Reviewerly/exHarmony huggingface dataset:
U-shaped impacts of institutional quality and ICT penetration on economic growth: Evidence from selected emerging countries

This research aims to consider determinants of economic growth, such as financial development, institutional quality, and information and communication technology (ICT) penetration, which have been explored separately in previous studies and have produced mixed findings in different regions. Using the two-step system generalized method of moments (GMM) estimator for a dynamic panel dataset of 35 selected emerging countries, several research findings could be drawn as the following. First, both ICT penetration and institutional quality have significantly positive effects on economic growth. On the contrary, economic development could be hampered by financial development. Second, there are the U-shape and inverted U-shape relations for the cases of institutional quality and ICT penetration on economic growth, respectively. Third, the interaction effect of ICT penetration and financial development could enhance economic growth while the negative impact of financial development on economic development is amplified by the high level of institutional quality. These findings are robust to the global financial crisis control and the use of an alternative ICT proxy when setting up the approach of principal component analysis. Given these findings from emerging countries, some policy challenges that policy-makers should address by simultaneously considering each growth driver, such as financial development, ICT evolution, and institutional quality and interactions between them, as well as the non-monotonic effect of ICT penetration and institutional quality to facilitate economic growth. U-shaped impacts of institutional quality and ICT penetration on economic growth: Evidence from selected emerging countriesAll authorsThanh Phuc Nguyen , Thi Thu Hong Dinh, Ngoc Tho Tran & Trang Duong Thi Thuyhttps://doi.org/10.1080/23322039.2022.2139887Published online:04 November 2022Display full size
Quality assessment of surface and sediment pore waters and their behaviours using WQI index and geostatistics approaches. Case study: El Malleh dam reservoir, Morocco

The El Malleh dam reservoir is an important water source for irrigation and protection against flooding in the Mohammedia region of the kingdom of Morocco. However, flash floods threaten the sustainability of the reservoir by sedimentation and water quality contamination. Pollution sources could arise from sediment mobilization in the reservoir bottom, or carry by water flow from the watershed to the dam reservoir. The current study evaluates the surface and pore water quality, focuses on their behaviour based on the laboratory results, the Water Quality Index (WQI) and statistical analysis. The results show that the concentration of nutrients was not significant. The concentrations of major elements differ from surface waters to pore waters and from one parameter to another. For instance, the Cl concentration in the surface water ranged from 852 mg L−1 to 1065 mg L−1, and from 10 mg L−1 to 7810 mg L−1 for pore water. Besides that, the calculation of WQI indicated that the sediment pore water quality was unsuitable, and the surface waters quality was in a poor and very poor category.
APRO: Secret Key Based Data Encryption and Mutual Authentication with Privacy Preservation Model in IoT Using Autoregressive Poor and Rich Optimization

An essential security component for the Internet of Things (IoT) is device authentication. IoT devices are widely dispersed in public and open areas, making them vulnerable to attacks. To detect the security violations, an authentication with a privacy preservation model is developed in this research using devised Autoregressive Poor and Rich Optimization (APRO)-based secret key. Here, the secret key used to perform the encryption process is generated by the proposed optimization algorithm in such a way that the data encryption mechanism is carried out by considering the Box-Cox transformation. However, the authentication process is achieved between the server and the Authorization center (AC) through the generation of authentication and verification messages. The original data is retrieved back by employing the decryption process along with the usage of the retrieval key. The proposed method achieved a minimum computation time of 0.062 sec, higher encryption quality of 92.56%, and minimum memory usage of 2.86MB.
MOHAQ: Multi-Objective Hardware-Aware Quantization of recurrent neural networks

The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet changes in the hardware platform and application. This article introduces a Multi-Objective Hardware-Aware Quantization (MOHAQ) method, which considers hardware performance and inference error as objectives for mixed-precision quantization. The proposed method feasibly evaluates candidate solutions in a large search space by relying on two steps. First, post-training quantization is applied for fast solution evaluation (inference-only search). Second, we propose the ”beacon-based search” to retrain selected solutions only and use them as beacons to estimate the effect of retraining on other solutions. We use speech recognition models on TIMIT dataset. Experimental evaluations show that Simple Recurrent Unit (SRU)-based models can be compressed up to 8x by post-training quantization without any significant error increase. On SiLago, we found solutions that achieve 97% and 86% of the maximum possible speedup and energy saving, with a minor increase in error on an SRU-based model. On Bitfusion, the beacon-based search reduced the error gain of the inference-only search on SRU-based models and Light Gated Recurrent Unit (LiGRU)-based model by up to 4.9 and 3.9 percentage points, respectively.
Affective feature knowledge interaction for empathetic conversation generation

A popular chatbot can generate natural and human-like responses, and the crucial technology is the ability to understand and appreciate the emotions and demands expressed from the perspective of the user. However, some empathetic dialogue generation models only specialise in commonsense and neglect emotion, which can only get a one-sided understanding of the user's situation and makes the model unable to express emotion better. In this paper, we propose a novel affective feature knowledge interactive model named AFKI, to enhance response generation performance, which enriches conversation history to obtain emotional interactive context by leveraging fine-grained emotional features and commonsense knowledge. Furthermore, we utilise an emotional interactive context encoder to learn higher-level affective interaction information and distill the emotional state feature to guide the empathetic response generation. The emotional features are to well capture the subtle differences of the user's emotional expression, and the commonsense knowledge improves the representation of affective information on generated responses. Extensive experiments on the empathetic conversation task demonstrate that our model generates multiple responses with higher emotion accuracy and stronger empathetic ability compared with baseline model approaches for empathetic response generation.
Iterative embedding distillation for open world vehicle recognition

Vehicle recognition poses a practical but challenging problem in many real-world surveillance applications. Since vehicle recognition is an open-set problem, it is a critical issue to learn a discriminative visual embedding space rather than a well-performing classifier. In this paper, we propose an iterative embedding distillation (IED) framework for open-set vehicle recognition. The soft target in knowledge distillation is utilized to establish the interclass relations from an instance level rather than a category level. Towards the open-set problem, we extend knowledge distillation to embedding distillation in an iterative learning way, in which three types of loss functions are studied to iteratively transfer the distributions of embeddings from the teacher network to the student network. To demonstrate the universal nature of IED, we implement the IED framework on two basic convolutional neural networks and verify it using the cross-dataset testing protocols without retraining or fine-tuning. Extensive experimental results show that IED obtains quite encouraging results and outperforms state-of-the-art methods on various large-scale vehicle recognition datasets including VeRi-776, Vehicle-ID, Vehicle-1M, VD1 and VD2.
Genetic Algorithm Based Aggregation for Federated Learning in Industrial Cyber Physical Systems

During the last decade, Industrial Cyber-Physical Systems (ICPS) have attracted a significant amount of interest from industries as well as academic institutions. These kinds of systems have proved to be very complicated, and it may be a difficult task to get a handle on their architecture and make sure everything works properly. By putting up a framework for federated learning that we’ve dubbed FedGA-ICPS the purpose of this study is to address some of the difficulties that are associated with the performance and decision-making aids provided by ICPS. To begin, we launch an ICPS modeling formalism with the goal of specifying the structure and behaviour of such systems. FedGA-ICPS then conducts an analysis of the performance of the industrial sensors based on the data supplied by the ICPS from the industrial sensors by putting forth locally integrated learning models. Following that, a genetic algorithm drives federated learning in order to quicken and enhance the aggregation process. In the end, transfer learning is used so that the learned parameters of the models may be distributed across a variety of limited entities. FedGA-ICPS has been implemented on MNIST, and the results have been rather significant.
A computationally efficient unscented Kalman smoother for ameliorated tracking of subatomic particles in high energy physics experiments

The focus of this paper is to discuss improvements with respect to smoothing and its efficient implementation with the Unscented Kalman Filter (UKF) for particle tracking in High Energy Physics (HEP) experiments. At present, in almost all of the HEP experiments, smoothing is typically achieved using the Rauch-Tung-Striebel smoother or Kalman smoother that works naturally with the existing particle tracking algorithm, the Extended Kalman Filter. However, it does not work directly with the UKF that has recently been shown to improve particle tracking in the Muon Ionization Cooling Experiment due to the fact that Jacobian matrices are not available in the UKF framework. Although a smoother, the Unscented Rauch-Tung-Striebel (URTS) smoother, exists that naturally works with the UKF, it is not computationally efficient provided that particle tracking is a large dimensional problem. Therefore, the existing smoothers do not provide an efficient solution to improve the overall particle tracking process for such problems where computational complexity is one of the main challenges. In this paper, an alternative to the URTS method is presented that does not only implement directly with the UKF but also requires less computational effort. We name it as Jacobian Equivalent Rauch-Tung-Striebel smoother.
Altering OWL Ontologies for Efficient Knowledge Organization on the Semantic Web

The increase in the number of users on the internet and the advancement in information technology have spiked the generation of information to an unprecedented level making information retrieval and web mining a difficult task. Semantic technologies can help improve the results of web mining by providing constructs that can help represent the web documents in a machine-understandable manner. To keep providing semantically rich services while keeping this surge in the amount of information in mind, we have to work towards ways to make the process of information management efficient while retaining its effectiveness. One of the ways to accomplish the above task is to improvise the knowledge organization in a manner that every piece of information is in its designated place. This paper discusses and addresses the problems with current knowledge organization methodologies and presents an algorithm to alter the available OWL ontologies. The authors were able to get a noticeable improvement in the amount of storage used by the ontology with fewer axioms without losing any information.
The Stationary Problem of Two-Phase Filtration by the Monte-Carlo Method: Solutions and Applications

This article is devoted to solving the problems of applying Monte-Carlo algorithms to filtration problems. The "random walk by spheres" and "random walk by boundary" algorithms of Monte-Carlo methods are used to solve the stationary problem of filtration of two immiscible inhomogeneous incompressible liquids in a porous medium. Estimates constructed using the "random walk by spheres" and "random walk by boundary" algorithms of Monte-Carlo methods will be mostly ε−biased. Unbiased estimates are in most cases unrealizable on a computer, since with a probability of 1 they do not go to the boundary of the region, and therefore are of little use. In practice, they are usually limited to only the first two points of evaluation.
RBSP-Boosting: A Shapley value-based resampling approach for imbalanced data classification

Addressing the problem of imbalanced data category distribution in real applications and the problem of traditional classifiers tending to ensure the accuracy of the majority class while ignoring the accuracy of the minority class when processing imbalanced data, this paper proposes a method called RBSP-Boosting for imbalanced data classification. First, RBSP-Boosting introduces the Shapley value and calculates the Shapley value for each sample of the dataset through the truncated Monte Carlo method. Moreover, the proposed method removes the noise data according to the Shapley value and undersamples the samples with Shapley values less than zero in the majority class. Then, it takes the Shapley value as the weight of the sample and oversamples the minority class according to the weight. Finally, the new dataset is trained on the classifier through the AdaBoost classifier. Experiments are conducted on nine groups of UCI and KEEL datasets, and RBSP-Boosting is compared with four sampling algorithms: Random-OverSampler, SMOTE, Borderline-SMOTE and SVM-SMOTE. Experimental results show that the RBSP-Boosting method in the three evaluation metrics of AUC, F-score and G-mean, compared with the best performance of the four comparison algorithms, increases by 4.69%, 10.3% and 7.86%, respectively. The proposed method can significantly improve the effect of imbalanced data classification.
Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners

We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the natural Bayesian analog to boosting decision trees. In this article, we use Gibbs distributions on random partitions to induce sparsity in ensembles of weak learners. Looking at BART as a special case, we show that the class of Gibbs priors includes two recently proposed models—the Dirichlet additive regression trees (DART) model and the spike-and-forest model—as extremal cases, and we show that certain Gibbs priors are capable of achieving the benefits of both the DART and spike-and-forest models while avoiding some of their key drawbacks. We then show the promising performance of Gibbs priors for other classes of weak learners, such as tensor products of spline basis functions. A Pólya Urn scheme is developed for efficient computations. Supplementary materials for this article are available online.
Dual-track feedback aggregation recommendation model for programming training

Programming online judges (POJs) are widely used to train programming skills, and exercise recommendation algorithms in POJs have attracted wide attention. The current programming recommendation algorithms cannot make full use of the feedback of user–item pairs and cannot effectively express students’ mastery of exercises. Therefore, we propose a dual-track feedback aggregation recommendation model for programming training (DTFARec). In this model, multiple types of feedback fusion mechanism (MTFFM) and dual-track method (DTM) are proposed to solve this problem and can better express students’ mastery of exercises. The MTFFM uses an attention mechanism to learn different feedback information, and the DTM is able to fuse information from both feedback and interactive aspects. The experimental results on a real-world dataset show that the model has better recommendation performance than the best performing benchmark and that our method can effectively model students’ mastery of exercises.
A Dataset to Investigate First-Person Shooter Players

Datasets are multi-purpose research tools, enabling researchers to design, develop, and test solutions to classical computer sciences problems and novel research questions. In the gaming domain, however, there are few high-quality datasets providing both: (1) visual gameplay data and (2) additional information about the gameplay, such as user input. As a result, game researchers most of the time have to collect, process, and annotate gameplay data in time-consuming data collection studies themselves. We start closing this gap, by presenting a novel Counter-Strike: Global Offensive dataset. The contributed dataset is a collection of 12 high-skilled players playing Counter-Strike: Global Offensive. We showcase two deep learning-based examples using the presented dataset, demonstrating its versatility.
Poster

Over the last two years, governments all over the world have used a variety of containment measures to control the spread of \covid, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented in actuality. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, concretely detailed social contact graphs. As this data is inherently privacy-critical, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing sensitive information.
Research on Autonomous Tracking Algorithm of Robot

Robotics is an important achievement in the field of intelligence. The mobile robot is more widely used than the fixed robot, has a very heavy role and status. Two important technologies of mobile robot autonomous mobile system include map construction and path planning. Map construction and path planning can ensure that the robot is in an unfamiliar environment, and use sensors to achieve localization and autonomous movement. This paper mainly studies SLAM algorithm and path planning algorithm used in map construction.
A DWT Digital Watermarking Algorithm Based on 2D-LICM Hyperchaotic Mapping

In this paper, we studied a DWT digital watermarking algorithm based on the 2D-LICM hyperchaotic mapping. The algorithm uses 2D-LICM hyperchaotic mapping to determine the embedding position of watermarking and performs first-order two-dimensional discrete wavelet decomposition on the original carrier image. The watermark information is embedded into the high-frequency coefficients in the horizontal direction according to the location information of the watermark embedding. The watermark image with watermark information can be obtained by inverse wavelet transform. The process of extracting a watermark is the reverse process of embedding a watermark. According to the experimental results, this algorithm is a blind watermark algorithm. The embedded watermark has good transparency, good robustness against noise attacks, JPEG compression attacks, cropping attacks, and geometric scaling attacks, and the algorithm also has high security.
Research on 3D Path Planning Based on Ant Group Algorithm

Ant colony algorithm is mostly applied to enterprise, underwater detection, logistics optimization, is to follow the ant population based on spatial consciousness planning model, relative to two-dimensional path planning has more obstacles, more mixed space, more information storage, more complex operation logic and difficult to direct global planning, in the dynamic environment, put forward the global path planning and local path planning. Based on static knowledge of static environment, I propose an ant colony algorithm. On this basis, modify the ant path search strategy to jump out of some special shape obstacles; establish the probabilistic selection formula of the basic ant colony algorithm, add the target node information to the algorithm convergence; select the formula, and add the volatile coefficient to improve the adaptability of the algorithm. In the planning process, by analyzing the distribution of obstacles, the corresponding avoidance strategies are proposed for the different positions of obstacles. Simulation experiments show that the proposed algorithm can effectively realize the robot obstacle avoidance target in a complex 3D environment, and obtain the touch-free optimal or suboptimal path.
Classification method for communication modulation signal identification based on multiple feature extraction and Cubic SVM

Facing the complex electromagnetic environment, the modulation mode of communication signal is also becoming more complex, leading to the existing methods for identifying modulation modes of communication signals can not accurately and quickly identify the modulation mode of communication signal. Signal modulation mode recognition mainly includes two parts: extraction of feature parameters and selection of classifier. This paper proposes a communication modulation recognition method based on multiple feature extraction and cubic support vector machin(SVM). Using time and frequency domain, wavelet singular entropy feature extraction, and cubic SVM model, the speed and accuracy of signal modulation mode recognition is significantly improved, saving a lot of training time and significantly improving the recognition accuracy. The proposed method achieves an accuracy of 99.59% with a training time of only 5.4034 seconds.
Multi-vehicle Tracking System Based on Structured and Cascade Control

According to the issue of multi vehicle following cooperation in limited resources, an intelligent following vehicle control system is proposed. The control system takes the low-power chip MSP430F5529 as the main control core, the tracking system adopts seven gray sensors, and the speed control system adopts a 13 wire encoder to obtain the speed in real time. It combines with the variable integral structure PID to achieve accurate speed control and tracking. The distance measurement system uses ATK-MS53LOM to obtain the distance between two vehicles in real time through laser, and then uses PD controller to adjust the average speed, and in addition, the communication system adopts wireless serial port to transmit information. In addition, reasonable adjustment of the mechanical structure of the body can enhance the grip of the tires, which can better achieve multi vehicle coordination. Under the background of TI Cup Zhejiang Undergraduate Electronic Design Competition in 2022, the experimental system was established. Through many experiments and debugging, it can complete the communication, following, overtaking and other functions of two cars. The speed can be controlled within 0.3m/s to 1m/s, and the completion rate is more than 95%.