Reviewerly/exHarmony huggingface dataset:
Error-aware Markov blanket learning for causal feature selection

Causal feature selection has attracted much attention in recent years, since it has better robustness than the traditional feature selection. Existing causal feature selection algorithms aim to identify a Markov blanket (MB) of the class variable. The MB of the class variable implies potential local causal relations around the class variable and has been proven to be the optimal feature subset for feature selection. Since almost all existing causal feature selection methods employ conditional independence (CI) tests to learn MBs, in practical settings, existing causal feature selection algorithms encounter the problem of CI test errors, which seriously deteriorates the performance of those existing methods. To solve this issue, in this paper, we propose an Error-Aware Markov Blanket learning (EAMB) algorithm with two novel subroutines to tackle the CI test error problem. Specifically, EAMB first identifies the MB of the class variable using one subroutine, and then utilizes the other subroutine to selectively recover the missed true MB features from the discarded features. The extensive experiments on 13 real-world datasets validate the effectiveness of EAMB against fourteen state-of-the-art causal feature selection algorithms and four well-established traditional feature selection methods.
A Flexible and Efficient FPGA Accelerator for Various Large-Scale and Lightweight CNNs

To enable efficient deployment of convolutional neural networks (CNNs) on embedded platforms for different computer vision applications, several convolution variants have been introduced, such as depthwise convolution (DWCV), transposed convolution (TPCV), and dilated convolution (DLCV). To address the utilization degradation issue occurred in a general convolution engine for these emerging operators, a highly flexible and reconfigurable hardware accelerator is proposed to efficiently support various CNN-based vision tasks. Firstly, to avoid workload imbalance of TPCV, a zero transfer and skipping (ZTS) method is proposed to reorganize the computation process. To eliminate the redundant zero calculations of TPCV and DLCV, a sparsity-alike processing (SAP) method is proposed based on weight-oriented dataflow. Secondly, the DWCV or pooling layers are configured to be directly executed after standard convolutions without external memory accesses. Furthermore, a programmable execution schedule is introduced to gain better flexibility. Finally, the proposed accelerator is evaluated on Intel Arria 10 SoC FPGA. Experimental results show state-of-the-art performance on both large-scale and lightweight CNNs for image segmentation or classification. Specifically, the accelerator can achieve a processing speed up to 339.9 FPS and computational efficiency up to 0.58 GOPS/DSP, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3\times $ </tex-math></inline-formula> better than the prior art evaluated on the same network.
Multi-agent pathfinding with continuous time

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that each agent reaches its goal and the agents do not collide. In recent years, variants of MAPF have risen in a wide range of real-world applications such as warehouse management and autonomous vehicles. Optimizing common MAPF objectives, such as minimizing sum-of-costs or makespan, is computationally intractable, but state-of-the-art algorithms are able to solve optimally problems with dozens of agents. However, most MAPF algorithms assume that (1) time is discretized into time steps and (2) the duration of every action is one time step. These simplifying assumptions limit the applicability of MAPF algorithms in real-world applications and raise non-trivial questions such as how to discretize time in an effective manner. We propose two novel MAPF algorithms for finding optimal solutions that do not rely on any time discretization. In particular, our algorithms do not require quantization of wait and move actions' durations, allowing these durations to take any value required to find optimal solutions. The first algorithm we propose, called Continuous-time Conflict-Based Search (CCBS), draws on ideas from Safe Interval Path Planning (SIPP), a single-agent pathfinding algorithm designed to cope with dynamic obstacles, and Conflict-Based Search (CBS), a state-of-the-art search-based MAPF algorithm. SMT-CCBS builds on similar ideas, but is based on a different state-of-the-art MAPF algorithm called SMT-CBS, which applied a SAT Modulo Theory (SMT) problem-solving procedure. CCBS guarantees to return solutions that have minimal sum-of-costs, while SMT-CCBS guarantees to return solutions that have minimal makespan. We implemented CCBS and SMT-CCBS and evaluated them on grid-based MAPF problems and general graphs (roadmaps). The results show that both algorithms can efficiently solve optimally non-trivial MAPF problems.
RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment

This paper proposes a hybrid metaheuristic based resource allocation framework named RAFL for load balancing in cloud computing environment. The objective is to proactively minimize the load imbalance across active physical machines and among their considered resource capacities (e.g., CPU and RAM). This prevents overloading/underloading of active physical machines and utilizes their considered resource capacities in a balanced manner. In the proposed framework, a phasor particle swarm optimization and dragonfly algorithm based hybrid optimization algorithm named PPSO-DA is used to generate an optimal resource allocation plan for balancing the load. Simulation experiments are performed using CloudSim simulator to measure the metrics of load imbalance across active physical machines and among their considered resource capacities. Results show that the proposed PPSO-DA algorithm outperforms phasor particle swarm optimization, dragonfly algorithm, comprehensive learning particle swarm optimization, memory based hybrid dragonfly algorithm, sine cosine algorithm, and elephant herding optimization, in finding an optimal resource allocation for balancing the load. The statistical analysis and benchmark testing also validates the relative superiority of PPSO-DA.
Semantic-aware event link reasoning over industrial knowledge graph embedding time series data

The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an industrial knowledge graph embedding time series data. Its knowledge graph skeleton is constructed through a specific manufacturing process. NLTK is used to transform technical documents into a structured industrial knowledge graph. We employ deep learning (DL)-based models to obtain semantic information related to product quality prediction using time series data collected from IoT devices. Then the prediction information is attached to the specified node in the knowledge graph. Thus, the knowledge graph will describe the dynamic semantic information of manufacturing contexts. Meanwhile, a dynamic event link reasoning model that uses graph embedding to aggregate manufacturing processes information is proposed. The implicit information with industrial temporal knowledge can be further mined and inferred. The case study has shown that the proposed knowledge graph link reasoning reflects dynamic temporal characteristics. Compared to the classical knowledge graph prediction models, our model is superior to the baseline methods.
State-Only Imitation Learning for Dexterous Manipulation

Modern model-free reinforcement learning methods have recently demonstrated impressive results on a number of problems. However, complex domains like dexterous manipulation remain a challenge due to the high sample complexity. To address this, current approaches employ expert demonstrations in the form of state-action pairs, which are difficult to obtain for real-world settings such as learning from videos. In this paper, we move toward a more realistic setting and explore state-only imitation learning. To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations. The inverse dynamics model and the policy are trained jointly. Our method performs on par with state-action approaches and considerably outperforms RL alone. By not relying on expert actions, we are able to learn from demonstrations with different dynamics, morphologies, and objects. Videos available on the ${\text{project page}}$.
Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization

This work introduces a program to enhance images taken in low-light. Fuzzy set theory is creating a significant shift in image processing. Interval-valued intuitionistic fuzzy sets (IVIFS) based on intuitionistic fuzzy sets constructed from fuzzy sets are used to enhance images taken in low-light. In the proposed method, first the given low-light image is fuzzified by normal fuzzification. Then the fuzzified image is converted to an interval-valued intuitionistic fuzzy image. This image will be proposed enhanced image after applying the contrast limited adaptive histogram equalization (CLAHE). The experimental results reveal that the proposed method gives better results when compared with other existing methods like histogram equalization (HE), CLAHE, brightness preserving dynamic fuzzy histogram equalization (BPDFHE), histogram specification approach (HSA). Based on the performance analysis like entropy and correlation coefficient (CC), the proposed method gives better results.
Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots

Chatbots are often designed to mimic social roles attributed to humans. However, little is known about the impact of using language that fails to conform to the associated social role. Our research draws on sociolinguistic to investigate how a chatbot’s language choices can adhere to the expected social role the agent performs within a context. We seek to understand whether chatbots design should account for linguistic register. This research analyzes how register differences play a role in shaping the user’s perception of the human-chatbot interaction. We produced parallel corpora of conversations in the tourism domain with similar content and varying register characteristics and evaluated users’ preferences of chatbot’s linguistic choices in terms of appropriateness, credibility, and user experience. Our results show that register characteristics are strong predictors of user’s preferences, which points to the needs of designing chatbots with register-appropriate language to improve acceptance and users’ perceptions of chatbot interactions.
IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization

Federated learning (FL) through its novel applications and services has enhanced its presence as a promising tool in the Internet of Things (IoT) domain. Specifically, in a multiaccess edge computing setup with a host of IoT devices, FL is most suitable since it leverages distributed client data to train high-performance deep learning (DL) models while keeping the data private. However, the underlying deep neural networks (DNNs) are huge, preventing its direct deployment onto resource-constrained computing and memory-limited IoT devices. Besides, frequent exchange of model updates between the central server and clients in FL could result in a communication bottleneck. To address these challenges, in this article, we introduce GWEP, a model compression-based FL method. It utilizes joint quantization and model pruning to reap the benefits of DNNs while meeting the capabilities of resource-constrained devices. Consequently, by reducing the computational, memory, and network footprint of FL, the low-end IoT devices may be able to participate in the FL process. In addition, we provide theoretical guarantees of FL convergence. Through empirical evaluations, we demonstrate that our approach significantly outperforms the baseline algorithms by being up to 10.23 times faster with 11 times lesser communication rounds, while achieving high-model compression, energy efficiency, and learning performance.
Quantification of some intrinsic soil properties using proximal sensing in arid lands: Application of Vis-NIR, MIR, and pXRF spectroscopy

The aim of the current research was to examine the effectiveness of Vis-NIR-SWIR (visible, near-infrared, and shortwave infrared spectroscopy: 350–2500 nm), MIR (mid-infrared spectroscopy: 4000–400 cm−1), and pXRF (portable x-ray fluorescence) to characterize and estimate clay, sand, silt, calcium carbonate (CaCO3), gypsum (CaSO4.2H2O), soil organic carbon (SOC) contents and electrical conductivity (EC) in Afzar district, Fars province, southern Iran. The study area was divided into non-saline (A) and saline (B) regions, and then a total of 300 soil samples from these areas were collected for laboratory analysis. The partial least-squares regression (PLSR) method was used to predict soil properties from Vis-NIR-SWIR, mid-IR, and pXRF spectra. In general, Vis-NIR-SWIR showed better results for predicting soil texture (R2 for area A: clay = 0.78, sand = 0.80 and silt = 0.69; R2 for area B: clay = 0.63, sand = 0.67 and silt = 0.47) in the arid regions than mid-IR (R2 for area A: clay = 0.71, sand = 0.75 and silt = 0.63; R2 for area B: clay = 0.61, sand = 0.57 and silt = 0.37), and pXRF (R2 for area A: clay = 0.49, sand = 0.60 and silt = 0.50; R2 for area B: clay = 0.41, sand = 0.41 and silt = 0.23). Based on the RPI index, three approaches alone could successfully predict CaCO3 in areas A and B (R2 > 0.75 and RPIQ >2.02). The model prediction results for gypsum showed that although the value of the determination coefficient is high (R2 > 0.95), but according to the low value of the RPIQ index (RPIQ<1.26), the model prediction performance is untrusted, and this is probably due to unsuitable distribution of gypsum in this area. The results showed that the pXRF technique alone could not predict SOC in these areas (R2 < 0.27 and RPIQ <1.08), and it is probably better to use it in combination with Vis-NIR-SWIR and mid-IR data. pXRF for predicting soil salinity in areas A and B was acceptable and successful, respectively. Also, the results showed that the Vis-NIR-SWIR and mid-IR ranges could not predict salinity in these regions and should be used in combination with pXRF data. In conclusion, the Vis-NIR-SWIR, mid-IR, and pXRF spectroscopy are presented here as useful and reliable tools for direct applications in pedology, particularly in the digital mapping of soil surface properties.
Dual Watermarking for Security of COVID-19 Patient Record

In recent years, smart healthcare systems have gained popularity due to the ease of sharing e-patient records over the open network. The issue of maintaining the security of these records has attracted many researchers. Thus, robust and dual watermarking based on redundant discrete wavelet transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD) are put forward for CT scan images of COVID-19 patients. To ensure a high level of authentication, multiple watermarks in form of Electronic Patient Record (EPR) text and medical image are embedded in the cover. The EPR is encoded via turbo code to reduce /eliminate the channel noise if any. Further, both imperceptibility and robustness are achieved by a fuzzy inference system, and the marked image is encrypted using a lightweight encryption technique. Moreover, the extracted watermark is denoised using the concept of deep neural network (DNN) to improve its robustness. Experiment results and performance analyses verify the proposed dual watermarking scheme.
An improved type-reduction algorithm for general type-2 fuzzy sets

General type-2 fuzzy systems (GT2 FLSs) provide a more flexible way of overcoming an uncertain lack of uniformity in different applications. Centroid type reduction is one of the major component of GT2 FLSs, it is currently one of the key factors restricting GT2 FLSs efficiency. This paper proposes a novel and efficient method for centroid type reduction. The method is based on α-plane representation, where a general type-2 fuzzy set is decomposing into a series of α planes. The centroid for each plane is the calculated, layer by layer, from the top down, until the α=0 plane is reached. In each α plane, a centroid type reduction calculation is performed using an improved enhanced opposite direction searching algorithm (IEODS). Finally, the centroids obtained for each plane are aggregated to obtain a type-1 fuzzy set, which form the centroid of general type-2 fuzzy set. Experiments show that this method results in less calculation time and fewer iterations than other α-plane representation-based reduction methods. By providing more efficient type reduction, the method improves the applicability of general type-2 fuzzy systems in more complex environments and embedded platforms, thereby enhancing their scope for future development.
A differentially private nonnegative matrix factorization for recommender system

Nonnegative matrix factorization (NMF)-based models have been proven to be highly effective and scalable in addressing collaborative filtering (CF) problems in the recommender system (RS). Since RS requires tremendous user data to provide personalized information services, the issue of data privacy has gained prominence. Although the differential privacy (DP) technique has been widely applied to RS, the requirement of nonnegativity makes it difficult to successfully incorporate DP into NMF. In this paper, a differentially private NMF (DPNMF) method is proposed by perturbing the coefficients of the polynomial expression of the objective function, which achieves a good trade-off between privacy protection and recommendation quality. Moreover, to alleviate the influence of the noises added by DP on the items with sparse ratings, an imputation-based DPNMF (IDPNMF) method is proposed. Theoretic analyses and experimental results on several benchmark datasets show that the proposed schemes have good performance and can achieve better recommendation quality on large-scale datasets. Therefore, our schemes have high potential to implement privacy-preserving RS based on big data.
SmartDID: A Novel Privacy-Preserving Identity Based on Blockchain for IoT

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.
Knowledge extraction and retention based continual learning by using convolutional autoencoder-based learning classifier system

An ideal artificial intelligence-based autonomous system, interacting with a dynamic environment, is required to learn continuously as human do. Human beings retain the learned knowledge, accumulate, and utilize it to solve related problems. Currently, most artificial intelligence-based systems lack this capability and work in an isolated learning paradigm. In this paper, we present a novel continual learning model to solve the challenging problem of real-world images classification. The proposed model is capable of learning continuously by utilizing the previously learned knowledge. It can handle both multi-task and single incremental task scenarios as opposed to various existing models that cover only the multi-task scenarios. In the proposed model, a deep convolutional autoencoder is presented to extract features from images. In addition, a learning classifier system with an effective knowledge encoding scheme is proposed for mapping real-world images to code fragment-based compact knowledge representation. Experiments are conducted on three benchmark image datasets to validate the model: (i) CORe50, (ii) iCubWorld28, and (iii) STL-10. Experiments results demonstrate that the proposed model outperforms the baseline method as well as various state-of-the-art methods for both continual learning scenarios.
Directional mutation and crossover for immature performance of whale algorithm with application to engineering optimization

Abstract In recent years, a range of novel and pseudonovel optimization algorithms has been proposed for solving engineering problems. Swarm intelligence optimization algorithms (SIAs) have become popular methods, and the whale optimization algorithm (WOA) is one of the highly discussed SIAs. However, regardless of novelty concerns about this method, the basic WOA is a weak method compared to top differential evolutions and particle swarm variants, and it suffers from the problem of poor initial population quality and slow convergence speed. Accordingly, in this paper, to increase the diversity of WOA versions and enhance the performance of WOA, a new WOA variant, named LXMWOA, is proposed, and based on the Lévy initialization strategy, the directional crossover mechanism, and the directional mutation mechanism. Specifically, the introduction of the Lévy initialization strategy allows initial populations to be dynamically distributed in the search space and enhances the global search capability of the WOA. Meanwhile, the directional crossover mechanism and the directional mutation mechanism can improve the local exploitation capability of the WOA. To evaluate its performance, using a series of functions and three models of engineering optimization problems, the LXMWOA was compared with a broad array of competitive optimizers. The experimental results demonstrate that the LXMWOA is significantly superior to its exploration and exploitation capability peers. Therefore, the proposed LXMWOA has great potential to be used for solving engineering problems.