detection model based on Variational Autoencoder and Genetic Algorithm for subspace outlier analysis of high-dimensional data (VAGA). The proposed VAGA model constructs a variational autoencoder (VAE) to preliminarily detect outliers. Then the genetic algorithm (GA) is used to search the abnormal subspace of the outliers obtained by the VAE layer to provide a basis for subspace outlier analysis. The subsequent clustering of the abnormal subspaces help filter out the false positives which are fed back to the VAE layer to adjust network weights. The comparative experiments performed on three public benchmark datasets show that the outlier detection results of the proposed VAGA model are highly interpretable and have better accuracy performance than the state-of-the-art outlier detection methods.
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition

Hand gesture recognition can benefit from directly processing 3D point cloud sequences, which carry rich geometric information and enable the learning of expressive spatio-temporal features. However, currently employed single-stream models cannot sufficiently capture multi-scale features that include both fine-grained local posture variations and global hand movements. We therefore propose a novel dual-stream model, which decouples the learning of local and global features. These are eventually fused in an LSTM for temporal modelling. To induce the global and local stream to capture complementary position and posture features, we propose the use of different 3D learning architectures in both streams. Specifically, state-of-the-art point cloud networks excel at capturing fine posture variations from raw point clouds in the local stream. To track hand movements in the global stream, we combine an encoding with residual basis point sets and a fully-connected DenseNet. We evaluate the method on the Shrec'17 and DHG dataset and report state-of-the-art results at a reduced computational cost. Source code is available at https://github.com/multimodallearning/hand-gesture-posture-position.
Image binarization method for markers tracking in extreme light conditions

Image binarization is one of the fundamental methods in image processing and it is mainly used as a preprocessing for other methods in image processing. We present an image binarization method with the primary purpose to find markers such as those used in mobile 3D scanning systems. Handling a mobile 3D scanning system often includes bad conditions such as light reflection and non-uniform illumination. As the basic part of the scanning process, the proposed binarization method successfully overcomes the above problems and does it successfully. Due to the trend of increasing image size and real-time image processing we were able to achieve the required small algorithmic complexity. The paper outlines a comparison with several other methods with a focus on objects with markers including the calibration system plane of the 3D scanning system. Although it is obvious that no binarization algorithm is best for all types of images, we also give the results of the proposed method applied to historical documents.
Black Twitter is gold

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor
Energy Cost of Quantum Circuit Optimisation: Predicting That Optimising Shor’s Algorithm Circuit Uses 1 GWh

Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.
The profile of future consumer with microchip implant: Habits and characteristics

Abstract Passive radio frequency identification microchip implants (MIs) have been the subject of public debate for nearly two decades, due to differing views on issues of privacy and impact on the human body, as well as actual successful applications in healthcare. Nonetheless, advances in technology imply that consumers may use MIs for identification, shopping, and other activities in the future. The purpose of this study was to identify the characteristics and habits of individuals who are more likely to use a MI in the near future. A cross‐sectional study was conducted in five countries: Poland, Croatia, Slovenia, Ukraine, and the Russian Federation. Convenience sampling and an online questionnaire were used to collect data from 2037 respondents. Multiple regression analysis was used to test the research questions, and the results showed the predictors of willingness to use MIs. The potential MI user is male, has multiple social media profiles, and has a history of losing keys, wallets, or ID cards. He tends to use credit cards, contactless payments, and payment services when shopping. His willingness to use a MI decreases with age. Based on these findings, retailers and other service providers can adapt their processes to successfully support the future consumer with MI.
Culture teaching in EFL classes: Teachers’ beliefs, attitudes, and classroom practices

It is inevitably believed that culture teaching is the pivotal feeling of integrating culture into the teaching of a language, including in the EFL setting. This study aims to explore the English teachers’ beliefs, attitudes, and the reflection of their beliefs and attitudes on the teaching syllabi. The sequential explanatory mix-methods design was applied in junior high schools in Ngawi. The data were obtained from 144 English teachers’ answers to a questionnaire and interviews with six teachers. Then, the data were analysed by using descriptive statistics, the independent sample T-test, and the Mann-Whitney test. The results indicated that the majority of junior high school English teachers believed in the importance of incorporating culture into their teaching of the language taught and students’ learning process. Moreover, both state and private junior high school English teachers showed similar beliefs and attitudes related to culture teaching. When they taught English, the culture associated with that language had also been taught so that the misconception of learning the language can be minimized. The result of teachers’ practices strongly indicates that the English teachers in Ngawi had implemented the teaching of culture and inserted various cultural elements in the process of their teaching and learning in the EFL classes.
Adaptive Model Scheduling for Resource-efficient Data Labeling

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.
Lightweight Direct Acyclic Graph Blockchain for Enhancing Resource-Constrained IoT Environment

Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (<i>IoT</i>). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (<i>Multizone-DAG-Blockchain</i>) framework is proposed for the fog-based IoT environment. In this context, fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes, thus preserving the energy consumption of the IoT devices. Both IoT and fog nodes are initially authenticated using a non-cloneable physical function- based validation mechanism (<i>DPUF-VM</i>) in which multiple authentication certificates are verified in the blockchain. Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q- Curve algorithm. In the cloud, sensitive data is stored as ciphertext. Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes. The fog node first performs a redundancy analysis using the Jaccard Similarity (JS) measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network (<i>N2IN</i>) algorithm. A lightweight proof-of-authentication (<i>PoAh</i>) algorithm is presented and executed by the optimal consensus node selected by the bi- objective spiral optimization (<i>BoSo</i>) algorithm for transaction validation. The proposed work is modeled in Network Simulator 3.26 (ns-3.26), and the performance is evaluated in terms of energy consumption, storage cost, response time, and throughput.
A persona aware persuasive dialogue policy for dynamic and co-operative goal setting

In recent years, the popularity of virtual agents particularly task-oriented dialogue agents has increased immensely due to their effectiveness and simplicity in various domains such as industry, e-commerce, and health. In real-world, users do not have always a predefined and immutable goal, i.e., they may upgrade/downgrade/update their task goal dynamically depending upon their utility and the serving capability of the assisting agent. However, the existing Virtual Agents (VAs) in the dialogue literature relinquish and yield dialogue failure if they find any dynamic goal setting or goal unavailability scenarios. Motivated by these inabilities of existing VAs, we propose some intelligent and expert Dialogue Agents (A Unified Dialogue Agent and Multi-agent Dialogue system) that can deal with dynamic and goal unavailability situations to elevate both user satisfaction and the agent's utility particularly task success rate. The proposed architecture incorporates a goal guiding module namely Dynamic and Co-Operative Goal Driven Module (DyCoGDM), which traces goal status and resolves goal discrepancy through dynamic goal setting (Goal Formulator) and personalized persuasion (Goal Persuader) mechanisms. We also created and annotated a dialogue corpus because of unavailability of such corpus featured with dynamic and goal unavailability scenarios. Our proposed method outperforms several baselines and state of the art methods in all evaluation metrics. The proposed VA is capable of dealing with dynamic goals and goal unavailability scenarios effectively. The study found that the persona aware persuasive dialogue agent outperforms generalized persuasive dialogue agent by a large margin. Furthermore, we also observed that the task oriented reward is the most essential reward for training a reinforcement learning based agent and agents trained without task based reward do not even converge.
An AST Structure Enhanced Decoder for Code Generation

Currently, the most dominant neural code generation modelsare often equipped with a tree-structured LSTM decoder, which outputs a sequence of actions to construct an Abstract Syntax Tree (AST) via pre-order traversal. However, such a decoder has two obvious drawbacks. First, except for the parent action, other faraway and important history actions rarely contribute to the current decision. Second, it also neglects future actions, which may be crucial for the prediction of the current action. To deal with these issues, in this paper, we propose a novel AST structure enhanced decoder for code generation, which significantly extends the decoder with respect to the above two aspects. First, we introduce an AST information enhanced attention mechanism to fully exploit history actions, of which impacts are further distinguished according to their syntactic distances, action types and relative positions; Second, we jointly model the predictions of current action and its important future action via multi-task learning, where the learned hidden state of the latter can be further leveraged to improve the former. Experimental results on commonly-used datasets demonstrate the effectiveness of our proposed decoder.<sup>1</sup>
Multi-scale Attention Recalibration Network for crowd counting

Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in recent years. Nevertheless, how to efficiently address the problems of scale variation and complex backgrounds remain a major challenge. For this, we present an innovative Multi-scale Attention Recalibration Network termed MARNet for obtaining more accurate crowd counting. This is achieved mainly by introducing and integrating two significant modules into the proposed model. More concretely, a Feature Pyramid Module (FPM) is first designed to achieve multi-scale feature enhancement by utilizing multiple dilated convolutions with different rates, thus providing rich contextual information for subsequent operations. Besides, to adequately take advantage of these contextual information, a Feature Recalibration Module (FRM) is devised by integrating a Dimension Attention (DA) block with a Region Recalibration (RR) block. The DA block is mainly used for modeling the semantic dependencies between different dimensions of contextual information, while the RR block is responsible for reassigning attention weights for different regions based on the semantic dependencies. By the integration of the above two blocks, the proposed method can be targeted to capture the crowd features for accurately estimating crowd density. Extensive experiments on multiple publicly crowd counting datasets well demonstrate that our method significantly outperforms most existing methods in terms of the counting accuracy and the quality of the generated density map.