Reviewerly/exHarmony huggingface dataset:
Research on Railway Transportation Route Based on Dijkstra Algorithm

Dijkstra algorithm is an important basic algorithm for computer decision of railway transportation route. Taking Dijkstra as the shortest path algorithm, combined with the current situation of railway transportation in China, a specific path parameter description language is designed to realize the intelligent judgment of railway transportation path by computer. The speed of path calculation is more than 50,000 pieces/s, and the accuracy rate is 100%. It can meet the needs of different business on the path. It is the result of transforming theoretical knowledge of grate into productivity of railway transportation.
Adaptive Non-linear Pattern Matching Automata

Efficient pattern matching is fundamental for practical term rewrite engines. By preprocessing the given patterns into a finite deterministic automaton the matching patterns can be decided in a single traversal of the relevant parts of the input term. Most automaton-based techniques are restricted to linear patterns, where each variable occurs at most once, and require an additional post-processing step to check so-called variable consistency. However, we can show that interleaving the variable consistency and pattern matching phases can reduce the number of required steps to find all matches. Therefore, we take the existing adaptive pattern matching automata as introduced by Sekar et al and extend these with consistency checks. We prove that the resulting deterministic pattern matching automaton is correct, and show several examples where some reduction can be achieved.
An Improvised Framework for Privacy Preservation in IoT

Data privacy is now-a-days a special issue in era of Internet of Things because of the big data stored and transmitted by the public/private devices. Different types and levels of privacy can be provided at different layers of IoT architecture, also different mechanisms operate at different layers of IoT architecture. This article presents the work being done towards the design of a generic framework to integrate these privacy preserving mechanisms at different layers of IoT architecture and can ensure privacy preservation in a heterogeneous IoT environment. The data is classified into different levels of secrecy and appropriate rules and mechanisms are applied to ensure this privacy. The proposed framework is implemented and evaluated for its performance with security and execution time or primary parameters. Various scenarios are also evaluated, and a comparison is done with an existing mechanism ABE (Attribute Based Encryption). It has been found that the proposed work takes less time and is more secure due to short key length and randomness of the parameters used in encryption algorithm.
Weighted <scp>AutoEncoding</scp> recommender system

Abstract Recommender systems are information filtering tools that seek to match customers with products or services of interest. Most of the prevalent collaborative filtering recommender systems, such as matrix factorization and AutoRec, suffer from the “cold‐start” problem, where they fail to provide meaningful recommendations for new users or new items due to informative‐missing from the training data. To address this problem, we propose a weighted AutoEncoding model to leverage information from other users or items that share similar characteristics. The proposed method provides an effective strategy for borrowing strength from user or item‐specific clustering structure as well as pairwise similarity in the training data, while achieving high computational efficiency and dimension reduction, and preserving nonlinear relationships between user preferences and item features. Simulation studies and applications to three real datasets show advantages in prediction accuracy of the proposed model compared to current state‐of‐the‐art approaches.
Prior embedding multi-degradations super resolution network

Nowadays, multi-degradations super-resolution (MDSR) has gradually received wide attention for its ability to deal with multiple degradations including blur, noise in a low-resolution (LR) image. Deep neural networks based MDSR methods have gained rapid development by utilizing the degradation prior. Though achieving promising results, most existing MDSR models fail to fully utilize the degradation prior and efficiently reconstruct high-resolution (HR) image by simple concatenation and iterative operations. This paper proposes an end-to-end MDSR approach to effectively extract features from the degradation prior and embed the degradation features into image features via the degradation feature extraction module and the prior embedding module. In addition, the axis attention mechanism and the pixel attention mechanism are proposed to augment the representation power of image features. Extensive experiments demonstrate that our approach can simultaneously solve different degradations and produce state-of-the-art results on standard benchmark datasets, with advantages in terms of quantitative metrics, visual quality, and reconstruction efficiency.
Investigation of the Batch Size Influence on the Quality of Text Generation by the SeqGAN Neural Network

One of the problems of text generation using the LSTM neural network is a decrease in the quality of generation with an increase in the length of the generated text. There are various solutions to improve the quality of text generation based on generative adversarial neural networks. This work uses preliminary training of the LSTM neural network based on the MLE approach and further training based on the SeqGAN neural network. Based on the presented results, we can conclude that the SeqGAN-based approach allows to increase the quality of text generation according to the NLL and BLEU metrics. The study of the influence of the batch size, in the process of competitive training of the SeqGAN neural network, on the quality of text generation has been carried out. It is shown that with an increase in the batch size, in the process of adversarial learning, the quality of LSTM neural network training increases. In this work, the Monte Carlo algorithm is not used in the training process of the SeqGAN neural network. For training and testing algorithms, image captions from the COCO Image Captions data sample are used. The quality of text generation based on the NLL and BLEU metrics has been assessed. Examples of the results of generating texts with an assessment of the quality of examples according to the BLEU metric are given,
Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification

Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results. However, most human physical activity identification models can only be trained based on labeled data, and it is difficult to obtain enough labeled data, which leads to weak generalization ability of the model. A Pruning Growing SOM model is proposed in this paper to address the limitations of small-scale labeled dataset, which is unsupervised in the training stage, and then only a small amount of labeled data is used for labeling neurons to reduce dependency on labeled data. In training stage, the inactive neurons in network can be deleted by pruning mechanism, which makes the model more consistent with the data distribution and improves the identification accuracy even on unbalanced dataset, especially for the action categories with poor identification effect. In addition, the pruning mechanism can also speed up the inference of the model by controlling its scale.
Automated system for preventing suicides by train

Suicides in public have devastating consequences for families, the local authorities dealing with the aftermath of such incidents, and witnesses. Subways, metros, and trains are one of the most accessible modes of public transport and are emerging as one of the most widespread forms of vehicular suicide. Although measures like placing more guards on platforms and using glass barriers to prevent direct access to the railway tracks are being used in multiple railway stations across the world, their implementation in old stations is inefficient and expensive. This paper proposes a method that uses Image Analysis and other Computer Vision techniques on real-time closed-circuit television (CCTV) feed to detect behaviors that indicate suicidal intentions and, if detected, notify the rail personnel for appropriate countermeasures. The proposed method judges each individual based on eleven parameters, considered as indicators of suicidal intentions. Experimental observations on limited datasets, even with low video quality, have demonstrated the high precision of our system in predicting potential suicides by train.
Taxing Africa for Inclusive Human Development: The Mediating Role of Governance Quality

Given that the literature on the links between taxation and inclusive human development is ambiguous, it is important to investigate whether the mediating influence of governance in taxation for inclusive development exists. Thus, this study explores the linkages between the governance quality, taxation and inclusive human development (i.e., inequality-adjusted human development index) using the generalized method of moments (GMM) technique to establish the empirical findings on 52 African countries for the period 2010-2018. The following findings are established. First, there is an unconditional positive effect of taxation on inclusive human development. Second, the net effects of taxation on inclusive human development, associated with the interaction of the government revenue with governance quality variables, are positive for the most part. It is then evident that when taxation policies are combined with good governance initiatives, the ultimate impact of inclusive human development is likely to be enhanced.
Secure Inference via Deep Learning as a Service without Privacy Leakage

Cloud computing plays an important role in many applications today. There is a lot of machine learning as a service that provides models for users’ prediction online. However, in many problems which involve healthcare or finances, the privacy of the data that sends from users to the cloud server needs to be considered. Machine learning as a service application does not only require accurate predictions but also ensures data privacy and security. In this paper, we present a novel secure protocol that ensures to compute a scalar product of two real number vectors without revealing the origin of themselves. The scalar product is the most common operation that used in the deep neural network so that our proposed protocol can be used to allow a data owner to send her data to a cloud service that hosts a deep model to get a prediction of input data. We show that the cloud service is capable of applying the neural network to make predictions without knowledge of the user’s original data. We demonstrate our proposed protocol on an image benchmark dataset MNIST and an real life application dataset - COVID-19. The results show that our model can achieve 98.8% accuracy on MNIST and 95.02% on COVID-19 dataset with very simple network architecture and nearly no reduction in accuracy when compares with the original model. Moreover, the proposed system can make around 120000 predictions per hour on a single PC with low resources. Therefore, they allow high throughput, accurate, and private predictions.
a Schema Extraction of Document-Oriented Database for Data Warehouse

&#x0D; &#x0D; &#x0D; The data warehouse is a very famous solution for analyzing business data from heterogeneous sources. Unfortunately, a data warehouse only can analyze structured data. Whereas, nowadays, thanks to the popularity of social media and the ease of creating data on the web, we are experiencing a flood of unstructured data. Therefore, we need an approach that can "structure" the unstructured data into structured data that can be processed by the data warehouse. To do this, we propose a schema extraction approach using Google Cloud Platform that will create a schema from unstructured data. Based on our experiment, our approach successfully produces a schema from unstructured data. To the best of our knowledge, we are the first in using Google Cloud Platform for extracting a schema. We also prove that our approach helps the database developer to understand the unstructured data better.&#x0D; &#x0D; &#x0D;
A Novel Transfer-Learning Network for Image Inpainting

Image restoration techniques have developed rapidly in recent years. Some high-level vision tasks such as style transfer, automatic coloring, and large mask inpainting rely on deep learning methods to retrieve specific image attributes. However, due to the lack of a key remainder, the quality of image restoration remains at a low level. For instance, when the mask is large enough, traditional deep learning methods cannot imagine and fill a car on a bridge from a model. It is all dependent on the capacity of neural imagination. This is what an abstract neuron is good at. In this paper, we not only find specific neurons to guide semantic retrieval but also discover more neurons that serve as the indicator. In addition, we propose three principles to guarantee the leverage of reasonable visualization and coherent accuracy in terms of neuron guidance. A novel network called the Transfer-learning Network is designed to adopt the joint training strategy, multi-modal guided neurons, and multi-path attention edge algorithm for inpainting in a coarse-to-fine manner. This is the first time an extremely large mask is filled (35%–66%), guided by a high-level understanding of an image from abstract neuron reflection. Through ablation and combined experiments, the Transfer-learning Network validates that artificial neurons enhance the performance of joint training in multitasking vision problems. Therefore, this joint training framework meets the requirements of refining the background, i.e., removing meaningless noise more sharply and smartly between junior and advanced comprehensive vision tasks.
Secure and Fast Emergency Data Sharing in Vehicle Social Network Using Consortium Blockchain

Vehicle social network (VSN) emerges as a very meaningful paradigm in the Internet of Vehicles (IoV), which aims to provide various data sharing to enrich the current vehicle services. However, there is no trust among unfamiliar vehicles, which may cause the vehicle to choose a malicious data provider. Some solutions use blockchain technology to establish a trusted environment between vehicles to ensure the information sharing process. However, the single-chain blockchain structure cannot distinguish between emergency transactions, such as car accidents, and non-emergency transactions, so that urgent information cannot be spread quickly. To address this important problem, we propose a double-chain structure based on the consortium blockchain to achieve the safe sharing of data and the priority sharing of emergency transactions. The double-chain structure is divided into an emergency chain and a non-emergency chain. The transactions of the emergency chain are handled first. In addition, a reputation model based on social relationships is proposed to calculate the reliability of all entities in the vehicle social network to determine the best data provider and block generator. The simulation results show that our scheme can reduce the delay in completing emergency transactions and quickly find malicious nodes, which ensures the safe and rapid sharing of urgent data.
Mixed-precision Quantization with Dynamical Hessian Matrix for Object Detection Network

Mixed-precision quantization with adaptive bitwidth allocation for neural network has achieved higher compression rate and accuracy in classification task. However, it has not been well explored for object detection networks. In this paper, we propose a novel mixed-precision quantization scheme with dynamical Hessian matrix for object detection networks. We iteratively select a layer with the lowest sensitivity based on the Hessian matrix and downgrade its precision to reach the required compression ratio. The L-BFGS algorithm is utilized for updating the Hessian matrix in each quantization iteration. Moreover, we specifically design the loss function for objection detection networks by jointly considering the quantization effects on classification and regression loss. Experimental results on RetinaNet and Faster R-CNN show that the proposed DHMQ achieves state-of-the-art performance for quantized object detec-tors.
Optimizing Mission Times for Multiple Unmanned Vehicles with Vehicle-Target Assignment Constraints

This paper addresses a mission planning problem involving a team of unmanned vehicles located at a single depot location. Given a set of target locations, the problem aims to find a tour for each vehicle such that each target is visited at least once by some vehicle, each vehicle's tour starts and ends at the depot, and the time when the last vehicle returns to the depot is minimized. The key feature that differentiates our problem with the prior work is that we also consider vehicle-target constraints which does not allow a vehicle to visit any target. As the mission planning problem is NP-Hard, we resort to first developing simple heuristics to find a feasible solution and then improve the feasible solution using general variable neighbourhood search heuristics. Preliminary simulation results are also presented to quantify the improvements offered by the heuristics.
Emotions Based Voice Supportive Model Using SVM

The present technology of human beings has been increasing with the development of automobile. Present scenario of Automobile industry faces challenging tasks for satisfying customers by adapting latest technologies such as AI technique. Now a days, customers has given importance to safety travelling system such as cabs in urban areas or metropolitan cities in India. The most prior able issues are accidents by drowsiness of cab drivers and their heavy work load which in turn reflects their mood swings so this results in accidents and the life of driver and passenger is in danger. This paper deals with above issues and in order to compensate there is a need of companion to driver which helps him to be in conscious state all the time and also complete his work with less pressure. This idea is implemented by using Support vector machine algorithm. As it is a five-level process i.e., dataset training, face detection, feature extraction, prediction of emotion, voice approach. The dataset that is used to train the model is Olivetti faces to detect the emotions of a person and through this system the accuracy is calculated at frame/sec. In this regard by using SVM the system is developed which can act according to the emotions of that person and supports him through the speech delivery.
Stacked LSTM-Based Dynamic Hand Gesture Recognition with Six-Axis Motion Sensors

Hand gesture recognition can be exploited to benefit ubiquitous applications using sensors. Currently, the inherent complexity of human physical activities makes it difficult to accurately recognize gestures with wearable sensors, especially in real time. To this end, a real-time hand gesture recognition system is presented in this paper. In particular, sliding window technology and y-axis threshold are used to detect intended gestures from a continuous data stream and then the segmented data are classified by applying a stacked Long Short-Term Memory (LSTM) model. After noise is removed, six-axis sensor data from wrist-worn devices are fed into the model without requiring feature engineering. We use twelve common hand gestures to evaluate the performance of our model. The experimental results demonstrate the feasibility of our proposed system with an accuracy of 99.8% on average. Our approach allows for an accurate and nonindividual hand gesture recognition. It holds potential to be integrated into a smart watch or other wearable devices for intuitive human computer interaction.
Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.
STAFL: Staleness-Tolerant Asynchronous Federated Learning on Non-iid Dataset

With the development of the Internet of Things, edge computing applications are paying more and more attention to privacy and real-time. Federated learning, a promising machine learning method that can protect user privacy, has begun to be widely studied. However, traditional synchronous federated learning methods are easily affected by stragglers, and non-independent and identically distributed data sets will also reduce the convergence speed. In this paper, we propose an asynchronous federated learning method, STAFL, where users can upload their updates at any time and the server will immediately aggregate the updates and return the latest global model. Secondly, STAFL will judge the user’s data distribution according to the user’s update and dynamically change the aggregation parameters according to the user’s network weight and staleness to minimize the impact of non-independent and identically distributed data sets on asynchronous updates. The experimental results show that our method performs better on non-independent and identically distributed data sets than existing methods.
Uncertainty Estimation for Monocular 3D Object Detectors in Autonomous Driving

Uncertainty estimation for 3D object detectors plays a critical role in autonomous driving. This is because current state-of-the-art 3D object detectors can make severe errors in their detections, and not knowing the uncertainties in these results may cause catastrophic consequences for safety-critical autonomous vehicles. Prior researches have studied the uncertainty estimation problem for 2D and lidar-based 3D object detectors, but little attention has been paid to monocular 3D object detectors. In consideration of the extensive need to achieve 3D object detection with low-cost cameras, it is of great importance to perform uncertainty estimation for monocular 3D object detectors as well. Thus, in this paper, we propose a merging strategy to perform sampling-based uncertainty estimation for monocular 3D object detectors. Specifically, we adopt the popular Monte Carlo Dropout method to obtain the sampling results for uncertainty estimation, and propose to merge them by soft clustering and Bayesian Inference in the Bird's Eye View. During this process, in order to reduce computational cost of multiple sampling times, we propose the weighted Monte Carlo Dropout uncertainty calculation method that is able to capture uncertainties from very few sampling results. Finally, we verify the effectiveness of the proposed method and its superiority to the existing method on the challenging KITTI dataset.