Reviewerly/exHarmony huggingface dataset:
Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California

Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P- and S-wave arrivals, auto-identification of later seismic phases such as the Moho-reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine-identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high-quality PmP dataset (10,192 manual picks) in southern California, we develop PmPNet, a deep-neural-network-based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)-autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre-trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP dataset, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.
Motion generation of a soft sheet-like swimming robot using physical reservoir computing

In recent years, swimming robots have been developed to achieve efficient propulsion and high maneuverability. Previous researches have tried to achieve fish-like swimming by control based on physical models and top-down architectures, but have encountered problems due to high complexity of the underwater environment. Other researches have tried to overcome these problems by mimicking the physical properties of fish. To achieve more intelligent swimming, we argue that a control architecture that uses physical dynamics as a computational resource is needed. In this study, a soft sheet-like swimming robot achieves motion emulation and environment estimation through a control architecture based on physical reservoir computing.
CryptoCert

Blockchain is a distributed technology that works along with smart contracts to create immutable ledgers in different application domains. Extensive research is still ongoing on how to utilize blockchain in industries such as healthcare, education, and finance, just to name a few. This chapter proposes a blockchain-based academic credential verification. Replacing current verification systems in educational institutes with those based on blockchain proves to benefit all the stakeholders. The chapter evaluates this proposal and presents a system, CryptoCert, as a proof of concept. By removing the need for a third party for verification, a lot of time and money will be saved for the stakeholders. More importantly, blockchain’s immutability provides a unique and efficient way to curb the widespread fraud and falsification of academic credentials such as degrees, transcripts, and learning achievement certificates.
Image semantic segmentation based on improved DeepLabv3+ network and superpixel edge optimization

Image semantic segmentation is a fundamental problem in the field of computer vision. Although the existing semantic segmentation model based on fully convolutional neural network continuously optimizes the segmentation effect, the inherent spatial invariance of the network still leads to cause the loss of object edge details. Moreover, most models use the pixel-by-pixel loss to optimize the target, and the dependencies between pixels are ignored. When facing objects with smaller spatial structures in the image, the segmentation result is not satisfactory. Based on the theory of relative entropy and mutual information, we propose an overall objective loss function that integrates pixel similarity and image structure similarity. It can better pay attention to the structure and detail information of small objects in space by modeling the dependency relationship between pixels. We use the DeepLabv3+ network based on group normalization, with the improved ResNet50 as the backbone. After that, considering the particular advantages of superpixel segmentation for object edges, we propose a superpixel edge optimization algorithm, which combines pixel-level semantic features and superpixel-level regional information to obtain the semantic segmentation results after edge optimization. Experiments on PASCAL VOC 2012 and cityscapes datasets show that the proposed method improves the performance of semantic segmentation and shows better results in small target structures and object edge details.
Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network

The beauty of Indonesian women is distinguished by skin color, facial structure, hair color and body posture. For women today trying to look beautiful is a must. The way to make yourself look beautiful can be tricked by using make-up. But it's not that easy to use make-up because the type of make-up is differentiated based on the basic skin color, this is the problem for women in using make-up. Undertone is the basic color of the skin, there are three types of undertones, namely warm, cool and neutral. By knowing the type of undertone, it will make it easier for women to use make-up, namely to determine the appropriate shade based on the type of undertone. For this reason, a modeling of undertone image classification was made using the Convolutional Neural Network algorithm. This algorithm is claimed to be the best algorithm for solving object recognition and detection problems. The wrist vein color image dataset is required. The dataset used is 30 data per class, then preprocessing is carried out by homogenizing the image size to 64x64 pixels, then augmentation is carried out on each image by rotating and zooming. At this stage, the dataset will be divided into 3000 images which are divided into 80% training data and 20% testing data. Then it is processed through the convolution and pooling process at the feature learning stage, then the fully connected layer and classification stage where the feature learning results will be used for the classification process based on subclasses. Produces accuracy and training model values ​​reaching 98% with a loss value of 0.0214 and for accuracy from data validation it reaches 99% with a loss value of 0.0239 with model testing results of 99.5%.
A medical specialty outpatient clinics recommendation system based on text mining

Many prospective medical patients have difficulty determining which type of outpatient specialist to consult for their complaints, and their resulting inquiries impose an additional administration cost for hospitals. The symptoms data of outpatient treatment in different specialties are collected from various hospitals to establish a database fronted by a chatbot-based interface to develop a medical specialty outpatient clinic recommendation system. The proposed system integrates speech recognition, the Jieba word segmentation algorithm and the conditional random field algorithm to retrieve keywords during the dialogue process. The C4.5 decision tree model is used to provide clinical department referrals for the symptoms reported by patients. Through continuous revision, the system gradually reduces the error rate of outpatient recommendations, thus reducing patients' waiting times and the workload of frontline hospital staff. Eleven subjects were invited to use this system, and seven of them felt that this system could help them.
A Comparative Study of Cyber Threats on Evolving Digital Identity Systems

Identity empowers global citizens; it helps an individual to play an active part in international development. To do so, there is often a need for individuals to formally identify themselves. This empowers them to engage in financial services, voting rights, business rights, land titles, social protection, school and various other benefits. Organisations are becoming increasingly customer-centric and many are realising the importance of digital identity as a key to improve customer experience. However, a significant proportion of the human population does not have access to credentials, and in a number of instances, identities have been counterfeited or misused. Robust identification and associated management systems need to be introduced and implemented to address these challenges. Novel digitised decentralised approaches are being explored, and this emerging field promises to empower individuals with an identity; however, emerging threats and risks prevent such systems from being trusted. Firstly, this paper aims to describe the existing model of evolving Digital Identity Management System(IDMS) and investigate the related cyber security requirements and challenges to such systems. Secondly, the STRIDE threat modelling approach is described in the context of IDMS. Finally, the paper identifies the threat landscape of IDMS by employing the threat modelling approach.
Improved Redundant Rule-Based Stochastic Gradient Algorithm for Time-Delayed Models Using Lasso Regression

This paper proposes an improved redundant rule based lasso regression stochastic gradient (RR-LR-SG) algorithm for time-delayed models.The improved SG algorithm can update the parameter elements with different step-sizes and directions, thus it is more adaptive; while the lasso regression method can pick out the small weights from the redundant parameter vector, it therefore can obtain the time-delay easily.To show the effectiveness of the proposed algorithm, the convergence analysis is also given.The simulated numerical results are consistent with the analytically derived results of the proposed algorithm.
Face Detection using Deep Learning to ensure a Coercion Resistant Blockchain-based Electronic Voting

Features of proposed remote voting are non-repudiation (biometric authentication), coercion-resistance (face detection), universal verifiability (Blockchain), multi-party communication (asymmetric encryption),easy-of-use (android).
Minimal Supervision for Morphological Inflection

Neural models for the various flavours of morphological reinflection tasks have proven to be extremely accurate given ample labeled data, yet labeled data may be slow and costly to obtain.In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as small as five labeled inflection tables, accompanied by a large bulk of unlabeled text.Our bootstrapping method exploits the orthographic and semantic regularities in morphological systems in a two-phased setup, where word tagging based on analogies is followed by word pairing based on distances.Our experiments with the Paradigm Cell Filling Problem over eight typologically different languages show that in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy.Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems.We further show that our bootstrapping methods substantially outperform hallucination-based methods commonly used for overcoming the annotation bottleneck in morphological reinflection tasks.
Building a Privacy-Preserving Blockchain-Based Bidding System: A Crypto Approach

Blockchain-based bidding systems are becoming increasingly popular nowadays. Due to the properties of blockchain, bidding records are unchangeable. With existing encryption techniques, these bidding records can only be shared by the bidder and the seller. Although this scenario sounds secure, it does not consider a coercion case. A powerful coercer may force the bidding system to open the records stored on the blockchain, and the system loses privacy. To solve this problem, in this paper, we introduce a new encryption scheme called deniable matchmaking encryption (DME). This new encryption scheme provides deniability not only for the message, but also for the identities. We use the chameleon hash function to make fake message and fake identities indistinguishable from the real message and the real identities. Therefore, the bidding system can use fake information to answer the coercer, and user privacy is kept by the blockchain-based bidding system.
Reorganizing Virtual Machines as Docker Containers for Efficient Data Centres

With increase in the use of virtual machines in various fields, there is a need to enhance the balancing of huge workload in data centres. The traditional way of implementing the cloud is usually done with virtual machines. Working of virtualization can be reconsidered with different and enhanced technology, such as containers in data centre. Docker containers are gaining popularity due to its features such as increase in productivity by reducing the number of resources. In this chapter, virtual machines are compared with the containers, and virtualization techniques are examined so that the user can work with respect to the requirements. Unlike a virtual machine, a container does not have another software layer called Hypervisor. Due to these reasons, containerized applications have better performance characteristics than virtual machine-based applications. We will discuss the benefits of containers over hypervisor in containers. Nevertheless, containers execute directly in the kernel of the virtual machine. Docker containers use engine of the docker as an alternative to hypervisor.
Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images

Intensity inhomogeneity, hidden details, poor image contrast due to low capturing device quality, limited user experience, and inappropriate environment setting during data acquisition are major issues reported during the image enhancement process. Histogram Equalization (HE) approaches have been commonly deployed to overcome the above-listed problems, apart from improving image contrast. Nevertheless, the resulting images retrieved after undergoing these approaches are often affected by undesired artifacts, unnatural looks, and unpleasant washed-out effects. As such, this study introduces a new approach called Adaptive Clip Limit Tile Size Histogram Equalization (ACLTSHE). The ACLTSHE initially assigns the optimum clip limit (CL) and tile size minimum or maximum values. Then, a new fitness function called DataSignal is deployed to produce a set of non-dominated solutions by adaptively computing the optimum CL value. The performance of the proposed ACLTSHE approach was assessed and compared with conventional Clip Limit HE (CLHE) and several state-of-the-art approaches, such as Dynamic Clipped HE (DCLHE), Iterated Adaptive Entropy Clip Limit HE (IAECHE), Mean and Variance Sub-image HE (MVSIHE), and Adaptive Entropy Index HE (AEIHE). The outcomes were assessed both qualitatively and quantitatively by using six evaluation metrics, Discrete Entropy (DE), Absolute Mean Brightness Error (AMBE), Peak Signal-to-Noise Ratio (PSNR), Contrast Improvement Index (CII), Root Mean Square Error (RMSE), Structure Similarity Index (SSI) and Standard Deviation (SD). The quantitative evaluation of three dataset images (Pasadena-houses 2000, faces 1999, and BraTS 2019) verified that the proposed approach outperformed the compared approaches in terms of improved DE, enhanced contrast, and highlighted local details without losing the original image structures.
SENTIMEN ANALISIS OPERASI TANGKAP TANGAN KPK MENURUT MASYARAKAT MENGGUNAKAN ALGORITMA SUPPORT VECHTOR MACHINE, NAÏVE BAYES, BERBASIS PARTICLE SWARM OPTIMIZITION

&lt;p&gt;&lt;em&gt;It is known from various public sentiments conveyed through comments on social media twitter against the capture operations carried out by the corruption eradication commission (KPK) that currently it does not meet the expectations of the community, where officials who are only officials have small corruption rates, not corruption As for the classification algorithms that have strong accuracy at this time are Support Vector Machine and Naïve Bayes algorithms, calculation of Support Vector Machine method for tweet data from 78 positive tweet data and 78 negative tweet data, resulting in an accuracy of 80.77% and AUC 0.867. Whereas the results of accuracy with the Naïve Bayes method are 76.92% and AUC 0.729. Having a difference in accuracy of 3.3%, and after optimizing with the Operator Vector Machine (PSO) weight Particle Swarm Optimization the accuracy is 83.79% and AUC 0.910, while for Naïve Bayes (PSO) produces an accuracy of 80.13% and AUC 0.771 Has a difference in accuracy of 3.6%.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Diketahui dari berbagai sentimen masyarakat yang disampaikan melalui komentar di media sosial &lt;em&gt;twiter &lt;/em&gt;terhadap operasi tangkap tangan yang dilakukan oleh Komisi Pemberantasan Korupsi (KPK) nyatanya saat ini belum memenuhi harapan masyarakat, dimana pejabat yang di ott hanya pejabat yang mempunyai angka korupsi kecil, bukan korupsi yang besar adapun algoritma klasifikasi yang kuat akurasinya saat ini adalah algoritma&lt;em&gt; Support Vector Machine &lt;/em&gt;untuk data &lt;em&gt;tweet&lt;/em&gt; dari 78 data tweet positif dan 78 data tweet negatif, menghasilkan akurasi sebesar 80.77% dan AUC 0.867. Sedangkan hasil akurasi dengan metode&lt;em&gt; Naïve Bayes&lt;/em&gt; adalah 76.92% dan AUC 0.729. Memiliki selisih akurasi sebesar 3.3%, dan setelah di optimalisasi dengan oprator &lt;em&gt;Weight Partical Swarm Optimization&lt;/em&gt; untuk &lt;em&gt;Support Vector Machine (PSO)&lt;/em&gt; menghasilkan akurasi 83.79% dan AUC 0.910, sedangkan untuk &lt;em&gt;Naïve Bayes&lt;/em&gt; (PSO) menghasilkan akurasi sebesar 80.13% dan AUC 0.771 memiliki selisih akurasi sebesar 3.6%.&lt;/em&gt;&lt;/p&gt;
A CCA-PKE Secure-Cryptosystem Resilient to Randomness Reset and Secret-Key Leakage

In recent years, several new notions of security have begun receiving consideration for public-key cryptosystems, beyond the standard of security against adaptive chosen ciphertext attack (CCA2). Among these are security against randomness reset attacks, in which the randomness used in encryption is forcibly set to some previous value, and against constant secret-key leakage attacks, wherein the constant factor of a secret key’s bits is leaked. In terms of formal security definitions, cast as attack games between a challenger and an adversary, a joint combination of these attacks means that the adversary has access to additional encryption queries under a randomness of his own choosing along with secret-key leakage queries. This implies that both the encryption and decryption processes of a cryptosystem are being tampered under this security notion. In this paper, we attempt to address this problem of a joint combination of randomness and secret-key leakage attacks through two cryptosystems that incorporate hash proof system and randomness extractor primitives. The first cryptosystem relies on the random oracle model and is secure against a class of adversaries, called non-reversing adversaries. We remove the random oracle oracle assumption and the non-reversing adversary requirement in our second cryptosystem, which is a standard model that relies on a proposed primitive called LM lossy functions. These functions allow up to M lossy branches in the collection to substantially lose information, allowing the cryptosystem to use this loss of information for several encryption and challenge queries. For each cryptosystem, we present detailed security proofs using the game-hopping procedure. In addition, we present a concrete instantation of LM lossy functions in the end of the paper—which relies on the DDH assumption.
Statistical (Machine) Learning

In this chapter we explore the topic of statistical learning, which is more commonly referred to as machine learning, where we utilize statistical models to learn something about a data set, that can then be applied to new situations. A focus of this chapter is classification, where we are predicting a category (e.g., fraud or no-fraud), rather than a number, and study three techniques for classification: 1) logistic regression, 2) classification trees, and 3) random forests. We also revisit regression via a machine learning lens. We first explore how the application of linear regression changes in a machine learning context and then explore regression trees and regression random forests. We also study clustering algorithms, namely hierarchical and k-means clustering, that group data into meaningful groups. The last topic we consider in this chapter is advanced machine learning techniques, where we are introduced to neural networks (i.e., deep learning), bagging and boosting. All techniques are demonstrated in the R programming language.
Causal Structure Learning With One-Dimensional Convolutional Neural Networks

Causal structure discovery has an important guiding role in explanatory artificial intelligence. In order to discover causal relationships from observed data and restore causal structure graphs, we propose the Directed Acyclic Graph structure learning with Causal Convolutional Neural Networks (DAG-CCNN). First, we employ a nonlinear structural causal model (SCM) generation mechanism and propose an integrated neural network model which combines a fully-connected (FC) layer and one-dimensional convolutional (1D-Conv) layer. Then, a new acyclic algebraic representation is proposed, and a theorem and its proof are given. We use the eigenvalues of the weighted adjacency matrix instead of the Hadamard product of the adjacency matrix to represent the acyclicity of the graph, which essentially avoids the computational complexity associated with matrix multiplication. Finally, compared with DAG-GNN, NOTEARS and GraN-DAG, the experimental results show that the DAG-CCNN method has some advantages in performance on synthetic and real Sach data sets and the results of the restoring causal structure graph are more satisfactory.
Unravelling the Origins of Infobesity: The Impact of Frequency on Intensity

Infobesity is characterized by information overload whereby firms and decision makers collect more information than they need, or they can efficiently use.While recent studies have begun to unravel the antecedents of infobesity in organizations, there is a need to examine the relationship between the frequency and the degree of experiencing infobesity originating from enterprise systems.We use a research design that integrates inductive analytics and abductive discovery to uncover the interaction of multi-level antecedents of infobesity and conclude that the rate at which firms encounter infobesity drives the perception of the intensity at which the overload will be experienced.
Simple and Efficient ways to Improve REALM

Dense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25.One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrieval system that uses MLM based pretraining for improved downstream QA performance.However, the current REALM setup uses limited resources and is not comparable in scale to more recent systems, contributing to its lower performance.Additionally, it relies on noisy supervision for retrieval during fine-tuning.We propose REALM++, where we improve upon the training and inference setups and introduce better supervision signal for improving performance, without any architectural changes.REALM++ achieves ∼5.5% absolute accuracy gains over the baseline while being faster to train.It also matches the performance of large models which have 3x more parameters demonstrating the efficiency of our setup.