Reviewerly/exHarmony huggingface dataset:
ATS-O2A: A state-based adversarial attack strategy on deep reinforcement learning

In recent years, deep reinforcement learning has been widely applied in many decision-making tasks requiring high safety and security due to its excellent performance. However, if an adversary attacks when the agent making critical decisions, it is bound to bring disastrous consequences because humans cannot detect it. Therefore, it is necessary to study adversarial attacks against deep reinforcement learning to help researchers design highly robust and secure algorithms and systems. In this paper, we proposed an attack method based on Attack Time Selection (ATS) function and Optimal Attack Action (O2A) strategy, named ATS-O2A. We select the critical attack moment through the ATS function, and then combine the state-based strategy with the O2A strategy to select the optimal attack action which has profound influence as targeted action, finally we launch an attack by making targeted adversarial examples. In order to measure the stealthiness and effectiveness of the attack, we designed a new measurement index. Experiments show that our method can effectively reduce unnecessary attacks and improve the efficiency of attacks.
LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBM

Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules. The LightGBM module applies gradient boosting decision trees for feature processing, which improves LFDNN's ability to handle dense numerical features; the shallow model introduces the FM model for explicitly modeling the finite-order feature crosses, which strengthens the expressive ability of the model; the deep neural network module uses a fully connected feedforward neural network to allow the model to obtain more high-order feature crosses information and mine more data patterns in the features; finally, the Fusion module allows the shallow model and the deep model to obtain a better fusion effect. The results of comparison, parameter influence and ablation experiments on two real advertisement datasets shows that the LFDNN reaches better performance than the representative recommendation models.
Applications and Implementations of Blockchain Technology Across the Various Sectors

Blockchain was originally intended as a means to enable a trustless cryptocurrency—Bitcoin. However, an increasing number of sectors and stakeholders view the technology as an attractive option to address existing business issues and disrupt traditional industries. Blockchain is a distributed ledger with big promise in financial services. Blockchain application cases have mostly been discovered in banking. Blockchain technology can help insurers innovate insurance products, growth products and services, fraud detection, and price improvements and cutting costs. Blockchain technology allows organizations to access enormous talent pools, readily verify facts, and not rely on third parties or third employers. Blockchain has become a new frontier for venture capital, attracting banks, governments, and businesses. In this chapter, the authors present a novel approach or methodology for utilizing blockchain technology to manage businesses or other types of industries.
Locally informed gravitational search algorithm with hierarchical topological structure

Recently, gravitational search algorithm (GSA) has been successfully applied to solve various optimization problems. However, GSA tends to fall into local optimum because it ignores the environmental heterogeneity of agents. Therefore, locally informed gravitational search algorithm (LIGSA) based on neighborhood structure is proposed to balance exploration and exploitation. However, LIGSA ignores the differences in the evolutionary states between agents in the same neighborhood, and also ignores the differences in the evolutionary states between neighbors, which affects the performance of the algorithm. Therefore, a hierarchical locally informed gravitational search algorithm (HLIGSA) is proposed in this paper. This algorithm designs a hierarchical topology. In the lower layer, there are several non-overlapping neighborhoods, which constitute the whole population. Each agent in the neighborhood adaptively adjusts the gravitational constant according to its evolutionary state, so as to fully search the region of the neighborhood. The upper layer is a population composed of the best agents in each neighborhood, which performs gravity strategy or merging strategy for those neighborhoods that have lost the evolutionary capability, so as to balance the algorithm’s exploration and exploitation. The two layers work together to complete the entire search process. Experimental results show that HLIGSA outperforms many variants of GSA and many current state-of-the-art heuristic algorithms.
A comparative study between EPR steering and directional entanglement of a joint field-field system

In this paper, we investigate the presence of quantum EPR steering and entanglement, using EPR-Reid and directional entanglement criteria. In continuous variables, we study the effects of decoherence and thermal noise among the correlations of a joint field-field system. It consists of investigating the interaction between two quantum electrodynamic cavities during the passage of a two-level atom. Indeed, the main purpose of this paper is to examine the impact of the decoherence and thermal noise parameters on the behavior of EPR steering and directional entanglement. Hence, it is shown that the non-classical correlation, including steering and directional entanglement, is sensitive to the variation of the decoherence and thermal noise parameters. Furthermore, the outcomes provide an evidence about the asymmetry of steering correlations.
Cyclic policy distillation: Sample-efficient sim-to-real reinforcement learning with domain randomization

Deep reinforcement learning with domain randomization learns a control policy in various simulations with randomized physical and sensor model parameters to become transferable to the real world in a zero-shot setting. However, a huge number of samples are often required to learn an effective policy when the range of randomized parameters is extensive due to the instability of policy updates. To alleviate this problem, we propose a sample-efficient method named cyclic policy distillation (CPD). CPD divides the range of randomized parameters into several small sub-domains and assigns a local policy to each one. Then local policies are learned while cyclically transitioning to sub-domains. CPD accelerates learning through knowledge transfer based on expected performance improvements. Finally, all of the learned local policies are distilled into a global policy for sim-to-real transfers. CPD’s effectiveness and sample efficiency are demonstrated through simulations with four tasks (Pendulum from OpenAIGym and Pusher, Swimmer, and HalfCheetah from Mujoco), and a real-robot, ball-dispersal task. We published code and videos from our experiments at https://github.com/yuki-kadokawa/cyclic-policy-distillation.
Reinforcement learning compensated coordination control of multiple mobile manipulators for tight cooperation

This study presents a coordinated control method based on reinforcement learning for multiple mobile manipulators when strong constraints and close coupling are involved in the tightly cooperative tasks. The reinforcement learning strategy is specifically designed to deal with the unknown vibrations between the mobile manipulators and the common object. Firstly, the problem is converted into a Markov decision process. Next, the grasping forces of the end-effectors are regarded as the parameters to be optimized, and the system states and learning framework are described based on advantage actor–critic algorithm. Thirdly, an agent is trained through interacting with the environment based on a proposed reward policy. To eliminate joint dynamic errors caused by trajectories tracking, an adaptive controller is designed for each mobile manipulator. For the simulations and experiments, two mobile manipulators are employed for transporting a common plate under various conditions. The results demonstrate that the proposed method has better control effects than well-known controllers. This study combines the advantages of both reinforcement learning and model-based method via a coordinated controller designed with the characteristics of tight cooperation.
Graph signal processing based object classification for automotive RADAR point clouds

As the automotive industry evolves, visual perception systems to provide awareness of surroundings to autonomous vehicles have become vital. Conventional deep neural networks have been effective on 2D Euclidean problems during the previous decade. However, analyzing point clouds, particularly RADAR data, is not well-studied due to their irregular structures and geometry, which are unsuitable for 2D signal processing. To this end, we propose graph signal processing (GSP) based classification methods for RADAR point clouds. GSP is designed to process spatially irregular signals and can directly create feature vectors from graphs. To validate our proposed methods experimentally, publicly available nuScenes and RadarScenes point cloud datasets are used in our study. Extensive experiments on these challenging benchmarks show that our proposed approaches outperform state-of-the-art baselines.
A distributional assessment of rivalry in word formation

We contrast two views of rivalry in word formation. Under the classical, categorical view, two processes are rivals if they are semantically equivalent. Under the more nuanced, gradient view, two processes can be rivals at different degrees, depending on how frequently they are amenable to be deployed as alternatives to one another. We propose to use methods from distributional semantics to explore the usefulness of both views. Building on data from French, we first show that distributional differences between average difference vectors capture semantic similarity across derivational processes in a manner comparable to the expectations of expert morphologists. We then propose an operational implementation of the classical view of rivalry based on computational classifiers: processes are rivals if and only if a classifier is unable to discriminate between them. Experimentation with French data shows that this operationalization correctly captures the broad brushes of rivalry, but also reveals finer, gradient aspects of competition in the spirit of gradient rivalry.
Hub-hub connections matter: Improving edge dropout to relieve over-smoothing in graph neural networks

In recent years, graph neural networks (GNNs) have become the most widely used techniques for irregular data analysis. The core of GNNs lies in feature and/or label smoothing, which endows GNNs with promising expressive power. However, over-smoothing by excessive convolution on graph will inevitably result in performance degradation. While DropEdge has been shown to be an effective approach to tackle this problem, it views the over-smoothing as a macroscopic phenomenon and treats all edges equally. By tapping into the smoothness at a microscopic level, it is found that the smoothing processes in different regions are diverse. In particular, the first sign of over-smoothing generally emerges among connected hubs (i.e., nodes with high degrees). With this insight, we propose a novel edge dropout method to retrain early over-smoothing in graph learning, so as to relieve the global over-smoothing. Moreover, we introduce the siamese network architecture to alleviate the inconsistency problem inherent in Edge dropout methods (e.g. DropEdge). Extensive experiments on multiple benchmark datasets show that the proposed method consistently leads to improved performance and significantly outperforms the standard DropEdge with enhanced robustness.
A conceptual model of agile meetings' problems and their relationships with organizational issues in IT industry

Given that most IT companies apply agile principles, a lot of attention should be paid to the problems that arise when applying those principles. Agile meetings are the core of agile methodologies and deserve attention from both researchers and practitioners from industry. The aim of this paper is to present a conceptual model of problems in agile meetings and their relationships with organizational issues in IT industry. Meetings’ problems are identified through literature analysis, and the model is based on the interpretation of identified problems and their relationships. In addition, agile meetings are situated in organizational contexts and presented model also includes relationships between agile meetings’ problems and organizational issues identified in the literature. Each meeting problem, an organizational issue and all relationships in the model are discussed. The presented model can be used as a starting point for more deeper and more comprehensive inquiry of agile meetings, as well as for the development of guidelines and software tools for managing potential problems in IT industry.
Cost-constrained feature selection in multilabel classification using an information-theoretic approach

Feature selection is one of the key steps in building a predictive model in multi-label classification. However, most of the existing methods do not take into account information about the costs associated with considered features, such as the costs of performing diagnostic medical tests. We consider a problem of cost-constrained multilabel feature selection, which aims to select a feature subset relevant to multiple labels while satisfying a user-specific maximal admissible budget. This approach allows for building a model with high predictive power, for which the cost of making a prediction for a single instance does not exceed the user-specified budget. In this problem, the balance between the feature subset relevance and its cost should be considered concurrently, which is nontrivial in practice because their optimal balance is unknown. In this paper, we propose a novel criterion for cost-constrained multilabel feature selection that combines the relevance and cost of the candidate feature. The relevance measure is derived using the lower bound of the mutual information between the feature subset and label vector. Moreover, we propose an effective method for determining the cost-factor value that controls the trade-off between relevancy and cost. The experimental results on multilabel datasets with various characteristics demonstrate the superiority of the proposed method over conventional methods.
Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Abstract The trend of using artificial intelligence based chatbots for service encounters has seen a significant spike in recent times. Many studies have attempted to explain the adoption of the technology for various service applications; this study synthesizes the literature through a meta‐analysis, for which data from 70 independent studies, with a combined sample size of 20,026 was considered, to understand the influencers of the attitude and intention to use chatbots for service encounters. In addition, this study also sought to understand the moderating role of the culture, the technological characteristics captured through the variables transactional/non‐transactional, and informative/entertaining of the hypothesized relationships. Meta structural equation modelling (meta‐analytic SEM) was used to analyse the data. The results indicate playfulness, attitude, usefulness, facilitating condition and social influence of the chatbots are the key factors in influencing user adoption. Further, technological characteristics and culture were found to significantly moderate certain relationships in the model. Practical and theoretical implications have also been discussed.
Countering racial discrimination in algorithmic lending: A case for model-agnostic interpretation methods

In respect to racial discrimination in lending, we introduce global Shapley value and Shapley–Lorenz explainable AI methods to attain algorithmic justice. Using 157,269 loan applications during 2017 in New York, we confirm that these methods, consistent with the parameters of a logistic regression model, reveal prima facie evidence of racial discrimination. We show, critically, that these explainable AI methods can enable a financial institution to select an opaque creditworthiness model which blends out-of-sample performance with ethical considerations.
Exploring personalization via federated representation Learning on non-IID data

Federated Learning (FL) can learn a global model across decentralized data over different clients. However, it is susceptible to statistical heterogeneity of client-specific data. Clients focus on optimizing for their individual target distributions, which would yield divergence of the global model due to inconsistent data distributions. Moreover, federated learning approaches adhere to the scheme of collaboratively learning representations and classifiers, further exacerbating such inconsistency and resulting in imbalanced features and biased classifiers. Hence, in this paper, we propose an independent two-stage personalized FL framework, i.e., Fed-RepPer, to separate representation learning from classification in federated learning. First, the client-side feature representation models are learned using supervised contrastive loss, which enables local objectives consistently, i.e., learning robust representations on distinct data distributions. Local representation models are aggregated into the common global representation model. Then, in the second stage, personalization is studied by learning different classifiers for each client based on the global representation model. The proposed two-stage learning scheme is examined in lightweight edge computing that involves devices with constrained computation resources. Experiments on various datasets (CIFAR-10/100, CINIC-10) and heterogeneous data setups show that Fed-RepPer outperforms alternatives by utilizing flexibility and personalization on non-IID data.
Building a text retrieval system for the Sanskrit language: Exploring indexing, stemming, and searching issues

Stemming is an important pre-processing step in the text analysis domains such as text mining, text summarization and information retrieval (IR). In this study, we build a Sanskrit text collection and explore different indexing, stemming and searching strategies in Sanskrit. We also propose two stemmers: a ‘light’ and an ‘aggressive’ and evaluate their effectiveness in the text analysis task. The performance of the stemmers is evaluated in two ways: a direct and an indirect IR-based evaluation. In direct evaluation, we found that the stemmers are effective. In indirect evaluation, we apply different retrieval models such as BM25, TF–IDF, Divergence from Randomness (DFR) based and language models. The proposed stemmers are compared with GRAS stemmer, language-independent indexing (trunc-n) and no stemming approach. Among different stemming methods, aggressive stemmers provide the best performance. Hiemstra language model outperforms other retrieval models we experimented with. In statistical analysis, we found that the proposed stemming approaches produce significantly better results than the no-stemming approach.
Information Theory Pattern Recognition

the article discusses general approaches to the construction of pattern recognition algorithms based on the analysis of the recognition system in the information space. Based on the concept of interpreting an image as a set of geometric objects the problem of determining the correspondence of the image of a real object and its geometric model is considered. To increase the reliability of recognition, the problem of recognizing a scene image while compensating the information flow at the level of fuzzy sets is considered.
SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network

Sentiment analysis using the inbox message polarity is a challenging task in text mining, this analysis is used to differentiate spam and ham messages in mail. Polarity estimation is mandatory for spam and ham identification, whereas developing a perfect architecture for such classification is the hot demanding topic. To fulfill that, fuzzy based Recurrent Neural network-based Harris Hawk optimization (FRNN-HHO) is introduced, which performs post-classification over the classified messages (spam and ham). Previously the authors tried to classify the spam and ham messages from the collection of SMSs. But sometimes, the spam messages may incorrectly be classified within the ham classes. This misclassification may reduce the accuracy. The sentiment analysis process is performed over the classified messages to improve such classification accuracy. The spam and ham messages from the available data are classified using a Kernel Extreme Learning Machine (KELM) classifier. The sentiment analysis and classification based experimental evaluation is carried out using accuracy, recall, f-measure, precision, RMSE, and MAE. The performance of the proposed architecture is evaluated using threedifferent datasets: SMS, Email, and spam-assassin. The Area under the curve (AUC) of the proposed approach is found to be 0.9699 (SMS dataset), 0.958 (Email dataset), and 0.95 (spam assassin).
BV-ICVs: A privacy-preserving and verifiable federated learning framework for V2X environments using blockchain and zkSNARKs

As part of vehicle to everything (V2X) environments, intelligent connected vehicles (ICVs) generate a large amount of data, which can be exploited securely and effectively through decentralized techniques such as federated learning (FL). Existing FL systems, however, are vulnerable to attacks and barely meet the security requirements for real-world applications. If malicious or compromised ICVs upload inaccurate or low-quality local model updates to the central aggregator, they may reduce the accuracy of the global model, thereby reducing drivers safety and efficiency. This paper aims to alleviate these concerns by presenting BV-ICVs, a blockchain-enabled and privacy-preserving FL framework for ICVs in an edge-envisioned V2X environment. This system uses Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zkSNARKs) verification that is compiled as smart contracts to prevent malicious, compromised or even rational ICVs from uploading unreliable, erroneous or low-quality model updates. The verification process is embedded within the consensus of the underlying permissioned blockchain, which maximizes both the efficiency of the process and the utilization of computer resources. As demonstrated by discussions, security analysis, and numerical results, BV-ICVs reduced data poisoning attacks and increased the privacy protection and accuracy of FL.