Reviewerly/exHarmony huggingface dataset:
Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents

Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.
A Hybrid Cyber Defense Mechanism to Mitigate the Persistent Scan and Foothold Attack

As the prerequisite for the attacker to invade the target network, Persistent Scan and Foothold Attack (PSFA) is becoming progressively more subtle and complex. Even worse, the static and predictable characteristics of traditional systems provide an asymmetric advantage for attackers in launching the PSFA. To reverse this asymmetric advantage and resist the PSFA, two new defense ideas, called moving target defense (MTD) and deception-based cyber defense (DCD), have been suggested to provide the proactive selectable measures to complement traditional defense. However, MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability. Meanwhile, DCD is easy to be marked by the attacker, which will result in a great waste of defense resources and poor defense effectiveness. To address this shortcoming, we propose the hybrid cyber defense mechanism that combines the address mutation (belonging to MTD) and fingerprint camouflage (belonging to DCD) strategies. More specifically, we first introduce and formalize the attacker model of PSFA based on the cyber kill chain. Afterwards, the traffic direction technology is designed to realize the coordination between the strategy of address mutation and the strategy of fingerprint camouflage. Furthermore, we construct the fine-grained quantitative modeling of the attacker’s behaviors through an in-depth observation of actual network confrontation. Based on this, a dynamic defense strategy generation algorithm is presented to maximize the effectiveness of our hybrid mechanism. Finally, the experimental results show that our hybrid mechanism can greatly improve the time required for a successful attack and achieve a better defense effect than the single strategy.
Learning a Hierarchical Global Attention for Image Classification

To classify the image material on the internet, the deep learning methodology, especially deep neural network, is the most optimal and costliest method of all computer vision methods. Convolutional neural networks (CNNs) learn a comprehensive feature representation by exploiting local information with a fixed receptive field, demonstrating distinguished capacities on image classification. Recent works concentrate on efficient feature exploration, which neglect the global information for holistic consideration. There is large effort to reduce the computational costs of deep neural networks. Here, we provide a hierarchical global attention mechanism that improve the network representation with restricted increase of computation complexity. Different from nonlocal-based methods, the hierarchical global attention mechanism requires no matrix multiplication and can be flexibly applied in various modern network designs. Experimental results demonstrate that proposed hierarchical global attention mechanism can conspicuously improve the image classification precision—a reduction of 7.94% and 16.63% percent in Top 1 and Top 5 errors separately—with little increase of computation complexity (6.23%) in comparison to competing approaches.
Decision Support Systems to Promote Health and Well-Being of People During Their Working Age: The Case of the WorkingAge EU Project

The WorkingAge project aims at improving the psycho-physical condition of workers, with a special focus on ageing subjects. In this context, a Decision Support System, based on a hybrid data-driven/model-driven approach, fed with data coming from environmental and wearable sensors, aims to provide personalised advises to the worker. In this paper we briefly present the WorkingAge project and architecture, and then focus on the decision-making pipeline that, starting from raw data, generates the advises.
Review Research of Medical Image Analysis Using Deep Learning

In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.
One-Shot Voice Conversion Algorithm Based on Representations Separation

Voice Conversion (VC) is a method of converting the source speaker's speech into the target speaker's speech without changing the source speaker's speech content. The current VC methods have the following problems: (1) they are only applicable to a limited number of speakers, not to any speakers, as a result, the application scenarios are greatly restricted; (2) the representation (feature) separation(RS) effect of the current mainstream technology is not ideal on the source speaker speech and the target speaker speech; and (3) the voice conversion quality of most models is unsatisfactory, and hence needs to be improved. Therefore, in this paper, we constructed a one-shot VC model of Representation Separation, called RS-VC model, implemented by the encoder-decoder structure. The encoder is composed of a content encoder and a speaker encoder. The content encoder separates the content information of the source speaker voice and generates a content representation. The speaker encoder separates the target speaker information of the target speaker voice and generates a speaker representation. The decoder synthesizes the content representation and the speaker representation to generate the converted voice. In this paper, we obtained the optimized speaker verification model SVIGEN2E (Speaker Verification with Instance Normalization using Generalized End-to-End loss) by improving the speaker verification (SV) model. The model SVIGEN2E is used as the speaker encoder. This speaker encoder needs to be trained in advance prior to RS-VC model training, and the pre-trained model of SVINGE2E directly extracts speaker representation of the target speaker's voice, and is used for training and testing RS-VC model. A progressive training method is proposed then for training RS-VC model. Experiments show that the progressive training method can effectively improve the quality of the converted voice. Compared with the basic speaker verification model, both SVINGE2E and RS-VC deliver the impressive improvements in EER (Equal Error Rate).
Entanglement of two distinguishable atoms in a rectangular waveguide: Linear approximation with single excitation*

We investigate the entanglement dynamics of two distinguishable two-level systems (TLSs) characterized by energy difference δ located inside a rectangular hollow metallic waveguide of transverse dimensions a and b . The effects of energy difference δ and the inter-TLS distance on the time evolution of the concurrence of the TLSs are examined in the single excitation subspace when the energy separation of the TLS is far away from the cutoff frequencies of the transverse mode.
Estimation and diagnostic tools in reparameterized slashed Rayleigh regression model. An application to chemical data

In this paper, we introduce a regression model where the response variable is reparameterized slashed Rayleigh (RSR) distributed and which is indexed by mean and precision parameters. The proposed regression model is useful for situations where the variable of interest is continuous and restricted to the positive real line and is related to other variables through the mean and precision parameters. In addition, the RSR model has properties that its competitor distributions of the exponential family do not have. Estimation is performed by expectation maximization (EM) and extensions. Furthermore, we discuss residuals and influence diagnostic tools. Finally, we also carry out two applications to real-world data that demonstrate the usefulness of the proposed model.
PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

Classification of active compounds is necessary to trivialize their properties and activities and can be carried out by identifying functional groups and their structures based on the simplified molecular input line entry system (SMILES) code. This study classified active compounds based on the SMILES structure using a combination of the machine learning techniques, extreme learning machine (ELM), and particle swarm optimization (PSO). ELM facilitates learning very quickly and at with high predictive accuracy. The PSO covers the limitations of the ELM, i.e., randomly determining the weight and bias, trial, and error in determining the number of hidden neurons. PSO optimizes the weight, bias, and number of hidden neurons. This study’s results indicate that the classification of active compounds’ function based on the SMILES code can be done by the ELM and PSO-ELM with high accuracy. The PSO-ELM can improve the accuracy performance of the ELM classification by optimizing the weight, bias, and number of hidden neurons automatically. Moreover, the proposed PSO-ELM is better than the PSO-ELM for average accuracy, computation time, and standard deviation. The proposed algorithm has the highest average accuracy compared to the ELM by increasing the accuracy to 2.54%, 6.43%, and 3.85% for 2, 3, and 4 classes, respectively. A comparison with other machine algorithms shows that the proposed is superior.
Performance Analysis of Isolation Forest Algorithm in Fraud Detection of Credit Card Transactions

Losses incurred due to fraud on e-commerce transactions, especially those based on credit cards, continue to increase, resulting in large losses each year. One mechanism to minimize the risk of fraudulent credit card transactions is to utilize a detection technique for ongoing transactions. Credit card transaction data in its original state does not have a label, and the amount of fraud data on the training data is very small so that it belongs to a very unbalanced category, and the pattern of fraud continues to change. Isolation forest is an unsupervised algorithm that is efficient in detecting anomalies. Several techniques can be applied to improve the performance of the Isolation forest model. Previous studies used the ROC-AUC metric in analyzing the performance of Isolation Forests, which could provide incorrect information. This study made two contributions; the first is to present a performance analysis with both the ROC-AUC and AUCPR. Thus, it can be seen that the high ROC-AUC value does not guarantee the model has the reliability in detecting fraud. In comparison, the information provided through AUCPR is more appropriate to describe the ability of the model to capture data fraud. The second contribution is to propose several techniques that can be applied to improve the performance of the Isolation forest model, namely to optimize the determination of the amount of training data, feature selection, the amount of fraud contamination, and setting hyper-parameters in the modeling stage (training). Experiments were carried out using a real-life dataset from ULB. The best results are obtained when the validation data split ratio is 60:40, using the five most important features, using only 60% of fraud data, and setting hyper-parameters with the number of trees 100, 128 sample maximum, and 0.001 contamination. The validation performance of this model is precision 0.809917, recall 0.710145, F1-score 0.756757, ROC-AUC 0.969728, and AUCPR 0.637993, while for Testing results obtained precision 0.807143, recall 0.763514, F1-score 0.784722, ROC-AUC 0.97371, and AUCPR 0.759228.
Robust Video Watermarking using Tchebichef Transform and Singular Value Decomposition on the Selected Frame Based YCbCr Color Space

Watermarking is a copyright authentication technique. This research proposes a robust watermarking method with a combination of Tchebichef transformation and singular value decomposition (SVD). To maintain imperceptibility, embedding is done on one of the selected frames. Frames are randomly selected to increase watermark security. Frame selection is based on two integer keys processed by a linear congruential generator (LCG). The selected frame is then converted to its color space from RGB to YCbCr. Y channel (luminance) was selected to be processed by Tchebichef transformation based on block 8 × 8, the coefficient 0.0 for each block of the transformed results was selected and collected on a matrix. This matrix is then transformed with SVD and a singular matrix is selected for watermark embedding, this method is done to increase robustness. Based on the test results, the imperceptibility value is very good with an average value of 50.952dB, based on the PSNR as a measuring tool. Whereas in the robustness aspect, a value of 0.927 is generated based on the results of the measurement of the correlation between the watermark and the original watermark, where these results are the average extraction results without and with various attacks.
Optical-Amplifier-Compatible Long-Distance Secure Key Generation Based on Random Phase Fluctuations for WDM Systems

We proposed and experimentally demonstrated a secure key generation and distribution system that is compatible with optical amplifiers and standard wavelength-division multiplexing (WDM) transmission systems. The key is generated from the phase fluctuations induced by environmental instabilities. The key generation system is tested in a 240 km bidirectional fiber-pair link with multiple optical amplifiers. To demonstrate the compatibility with WDM systems, 38 WDM channels are transmitted together with the key distribution channel. The secret key is protected against eavesdropping and coherence detection attack by the wide-band property of the signal carrier and the fast-changing rate of the phase fluctuations.
Mask‐guided class activation mapping network for person re‐identification

In this Letter, the authors propose a novel mask-guided class activation mapping (MCAM) network for person re-identification, which learns background-invariant and view-invariant features. Specifically, a novel loss function named mask-guided mapping loss is meticulously formulated to utilise the human binary masks, which contain helpful body shape information as the reference standard, thereby guiding the model to place more emphasis on human body regions. Moreover, they propose a new weighted channel attention (WCA) module, which replaces the global average pooling with a global depthwise convolution layer. By virtue of this particular WCA module, the feature information distributed across the spatial space can be individually weighted and dynamically compressed into a more precise channel attention map. Extensive experiments have been carried out on three widely-used re-identification data sets. Compared with the baseline model, MCAM has gained rank-1 accuracy improvement of 2.0% on Market-1501, 6.0% on DukeMTMC-reID, and 7.5% on CUHK03-NP, confirming its effectiveness.
Criminal Investigation of the Corruption Crimes: Evidence and Procedure in an Interdisciplinary Approach

Approaching criminality in an interdisciplinary context has concerned practitioners in the last decades. The same situation is observed in criminal cases having as object the criminal investigation of corruption crimes, knowing the fact that this phenomenon is still into the judicial authorities' attention. In practice, there is a substantial jurisprudence on this topic. The current paper focuses on criminal investigation of the corruption in an interdisciplinary context, including elements of forensic investigation, gathering evidence by means of forensic science, as well as their administration during the criminal proceedings. Moreover, researching the current topic aims at analyzing phenomenon from the perspective of the judicial bodies' efforts of assuring functions of discovering and preventing corruption crimes, as well as punishing defendants during the criminal proceedings. In order to achieve this goal, the paper is designed in a qualitative research, with comparative elements and aspects of jurisprudence in criminal matters.
Video forgery detection based-on passive (blind) approach

Current communication technology can ease sharing multimedia data such as images, text, graphics, audio, and videos online or ofline.A multimedia editing tool can be downloaded freely and used eficiently to be editing the content of video clips.To solve integrity and originality, video clips are very challenging and require some complex methods.How to ensure that the content or the structure of the video provided or downloaded from the internet is original and just as recorded?Video forgery is a technique to generate fake videos with malicious intent by inserting, deleting, duplication their contents.Therefore, the video's originality can be questioned and need to be veriied.Video forgery detection is intended to determine the originality of video content, whether the video has undergone any unethical change.The video forgery detection technique has two approaches, active and passive.This study focuses on the detection of tampering video using a passive approach because it is not dependent on pre-embedded information to determine the originality of the video.The passive approach will be extracting this feature from the video and analyzing them for different forgery detection.A passive approach is better than the active one as it relies on fake video available and features and its properties without needing the original video.The research focus was to apply a passive approach to develop a new model for video forgery detection.This model will be used to build a powerful tool for detecting video authenticity.Therefore, it can help certain parties, especially those involved in legal activities.
Speech classification using combination virtual center of gravity and k-means clustering based on audio feature extraction

Voice recognition can be done in a variety of ways. Sound patterns can be recognized by performing sound feature extraction. The trainer sound data is built from the best sound data selection using a correlation coefficient based on the level of similarity between sound data for optimal sound features. Extraction of voting features on this research using the Virtual Center of Gravity method. This method calculates the distance between the sound data against the center point of gravity with visualizations in the 3-dimensional form of white, black, and grey pattern spaces. The preprocessing process generates a complex number of data consisting of real numbers and imaginary numbers. The number will be calculated the distance to the Virtual Center of Gravity's pattern space using Euclidean Distance. The sound feature testing is done using K-Means Clustering by means of a speech classification data based sound. The results showed an accuracy of 92.5%.
MCS-MCMC for Optimising Architectures and Weights of Higher Order Neural Networks

The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropagation (BP) algorithm. Yet, the current BP algorithm has several limitations including easily stuck into local minima, particularly when dealing with highly non-linear problems and utilise computationally intensive training algorithms. The current BP algorithm is also relying heavily on the initial weight values and other parameters picked. Therefore, in an attempt to overcome the BP drawbacks, we investigate a method called Modified Cuckoo Search-Markov chain Monté Carlo for optimising the weights in HONN and boost the learning process. This method, which lies in the Swarm Intelligence area, is notably successful in optimisation task. We compared the performance with several HONN-based network models and standard Multilayer Perceptron on four (4) time series datasets: Temperature, Ozone, Gold Close Price and Bitcoin Closing Price from various repositories. Simulation results indicate that this swarm-based algorithm outperformed or at least at par with the network models with current BP algorithm in terms of lower error rate.
Comparative Analysis of Past Earthquake Simulation and Test Operation

The Korea Meteorological Administration (KMA) provides an earthquake early warning (EEW) service by issuing prompt earthquake information for the public to prepare for severe earthquakes. The KMA has established high-quality seismic stations to provide prompt and precise EEW service and has been monitoring observed seismic waves in real time. The ambient signal exhibits characteristics of small repeated amplitudes owing to the background noise near the seismic station. If seismic waves from earthquakes or explosions are observed in seismic stations, their detected amplitudes are higher than those of background noise and possess specific features. Thus, the methods for differentiating between the observed signal and any signal attributed to earthquakes or noise can be the starting of EEW and the main technique. Because this technique is based on the empirical relationship derived using postprocessed data or specific criteria as the standard, it can have more advantages for optimizing the criteria or standards through observing several seismic events in seismic stations operated in real time. However, fewer earthquakes have occurred in the Korean Peninsula compared to the West of the United States, Japan, and Taiwan. It is difficult to set up the optimal criteria and validate it for seismic stations operating in Korea. In this study, we applied simulated methods to validate the possibility of setting up the optimal criteria. For the simulated results, the analyzed data did not include any delayed waveforms under the ideal condition. We compared the analyzed results observed in detail during the EEW-testing periods. Even though differences between the simulation and EEW-testing results were observed, it was proven that there was no problem in analyzing the earthquake information. In addition, we could design the optimization of module for detecting the P-wave in the Korean Peninsula.