Reviewerly/exHarmony huggingface dataset:
Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

&lt;span lang="EN-US"&gt;In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.&lt;/span&gt;
Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

&lt;span lang="EN-US"&gt;Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.&lt;/span&gt;
Device identification using optimized digital footprints

&lt;span lang="EN-US"&gt;The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.&lt;/span&gt;
Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

&lt;p&gt;&lt;span lang="EN-US"&gt;Today's world is no longer that of yesterday, the pace with which we live and also the speed is enormous and rapid, that overnight we discover the appearance of new technologies and solutions in all the fields, in particular, that of scientific research. Artificial intelligence plays the main role. Predicting the behavior of new materials using artificial neural networks has become a frequently adopted solution by researchers today. The performance of neural networks depends mainly on the activation functions used. This work was designed to mainly study the impact of these functions on the response speed of an artificial neural network in general, and particularly on the model we are working on to predict the thermomechanical behavior of innovative materials. By using TANSIG, PURELIN and LOGSIG in a feed forward back propagation training by Levenberg-Marquardt algorithm, we were able to generate 9 models. For each of these models, we were interested in analyzing the speed’s response of the network and studying its regression. Thus, this work was able to show us that choosing the right neuron activation function from one layer to another can clearly influence the performance of the results. Depending on the problem studied, the desired objective and the chosen architecture, the activation function can radically change the result and provide us with the expected efficiency. &lt;/span&gt;&lt;/p&gt;
Single parent mating in genetic algorithm for real robotic system identification

&lt;span lang="EN-US"&gt;System identification (SI) is a method of determining a mathematical model for a system given a set of input-output data. A representation is made using a mathematical model based on certain specified assumptions. In SI, model structure selection is a step where a model structure perceived as an adequate system representation is selected. A typical rule is that the final model must have a good balance between parsimony and accuracy. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the effectiveness of GA operators. This paper presents a mating technique named single parent mating (SPM) in GA for use in a real robotic SI. This technique is based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that eases the search for the optimal model. The results show that using three different objective functions (Akaike information criterion, Bayesian information criterion and parameter magnitude–based information criterion 2) respectively, GA with the mating technique is able to find more optimal models than without the mating technique. Validations show that the selected models using the mating technique are acceptable.&lt;/span&gt;
Editorial – 7th World Engineering Conference on Contemporary Technologies™ (WECON™ 2022)

Launched in 2008, World Engineering Conference on Contemporary Technologies™ (WECON™) is a flagship engineering conference of Chitkara University, Punjab, India. The 7th edition of the conference (WECON 2022) has been organized during May 20–21, 2022 in collaboration with Elsevier as publication partner and attended by 200 delegates in a virtual mode. Conference featured 50 paper presentations in 5 parallel tracks, 4 keynote talks, and 9 invited talks. In this editorial, an overview of the theme of WECON 2022 and technical program details of the conference are presented. Additionally, a brief history of the conference, and efforts of Chitkara University in organizing and supporting WECON for the last 14 years are also discussed. Finally, the editorial acknowledges all the committees and entities that have contributed toward the success of the conference.
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series

A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a kernel. In particular, this strategy is highly efficient (both in terms of accuracy and complexity) when the kernel is data-adapted using Kernel Flows (KF)~\cite{Owhadi19} (which uses gradient-based optimization to learn a kernel based on the premise that a kernel is good if there is no significant loss in accuracy if half of the data is used for interpolation). Despite its previous successes, this strategy (based on interpolating the vector field driving the dynamical system) breaks down when the observed time series is not regularly sampled in time. In this work, we propose to address this problem by directly approximating the vector field of the dynamical system by incorporating time differences between observations in the (KF) data-adapted kernels. We compare our approach with the classical one over different benchmark dynamical systems and show that it significantly improves the forecasting accuracy while remaining simple, fast, and robust.
Some Notes on the Four-parameter Kies Distribution

This short note is inspired by the work of [1] which is devoted on the four-parameter Kies distribution. Some of the results presented in this seminal paper need to be clarified. We present and prove the correct results following the original work.
Inferring Class-Label Distribution in Federated Learning

Federated Learning (FL) has become a popular distributed learning method for training classifiers by using data that are private to individual clients. The clients´ data are typically assumed to be confidential, but their heterogeneity and potential class-imbalance adversely impact the accuracy of the trained model. The class-imbalance may not be common knowledge or may even be confidential information itself. Thus, the inference of the class-label distribution of the training data is important both from a performance and from a privacy perspective. In this paper, we study the problem of class-label distribution inference from an adversarial perspective, based on model parameter updates sent to the parameter server. Firstly, we present conditions under which exact inference is possible. We then introduce four new methods to estimate class-label distribution in the general FL setting. We evaluate the proposed inference methods on four different datasets and our results show that they significantly outperform state of the art methods.
Interaural Time Difference Prediction Using Anthropometric Interaural Distance

This paper studies the feasibility of predicting the interaural time difference (ITD) in azimuth and elevation once the personal anthropometric interaural distance is known, proposing an enhancement for spherical head ITD models to increase their accuracy.The method and enhancement are developed using data in a Head-Related Impulse Response (HRIR) data set comprising photogrammetrically obtained personal 3D geometries for 170 persons and then evaluated using three acoustically measured HRIR data sets containing 119 persons in total.The directions include 360 • in azimuth and -15 • to 60 • in elevation.The prediction error for each data set is described, the proportion of persons under a given error in all studied directions is shown, and the directions in which large errors occur are analyzed.The enhanced spherical head model can predict the ITD such that the first and 99th percentile levels of the ITD prediction error for all persons and in all directions remains below 122 μs.The anthropometric interaural distance could potentially be measured directly on a person, enabling personalized ITD without measuring the HRIR.The enhanced model can personalize ITD in binaural rendering for headphone reproduction in games and immersive audio applications.
The differential fault analysis on block cipher FeW

Abstract Feather weight (FeW) cipher is a lightweight block cipher proposed by Kumar et al. in 2019, which takes 64 bits plaintext as input and produces 64 bits ciphertext. As Kumar et al. said, FeW is a software oriented design with the aim of achieving high efficiency in software based environments. It seems that FeW is immune to many cryptographic attacks, like linear, impossible differential, differential and zero correlation attacks. However, in recent work, Xie et al. reassessed the security of FeW. More precisely, they proved that under the differential fault analysis (DFA) on the encryption states, an attacker can completely recover the master secret key. In this paper, we revisit the block cipher FeW and consider the DFA on its key schedule algorithm, which is rather popular cryptanalysis for kinds of block ciphers. In particular, by respectively injected faults into the 30th and 29th round subkeys, one can recover about 55/80 ≈ 69% bits of master key. Then the brute force searching remaining bits, one can obtain the full master secret key. The simulations and experiment results show that our analysis is practical.
A summary report through home experiment on a dialogue system for older adults

Purpose Dialogue systems for older adults become more important because some of the older adults living alone have fewer chances to do that because they are alone; because of the COVID-19 situation, they have decreased the opportunity of conversation in the world.However, for academic studies, dialogue systems have been evaluated at laboratories.This is because the experimental conditions can be standardized.Moreover, on-site support is easier for the experimental staff than home-based experiments because some older adults are not familiar with ICT technologies such as tablets, robots, etc.Some researchers tackle developing a dialogue system specializing for older adults by Inoue et al. (2020); however, there is no information on what kind of robot interaction is effective for the cognitive function to the best of the authors' knowledge of the elderly.Hence home-based dialogue system experiments have become much more important.Method In a total, 11 groups consisted of a maximum of 8 persons per group, and the experiments were conducted with 1 group sequentially.The experiment is conducted along with a randomized controlled experiment with two sub-groups.An intervention group participant listens to the story and asks questions to the robots (Bono-06 reported in Tokunaga et al. ( 2019)) within 4 minutes.The other is a control group of participants who only listen to stories and speak a short greeting to the robots, which indicates they participate in the session within 1 minute.The experiment was designed to examine the effect of difference for cognitive training by listening to the stories and asking questions or just listening to the stories.In the experiment, participants listened to a total of 12 stories over a period of 2-3 weeks with our specializing system (DBSPS), as shown in Figure 1.Participants participated in 2 sessions per day at their home, except for the first day, where we had an exceptional practice session and the other two sessions.We remotely checked the progress of experiment status of the participants that we monitored with Slack bots (See Center of Figure 1).The essential of our system is reported in Tokunaga et al. (2021).Results and Discussion We have completed our experiments with 81 participants.The participants include those who have not familiar with smartphones or tablets.There were no lost to follow up.We found that the number of utterances is varied for each topic (Figure 2).The average number of questions seems to vary because some topics were easy to ask questions, others were not.We have confirmed through the interviews that many participants have enjoyed our experiment because due to COVID-19, older adults have less schedules than usual.Hence the participation to the experiment becomes pleasurable events in daily lives.We have received remarkable comments that some participants said, "I was glad to join the experiment so much."We have also received negative comments, "I felt thinking about the question was difficult" and "I felt nervous in the experiment."Those comments would be helpful to be designed for a home-based experiment.
An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations

Facing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to assess its potential. Remote sensing represents one of the low-cost solutions for solar energy assessment. Nevertheless, cloud cover is a main problem when validating the data. This study identifies satellite GHI profiles that cannot be used in energy production simulation. The validation is performed using parametric and non-parametric statistical tests. From the profile identified as invalid for simulation purposes, a site-adaptation methodology is proposed based on statistical learning using the machine learning algorithms “Best subset selection” and “Forward Stepwise Selection”. Linear and non-linear heuristic models are also proposed. The final AS7 model is selected through RMSE, MBE and adjusted R2 indicators and is valid for any sky condition. The results show an increase in R2 from 0.607 to 0.876.
SCA-Safe Implementation of Modified SaMAL2R Algorithm in FPGA

Cryptographic algorithms (RSA, DSA, and ECC) use modular exponentiation as part of the principal operation. However, Non-profiled Side Channel Attacks such as Simple Power Analysis and Differential Power Analysis compromise cryptographic algorithms that use such operation. In this work, we present a modification of a modular exponentiation algorithm implemented in programmable devices, such as the Field Programmable Gate Array, for which we use Virtex-6 and Artix-7 evaluation boards. It is shown that this proposal is not vulnerable to the attacks mentioned previously. Further, a comparison was made with other related works, which use the same family of FPGAs. These comparisons show that this proposal not only defeats physical attack but also reduces the number of resources. For instance, the present work reduces the Look-Up Tables by 3550 and the number of Flip-Flops was decreased by 62,583 compared with other works. Besides, the number of memory blocks used is zero in the present work, in contrast with others that use a large number of blocks. Finally, the clock cycles (latency) are compared in different programmable devices to perform operations.
Adaptive Query Compilation in Graph Databases

Abstract Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and to exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the same instructions for processing, this technique can lead to an improvement. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating and optimizing machine code becomes an issue for short-running queries or queries which could produce early results quickly.In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. As soon as the code generation is finished, the execution switches to the compiled code. Our evaluation shows that autonomously switching execution modes helps to hide compilation times and additionally the additional latencies of the underlying storage.
Fusion of ALBERT and BiGRU-Attention-CRF Models for Named Entity Recognition in Lakes

There are many lakes in China and they are widely distributed, so it is important to have comprehensive information on lakes to promote the implementation of major water network projects, ecological and environmental restoration of rivers and lakes, and smart water conservancy construction.This paper uses a deep learning model to identify and extract lake entities, and provides a reference for mining effective lake information from lake text.Firstly, the Named Entity Recognition (NER) method and annotation specification are customized, and a corpus of lake texts is established.Secondly, considering the small size of the self-built corpus and the sparsity of text information may affect the performance of the model and the recognition effect, a lightweight pre-training model (ALBERT) is introduced to generate high-quality word vectors as a two-way gating cycle The unit-conditional random field (BiGRU-CRF) model is
Speed of Processing: Tests and Factor Structure

In the present study we examined Speed of Processing derived from a number of cognitive tests; the response speeds were nearly automatic for tests with minimal cognitive load.The tests were given to students across ages from 8 to 20 in schools and colleges located in various parts of India.The structure of the constellation of the test responses was determined by factor analyses.We report one stable factor of speed for ages 8-10 that splits into two separate but correlated factors for ages 11-14,15-17, and 18-20 representing speed of response to tests that contain letters and numbers versus colour stimuli.Developmental changes in response speed across the four adjacent age groups were examined; the trajectory was not consistently incremental, especially for naming colours that did not increase beyond age 11-14.In conclusion, a fairly reasonable deconstruction of the concepts of RAN has been presented in this report.The major components are encoding and articulation, and the necessity of distinguishing alpha-numeric naming time and colour naming.The later requires the additional time for semantic access.At the end, we suggest that in a follow-up research RAN(alpha-numeric&colour) should be viewed as a part of a broader cognitive speed of processing.Our objective is to determine the association between tests of basic executive processes such as cognitive flexibility,attention &inhibition and working memory on the one hand and speed on the other.Consequently not to search for the tests that would not involve speed, but be aware of the value of the correlation of an executive test with sp eed.This will be useful information or example in investigations into the effect of ageing on executive functions.A worthy agenda for future research.
Learning to Detect Interesting Anomalies

Anomaly detection algorithms are typically applied to static, unchanging, data features hand-crafted by the user. But how does a user systematically craft good features for anomalies that have never been seen? Here we couple deep learning with active learning -- in which an Oracle iteratively labels small amounts of data selected algorithmically over a series of rounds -- to automatically and dynamically improve the data features for efficient outlier detection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10, and Galaxy-DESI data, significantly outperforming both standard anomaly detection and active learning algorithms with static feature spaces. Beyond improved performance, AHUNT also allows the number of anomaly classes to grow organically in response to Oracle's evaluations. Extensive ablation studies explore the impact of Oracle question selection strategy and loss function on performance. We illustrate how the dynamic anomaly class taxonomy represents another step towards fully personalized rankings of different anomaly classes that reflect a user's interests, allowing the algorithm to learn to ignore statistically significant but uninteresting outliers (e.g., noise). This should prove useful in the era of massive astronomical datasets serving diverse sets of users who can only review a tiny subset of the incoming data.