Reviewerly/exHarmony huggingface dataset:
Toward Privacy Preserving Federated Learning in Internet of Vehicular Things: Challenges and Future Directions

The Internet of vehicular things (IoVT) is turning into an indubitably evolving area of interest in either industrial or academic domains. The tremendous information exchanging between IoVT devices enable the development of a wide variety of vehicular applications i.e., intelligent transportation systems and autonomous driving system, etc. However, the sensitivity of this information resulted in growing security privacy concerns. Remarkably, federated learning (FL) is a promising paradigm of distributed learning from vehicular data of distinct agents without communicating the raw data among them. FL can appropriately use the computation power of manifold agents to develop efficient and privacy-preserving solutions for IoVT environment. Thus, this study figures out the potential of the FL approach in developing efficient decentralized solutions that consider the security and privacy concerns of the IoVT system. A federated graph convolutional recurrent network (Fed-GCRN) is introduced to learn spatial-temporal information for traffic flows forecasting. The Fed-GCRN introduce an adaptive differential privacy mechanism to realize a better privacy performance tradeoff. Finally, the current challenges related to FL are discussed along with the hopeful future directions that enable the development of more intelligent, secure, and private IoVT applications.
PDV: Permissioned Blockchain based Personal Data Vault using Predictive Prefetching

We predict that in the not-so-distant future, most if not all documents will no longer be available in paper format. We envision a world in which one can store the entirety of their digital documents not unlike a filing cabinet storing paper documents but with the additional security, safety, and integrity. The vision is encapsulated in our Personal Data Vault (PDV). The PDV is a framework for storing, saving, protecting, and sharing a person’s life-time digital documents in a verifiable secure manner that can ascertain a document’s authenticity and integrity and afford its owner to share it securely. In PDV, each document is encrypted, compressed, and securely stored in the cloud, and indexes are entered in disturbed ledger of a Permissioned Blockchain (Hyperledger Iroha), which significantly prevents data leakage, and the ledger is immutable and tamper-proof to maintain integrity. All access rights are written in the form of an access-list in Smart Contracts, which are later used for verification. We used the concept of predictive prefetching in combination with the Markov tree to design a model that successfully predicts the next (or sequence of next) requests that may occur and pre-executes (execute before the request has occurred)
Research on Big Data Acquisition Bus Technology of NC Machine Tool

Abstract Big data technology has strongly promoted the development of social productive forces, and commercial applications are continuously expanding in all walks of life. It also uses the power of Internet technology to provide strong technical support for the development of the entire industry. In terms of the production of CNC machine tools, due to the use of big data technology, CNC machine tools have been rapidly produced on a large scale, which has played an important role in the upgrading of the entire CNC machine tools and the improvement of quality and efficiency. This article takes the technology of big data acquisition bus for CNC machine tools as the topic, through researching and discussing its concept, meaning and related strategies, in order to effectively improve the production scale and reference. Chinese Library Classification number : TP23 Document Identification code : A
GreenHetero: Adaptive Power Allocation for Heterogeneous Green Datacenters

In recent years, the design of green datacenters and their enabling technologies, including renewable power managements, have gained a lot of attraction in both industry and academia. However, the maintenance and upgrade of the underlying server system over time (e.g., server replacement due to failures, capacity increases, or migrations), which make datacenters increasingly more heterogeneous in their key processing components (e.g., capacity and variety of processors, memory and storage devices), present a great challenge to optimal allocation of renewable power supply. In other words, the current heterogeneity-unaware power allocation policies have failed to achieve optimal performance given a limited and time varying renewable power supply. In this paper, we propose a dynamic power allocation framework called GreenHetero, which enables adaptive power allocation among heterogeneous servers in green datacenters to achieve the optimal performance when the renewable power varies. Specifically, the GreenHetero scheduler dynamically maintains and updates a performance-power database for each server configuration and workload type through lightweight profiling method. Based on the database and power prediction, the scheduler leverages a well-designed solver to determine the optimal power allocation ratio among heterogeneous servers at runtime. Finally, the power enforcer is used to implement the power source selections and the power allocation decisions. We build an experimental prototype to evaluate GreenHetero. The evaluation shows that our solution can improve the average performance by 1.2x-2.2x and the renewable power utilization by up to 2.7x under tens of representative datacenter workloads compared with the heterogeneity-unaware baseline scheduler.
Exploring the common principal subspace of deep features in neural networks

We find that different Deep Neural Networks (DNNs) trained with the same dataset share a common principal subspace in latent spaces, no matter in which architectures (e.g., Convolutional Neural Networks (CNNs), Multi-Layer Preceptors (MLPs) and Autoencoders (AEs)) the DNNs were built or even whether labels have been used in training (e.g., supervised, unsupervised, and self-supervised learning). Specifically, we design a new metric $${\mathcal {P}}$$ -vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $${\mathcal {P}}$$ -vectors. Small angles (with cosine close to 1.0) have been found in the comparisons between any two DNNs trained with different algorithms/architectures. Furthermore, during the training procedure from random scratch, the angle decrease from a larger one (70°–80° usually) to the small one, which coincides the progress of feature space learning from scratch to convergence. Then, we carry out case studies to measure the angle between the $${\mathcal {P}}$$ -vector and the principal subspace of training dataset, and connect such angle with generalization performance. Extensive experiments with practically-used Multi-Layer Perceptron (MLPs), AEs and CNNs for classification, image reconstruction, and self-supervised learning tasks on MNIST, CIFAR-10 and CIFAR-100 datasets have been done to support our claims with solid evidences.
Graph Rules Hierarchy as a Tool of Collaborative Game Narration Creation

This paper presents a computer tool for collaborative narration creation based on graph rules hierarchy that was developed and tested with collaboration of students of Jagiellonian University in Poland. The tool is made of two modules: internal representation of game world graph model and animation system: The former describes the narration points and story arcs and the latter uses basic animations and blends them together to create animated sequences. The opportunity to develop the system as a group effort allowed to identify and address workflow bottleneck and proved that using layered graph representation of the functional elements of the story, characters, locations, and objects is viable method to design plots and quest for video games. Dynamically changing graph can be visualized at any stage and helps to perceive the story as animated sequences. This on the other hand if used for video games story design enables to observe character actions, possible paths the player can take and quickly iterate on the story components. The contribution of this paper is to propose an innovative tool which allow the designers and animators to work together within one platform.
Detail enhancement for infrared images based on Relativity of Gaussian-Adaptive Bilateral Filter

Detail enhancement is the key to the display of infrared image. For the infrared image detail enhancement algorithms, it is very important to present a good visual effect for people effectively. A novel algorithm for detail enhancement of infrared images is proposed in this paper. The method is based on the relativity of Gaussian-adaptive bilateral Filter. The algorithm consists of three steps. The first step is to divide the input image into the base layer and the detail layer by the relativity of Gaussian-adaptive bilateral filter. In the second step, the detail layer is multiplied by the proposed weight coefficient, and the base layer is processed by histogram projection. The third step is to combine the detail layer and the base layer processed in the second step and output it to the 8-bit domain display. Compare with other methods, the new algorithm reduces the running time greatly. The experimental results showed that the proposed algorithm improves the contrast of infrared images effectively.
Workplace Ostracism and Knowledge Hiding: The Mediating Role of Job Tension (Case Study of Nurses of Public Hospitals Employee's in Lorestan)

Workplace Ostracism and Knowledge Hiding: The Mediating Role of Job Tension (Case Study of Nurses of Public Hospitals Employee's in Lorestan)
Chaotic Sensors and Tags: Design and Performances

Chaotic tags and sensors can be conveniently made using simple chaotic circuits of reduced complexity that can compete with tags and sensors based on a combination of capacitance-, inductance-, or resistance-to-voltage converters and additional microcontrollers. The design of such devices is exemplified and details of the design discussed.
An Intelligent System for Spam Message Detection

About more than half of worldwide email traffic, amounting several billions per day consists of spam causing considerable disturbance in telecommunications. This upheaval volume of unwanted messages implies an intense need for reliable and robust spam filters. Conventional filtering methods have largely failed to tackle the adaptive nature of spam messages. Machine learning methods, on the contrary, may have the ability to intelligently detect and filter spams. Here, we present a system of spam message prediction based on appropriate lexical analysis like tokenization, stop-words removal, stemming, lemmatization, and feature extraction. Impressive results, i.e., over 97% accuracy with random forest classifier, have been obtained in several experiments on the UCI spam collection dataset. We have also hosted the developed spam-detection system on Heroku as platform-as-a-service (PAAS).
Identity-Based Identity-Concealed Authenticated Key Exchange

Identity-based authenticated key exchange (ID-AKE) allows two parties (whose identities are just their public keys) to agree on a shared session key over open channels. At ESORICS 2019, Tomida et al. proposed a highly efficient ID-AKE protocol, referred to as the TFNS19-protocol, under the motivation of providing authentication and secure communication for huge number of low-power IoT devices. The TFNS19-protocol currently stands for the most efficient ID-AKE based on bilinear pairings, where each user remarkably performs only a single pairing operation. But it does not consider users’ identity privacy, and the security is based on relatively non-standard assumptions.In this work, we formulate and design identity-based identity-concealed AKE (IB-CAKE) protocols. Here, identity concealment means that the session transcript does not leak users’ identity information. We present a simple and highly practical IB-CAKE protocol, which is computationally more efficient than the remarkable TFNS19-protocol in total. We present a new security model for IB-CAKE, and show it is stronger than the ID-eCK model used for the TFNS19-protocol. The security of our IB-CAKE protocol is proved under relatively standard assumptions in the random oracle model, assuming the security of the underlying authenticated encryption and the gap bilinear Diffie-Hellman (Gap-BDH) problem. Finally, we provide the implementation results for the proposed IB-CAKE scheme, and present performance benchmark.
Recommender System: Personalizing User Experience or Scientifically Deceiving Users?

Recommender system is taking the lead among many things that the digital world offers today, to every customer visiting online portals for any service. Since its popularity from the time of Netflix competition, recommender system has become more visible and an important marketing and sales tool for corporates augmenting their offers online. Ongoing research initiatives in recommender systems, large datasets available for users across the globe, and corporate collaborations have led to improvised algorithms, and reduced errors in estimating predictions. Software and hardware tools that enable easy gathering of implicit and explicit data have helped recommender system to quickly adapt to the needs of the users. It is in this background the possibility of recommender system inducing the customer to pre-determined items by presenting fabricated predictions, as if it is resultant of scientific principles, need to be considered. In this paper, we give an overview of the recommender system, discuss how various components of the recommender system may be manipulated to allure innocent customers with false ratings, and also discuss the importance of engaging stakeholders to develop a robust recommender system.
Product Recommendation Algorithm for Score Prediction Based on Joint Feature Vector Extraction

In order to make full use of multiple types of data and improve the accuracy of the recommendation algorithm, a scoring prediction model combining multi-source heterogeneous data such as product attributes, user reviews and friend relationships is proposed for product recommendation. In this algorithm, the product attributes and user comment information are combined, the combination method of LDA and Word2vec is adopted to extract user-commodity joint features, a community division algorithm is used to divide users into communities, features and product ratings are integrated to establish a rating prediction model and the recommended results are obtained by grading forecasting and ranking. The experiment results show that, compared with collaborative filtering and existing recommendation algorithms, the proposed model combining multi-source heterogeneous data can reduce the root mean square error of prediction scores, improve the accuracy of prediction, and increase the accuracy of product recommendation.
Study of Various Dimensionality Reduction and Classification Algorithms on High Dimensional Dataset

A potential drawback of huge data is that it makes analysis of the data hard and also computationally infeasible. Health care, finance, retail, and education are a few of the data mining applications that involve very high-dimensional data. A large number of dimensions introduce a popular problem of “Curse of Dimensionality”. This problem makes it difficult to perform classification and engenders lower accuracy of machine learning classifiers. This paper computes a threshold value (35%) to which if the data is reduced, the best accuracy can be obtained. Further, this research work considers an image dataset of very high dimensions on which different dimensionality reduction techniques such as PCA, LDA, and SVD are performed to find out the best dimension fit for an image dataset. Also, various ML classification algorithms, such as Logistic Regression, Random Forest Classifier, Naive Bayes, and SVM are applied to find out the best classifier for the dimensionally reduced dataset. Finally, this research work has concluded that, PCA+SVM, LDA+Random Forest, and SVD+SVM have produced the best results out of all the possible combinations from the comparative study.
Twitter Association Rule Mining using Clustering and Graph Databases

In this scenario, the need to efficiently analyze this kind of data is increasing because of characteristics of such big data, especially their huge and sometimes unpredictable variety. Twitter alone, with 320 M active users every month and more than 500 M tweets per day, could represent an important source of information. For this research, we are focusing solely on social networks. The reason for this choice is that they are increasingly becoming a platform where people will comfortably update their status and share or retrieve information about the world in real time. Often news is spreading through them faster than in traditional channels because user capillarity worldwide makes it possible. In particular, we will focus on Twitter, because its micro-blogging nature makes it suitable for this kind of purpose. It questions the concept of a small private community of friends in favor of less private, less personal broadcast communications of common interest. Another reason why we chose Twitter is because semantic value of hashtags, their power in summarizing tweet content and the spreading model through the social network that allows us to highlight clusters of topics by focusing on these tags.One of the objectives of this thesis is to show how data mining can provide useful techniques to deal with these huge datasets for retrieving information to detect and analyze trending topics and the corresponding user's interactions with them. We identified in Association Rules identification and evolution in time, a systematic approach to conduct the analysis.
Double framed moduli spaces of quiver representations

Motivated by problems in the neural networks setting, we study moduli spaces of double framed quiver representations and give both a linear algebra description and a representation theoretic description of these moduli spaces. We define a network category whose isomorphism classes of objects correspond to the orbits of quiver representations, in which neural networks map input data. We then prove that the output of a neural network depends only on the corresponding point in the moduli space. Finally, we present a different perspective on mapping neural networks with a specific activation function, called ReLU, to a moduli space using the symplectic reduction approach to quiver moduli.
The Application of Multimedia Network Technology in the Autonomous Learning of University Students’ Speech

Multimedia provides new conditions for students to learn English phonetics, the application of multimedia in English phonetic autonomous learning, and the role of teachers in cultivating students’ English phonetic autonomous learning in a multimedia environment. It aims to adapt to the new development of modern science and technology, cultivate students’ ability to learn English pronunciation independently, and improve students’ English proficiency.
Safe distance reminder system on ship against port for the standing process using image processing

Abstract Since ancient times, Indonesia has been known as a country that conducts trading activities. However, the number of accidents when the ship is about to dock is an indication of the need to improve the marine transportation system. The factors that cause accidents include human, technical and weather errors. Seeing these conditions and also related to the increasingly rapid development of technology, modern solutions for detecting objects using cameras can be developed. By using a PC as a video processor from a camera that works in real time and can classify several ship objects using the Convolutional Neural Network (CNN) method. As well as being able to estimate the distance of the object where the ship must turn off the engine before docked with the camera using Stereo Vision. and the results of the algorithm processing can be translated into a signal that will be sent to the harbormaster and also the ship that will dock as an output. Then the ship that will dock can find out the distance where the ship has to turn off the engine through the signal sent or siren. The average total accuracy of the system which is able to detect ships is 97.15%.
Towards Silhouette-Aware Human Detection in Depth Images

Detecting humans in depth images attracts increasing attention thanks to the advantage of depth modality in privacy protection. However, this task is challenging because the number of available training data is limited to date and the depth images, unlike RGB images, are short of rich texture features. Although many image synthesis methods and deep learning methods have been proposed and proven successful, especially for RGB images, it is unsatisfactory to directly apply them to depth images because of the intrinsical differences between the modalities. In view of that silhouette of humans becomes an essential discriminative cue in depth images in the absence of texture information, which is not well utilized by existing methods, in this paper, we thus propose a silhouette-aware network (SAN) to train the detection model and a depth image synthesis method that represses spurious silhouette to augment the training data. Besides, to further increase the diversity of training data, we collect a dataset of scene depth images (SDI), including both indoor and outdoor scenes, as background images when synthesizing training data. Experimental results show that (i) our proposed synthesis method can generate more realistic depth images and thus benefits the training of detection models, (ii) our collected SDI dataset can effectively enhance data diversity and thus improves the effectiveness of the obtained detection models, and (iii) our proposed silhouette-aware network (SAN) can effectively boost the human detection accuracy. Our dataset is available at https://pan.baidu.com/s/13hpuziavBNjS8KATClpXww, password: r9id
Development and Evaluation of Intraoperative Ultrasound Segmentation with Negative Image Frames and Multiple Observer Labels

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accuracy on the classifier-selected frames, that would be segmented, remains comparable to or better than those from standalone segmentation networks. Interestingly, the efficacy of the pre-screening classifier was affected by the sampling methods for training labels from multiple observers, a seemingly independent problem. We show experimentally that a previously proposed approach, combining random sampling and consensus labels, may need to be adapted to perform well in our application. Furthermore, this work aims to share practical experience in developing a machine learning application that assists highly variable interventional imaging for prostate cancer patients, to present robust and reproducible open-source implementations, and to report a set of comprehensive results and analysis comparing these practical, yet important, options in a real-world clinical application.