Reviewerly/exHarmony huggingface dataset:
Self-attention-based multi-agent continuous control method in cooperative environments

Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning. Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. In this paper, a new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network and the value decomposition method used to solve the uneven problem. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents. First, a new update method is proposed for policy networks that promotes learning efficiency. Second, the utilization of samples is improved, at the same time reflecting the ability of perspective-taking among groups. Finally, the “deceptive signal” in training is eliminated and the learning degree among agents is more uniform than in the existing methods. Multiple experiments were conducted in two typical scenarios of a multi-agent particle environment. Experimental results show that the proposed algorithm can perform better than the state-of-the-art ones, and that it exhibits higher learning efficiency with an increasing number of agents.
A Comparative Study of MongoDB, ArangoDB and CouchDB for Big Data Storage

A distinctive aspect of the current era is the ferocious amount of data that is generated and processed in a daily basis. There is no wonder that this epoch is generally characterized as the “Era of Big Data”. Thus, many enterprises and research initiatives strive to find a way to effectively and efficiently collect, store and analyze Big Data in order to improve their services and make efficient decisions. Those approaches refer to several domains such as healthcare, transportation, governance, or insurance. Towards this direction, in this paper we contribute into the selection of the most appropriate database for efficiently storing and retrieving Big Data. More specifically, taking into account the nature of Big Data and the main categories of databases that currently exist, three (3) NoSQL document-based databases were considered for this comparative study, namely the ArangoDB, the MongoDB and the CouchDB. The performance of these databases was measured based on specific metrics and criteria, including the total execution time for the same CRUD operations and their corresponding demands for resources, concluding to the most suitable database for storing Big Data.
FDA-GAN: Flow-Based Dual Attention GAN for Human Pose Transfer

Human pose transfer aims at transferring the appearance of the source person to the target pose. Existing methods utilizing flow-based warping for non-rigid human image generation have achieved great success. However, they fail to preserve the appearance details in synthesized images since the spatial correlation between the source and target is not fully exploited. To this end, we propose the Flow-based Dual Attention GAN (FDA-GAN) to apply occlusion- and deformation-aware feature fusion for higher generation quality. Specifically, deformable local attention and flow similarity attention, constituting the dual attention mechanism, can derive the output features responsible for deformable- and occlusion-aware fusion, respectively. Besides, to maintain the pose and global position consistency in transferring, we design a pose normalization network for learning adaptive normalization from the target pose to the source person. Both qualitative and quantitative results show that our method outperforms state-of-the-art models in public iPER and DeepFashion datasets.
PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI

In this paper, we propose a deep nonlinear CNN model, named as PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI. Our PU-M-Net consists of four pathways in "M" shape, and merges them by abundant skip connections. By this means, our PU-M-Net can improve the flow of deep features, promote the fusion of shallow information and deep features, and enhance the utilization of initial features in phase unwrapping. We propose a structure-consideration loss function by combining the MAE and MS-SSIM loss functions, and also construct an available pertinent dataset for ESPI phase unwrapping by means of the phase shifting method. With the proposed dataset and the proposed loss function, we train the PU-M-Net successfully under a limited training condition. With the trained network, we directly get the results from the original wrapped phases. We test our method on many simulated and experimental ESPI wrapped phases, and compare it with a least-squares method, a quality-guided method, and two deep learning methods. We also compared it with the PU-M-Net trained by MSE loss function. The performance is evaluated quantitatively and qualitatively in terms of phase unwrapping and structure protection. Results demonstrate that our method can reduce speckles and protect structures in phase unwrapping, and get the better results than the compared methods. Besides, it does not require any parameter fine-turning and any pre-process or post-process procedure, even for the case of high-level speckles or high-dense wrapped phases. Moreover, it has full advantages on excellent generalization and batch performance, and can be used to process a great number of images rapidly. Indeed, it has been applied to the dynamic measurement successfully in this paper.
Shard scheduler

We propose Shard Scheduler, a system for object placement and migration in account-based sharded blockchains. Our system calculates optimal placement and decides on object migrations across shards. It supports complex multi-account transactions caused by smart contracts. Placement and migration decisions made by Shard Scheduler are fully deterministic, verifiable, and can be made part of the consensus protocol. Shard Scheduler reduces the number of costly cross-shard transactions, ensures balanced load distribution and maximizes the number of processed transactions for the blockchain as a whole. To this end, it leverages a novel incentive model motivating miners to maximize the global throughput of the entire blockchain rather than the throughput of a specific shard. In our simulations, Shard Scheduler can reduce the number of costly cross-shard transactions by half while ensuring equal load and increasing throughput more than 2 fold when using 60 shards. We also implement and evaluate Shard Scheduler on Chainspace, more than doubling its throughput and reducing user-perceived latency by 70% when using 10 shards.
A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications

With the expansion of smartphone and financial technologies (FinTech), mobile money emerged to improve financial inclusion in many developing nations. The majority of the mobile money schemes used in these nations implement two-factor authentication (2FA) as the only means of verifying mobile money users. These 2FA schemes are vulnerable to numerous security attacks because they only use a personal identification number (PIN) and subscriber identity module (SIM). This study aims to develop a secure and efficient multi-factor authentication algorithm for mobile money applications. It uses a novel approach combining PIN, a one-time password (OTP), and a biometric fingerprint to enforce extra security during mobile money authentication. It also uses a biometric fingerprint and quick response (QR) code to confirm mobile money withdrawal. The security of the PIN and OTP is enforced by using secure hashing algorithm-256 (SHA-256), a biometric fingerprint by Fast IDentity Online (FIDO) that uses a standard public key cryptography technique (RSA), and Fernet encryption to secure a QR code and the records in the databases. The evolutionary prototyping model was adopted when developing the native mobile money application prototypes to prove that the algorithm is feasible and provides a higher degree of security. The developed applications were tested, and a detailed security analysis was conducted. The results show that the proposed algorithm is secure, efficient, and highly effective against the various threat models. It also offers secure and efficient authentication and ensures data confidentiality, integrity, non-repudiation, user anonymity, and privacy. The performance analysis indicates that it achieves better overall performance compared with the existing mobile money systems.
A novel DICOM image encryption with JSMP map

Recent times have seen a massive rise in the transmission and storage of DICOM images. This work proposes a cryptosystem with the sturdy JSMP map, for the pixel data of the DICOM file. The proposed system is designed to work in the bit plane level for high resilience. To achieve an effective cryptosystem, the number of bit planes generated depends on the bits stored attribute and not on the conventional method of bits allocated of each pixel. Only the MSB bit planes that contribute more to the details of the image are shuffled with the JSMP chaotic keys. Then the shuffled image is reconstructed by combining all the MSB and LSB bit planes. Finally, a robust cipher image is generated by diffusing the reconstructed image with a random image generated with JSMP map. The novelty and uniqueness of the system lie in its capability of encrypting DICOM images with any pixel depth. The distinctive feature of this system is the unique key generation algorithm and the variable number of keys, which contributes to high sensitivity and entropy. Performance measures of the encrypted image illustrate the robustness of the cryptosystem in the health information system.
ClawGAN: Claw connection-based generative adversarial networks for facial image translation in thermal to RGB visible light

Thermal cameras work well in harsh environments, but the quality of infrared images is not as high as visible light. Thermal to visible image translation can get rid of the image modal differences caused by various spectral characteristics. Nowadays, Generative Adversarial Network (GAN) can transform the images from one domain to another domain, but the generated images are still in a single channel in case of facial thermal to visible. In this paper, we propose a claw connection-based generative adversarial networks framework named ClawGAN for the facial thermal images to RGB visible images translation. We proposed the mismatch metric (MM) to measure the mapping relationship of paired images and use template matching to reduce MM of the dataset. Based on the CycleGAN framework, the synthesized loss and the generative reconstructed loss are added to the adversarial loss and the cycle-consistency loss to form a new objective function. And a claw-connected network is invoked to replace the U-net network structure of the generator for more feature preservation. The model is judged from subjective evaluation and objective evaluation based on image quality metrics such as PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity), FID (Fréchet inception distance), and face recognition accuracy. We divided the open datasets into bright light and dark light to research the effect of illumination. The experiments show that the proposed method has the lowest FID and the highest face recognition accuracy compared to the state-of-the-art methods. The proposed ClawGAN retains the structural features of thermal images while not only enhancing the quality of images but also effectively improving the observability of image translation results in both bright and dark light. The code is available at https://github.com/Luoyi3819/ClawGAN.
Universal Deep Network for Steganalysis of Color Image Based on Channel Representation

Up to now, most existing steganalytic methods were designed for grayscale images, and are not suitable for the color images that are widely used in social networks. In this paper, we design a universal color image steganalysis network (called UCNet) for the spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganalytic features in each color channel, the preprocessing module first separates the input image into three channels based on the corresponding embedding spaces (i.e., RGB in the spatial domain, and YCbCr in the JPEG domain), and then extracts the image residuals with 62 fixed high-pass filters. Finally, all truncated residuals are concatenated for subsequent analysis, rather than adding them together in the first layer as in existing CNN-based steganalyzers. To accelerate network convergence and effectively reduce the number of parameters, the convolutional module contains three carefully designed types of layers with different shortcut connections and group convolution structures, to further learn the high-level steganalytic features. In the classification module, we employ global average pooling and a fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results that are comparable with other modern CNN-based steganalyzers (e.g., SRNet and LC-Net) in both the spatial and JPEG domains, with relatively few memory requirements and short training times. Furthermore, we also provide some necessary descriptions and carry out numerous ablation experiments to verify the rationality of the network design.
SECOND SKIN Project: BioIntegrated Wireless Sensors for the Epidermal Monitoring and Restoring of Sensorial Injuries

SECOND SKIN was a two-year project addressing bio-integrated wireless-sensing epidermal membranes. Being flexible and elastic, they are suitable to be placed in direct contact with the human skin. As the Radio Frequency Identification (RFID) technology is exploited for data acquisition and communication, they are able to work without a battery and can be interrogated remotely through fixed and/or wearable readers. The innovations of the project spanned from the definition of designing methodologies and manufacturing techniques for prototyping, up to real-world experimental evaluations to assess usability, durability and insensitivity to body variability. The project findings have now led to the consolidation of the RFID technology potentialities with respect to the monitoring of the human body’s biophysical data.
IPMOD: An efficient outlier detection model for high-dimensional medical data streams

Outlier detection in high-dimensional medical data streams in real-time is critical and challenging research, which is of great help to disease prevention and source analysis. Although academia has done a lot of research on outlier detection of time series data streams, these methods have the following two shortcomings: (1) Insufficient detection accuracy on high-dimensional data streams; (2) Insufficient accuracy in dynamic data streams scenarios low. To this end, we propose a sliding window model based on efficient pruning and information entropy, namely IPMOD(Information Entropy-Pruning Multi-dimensional Outlier Detection). In IPMOD, we first designed a new index weight measurement method combined with information entropy to quantify the weight of different indexes in multi-dimensional data, to determine the influence of different attributes on the prediction results. Then we designed a new sliding window and sub-sequence measurement mechanism to judge whether the data is abnormal based on the distance between the target sequence and the non-self-match. After that, we designed a pruning strategy to further reduce the computational complexity of the algorithm. The final comprehensive experiment shows that our proposed scheme not only has higher detection accuracy than many current schemes on multiple sets of real data-sets but also can quickly detect outliers in different medical data streams in real-time.
Backdoor Attacks on Image Classification Models in Deep Neural Networks

Deep neural network (DNN) is applied widely in many applications and achieves state-of-the-art performance. However, DNN lacks transparency and interpretability for users in structure. Attackers can use this feature to embed trojan horses in the DNN structure, such as inserting a backdoor into the DNN, so that DNN can learn both the normal main task and additional malicious tasks at the same time. Besides, DNN relies on data set for training. Attackers can tamper with training data to interfere with DNN training process, such as attaching a trigger on input data. Because of defects in DNN structure and data, the backdoor attack can be a serious threat to the security of DNN. The DNN attacked by backdoor performs well on benign inputs while it outputs an attacker-specified label on trigger attached inputs. Backdoor attack can be conducted in almost every stage of the machine learning pipeline. Although there are a few researches in the backdoor attack on image classification, a systematic review is still rare in this field. This paper is a comprehensive review of backdoor attacks. According to whether attackers have access to the training data, we divide various backdoor attacks into two types: poisoning-based attacks and non-poisoning-based attacks. We go through the details of each work in the timeline, discussing its contribution and deficiencies. We propose a detailed mathematical backdoor model to summary all kinds of backdoor attacks. In the end, we provide some insights about future studies.
Usual and Unusual Human Activity Recognition in Video using Deep Learning and Artificial Intelligence for Security Applications

The main objective of Human Activity Recognition (HAR) is to detect various activities in video frames. Video surveillance is an import application for various security reasons, therefore it is essential to classify activities as usual and unusual. This paper implements the deep learning model that has the ability to classify and localize the activities detected using a Single Shot Detector (SSD) algorithm with a bounding box, which is explicitly trained to detect usual and unusual activities for security surveillance applications. Further this model can be deployed in public places to improve safety and security of individuals. The SSD model is designed and trained using transfer learning approach. Performance evaluation metrics are visualised using Tensor Board tool. This paper further discusses the challenges in real-time implementation.
An Empirical Analysis of KDE-based Generative Models on Small Datasets

One of the approaches to deal with the small dataset problem is synthetic data generation. Kernel density estimation is a common method to approximate the underlying probability distribution of a small dataset. The present paper aims to analyze the generation capability of KDE-based models by evaluating their samples. For this purpose, we introduce a framework for synthetic dataset quality estimation which also accounts for the overfitting of a generative model. The performance of KDE is analyzed on samples from theoretical distributions and real datasets. The results state that KDE generates synthetic samples of a good quality and outperforms its competitors on small datasets.
FCMpy: a python module for constructing and analyzing fuzzy cognitive maps

FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.
An information entropy-based evolutionary computation for multi-factorial optimization

Recently, a new category of problems known as multi-factorial optimization (MFO) is gaining momentum in the field of evolutionary computation (EC). This paper aims at improving the aggregate performance of an underlying EC model in a multi-tasking environment by implementing simple strategies with minimal parameter tuning effort. Firstly, an enhanced Simulated Binary Crossover (SBX)-based unary variation operator to improve EC performance is devised. Secondly, to overcome the challenge in parameter tuning and operators selection, an adaptive control strategy underpinned by information entropy is proposed. We study the measure of entropy to quantify the uncertainty of evolutionary search and use the information to adapt the algorithmic parameters. Thirdly, the MFO problems are solved using the proposed methods. Three experiments are carried out to attest the methodology efficacy. The first experiment benchmarks the proposed method against twelve state-of-the-art single objective optimization algorithms in the CEC2014 competition. The second experiment compares the performance of the proposed method using a recent benchmark MFO problem. We further extend the investigation into the analysis of parameter sensitivity and solicit insights pertaining to the algorithm characteristics. Overall, the empirical results on various benchmark problems are promising. The third experiment offers a solution to a multi-mode resource-constrained project scheduling problem in the real-world construction industry. The results indicate improvements of 2.29% in project quality and 8.23% in project duration subject to an increase of 4.78% in project cost, which are well within the acceptance limits of the decision makers. • An enhanced SBX-based unary variation operator for continuous optimization problems. • Parameter tuning with adaptive control scheme underpinned by information entropy. • A real multi-mode resource-constrained scheduling problem in construction industry.