Reviewerly/exHarmony huggingface dataset:
A Theory of Independent Mechanisms for Extrapolation in Generative Models

Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating process, such that one can intervene on each node independently of the others. However, the nodes of this graph are usually unobserved, leading to overparameterization and lack of identifiability of the causal structure. We develop a theoretical framework to address this challenging situation by defining a weaker form of identifiability, based on the principle of independence of mechanisms. We demonstrate on toy examples that classical stochastic gradient descent can hinder the model's extrapolation capabilities, suggesting independence of mechanisms should be enforced explicitly during training. Experiments on deep generative models trained on real world data support these insights and illustrate how the extrapolation capabilities of such models can be leveraged.
Identifying natural images and computer-generated graphics based on Convolutional Neural Network

Aiming at the identification of natural images and computer-generated graphics, an image source pipeline forensics method based on convolutional neural network (CNN) is proposed. In this method, Inception-v3 is used as the basic network, and the pre-trained model parameters in ImageNet are adopted. The top-level classification layer of Inception-v3 is replaced by two fully-connected Softmax classifiers. With the transfer learning, a new network model is constructed. The network is fine-tuned by a database with 10,000 images to identify natural images and computer-generated graphics. Experimental results and analysis show that it can effectively identify natural images and computer-generated graphics, and it is robustness against JPEG compression, scaling, rotation, noise and other post-processing operations. Furthermore, the effect of Softmax classifier and SVM classifier on the experimental results are analysed.
SURAKSHA E-Caretaker: Elders Falling Detection and Alerting System using Machine Learning

People become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain.
Consumer financial fraud in the United Arab Emirates

Purpose This paper aims to study the explanatory power of demographics, financial behaviors and financial literacy on instances of consumer financial fraud (CFF) among Emirati households. Design/methodology/approach This study is based on a survey applied to the United Arab Emirates’ (UAE’s) largest federal higher education institution. The authors analyzed the data using generalized linear models, specifically generalized regressions based on both the logit and the probit models. Independent sample tests were also applied to compare the different subgroups considered in this study. Findings The authors found that the CFF victims seem to be older with more years of post-secondary education and high monthly credit card balances. When analyzing the probability of Emirati students becoming CFF victims, the authors found that only age, instances of lack of monthly income to cover living costs, and average monthly credit card balance, all have significant and positive explanatory power on the probability of becoming a CFF victim. However, when analyzing the aggregate subsample of all Emirati respondents, only the credit card balance has a positive and significant relationship with such a probability. Research limitations/implications The authors used a non-probability sampling method that produced some biases, including a gender bias and an age-related bias. These biases preclude us from making valid inferences and generalizations about the Emirati population. Originality/value To the best of authors’ knowledge, no previous research article has studied CFF in the UAE, which constitutes this study’s original contribution.
The guaranteed convergence mayfly optimization algorithm

Although the updating equations of velocities for individuals in the mayfly optimization (MO) algorithm were not the same as that for individuals in the particle swarm optimization (PSO) algorithm, there was still a similarity among the equations. Therefore, when the swarms were approaching to the local or global optima, the individuals would be trapped in local optima in the same way. Consequently, the guaranteed convergence improvement should also be introduced to the MO algorithm. This work was reported in this paper, and simulation experiments were carried out. The results verified that the guaranteed convergence MO algorithm would also perform better than the original one.
A Note on Privacy in Constant Function Market Makers.

Constant function market makers (CFMMs) such as Uniswap, Balancer, Curve, and mStable, among many others, make up some of the largest decentralized exchanges on Ethereum and other blockchains. Because all transactions are public in current implementations, a natural next question is if there exist similar decentralized exchanges which are privacy-preserving; i.e., if a transaction's quantities are hidden from the public view, then an adversary cannot correctly reconstruct the traded quantities from other public information. In this note, we show that privacy is impossible with the usual implementations of CFMMs under most reasonable models of an adversary and provide some mitigating strategies.
Deep Learning-Based, Real-Time, False-Pick Filter for an Onsite Earthquake Early Warning (EEW) System

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.
Seismic data reconstruction based on super resolution convolutional neural network

Field seismic records are often incomplete for missing some data. Reconstruction of the missed seismic data is necessary for seismic data processing and interpretation. Deep learning method can automatically mine data sets for hidden features or relationships. We developed a seismic data reconstruction method from under-sampled or missing traces based on a deep learning method, called super-resolution convolutional neural network (SRCNN). The key idea of our new seismic reconstruction method is significantly different from that of many previous interpolation methods. Our method views the seismic data reconstruction as a problem of image super-resolution in computer vision and directly learns an end-to-end mapping that indicates the hidden relationship between the incomplete and the complete seismic data from training data by means of feature extraction. The mapping is represented as a deep convolutional neural network (CNN) that takes the incomplete seismic data as the input and outputs the complete one. In numerical experiments, a three-layer convolution is utilized to recover field seismic data, and the results show the competitive performance of our method in comparison with the traditional f-x interpolation and POCS interpolation method.
Automatic Machine Learning Method for Hyper-parameter Search

Abstract Automatic Machine Learning (AutoML) uses automated data-driven methods to realize the selection of hyper-parameters, neural network architectures, regularization methods, etc., making machine learning techniques easier to apply and reducing dependence on experienced human experts. And hyper-parameter search based on automatic machine learning is one of the current research hotspots in the industry and academia. We mainly introduce the hyper-parameter search framework based on automatic machine learning and the common hyper-parameter search strategies. Combined with specific data sets, the classification accuracy of the model under different hyper-parameter search strategies is compared to find the model parameter configuration that can maximize the classification accuracy. Compared with the experience-based parameter adjustment method, the hyper-parameter search based on automatic machine learning can reduce labor costs, improve training efficiency, and automatically construct a dedicated convolutional neural network to maximize the model effect.
GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments

In remote procedure call (RPC)-based graphic processing unit (GPU) virtualization environments, GPU tasks requested by multiple-user virtual machines (VMs) are delivered to the VM owning the GPU and are processed in a multi-process form. However, because the thread executing the computing on general GPUs cannot arbitrarily stop the task or trigger context switching, GPU monopoly may be prolonged owing to a long-running general-purpose computing on graphics processing unit (GPGPU) task. Furthermore, when scheduling tasks on the GPU, the time for which each user VM uses the GPU is not considered. Thus, in cloud environments that must provide fair use of computing resources, equal use of GPUs between each user VM cannot be guaranteed. We propose a GPGPU task scheduling scheme based on thread division processing that supports GPU use evenly by multiple VMs that process GPGPU tasks in an RPC-based GPU virtualization environment. Our method divides the threads of the GPGPU task into several groups and controls the execution time of each thread group to prevent a specific GPGPU task from a long time monopolizing the GPU. The efficiency of the proposed technique is verified through an experiment in an environment where multiple VMs simultaneously perform GPGPU tasks.
Federated Learning in Named Entity Recognition

This article is devoted to the implementation of the federated approach to named entity recognition. The novel federated approach is designed to solve data privacy issues. The classic BiLSTM-CNNs-CRF and its modifications trained on a single machine are taken as baseline. Federated training is conducted for them. Influence of use of pretrained embedding, use of various blocks of architecture on training and quality of final model is considered. Besides, other important questions arising in practice are considered and solved, for example, creation of distributed private dictionaries, selection of base model for federated learning.
Crack Detection and Geometry Measurements using Digital Image Processing

Digital image processing (DIP), is the use of a digital computer to process digital images through an algorithm. In this work DIP used to perform crack/defect Detection and geometry measurements in terms of angle, length, and width by building a MATLAB code. In this research, the test of tensile were applied on two flat samples, the two flat samples are painted in a speckle pattern on the tested surface to use DIP features. The DIP gives a low-cost optical, and an appropriate technique to detect crack/defect as well as geometry measurements with the load increment in terms of angle, length and width without any contact with the tested surfaces. The tests directed for both of flat Aluminum alloy and Copper alloy specimens. In order to guarantee the efficiency of the planned DIP systems, the comparison is considered between the test results of this technique with the measurements gained by actual physical by using calibrated digital vernier calliper (IP 54) to measure the length of the crack with 0-300 mm range and 0.01 mm resolution. The DIP obtained percentage of accuracy varies from (97) % to (99) % by comparison with the measurements gained by actual physical way.
Micro-Id-Gym: Identity Management Workouts with Container-Based Microservices

Identity Management (IdM) solutions are increasingly important for building trust in current and future digital ecosystems. Unfortunately, not only their secure deployment but even their usage are non-trivial activities that require a good level of security awareness. For this, we introduce Micro-Id-Gym, an easy to configure training environment in which users can develop hands-on experiences on how IdM solutions work and better understand the underlying security issues.
An Analysis on Affecting Factors of Healthcare Applications Continuous Usage Intention and their Relationships

Many studies have been conducted on adoption of healthcare applications. However, few studies have paid their attention to continuous usage intention of healthcare applications even though success of healthcare applications is determined by Continuous usage intention rather than adoption. Therefore, this study tried to identify the factors affecting the continuous usage intention of healthcare applications and examined the relationship between them empirically. In addition, the role of gender and age in the relationship was investigated. The results showed that self-efficacy and innovation had a significant effect on perceived ease of use of healthcare application, while self-efficacy, health information seeking, and enjoyment had a significant effect on perceived usefulness. Perceived ease of use and usefulness showed positive impact on continuous usage intention of healthcare application. Finally, age and gender had a moderator effect the relationship between perceived usefulness and continuous usage intention. This study is expected to make contributions to the existing research by identifying the factors affecting the Continuous usage intention of healthcare applications, but also to give a guideline for managers who want to provide healthcare application services.
Text Categorization of Marathi News Articles Using Machine Learning

Nowadays, when the digitization is at its peak, handling the huge online generated data has become the most challenging task for a data analyst. Like all other tasks, reading new articles online has become the new trend, and then let it be in e-paper form or HTML form. Marathi has the fourth-largest number of native speakers in India. People in India prefer reading the news online in their native languages. Considering this scenario, it has become very important to categorize the news in different categories as per the user’s choice of news like sports, politics, entertainment, etc. to fetch the news of specific category in more easy and faster manner. Categorizing the text manually is very hard and time-consuming task; hence, it can be replaced with the automatic categorization of text using latest machine learning concepts. This paper proposes a way to categorize the given Marathi news article to one of the predefined categories by using different preprocessing techniques, most popular term-weighting scheme TF-IDF and K-NN machine learning classification algorithm. The percentage classification and recognition rate achieved is 91.27%.
Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors

In this research, we look at the notions of objectivity and subjectivity and create word embeddings from them for the purpose of sentiment analysis. We created word vectors from two datasets, the Wikipedia English Dataset for objectivity and the Amazon Product Reviews Data dataset for subjectivity. A model incorporating an Attention Mechanism was proposed. The proposed Attention model was compared to Logistic Regression, Linear Support Vector Classification models, and the former was able to achieve the highest accuracy with large enough data through augmentation. In the case of objectivity and subjectivity, models trained with the objectivity word embeddings performed worse than their counterpart. However, when compared to the BERT model, a model also with Attention Mechanism but has its own word embedding technique, the BERT model achieved higher accuracy even though model training was performed with only transfer learning.
Local Outlier Detection for Multi-type Spatio-temporal Trajectories

Outlier detection has become one of the core tasks in spatio-temporal data mining. It plays an essential role in data quality improvement for the machine learning models and recognizing the anomalous patterns, which may remarkably deviate from expected patterns among the trajectory datasets. In this work, we propose a clustering-based technique to detect local outliers in trajectory datasets by utilizing spatial and temporal attributes of moving objects. This local outlier detection involves three phases. In the first phase, we apply a temporal partition procedure to divide the raw trajectory into multiple trajectory segments and extract trajectory features from spatial and temporal attributes for each trajectory segment. Then, we generate template features of trajectory segments by applying a clustering schema in the second phase. Finally, we use the abnormal score - a novel dissimilarity measure, which quantifies the disparity among the query and template trajectory segments in terms of trajectory features and hence determines the local outliers based on the distribution of abnormal score. To demonstrate the effectiveness of our method, we conduct three case studies on the real-life spatio-temporal trajectory datasets from the solar astroinformatics domain (i.e., solar active regions, coronal mass ejections, polarity inversion lines (PIL)). Our experimental results show that our local outlier detection approach can effectively discover the erroneous reports from the reporting module and abnormal phenomenon in various spatio-temporal trajectory datasets.
Reviewing Commonalities between Agile Software Development Methodology and Grounded Theory Methodology

Agile development method has attracted widespread corporate and academic attention as the limitations of traditional software development method become apparent. Similarly focusing on inductive knowledge creation, grounded theory method has achieved seminal status in social science. But there is no attempt so far to identify synergies between these two popular methodologies of diverse fields, so that social science researchers can benefit by using principles and practices of agile methodology. This paper identifies the similarities between these two methods in terms of the underlying philosophy as well as important practices. Focusing on these similarities, multiple recommendations regarding enriching grounded theory method with components of agile methodologies were made. The paper serves as an initial work on an important emerging methodology debate, which need to be further explored, enhanced and detailed.
ACRIPPER: A New Associative Classification Based on RIPPER Algorithm

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.
On Galois NFSRs Equivalent to Fibonacci Ones

Nonlinear feedback shift registers (NFSRs) are used in many stream ciphers as their main building blocks. According to implementation configurations, NFSRs are generally classified as Fibonacci NFSRs and Galois NFSRs. Compared to Fibonacci NFSRs, Galois NFSRs have potentially shorter propagation time and higher throughput. Moreover, if a Galois NFSR is equivalent to a Fibonacci NFSR in the sense that they have the same set of output sequences, then this particular Galois NFSR can overcome some drawbacks of a general one, and therefore may improve the security of NFSR-based stream ciphers. Previous work has found some types of Galois NFSRs equivalent to Fibonacci NFSRs, and has shown many Galois NFSRs equivalent to a given Fibonacci NFSR with the same stage number. This paper gives another type of Galois NFSRs equivalent to a given Fibonacci NFSR, in which their all corresponding states have either equal or complementary components at the same positions. As an application of this result, the paper shows that the stream ciphers Grain, Trivium and Acorn have used the NFSRs with the lowest cost of hardware implementation among their own equivalent Galois NFSRs of this type. The paper also enumerates the Galois NFSRs equivalent to a given Fibonacci NFSR with the same stage number. Moreover, it reveals some common characterizations of Galois NFSRs that are equivalent to Fibonacci ones from the perspectives of their stage number and feedback functions, helpful to the design of stream ciphers.
Why Blockchain Technology Will Never Replace The Land Registry Office

Many innovation enthusiasts bask the virtues of the idea of applying Blockchain to create an autonomous decentralized land registration system. I argue that this idea appears to run counter to the basic characteristics of real estate market and therefore is undesirable. Though Blockchain has several other technological advantages, most of them can be achieved through existing alternatives that challenge the logic of adopting a riskier technology with many drawbacks and unknowns.