Reviewerly/exHarmony huggingface dataset:
Reinforcement learning based metric filtering for evolutionary distance metric learning

Data collection plays an important role in business agility; data can prove valuable and provide insights for important features. However, conventional data collection methods can be costly and time-consuming. This paper proposes a hybrid system R-EDML that combines a sequential feature selection performed by Reinforcement Learning (RL) with the evolutionary feature prioritization of Evolutionary Distance Metric Learning (EDML) in a clustering process. The goal is to reduce the features while maintaining or increasing the accuracy leading to less time complexity and future data collection time and cost reduction. In this method, features represented by the diagonal elements of EDML matrices are prioritized using a differential evolution algorithm. Further, a selection control strategy using RL is learned by sequentially inserting and evaluating the prioritized elements. The outcome offers the best accuracy R-EDML matrix with the least number of elements. Diagonal R-EDML focusing on the diagonal elements is compared with EDML and conventional feature selection. Full Matrix R-EDML focusing on the diagonal and non-diagonal elements is tested and compared with Information-Theoretic Metric Learning. Moreover, R-EDML policy is tested for each EDML generation and across all generations. Results show a significant decrease in the number of features while maintaining or increasing accuracy.
Analog Circuit Incipient Fault Diagnosis from Raw Signals using Multi-Layer Extreme Learning Machine

Analog circuits play important roles in modern electronic systems. Incipient fault diagnosis of analog circuits is a recognized challenging research direction due to the difficulty of fault feature extraction and identification. This paper proposes an early fault diagnosis algorithm for analog circuits based on multilayer extreme learning machine (ML-ELM).Its basic idea comes from the Auto-encoder(AE) and the Extreme Learning Machine (ELM). The proposed method which combines the characteristics of both, so it has the ability of feature extraction and fast training speed. In this method, the time domain sampling signal of the circuit can be directly used as the fault sample, and the unsupervised feature extraction is carried out layer by layer through the deep network, the diagnosis results are obtained after the supervised classification by the traditional ELM algorithm. The whole process does not rely on multi-step iterative and reverses fine-tuning. The experimental results of the Sallen-Key band-pass filter circuit and Leapfrog low-pass filter circuit show that the method has not only high diagnosis accuracy, but also fast diagnosis speed. Because this method gets rid of the tedious manual feature extraction and complex signal processing, it improves the training efficiency and makes the method more universal.
A Novel Resource Allocation and Scheduling Based on Priority Using Metaheuristic for Cloud Computing Environment

In cloud computing systems, current works do not challenge the database failure rates and recovery techniques. In this chapter, priority-based resource allocation and scheduling technique is proposed by using the metaheuristic optimization approach spotted hyena optimizer (SHO). Initially, the emperor penguins predict the workload of user server and resource requirements. The expected completion time of each server is estimated with this predicted workload. Then the resources activities are classified based on the criteria of the deadline and the asset. Further, the employed servers are classified based on the workload and the estimated completed time. The proposed approach is compared with existing resource utilization techniques in terms of percentage of resource allocation, missed deadlines, and average server workload.
Time Series Analysis for Understanding Local Policy Impact of COVID-19 Cases in East Java

Daily new cases of COVID-19 as a series of data points ordered in time is one representation of time series data.Our works expect to understand the data characteristics that is related to existing government policies.This paper aims to highlight the impact report and analyses of some COVID-19 policies in East Java province districts.The study is focused on the policy execution before and during the new normal situation of COVID-19 using time series data of new cases as possible and easily observable results.Aside from time series analysis, some visual analysis is performed as well.The experiments focused on some questions related to the policy effectiveness: finding patterns of daily new cases to instigate the need for other local policies and understanding any precedence on occurred cases for nearby districts.Although the second question is not confirmable, the first question verifies more tightened social distancing is still necessary for the new normal.
A Robust Facial Landmark Detection in Uncontrolled Natural Condition

Abstract Facial landmark detection in uncontrolled natural condition is a challenging task due to illumination, occlusion, expression, posture, blur and other factors. In order to make a stable and accurate prediction, a novel data augmentation strategy based on random mask is used to obtain an efficient training data set. Our work is motivated by the ability of artist’s spatio imagination from part to whole. The artist can find the corresponding key points according to the incomplete image, so as to realize the whole from the part. In the experiment, the corresponding incomplete image is first generated based on the random mask and the complete image, and then the incomplete image is fed into the neural network to fit the mapping between the incomplete image and the facial landmarks. In order to evaluate the performance, a simple regression convolutional neural network based on the improved VGG16 [1] was trained, and the latest technical results were obtained. Furthermore, our method is tested over both complete and incomplete face images. Extensive experiments reveal that our model can work well on different human face data set.
Comparative Analysis of Cryptographic Key Management Systems

Managing cryptographic keys can be a complex task for an enterprise and particularly difficult to scale when an increasing number of users and applications need to be managed. In order to address scalability issues, typical IT infrastructures employ key management systems that are able to handle a large number of encryption keys and associate them with the authorized requests. Given their necessity, recent years have witnessed a variety of key management systems, aligned with the features, quality, price and security needs of specific organisations. While the spectrum of such solutions is welcome and demonstrates the expanding nature of the market, it also makes it time consuming for IT managers to identify the appropriate system for their respective company needs. This paper provides a list of key management tools which include a minimum set of features, such as availability of secure database for managing keys, an authentication, authorization, and access control model for restricting and managing access to keys, effective logging of actions with keys, and the presence of an API for accessing functions directly from the application code. Five systems were comprehensively compared by evaluating the attributes related to complexity of the implementation, its popularity, linked vulnerabilities and technical performance in terms of response time and network usage. These were Pinterest Knox, Hashicorp Vault, Square Keywhiz, OpenStack Barbican, and Cyberark Conjur. Out of these five, Hachicorp Vault was determined to be the most suitable system for small businesses.
Research on cigarette packet recognition and stacking technology based on machine vision

In order to improve the automation and intelligence level of tobacco packet distribution and to automatically complete the stacking flow operation of tobacco packet, a set of automatic stacking robot systems based on machine vision were designed. This system includes the visual system and sorting palletizing system. In the visual system shape area, k-means clustering is adopted to establish the standard template library. A fast matching algorithm based on the shape of template was put forward, which could identify smoke a few shapes and cigarette package fast and accurately. Then the upper machine data information was sent to the palletizing system to finish the tobacco package, cigarette package group pile, and pallet cigarette packet scheduling algorithm of automatic palletizing homework. The field test results showed that the shape recognition rate of cigarette packs was over 99.90%, the shape matching time of cigarette packs was less than 80ms, the stacking efficiency was 5.04-5.14s per packet, the stacking tilt value was less than 3cm, the packing void rate was less than 5%, and the stacking error rate was less than 0.10%, which met the requirements of cigarette packing stacking. The system can provide technical support for improving tobacco logistics automation.
Network Slimming Method for SAR Ship Detection Based on Knowlegde Distillation

This paper proposes a network slimming method for synthetic aperture radar (SAR) ship detection based on knowledge distillation. Firstly, the generic objection detection network is pruned regularly and extremely on filter-level to get lightweight models under different global pruning ratios. Secondly, the knowledge distillation framework based on Kullback Leibler (KL) Divergence is used to train small student network and large teacher network from scratch synchronously to restore the accuracy of student network. To verify the effectiveness of the proposed method, sufficient experiments are conducted on the widely used SAR Ship Detection Dataset (SSDD). YOLO [email protected] is selected as the baseline model while YOLO [email protected] as the teacher network. Results show that, with our method, a student model with only 15.4M parameters (25% of the baseline model) can achieve high pruning ratio while still maintaining encouraging performance. Compared with the baseline model, there are only 1% and 0.9% differences on average precision (AP) and average recall (AR), respectively. Compared with traditional fine-tuning method which only restores 0.6% AP, the model slimming method based on knowledge distillation proposed in this paper restores 2.4% AP with obvious advantages.
Identification of Palatal Fricative Fronting Using Shannon Entropy of Spectrogram

In this paper, an attempt has been made to identify palatal fricative fronting in children speech, where postalveolar /sh/ is mispronounced as dental /s/. In children’s speech, the concentration of energy (darkest part) of spectrogram for /s/ ranges 4000 Hz to 8000 Hz, whereas it ranges 3000 Hz 8000 Hz for /sh/. Gammatonegram follows the frequency subbands of the ear (wider for higher frequencies). Various spectral properties such as spectral centroid, spectral crest factor, spectral decrease, spectral flatness, spectral flux, spectral kurtosis, spectral spread, spectral skewness, spectral slope and Shannon entropy of the spectrogram (interval of 2000 Hz), extracted from the Gammatonegram are proposed for the characterization of /sh/ and /s/. The dataset recorded from 60 native Kannada speaking children of age between 3 1/2 to 6 1/2 years is considered for the analysis from NITK Kids’ Speech Corpus. Support vector machine (SVMs) is considered for the classification. Various combinations of the proposed features are considered for the evaluation, along with the MFCCs(39) and LPCCs(39). Combination of MFCCs(39), LPCCs(39) and Entropy(4) is observed to achieve highest mispronunciation identification performance of 83.2983%.
Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation

We propose a novel NMF algorithm, named Total Variation constrained Graph-regularized Convex Non-negative Matrix Factorization (TV-GCNMF), to incorporate total variation and graph Laplacian with convex NMF. In this model, the feature details of the data are preserved by a diffusion coefficient based on the gradient information. The graph regularization and convex constraints reveal the intrinsic geometry and structure information of the features; thereby, obtaining sparse and parts-based representations. Furthermore, we give the multiplicative update rules and prove convergence of the proposed algorithm. The results of clustering experiments on multiple datasets, under various noise conditions, show the effectiveness and robustness of the proposed method compared to state-of-the-art clustering methods and other related work.
Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling

Layer-wise learning, as an alternative to global back-propagation, is easy to interpret, analyze, and it is memory efficient. Recent studies demonstrate that layer-wise learning can achieve state-of-the-art performance in image classification on various datasets. However, previous studies of layer-wise learning are limited to networks with simple hierarchical structures, and the performance decreases severely for deeper networks like ResNet. This paper, for the first time, reveals the fundamental reason that impedes the scale-up of layer-wise learning is due to the relatively poor separability of the feature space in shallow layers. This argument is empirically verified by controlling the intensity of the convolution operation in local layers. We discover that the poorly-separable features from shallow layers are mismatched with the strong supervision constraint throughout the entire network, making the layer-wise learning sensitive to network depth. The paper further proposes a downsampling acceleration approach to weaken the poor learning of shallow layers so as to transfer the learning emphasis to deep feature space where the separability matches better with the supervision restraint. Extensive experiments have been conducted to verify the new finding and demonstrate the advantages of the proposed downsampling acceleration in improving the performance of layer-wise learning.
Grouping PMU Signals for Guaranteed Recovery under Corruption: Insights and Recommendations

A connection is established between the system-theoretic notion of modal observability, and the indices guaranteeing robust signal recovery- denseness and restricted isometry. Insights are derived for grouping signals to ensure guaranteed recovery of synchrophasor measurements corrupted with sparse and spurious anomalies. The propositions on signal grouping are validated on simulated data from a IEEE test system, as well as field PMU measurements from a US utility.
Optical Flow Based Background Subtraction with a Moving Camera: Application to Autonomous Driving

In this research we present a novel algorithm for background subtraction using a moving camera. Our algorithm is based purely on visual information obtained from a camera mounted on an electric bus, operating in downtown Reno which automatically detects moving objects of interest with the view to provide information for collision avoidance and number of vehicles for an autonomous vehicle. In our approach we exploit the optical flow vectors generated by the motion of the camera on the bus while keeping parameter assumptions at a minimum. At first, we estimate the Focus of Expansion which is used to model and simulate 3D points given the intrinsic parameters of the camera and perform multiple linear regression to estimate the regression equation parameters and implement on the real data set of every frame to identify moving objects. We validated our algorithm using data taken from a common bus route in the city of Reno.
Implication of Predictive Maintenance for Industrial Marketing

This chapter discusses the implication of predictive maintenance (PM) for industrial marketing companies. Using an illustrative case study from the Indian industrial air compressor market, it shows that predictive maintenance solutions will change the way of conventional sales and marketing. Sellers need to focus on early innovation adopters among its customers. They also need to engage with existing customers early on in the purchase process and highlight how PM can reduce the total cost of ownership. PM can be sold effectively to different types of customers- transactional, value-oriented, and collaborative. Industrial marketers have to position the solution appropriately to gain competitive advantage.
A Catalog of Object-Relational Mapping Code Smells for Java

Bad choices during software development might lead to maintenance and performance issues. Code smells are typically used to indicate such problems. A number of smells have been proposed, usually focused on generic code problems. In this work, we focus on the specifics of Object-Relational Mapping (ORM) code in Java. Developers use ORM frameworks to abstract the complexity of accessing a database. However, when poorly used, frameworks can lead to problems that might affect the overall performance of the system. Therefore, we present a catalog of eight smells extracted from the state of research and practice, through a combination of rapid review and grey literature review. For each smell, we also present a suggested solution and rationale. To evaluate the catalog, we conducted a survey with 86 respondents. The majority of the respondents agree both that the code smells are a problem, as well as that the suggested solution is adequate. In conclusion, this work contributes with a systematic way of describing ORM code smells and an initial catalog, which can be useful for researchers and practitioners, positively evaluated by our initial results.
A metric learning network based on attention mechanism for Power grid defect identification

Abstract Aiming at the current situation that deep learning has a strong dependence on sufficient samples and it is difficult to obtain sufficient labeled samples in practical applications, a few-shot image classification method combining attention mechanism is proposed. Firstly, the feature extraction module adds an attention mechanism to increase the weight of important parts, pay attention to the more important parts of the image, and obtain the corresponding feature vector. Secondly, the relevance measurement module is used to calculate the similarity between features to determine the category of the query image. Experiments have proved that our proposed model improves the accuracy of about 1.2% compared with the existing model. At the same time, the model is applied to the classification of power grid defects.
Making Kids Learning Joyful using Artistic Style Transferred YouTube VCs

The rise of E-learning systems offers a large number of open online educational videos through portals like YouTube, MOOCs, which result in an enormous volume of data for every inquisitive e-learner. This work proposes a novel approach for recommending educational video clips (VCs) for children's joyful learning. It caters to the need for hard-pressed parents, who are unable to tutor their children efficaciously. As children are unable to choose the recommended VCs on their own, the proposed system offers a precise summary of the recommended VCs for parents to effectively select the learning content for their children. For a joyful learning experience, the selected VCs are then style transferred using our deep style transfer network. The extensive evaluation methods demonstrate the effectiveness and practicability of the proposed approach.
Web Service Recommendation based on Knowledge Graph Convolutional Network and Doc2Vec

With the rapid development of Internet, the number of Web services is increasing sharply, which makes it more difficult for Mashup developers to find suitable Web services. Nowadays, there are numerous methods to improve Web service recommendation, but it is still a challenging problem to recommend Web services with both good accuracy and satisfying diversity. Collaborative filtering is a common algorithm in recommendation system, but it often faces serious cold start and sparsity problems. To alleviate the above problems, this paper proposes a Web service recommendation method based on knowledge graph convolutional network and Doc2Vec. First of all, it constructs the knowledge graph of Web services based on the additional information such as the categories, developers, scope of application of Web services, and adopts knowledge graph convolutional networks to mine the higher-order relationship between Web service and the preference information of Mashups. Secondly, it employs Doc2Vec to mine the semantics of Web service description documents, and integrates the Mashup preference information and the Mashup semantic information in the training process, so as to predict Web services needed for Mashup development. Finally, the experiment is conducted on the latest Programmable Web dataset and the experimental results show that the recommended performance of the proposed method is better than that of FM, NCF, CKE, RippleNet, KGCN.
How to find assembly plans (fast): Hierarchical state space partitioning for efficient multi-robot assembly

The programming of cooperating teams of robots in automation and in particular for assembling small batch sizes is a tedious task. Apart from that, the use of AI-based planning for multiple robots is computationally expensive. Using state space techniques, the state space increases dramatically for numerous possibilities in an indiscrete, continuous world. Hence, in this work we present a two-layer planning algorithm for multi-robot assembly which automatically partitions the state space in order to reduce complexity and to speed up planning time. We depict formal considerations about planning complexity and show how and why the state space size and, thus, planning time is strongly lower compared to common approaches.
On the influence of different perspectives on evaluating the teamwork quality in the context of agile software development

Background: The literature reports that different perspectives (i.e., roles) within an agile software development team (ASD) perceive the impact of teamwork quality (TWQ) on team performance in different ways. However, nothing is said about the perspective influence on the assessment TWQ construct itself. Aims: To fill this gap, this study provides a more in-depth insight into how the perspective affects the perception of the variables in TWQ construct. Method: We performed a comparative analysis in which we collected TWQ-related data from 21 ASD teams from 2 software development companies. We interviewed 130 professionals from different roles (i.e., developers, technical leader, and manager). We compared the results for the associated perspectives using the Mean Relative Error (MRE). Results: The leader and manager perspectives show reasonable agreement when evaluating three variables that compose the TWQ (i.e., Cohesion, Self-Organizing, and Team Orientation). Developers and managers strongly agreed on four variables (i.e., Communication, Cohesion, Self-Organizing, and Collaboration). For developers and leaders, only the Coordination variable showed reasonable agreement. Conclusions: We believe that more studies are needed to generalize the results. However, the research indicates that different perspectives evaluate the variables that compose the TWQ construct in different ways. Closer discussions and definitions of more objective metrics are advisable to assess these variables, aiming to align expectations between perspectives and consensus when measuring efforts to achieve continuous improvement team's.