Reviewerly/exHarmony huggingface dataset:
Earthquake Detection Using a Nodal Array on the San Jacinto Fault in California: Evidence for High Foreshock Rates Preceding Many Events

Abstract We use a dense seismic array of 1,108 vertical‐component geophones within a 600‐m footprint to detect thousands of small earthquakes near an active strand of the San Jacinto fault zone in southern California during a 26‐day period. We first correct site effects using multichannel cross‐correlations of the P‐waves of 256 cataloged earthquakes, and then perform beamforming analysis on the continuous waveforms in a slowness range from −0.4 to 0.4 s/km in both the east and north directions. At each time step, we identify the beam slowness with maximum amplitude and apply a picking algorithm to identify 13,408 events. These detections include over 55.6% of the events in the Quake Template Matching (QTM) catalog for all of southern California during the same time period and 70% of those within 100 km of the array. In addition, we detect over 10,000 new events, not in the QTM catalog. Many of these events can also be seen in records from nearby borehole seismic stations. Measured slownesses for the catalog and newly‐discovered events group into clusters that can be associated with QTM earthquake locations, but with slowness values considerably distorted from predictions based on a 1‐D velocity model, presumably owing to strong velocity heterogeneity near the San Jacinto Fault. Amplitudes of the detected events obey a Gutenberg‐Richter distribution with a b ‐value close to one. Foreshocks are common among these detected events, increasing in rate before mainshocks following an inverse Omori's law.
Spline representation and redundancies of one-dimensional ReLU neural network models

We analyze the structure of a one-dimensional deep ReLU neural network (ReLU DNN) in comparison to the model of continuous piecewise linear (CPL) spline functions with arbitrary knots. In particular, we give a recursive algorithm to transfer the parameter set determining the ReLU DNN into the parameter set of a CPL spline function. Using this representation, we show that after removing the well-known parameter redundancies of the ReLU DNN, which are caused by the positive scaling property, all remaining parameters are independent. Moreover, we show that the ReLU DNN with one, two or three hidden layers can represent CPL spline functions with K arbitrarily prescribed knots (breakpoints), where K is the number of real parameters determining the normalized ReLU DNN (up to the output layer parameters). Our findings are useful to fix a priori conditions on the ReLU DNN to achieve an output with prescribed breakpoints and function values.
An Information Security Policy Development Process in Higher Education Institution: A Case Study Approach

Higher Education Institutions (HEIs) are exposed to cybersecurity challenges due to the open campus cultures of information and computing resources. Moreover, universities need to ensure that data is always protected from potential corruption, destruction or theft. Therefore, many experts have agreed that the establishment and implementation of an information security policy (ISP) is one of the most crucial factors in the protection of an organization's information assets. The success of implementing information security policies does not only depend on the awareness, acceptance and employees' compliance toward security policies but also on the good design and functioning of the policy. However, security policies are not easy to develop, the policies may differ significantly in the way the policy and procedures are set forth because of different types of organizations, cultures, technology changes and users. In designing a security policy, the policy's clarity, comprehensiveness, and flexibility are important to enable users to comply with any security policies. This paper presents the information security development guidelines and the experiences on the development process of Information Security policies in the HEI. This study discovered three case studies at HEI based on a conceptual policy development model. There are 44 semi-structured questions were derived from six themes of the policy development process for interviews session with IT managers. The results of this study propose nine steps for the development process of ISP and the barriers during development and implementation, especially in HEI.
Formal Specification and Verification for Organization-based systems : A Survey

Recently, developing organizational multi-agent systems (MAS) is very difficult due to their dynamism and complexity. Consequently, checking the correctness of these MAS is a complicated task. This paper proposes a survey of the relevant works dealing with formalizing organizational models in multi-agent systems. For this purpose, we analyse the essential studies in the domain. Then, we report an excellent discussion of these relevant approaches according to specific criteria. Lastly, we propose certain challenges to improve the formal specification process in organizational multi-agent systems.
Ensemble Image Colorization using Convolutional Neural Network

Colorization of grayscale images brings more life to images and presents a more realistic view of the objects present in the real world. It can be applied to the restoration of historic monochrome images and films as well as in assisting cartoon artists. Traditionally, image colorization involved scribbling color hints or choosing a reference color image to be able to transfer the color traits into the input grayscale image. In this study, the burden of requiring user intervention in the colorization process is removed by using a convolutional neural network(CNN) that is trained on a large dataset of color images. Previous colorization studies have produced useful colorizations for elements of an image's global semantics, such as the sky, grass, and trees, but they also revealed color bleeding. We propose an approach for CNN-based colorization using ensemble colorization where separate colorizations by two different CNNs are further processed by a refinement network. Mode cost is evaluated using L2 regression loss and by performing a “Colorization Turing Test”. Survey results reveal that people are fooled by most of the hand-picked colorized images considering that they are our network's best colorization results.
Improving Recommender Systems Performance with Cross-domain Scenario: Anime and Manga Domain Studies

Recommendation systems have been an integral part of supporting various applications used in daily life by presenting information according to users' preferences and solving information overload problems. Most of the time, users only interact with a small portion of information in the system, which leads to sparsity in the user and item interaction matrix. This sparsity leads to performance degradation of the recommenders. Cross-domain recommendation system is one technique that can be used to alleviate the impact of sparsity. It works by transferring useful knowledge from additional domains that have some relation to users or items in the system. However, its implementation depends on several aspects that are not commonly fulfilled by common datasets that are available. This study tries to implement cross-domain recommendation techniques in a new domain of anime and manga. These domains have close relationships and support the use of cross-domain recommendations. This study compares performance in single and cross-domain scenarios, along with sensitivity analysis. The use of cross-domain scenarios for manga recommendations resulted in a decrease in RMSE of 0.075 and relatively stable accuracy above 90% even at the sparsity level of 0.01%. The result shows that cross-domain recommenders have lower RMSE and better accuracy than single-domain recommenders in cases when anime domains are used as auxiliary domains for manga recommendation, but not otherwise.
Cloud-Edge Inference under Communication Constraints: Data Quantization and Early Exit

The inference delay of deep neural networks (DNN) cannot always fulfill the application requirements due to the data transmission to the cloud, which can be effectively alleviated by cloud-edge collaboration via DNN partitioning. However, the communication capability between cloud and edge is usually limited. In this paper, we propose a threshold-based data quantization and exit (TDQE) framework, where the classification thresholds divide the data to different parts and determine to either quantize the data for transmitting under the communication constraints or early exit the DNN at the partition point. To solve the optimal solutions of the thresholds, we model an accuracy optimization problem under communication constraints, and solve it through the linear programming. In order to reduce the impact of quantization on accuracy due to difference parts of data, we further adjust the quantization ranges for each part of data to refine the quantization performance. Based on the optimization results, TDQE algorithm is proposed to construct the DNN partitioning with classified data processing. Finally, to evaluate the proposed method, we compare it with two traditional DNN partitioning algorithms via the simulation results. The results show that the proposed algorithm outperforms the other two baselines with respect to the accuracy and meets the real-time requirements.
Mirror classes as a tool for International educational development

Mirror classes allow better development of educational and research competencies in undergraduate students of Latin American universities to be applied as a regular pedagogic strategy. Using the technique of observation and analysis of the different articles reviewed, gathering information through instruments such as a comparative table of results, on the pedagogical approach, leaving the traditional role, significantly improving their educational practice; looking for innovation, the ideal mechanism and strategies that are useful to achieve teaching-learning with technological innovation. The results obtained help to show that the academic learning strategy “Mirror Class” is an active communication tool that breaks down the barriers of traditional education and allows interaction with students and teachers from Latin American countries and other continents who share a high level of knowledge and encourage research in students. This type of work may be referred to in other countries as inter-university networking or collaborative international online learning (COIL). The difference in the time worked is from two to four sessions in the mirror classes to a work of 10 to 12 weeks in other cases.
Implementation of LSSVM in Classification of Software Defect Prediction Data with Feature Selection

Software defect prediction enhances the quality, efficiency, and effectiveness of time and expenses for software testing by focusing on defect modules. Software defect prediction technology uses machine learning to predict defect modules, making allocating limited resources easier quickly. Software defect prediction datasets naturally have imbalanced class problems with significantly few defective modules compared to non-defective modules. In this study, software defect prediction data was classified by implementing the LSSVM algorithm with ReliefF (K=10) feature selection and applying the SMOTE method to overcome the imbalanced class problem in the dataset. The datasets used are software defect prediction datasets of the public NASA MDP Promise project, namely CM1, PC1, KC1, and KC2. Dataset divided into training and testing data using Fold Cross-Validation with ten folds. The classifier achieved the highest average accuracy on the PC1 dataset, which was 93,87%, while the highest Area Under the ROC Curve (AUC) was achieved by the classifier for the KC2 dataset, which was 78,35%. The results also indicate that AUC values for classifiers that use SMOTE always higher than non-SMOTE in each dataset
FPGA Implementation of An Event-driven Saliency-based Selective Attention Model

Artificial vision systems of autonomous agents face very difficult challenges, as their vision sensors are required to transmit vast amounts of information to the processing stages, and to process it in real time. One first approach to reduce the data transmission is to use event-based vision sensors, whose pixels produce events only when there are changes in the input. However, even for event-based vision, transmission and processing of visual data can be quite onerous. Currently these challenges are solved by using high-speed communication links and powerful machine vision processing hardware. But if resources are limited, instead of processing all the sensory information in parallel, an effective strategy is to divide the visual field into several small sub-regions, choose the region of highest saliency, process it and shift serially the focus of attention to regions of decreasing saliency. This strategy, commonly used also by the visual system of many animals, is typically referred to as "selective attention". Here we present a digital architecture implementing a saliency-based selective visual attention model for processing asynchronous event-based sensory information received from a Dynamic Vision Sensor (DVS). For ease of prototyping, we use a standard digital design flow and map the architecture on a Field Programmable Gate Array (FPGA). We describe the architecture block diagram highlighting the efficient use of the available hardware resources demonstrated through experimental results exploiting a hardware setup where the FPGA interfaced with the DVS camera.
A DNN-based Decoding Scheme for Communication Transmission System over AWGN Channel

A communication transmission system with channel coding and deep neural network (DNN)-based decoding is considered. A DNN-based decoding scheme is proposed for reliable transmission. The decoding scheme is accomplished by efficient local decoding at all the neurons and interactions in the input, hidden and output layer. Specifically, firstly, the nonlinear operations at each neuron and the linear operations of the weights and biases at each edge are performed by the local decoding. Secondly, the weights and biases are updated by gradient descent (GD) algorithm, based on the estimated loss value. This process above is performed iteratively until the message sequence has been recovered. Simulation results show that our proposed decoding scheme performs well. Moreover, our decoding scheme performs significantly better than the conventional hard decision.
A novel hybrid Aquila optimizer with Whale optimziation algorithm for global optimization, feature selection and optimizing SVM parameters

“Support vector machine (SVM)” is one of the well-known machine learning algorithms used for classification and regression tasks. The “SVM” parameters and features selection have a significant impact on the “SVM” model's complexity and classification accuracy. Finding the best set of features and selecting the appropriate parameters of “SVM” is considered an optimization problem. Different metaheuristic algorithms are employed in the literature to choose the best set of features and to optimize “SVM” parameters simultaneously. This paper presents a new algorithm hybrid based on “Aquila Optimizer” and “Whale Optimization Algorithm” with Adaptive inertial weight, called AOWOA. The AOWOA is used for global optimization and feature selection and optimizing “SVM” parameters to achieve a higher classification accuracy. We utilized two experiment series to test the presented (AOWOA) algorithm. In the first experiment, 13 standard “benchmark functions” are used and the AOWOA algorithm is compared to AO, MVO, WOA, MFO, PSO, and SSA. In the second experiment, the results of AOWOA-SVM are compared with four metaheuristic algorithms: MVO, GWO, WOA, and BAT using ten labeled datasets. The experiment results proved that AOWOA can reduce the number of features, find the optimal parameters of “SVM”, and avoids local optima with high classification accuracy in most datasets compared to other algorithms.
Predicted Model based on Boltzmann Restricted Machine for Web Services Recommendation

With the growing number of Web services, it becomes more difficult for users to choose the best ones that meet their needs and get the best quality of service (QoS). Many times, a user will only invoke a small number of services, which leaves many QoS values for those services blank. Therefore, users can't select the best services based on their QoS values. This problem can be solved by proposing a predicted model for recommending appropriate services. This model uses the Restricted Boltzmann Machine (RBM) to predict which of the services with missing QoS values we can recommend to users. We evaluate our model using the WSDREAM dataset. Experimental results indicate that the proposed model is well performed and gets better results compared to other models.
Revamping Employee Engagement Practices by Augmenting Data Analytics

With the advent of COVID-19, the corporate world underwent a massive change. This stressed the need of effective employee engagement more than ever. Therefore, this study aims to understand the various dimensions of employee engagement and the use of Natural Language Processing to ensure an engaged workforce. To understand the problem, primary data collection was done with the help of interviews. The authors conducted interviews of 100 professionals from manufacturing, FMCG and IT companies in India to understand the problem from the root level. The paper then proposes a model, i.e., the PAUSE model which aims at categorizing the broad topic of employee engagement into five distinct categories. These categories tell us about the various areas in which employees can feel a sense of detachment or disengagement towards the organization. The model then suggests corrective actions that can be taken at the ground level to improve engagement in the targeted category.
Cascaded Coded Distributed Computing Schemes Based on Symmetric Designs

Coded distributed computing (CDC) is an efficient method to reduce the communication load in general distributed computing frameworks such as MapReduce. In these systems, one usually needs to split the data set into disjoint files and design several output functions to complete a computational task. Li et al. provided some CDC schemes achieving optimal communication load. However, as the number of computing nodes increases, the numbers of input files and output functions of such schemes grow too fast to be applied in practice. In this paper, several infinite families of cascaded CDC schemes, where each output function is computed multiple times, are constructed by taking advantage of symmetric designs. Most importantly, the numbers of input files and output functions of each new scheme are linear with the number of computing nodes and the communication load of each new scheme approximates to that of the CDC scheme derived by Li et al. when the number of nodes becomes large.
SVM ensemble training for imbalanced data classification using multi-objective optimization techniques

Abstract One of the main problems with classifier training for imbalanced data is defining the correct learning criterion. On the one hand, we want the minority class to be correctly recognized, and on the other hand, we do not want to make too many mistakes in the majority class. Commonly used metrics focus either on the predictive quality of the distinguished class or propose an aggregation of simple metrics. The aggregate metrics, such as Gmean or AUC , are primarily ambiguous, i.e., they do not indicate the specific values of errors made on the minority or majority class. Additionally, improper use of aggregate metrics results in solutions selected with their help that may favor the majority class. The authors realize that a solution to this problem is using overall risk. However, this requires knowledge of the costs associated with errors made between classes, which is often unavailable. Hence, this paper will propose the semoos algorithm - an approach based on multi-objective optimization that optimizes criteria related to the prediction quality of both minority and majority classes. semoos returns a pool of non-dominated solutions from which the user can choose the model that best suits him. Automatic solution selection formulas with a so-called Pareto front have also been proposed to compare state-of-the-art methods. The proposed approach will train a svm classifier ensemble dedicated to the imbalanced data classification task. The experimental evaluations carried out on a large number of benchmark datasets confirm its usefulness.
Four‐branch Siamese network based on sketch‐specific data augmentation for sketch recognition

The shortened abstract is as follows: Sketch recognition has become an important hotspot issue because of sketch's intuitiveness and visualization. The existing sketch-recognition methods based on handcrafted features and deep features are insufficient in the recognition of the local information of sketches, and the recognition accuracy is not ideal. Accordingly, this paper proposes a four-branch Siamese network based on sketch-specific data augmentation to generate discriminative feature representations and improve the sketch-recognition accuracy. A sketch is an ordered list of strokes, we adopt the semantic information of strokes as the decomposition criteria to divide a sketch into three disjoint local blocks, and then combine the local blocks in pairs to form three new sketches. In order to give full play to the positive effect of local blocks on category prediction and enhance the fine-grained capability of the network, three newly generated sketches and the original sketch are combined to construct a four-branch Siamese network. Each branch network adopts the Sketch-A-Net architecture with the fully connected layer removed as the basic network, and we improve it by adding shortcut connection layer and multi-scale weighted bilinear coding (MWBC) modules. Compared with the state-of-the-art methods, the experimental results on the TU-Berlin dataset demonstrate the excellent performance of our model.
Towards Role-Based Context-Aware Monitoring Systems

Monitoring IT systems during operation is one of the few methods that help administrators track the health of the monitored system, predict and detect faults, and assist in system repair and fault prevention. However, existing monitoring solutions still suffer from several trade-offs, as current implementations impose architectural constraints on monitored systems that result in less flexibility in deployment and operation. While there are excellent monitoring systems available for some application areas, others are not adequately supported. In addition, most monitoring software is specialized to work with specific data formats, protocols, and data collection mechanisms, further limiting its flexibility. The role-based approach to modelling and implementing software promises an intuitive way to increase flexibility in modelling and implementing information technology systems. Coupled with control loop technology from the field of self-adaptive systems, we created a reusable framework of static role-playing building blocks to overcome these limitations. In this chapter, we present and discuss an ongoing research project on new concepts for creating flexible role-based monitoring systems that function in most application domains. For that, we discuss the results of a study that compared the flexibility of a variety of monitoring systems and compare the flexibility of our concept to them based on 11 criteria. We illustrate our concept by presenting and discussing a role-based model for control loop components. We further discuss our implementation of the concept as well as how it can be applied to different problem domains. Finally, we discuss the results of a quantitative evaluation of the role-based monitoring approach and present an outlook of future tasks that need to be solved in this research project.