Reviewerly/exHarmony huggingface dataset:
Preliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking Using Machine Learning with STA/LTA As the Input Parameters

Abstract The Short Term Averaging/Long Term Averaging (STA/LTA) has been widely used to detect earthquake arrival time. The method simply calculates the ratio of moving average of the waveform amplitude at short and long-time windows. However, although STA/LTA signals can distinguish between real events and noise, we still recognize some lack of accuracies in first P wave arrival pickings. In this study, we attempt to implement one machine learning method popularly, Artificial Neural Network (ANN) that employ input, hidden and output layer similar as human brain works. Note that in this study, we also try to add input parameters with another derivative signal attributes such as Recursive STA/LTA and Carl STA/LTA. The processing step started by collecting event waveforms from the Agency of Meteorology, Climatology and Geophysics. We chose regional events with moment magnitude higher than 3 in the Maluku region Indonesia. Next, we apply all STA/LTA attributes to the input waveforms. We also tested our STA/LTA with synthetic data and additional noise. Further step, we manually picked the arrival of P wave events and used this as the output for ANN. In total, we used 100 events for arrival data training in P wave phases. In the validation process, an accuracy of more than 0.98 can be obtained after 200 iterations. Final outputs showed, that in average, the difference between manual picking and automatic picking from ANN is 0.45 s. We are able to increase the accuracy by band pass filter (0.1 – 3 Hz) all signal and improve the mean into 0.15s difference between manual picking and ANN picks.
Blockchain and European Higher Education Systems A snapshot on the diffusion process of Blockchain In-novation into European Academia Comparative study of Blockchain in Higher Education Systems of Estonia, Germany, Greece, the Netherlands, and Spain

The task of this study is the comparative analysis of the diffusion process of Blockchain into the national higher education systems as well as analysis of best-practice examples. It is part of the EU Erasmus+ project BlockWASTE, which has as its overall objective to promote the application of Blockchain in municipal waste management through the development of targeted university curricula and study plans on the Circular Economy and Blockchain, training manuals and other educational tools. With this goal in mind, the first question to be answered is where there is a need for Blockchain. This again translates into questions like What is the status quo of the diffusion of Blockchain innovation in university education and research in the participating partner countries of the BlockWASTE project? What are the main drivers and reasons behind the fact that the diffusion process for the integration of Blockchain innovation in science and teaching is faster in some countries and slower in other countries despite good framework conditions? But the present study is not only intended to be a snapshot of the diffusion process of innovation in higher education of different European countries. A major focus of the study is to learn from best practice examples of university education in Blockchain. In the framework of the BlockWASTE project, this im-plies analyzing the innovation downstream and universities’ own development of curricular and educational material and tools.
Recognizing Long Grammatical Sequences using Recurrent Networks Augmented with an External Differentiable Stack

Recurrent neural networks (RNNs) are a widely used deep architecture for sequence modeling, generation, and prediction. Despite success in applications such as machine translation and voice recognition, these stateful models have several critical shortcomings. Specifically, RNNs generalize poorly over very long sequences, which limits their applicability to many important temporal processing and time series forecasting problems. For example, RNNs struggle in recognizing complex context free languages (CFLs), never reaching 100% accuracy on training. One way to address these shortcomings is to couple an RNN with an external, differentiable memory structure, such as a stack. However, differentiable memories in prior work have neither been extensively studied on CFLs nor tested on sequences longer than those seen in training. The few efforts that have studied them have shown that continuous differentiable memory structures yield poor generalization for complex CFLs, making the RNN less interpretable. In this paper, we improve the memory-augmented RNN with important architectural and state updating mechanisms that ensure that the model learns to properly balance the use of its latent states with external memory. Our improved RNN models exhibit better generalization performance and are able to classify long strings generated by complex hierarchical context free grammars (CFGs). We evaluate our models on CGGs, including the Dyck languages, as well as on the Penn Treebank language modelling task, and achieve stable, robust performance across these benchmarks. Furthermore, we show that only our memory-augmented networks are capable of retaining memory for a longer duration up to strings of length 160.
A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of Timestamp Influence on Bitcoin Value

The transaction and market of bitcoin is volatile, meaning it's uncertain because it changes frequently.There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps.It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction.A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions.That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction timestamp influence on Bitcoin value.Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used.Simulation analysis indicates that bitcoin digital currency's performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%.The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.
HAO Unity

Many real-world applications have to face the problem of diversity in data formats and semantics. Currently, how to deal with heterogeneous data effectively is still a big challenge. With the rise of knowledge graphs, more and more applications are built upon graph-like data models, which benefit from flexible schemas and convenient support for relationship queries. We propose a graph-based unifying system for heterogeneous data unification, which helps to (1) transform data in many other formats into graphs, or conversely, from graph to other formats, (2) integrate graph data based on HAO intelligence, which achieves schema integration and entity consolidation, and (3) explore data at different levels via querying the integrated graphs. In this paper, we introduce the overall system architecture, explain in detail the implementation, and display the usage in two practical scenarios.
How Good is Too Good? A Subjective Study on Over Enhancement of Images

For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.
A Comparative Study for Open Set Semantic Segmentation Methods

Typical semantic segmentation methods do not recognize unknown pixels during the test or deployment stage. This capability is critical for open-world environment applications where unseen objects appear all the time. Recently, to solve those limitations, Open Set Semantic Segmentation (OSSS) was introduced. This task aims to produce known and unknown pixels semantic segments. However, due to its recent introduction, few works are found in the literature, and consequently, few datasets are publicly available. This work carried out a comparative study between the existing OSSS methods on a new synthetic dataset of images and the well-known PASCAL VOC 2012 dataset. The compared methods include SoftMax-T, OpenMax-based, and OpenIPCS. The results are encouraging and show some of the advantages and main limitations of each technique. However, in general, they demonstrate that the problem of OSSS remains open and demands further research aiming at real applications, such as autonomous driving and robotics.
Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.
Exploring the mechanisms of phishing

Social engineering can be defined as a form of attack involving human interactions. 1,2 It is used to force users or victims to make psychological mistakes so that they end up making a security mistake, giving up crucial information. Social engineering is about tricking users with the help of fake websites that look the same as legitimate ones or impersonating someone trustworthy, and it includes activities such as exploiting normal human behaviours like kindness and greed. Human behaviour is such that people tend to fall easily for anything they think is free, or anything they see as trustworthy.
WristPrint: Characterizing User Re-identification Risks from Wrist-worn Accelerometry Data

Public release of wrist-worn motion sensor data is growing. They enable and accelerate research in developing new algorithms to passively track daily activities, resulting in improved health and wellness utilities of smartwatches and activity trackers. But, when combined with sensitive attribute inference attack and linkage attack via re-identification of the same user in multiple datasets, undisclosed sensitive attributes can be revealed to unintended organizations with potentially adverse consequences for unsuspecting data contributing users. To guide both users and data collecting researchers, we characterize the re-identification risks inherent in motion sensor data collected from wrist-worn devices in users' natural environment. For this purpose, we use an open-set formulation, train a deep learning architecture with a new loss function, and apply our model to a new data set consisting of 10 weeks of daily sensor wearing by 353 users. We find that re-identification risk increases with an increase in the activity intensity. On average, such risk is 96% for a user when sharing a full day of sensor data.
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text

Communicating with humans is challenging for AIs because it requires a shared understanding of the world, complex semantics (e.g., metaphors or analogies), and at times multimodal gestures (e.g., pointing with a finger, or an arrow in a diagram).We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community.In Iconary, a Guesser tries to identify a phrase that a Drawer is drawing by composing icons, and the Drawer iteratively revises the drawing to help the Guesser in response.This back-and-forth often uses canonical scenes, visual metaphor, or icon compositions to express challenging words, making it an ideal test for mixing language and visual/symbolic communication in AI.We propose models to play Iconary and train them on over 55,000 games between human players.Our models are skillful players and are able to employ world knowledge in language models to play with words unseen during training.Elite human players outperform our models, particularly at the drawing task, leaving an important gap for future research to address.We release our dataset, code, and evaluation setup as a challenge to the community at github.com/allenai/iconary. Annotations 18.5%, 22.0%A noun is drawn by composing multiple icons, such as drawing 'sprinkler' with a spray bottle, drop, and fountain icons.Composition 47.5%, 64.0%Arrows, crosses, checkmarks, or circles guide interpretation, such as an arrow to indicate a part or crosses to specify incorrect options.Repurposing 22.5%, 32.0%An icon is used to represent an object different than its intended meaning, such as a scarf for 'scabbard', or box + ring for 'boxing ring'.Verb Scene 25.0%, 44.5% Multiple icons are arranged in a scene to indicate the verb.Verb Icon 41.0%, 25.5%A single icon is used to indicate the verb, such as hammer for 'building' or eyes for 'reading'. Verb Arrows22.0%, 25.5%The verb is indicated by using arrows to show motion.
Monte Carlo Game Solver

We present a general algorithm to order moves so as to speedup exact game solvers. It uses online learning of playout policies and Monte Carlo Tree Search. The learned policy and the information in the Monte Carlo tree are used to order moves in game solvers. They improve greatly the solving time for multiple games.
YouTube vlog channels in basic mathematics as e-learning during the COVID-19 pandemic

This research aimed to produce YouTube vlog channels that are valid, practical, and effective to be used in facilitating e-learning in a basic mathematics course. The method used in this research is Research and Development (RD), which consists of four stages, namely define, design, develop, and disseminate. The subjects in this study were 62 third-semester students of the Mathematics Education Study Program at PGRI University of Madiun, Indonesia, where 50 students were field test subjects and 12 students were limited test subjects. The instruments used in this research were a validation sheet, a student response questionnaire, and a student learning outcomes test. This research produced YouTube vlog channels. Based on the validation results on teaching materials, an average score of 77% was obtained which was declared valid. Based on the student response questionnaire at the field test stage, it was obtained an average value of 75.46% which was stated to be practical. While at the limited test stage obtained a score of 81.94% which is also declared practical. Based on the learning outcomes test in the field test, the percentage of completeness was 81.82% which stated that the YouTube vlog channel was effective. Meanwhile, in the limited test stage, the completeness percentage was 82% which was also declared effective. Therefore, the produced YouTube vlog channel is feasible for use in e-learning environment.
Modified Tunicate Swarm Algorithm for Nonlinear Optimization Problems

Due to the difficulty and complexity of optimization problems and the traditional methods cannot find the optimal global solutions after a few iterations, researchers have been proposed and improved a large number of algorithms to avoid the demerits of the classical methods. One of the newest meta-heuristic algorithms is Tunicate Swarm Algorithm (TSA) which shows how the tunicates reach the food source using two important techniques Swarm intelligence and jet propulsion. In this paper, we will modify the classical Tunicate Swarm Algorithm. We proved the high performance and good convergence of the proposed methodology by applying the new modification to 10 unconstrained benchmark functions compared with other algorithms from the literature review and one of the constrained problems (Economic dispatch Problem).
AI and the Everything in the Whole Wide World Benchmark

There is a tendency across different subfields in AI to valorize a small collection of influential benchmarks. These benchmarks operate as stand-ins for a range of anointed common problems that are frequently framed as foundational milestones on the path towards flexible and generalizable AI systems. State-of-the-art performance on these benchmarks is widely understood as indicative of progress towards these long-term goals. In this position paper, we explore the limits of such benchmarks in order to reveal the construct validity issues in their framing as the functionally general broad measures of progress they are set up to be.
A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

&lt;p class="0abstract"&gt;Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.&lt;/p&gt;
Logic Rules for Automated Synthesis of Function Models Using Evolutionary Algorithms

Abstract This paper presents the development of logic rules for evaluating the fitness of function models synthesized by an evolutionary algorithm. A set of 65 rules for twelve different function verbs are developed. The rules are abstractions of the definitions of the verbs in their original vocabularies and are stated as constraints on the quantity, type, and topology of flows connected to the functions. The rules serve as an objective and unambiguous basis of evaluating the fitness of function models developed by a genetic algorithm. The said algorithm and the rules are implemented in software code, which is used to both demonstrate and validate the efficacy of the rule-based approach of converging function model synthesis using GAs.
Analysis of «Leader – Followers» Algorithms in Problem of Trajectory Planning for a Group of Multi-rotor UAVs

Trajectory planning a group of robots is an urgent task of basic robotics. The paper evaluates the efficiency of using the path planning algorithms A*, RRT, RRT* and the visibility graph (VG) to solve the group flight problem using the «leader-follower» strategy. According to this strategy, it is important to provide a trajectory without collisions with obstacles for the robot leader, and the trajectories of the followers are determined relative to the position of the leader. The paper presents results of comparing of path planning algorithms, as well as solving the problem of moving a group of 7 UAVs in the environment with obstacles. The processing time for calculating the trajectory for the leader of the group does not exceed 1.3 s in the working area in the form of a square with a side of 100 m, the processing time for solving the flight task performed by the UAV group takes no more than 2 min, provided that the speed of the UAV is 11 m/s. The proposed strategy is flexible to modifications, therefore, further research of the leader-follower strategy will be aimed at modifying the A* algorithm when finding a path for a group leader in an environment with dynamic obstacles.
TABAS: Text augmentation based on attention score for text classification model

To improve the performance of text classification, we propose text augmentation based on attention score (TABAS). We recognized that a criterion for selecting a replacement word rather than a random selection was necessary. Therefore, TABAS utilizes attention scores for text modification, processing only words with the same entity and part-of-speech tags to consider informational aspects. To verify this approach, we used two benchmark tasks. As a result, TABAS can significantly improve performance, both recurrent and convolutional neural networks. Furthermore, we confirm that it provides a practical way to develop deep-learning models by saving costs on making additional datasets.