Reviewerly/exHarmony huggingface dataset:
A novel method for measuring the length of hot large forgings based on machine vision system

In order to measure the length of hot large forgings, a novel method based on machine vision system is proposed. Firstly, according to the characteristics that the light strips in the images acquired by machine vision system are continuous and have peaks, the light strip is detected. Secondly, after calculating the sub-pixel edge points of each light strip using an improved sub-pixel edge detection algorithm, the sub-pixel edge points of each light strip are sorted according to the order of the abscissa from small to large. Three-dimensional (3D) points of each edge can be obtained separately by matching and 3D reconstruction. Finally, the 3D points of each edge are projected onto its fitting plane, and the projected points are fitted with a quintic polynomial curve. According to the curvature of the fitted curve, the edge point of the forging can be detected. The distance from the starting point to the edge point is the length of forging at the position of light strip edge. The length measurement experiment shows that the method can be used to measure the length of hot large forgings. The relative error of the length measurement system is 0.547%. The time for measuring the length of hot forging is 6.8 s.
Enhancing Hybrid Encryption Techniques for Secured Data Processing for Small Medium Enterprises in cloud

Cloud computing is a promising area in the field of Information Technology. Nowadays, a good amount of fast, advanced and demanding technologies are available in cloud computing for accessing, sharing, and transmitting large data over the network. Along with this growth, the existence of data hackers and attackers has also increased. Every organization always seek error free and secured data transmission over the cloud. Moreover, the users also expect their data, stored on to the cloud should be more secured, confidential, and authorized. The encrypted data stored at server and it is decrypted at client side. In this article, we proposed cryptographic mechanisms that verify the uniqueness of data stored at cloud is same as original data put by the client. Additionally, we have also explored the existing cryptographic algorithms, namely DES, AES, Paillier and Blowfish for the comparison. For evaluation and performance of an algorithm, we have considered three parameters such as, File size, Encryption time/Decryption time and Memory usage for dissimilar formats like text file, audio file, image file, video file, etc. at different configurations for individual users.
A Procedure to Continuously Evaluate Predictive Performance of Just-In-Time Software Defect Prediction Models During Software Development

A procedure to continuously evaluate predictive performance of just-in-time software defect prediction models during software development
DECENT: Decentralized and Efficient Key Management to Secure Communication in Dense and Dynamic Environments

Intelligent Transportation Systems (ITS), one aspect of the Smart City paradigm, aim to improve the efficiency, convenience, and safety of travelers. The integration of (vehicular) communication technologies allows communication between the on-board communication units (OBUs) of vehicles, roadside units (RSUs), and vulnerable road users (VRUs), and contribute to the efficacy of ITS applications. However, these additional sources of information must be reliable and accurate. Security primitives such as confidentiality, integrity, and authenticity are required, but only achievable when supported with a suitable cryptographic key management scheme. This paper presents the design of a decentralized and efficient key management scheme, abbreviated as the DECENT scheme. This scheme provides secure multi-hop communication in dense and dynamic network environments while functioning in a self-organized manner. Through threshold secret sharing techniques, network nodes act as a distributed trusted third party (TTP) such that a threshold number of nodes can collaborate to execute key management functions. These functions include decentralized node admission and key updating. Novelties include (i) the unique self-healing characteristic, meaning that DECENT is capable of independently recovering from network compromise, and (ii) guidelines for choosing an appropriate security threshold in any deployment scenario which maximizes the level of security while simultaneously guaranteeing that decentralized key management services can be provided.
Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation

Cross-domain recommendation aims to model representations of users and items with the incorporation of additional knowledge from other domains, so as to alleviate the data sparsity issue. While recent studies demonstrate the effectiveness of cross-domain recommendation systems, there exist two unsolved challenges: (1) existing methods focus on transferring knowledge to generate shared factors implicitly, which fail to distill domain-shared features from explicit cross-domain correlations; (2) The majority of solutions are unable to effectively fuse domain-shared and domain-specific features. To this end, we propose Inter- and Intra-domain Relation-aware Cross-Domain Recommendation framework ( $$I^2$$ RCDR) to explicitly learn domain-shared representations by capturing high-order inter-domain relations. Specifically, we first construct a cross-domain heterogeneous graph and two single-domain heterogeneous graphs from ratings and reviews to preserve inter- and intra-domain relations. Then, a relation-aware graph convolutional network is designed to simultaneously distill domain-shared and domain-specific features, by exploring the multi-hop heterogeneous connections across different graphs. Moreover, we introduce a gating fusion mechanism to combine domain-shared and domain-specific features to achieve dual-target recommendation. Experimental results on public datasets show that the effectiveness of the proposed framework against many strong state-of-the-art methods.
Ideal Reference Point in Planning and Control for Automated Car-Like Vehicles

The choice of the reference point in automated vehicles impacts the vehicle’s driving behavior. However, this influence is often not considered for planning and control tasks. To find out where the reference point should be located best, we first consider its position to be ideal if the needed lane width on the left and right side of the planned path is equal when cornering with constant curvature. For constantly curved paths we derive the ideal reference point depending on the curvature, using the kinematics of a slip angle free bicycle model. For non-stationary cornering, we analyze different maneuvers and finally, we select the reference point on the front axle. Utilizing this knowledge, the extent of a forward moving vehicle can be reduced to a point model, which does not require the orientation of the vehicle. This enables a simple and still promising approach for collision checking, where the vehicle’s needed space is approximated by only one circle around the reference point. Finally, we analyze the influence of the reference point on a lateral feed-forward controller. Thus, we confirm the previously chosen reference point on the front axle for the equally distributed needed lane width and therefore recommend its use.
Detection of Fake and Clone Accounts in Twitter Using Classification and Distance Measure Algorithms

The social media applications like Facebook, Twitter, and others have gained a lot of name and fame and are represented as “Online Social Networks” (OSN). These networks are similar in their usage and can access with ease. With this popularity, people are sharing their personal sensitive information without checking any privacy policies, which made cyber-attacks easier nowadays. Most teenagers and women are getting affected by these cyber-attacks. Hackers take away the profile information of existing users in a profile cloning attack to create duplicate profiles, which are then used to compromise the identity of the original profile owners. This process is known as “Profile Cloning,” this cloning can be created on the same site or another site. Nowadays, hackers are creating fake profiles in cross-site, i.e., other social networks which are rare from the popular OSN walls. So, the users do not have any clue about their accounts on other platforms. Moreover, to simplify the registration process, the OSNs are not asking for authorization details which became an advantage for hackers to create millions of fake accounts. The proposed KNN algorithm is compared with the probability algorithm naïve Bayesian.
Signal Processing on Simplicial Complexes With Vertex Signals

In classical graph signal processing (GSP), the underlying topological structures are restricted in terms of dimensionality.A graph or a 1-complex is a combinatorial object that models binary relations, which do not directly capture complex high arity relations.One possible high dimensional generalization of graphs is a simplicial complex.In this paper, we develop a signal processing framework on simplicial complexes with vertex signals, which recovers the traditional GSP theory.We introduce the concept of a generalized Laplacian, which allows us to embed a simplicial complex into a traditional graph and hence perform signal processing similar to traditional GSP.We show that the generalized Laplacian satisfies several desirable properties, same as the graph Laplacian.We propose a method to learn 2-complex structures and demonstrate how to perform signal processing by applying the generalized Laplacian on both synthetic and real data.We observe performance gains in our experiments when 2-complexes are used to model the data, compared to the traditional GSP approach of restricting to 1-complexes.
The Impact of Augmented Reality on Student Learning and Emotions in Mathematics

The objective of this intervention project was to evaluate the impact of learning activities strengthened with Augmented Reality (AR) in Mathematics and the generation of positive and negative emotions in students who attended the third semester of the ITESM high school, Mexico City campus, where there had been a high percentage of failed students taking the subject “Algebraic and Transcendent Functions.” Evaluating the academic performance and the students' emotions before and after carrying out the activities, we obtained a p-value of 0.0014 in the non-parametric Wilcoxon test, indicating a statistically significant change in acquired learning. AR activities were implemented using different pedagogical strategies. We attained statistically significant results, increasing students' grades by 68%, achieving a grade point average of 93.47 out of 100 in the topics covered. After the intervention, negative emotions decreased by 84.2%, and positive emotions increased by 85%. We concluded that AR is an excellent technological tool to support remote mathematics classes, changing how students interact. The results also indicated that changes in pedagogical strategies are essential to the successful implementation of the remote teaching modality; also, using various stimuli changed the emotions experienced by the students.
Enhancing Session-Based Recommendation with Global Context Information and Knowledge Graph

Predicting a user’s next click by utilizing a short anonymous behavior is a challenging problem in the real-life session-based recommendation (SBR). Most existing methods usually learn the users’ preference from current session. However, they seldom consider global context information or knowledge graph and failed to distill high-quality item from similar sessions. In this work, we combine Global Context information with Knowledge Graph, and develop a new framework to enhance session-based recommendation (GCKG). Technically, we model a global knowledge graph, exploiting a knowledge aware attention mechanism for better learning item embeddings. Then, we leverage an attention network and a gated recurrent unit to learn session representations. Furthermore, session representations are augmented simultaneously through constructing a similar session referral circle. Comprehensive experiments demonstrate that GCKG significantly outperforms the state-of-the-art methods of existing SBR.
Microgrid Data Security Sharing Method Based on Blockchain under Internet of Things Architecture

The efficient and secure data sharing mechanism can support the microgrid to achieve more accurate business control, while the current data processing methods have the problems of large computing overhead and low data sharing security. Aiming at the current problems, this paper proposes a microgrid data sharing method based on blockchain technology based on the processing mode of cloud-edge-terminal architecture. Firstly, the elliptic curve encryption algorithm is used on the edge side to encrypt the data collected by the terminal equipment reliably, so as to improve the security and efficiency of microgrid key management. Then, in the cloud, the Reputation-Evaluation Practical Byzantine Fault Tolerant mechanism (REPBFT) based on smart contract and reputation evaluation can effectively manage the data sharing of edge computing devices, avoid the waste of network computing resources, and further improve the efficiency of microgrid data sharing. The simulation results show that when the number of edge devices reaches 25, the calculation and communication overhead of the proposed method are 63.46 ms and 2.66 KB, respectively, and when the processing data reaches 1024 KB, the security of the microgrid system is still 95%, which can realize safe and reliable data sharing and interaction, and can stably support the optimal operation of the microgrid.
Comparision of color spaces in DCD-based content-based image retrieval systems

Content-based image retrieval (CBIR) is one of the most applicable image processing techniques which includes two main steps: feature extraction and retrieval. A feature vector related to visual contents of image is extracted from the image in the feature extraction step. Three set features color, texture and shape are extracted from image in typical CBIR systems. Dominant color descriptor (DCD) is a method based on color information of the image. There are many color spaces to represent an image, so DCD can be implemented in any of these color spaces. In this paper color spaces RGB, CMY, HSV, CIE Lab, CIE Luv and HMMD are considered and effect of them in DCD features is investigated. Also, the CBIR precision is affected by the number of partitions in DCD method which is analyzed in this paper. Simulation results on Corel-1k dataset show that the HSV color space achieves better precision comparing the other color spaces.
Towards Accelerated and Robust Rreinforcement Learning with Transfer Learning

Transfer learning has been successfully used in some areas in deep learning, such as accelerate training progress in convolutional neural network (CNN). In another area, Reinforcement Learning (RL), time consumption during training is a serious problem due to the dataset. This is why many researchers try to find how to boost up training progress of Deep Reinforcement Learning. Therefore, this paper mainly focus on exploring whether transfer learning will have the same effect on RL as it performed in past CNN models. Thus, this research mainly tests one method of transfer learning, pretrained method, on OpenAI Gym games. In detail, Deep Q Learning was used in playing two Atari games SpaceInvaders and AirRaid, and the experiment for transfer learning was designed to see if what is learned from one game can be adapted to another, this experiment is mainly about utilizing pretrained network in different games and continue training it, including normal training and freezing training. Finally, the conclusion obtained from this research is that transfer learning not only robust training progress to reduce time consumption, but also lead to better performance for another task, potentially presenting the value of transfer learning for training in Deep Reinforcement Learning.
Multilingual and Crosslingual Speech Recognition Using Phonological-Vector Based Phone Embeddings

The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual and crosslingual speech recognition methods for low-resourced languages. A drawback suffered by previous methods in using phonological features is that the acoustic-to-PF extraction in a bottom-up way is itself difficult. In this paper, we propose to join phonology driven phone embedding (top-down) and deep neural network (DNN) based acoustic feature extraction (bottom-up) to calculate phone probabilities. The new method is called JoinAP (Joining of Acoustics and Phonology). Remarkably, no inversion from acoustics to phonological features is required for speech recognition. For each phone in the IPA (International Phonetic Alphabet) table, we encode its phonological features to a phonological-vector, and then apply linear or nonlinear transformation of the phonological-vector to obtain the phone embedding. A series of multilingual and crosslingual (both zero-shot and few-shot) speech recognition experiments are conducted on the CommonVoice dataset (German, French, Spanish and Italian) and the AISHLL-1 dataset (Mandarin), and demonstrate the superiority of JoinAP with nonlinear phone embeddings over both JoinAP with linear phone embeddings and the traditional method with flat phone embeddings.
Energy-efficient Training of Distributed DNNs in the Mobile-edge-cloud Continuum

We address distributed machine learning in multitier (e.g., mobile-edge-cloud) networks where a heterogeneous set of nodes cooperate to perform a learning task. Due to the presence of multiple data sources and computation-capable nodes, a learning controller (e.g., located in the edge) has to make decisions about (i) which distributed ML model structure to select, (ii) which data should be used for the ML model training, and (iii) which resources should be allocated to it. Since these decisions deeply influence one another, they should be made jointly. In this paper, we envision a new approach to distributed learning in multi-tier networks, which aims at maximizing ML efficiency. To this end, we propose a solution concept, called RightTrain, that achieves energy-efficient ML model training, while fulfilling learning time and quality requirements. RightTrain makes high-quality decisions in polynomial time. Further, our performance evaluation shows that RightTrain closely matches the optimum and outperforms the state of the art by over 50%.
ATF: An Alternating Training Framework for Weakly Supervised Face Alignment

In recent years, various face-landmark datasets have been published. Intuitively, it is significant to integrate multiple labeled datasets to achieve higher performance. Due to the different annotation schemes of datasets, it is hard to directly train models using them together. Although numerous efforts have been made in the joint use of datasets, there remain three shortages in previous methods, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , additional computation, limitation of the markups scheme, and limited support for the regression method. To solve the above issues, we proposed a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Alternating Training Framework</i> (ATF), which leverages the similarity and diversity across multiple datasets for a more robust detector. ATF mainly contains two sub-modules: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Alternating Training with Decreasing Proportions</i> (ATDP) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mixed Branch Loss</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{MB}$</tex-math></inline-formula> ). In particular, ATDP trains multiple datasets simultaneously via a weakly supervised way to take advantage of the diversity among them, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{MB}$</tex-math></inline-formula> utilizes similar landmark pairs to constrain different branches of the corresponding datasets. Besides, we extend the framework to easily handle three situations: single target detector, joint detector, and novel detector. Extensive experiments demonstrate the effectiveness of our framework for both heatmap-based and direct coordinate regression. Moreover, we have achieved a joint detector that outperforms state-of-the-art methods on each benchmark.
Chromosomes identification based differential evolution (CIDE): a new bio-inspired variant for network intrusion detection

Differential Evolution (DE) is perhaps one of the most efficient and versatile evolutionary techniques and its different variants have been proposed in the recent past. However, all these variants often suffer from the shortcomings of slow and/or premature convergence. Further, nearest neighbour (NN) is a frequently used classifier in the domain of data mining but its primitive version is noise sensitive and shows low accuracy. Different data reduction methods such as prototype selection, prototype generation, and DE are widely used to mitigate these drawbacks of NN. However, none of the methods substantially avoids all these shortcomings. This paper proposes a new variant of DE that is based on the biological phenomenon of the centromeric location of chromosomes. The proposed ‘Chromosomes Identification based Differential Evolution (CIDE)’ extracts the knowledge from databases and constructs effective and reliable decision rules for classification. In this paper, the performance of the proposed CIDE technique is computed and compared with 1-NN classifier optimized by fourteen state-of-art variants of DE, and 1-R classifier using six-real datasets as well as KDDCup’99 dataset to illustrate its superiority over the 1-NN and 1-R classifier. Finally, the performance of CIDE is also compared with Naïve Bayes, support vector machine, Neural Network, and Random Forest for the KDDCup’99 dataset using different performance metrics.
Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm

Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed. This method uses the K-means clustering algorithm to determine the data center and expansion function of the RBF and uses the least-mean-square algorithm to adjust the weights to obtain the nonlinear mapping relationship between the situation value before and after the situation and carry out the situation prediction. Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. Compared with the PSO-RBF model, AFSA-RBF model, and IAFSA-RBF model, the maximum relative error and minimum relative error of the IAFSA-PSO-RBF model are reduced by 14.27%, 8.91%, and 32.98%, respectively, and the minimum relative error is reduced by 1.69%, 12.97%, and 0.61%, respectively. This shows that the IAFSA-PSO-RBF model has reduced the prediction error interval, and the average relative error is 5%. Compared with the other three models, the accuracy rate is improved by more than 5%, and it has met the requirements for the prediction of the network security situation.