Reviewerly/exHarmony huggingface dataset:
A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN

Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several computer vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we used a deep learning model called Mask R-CNN that is trained on uninfected and Plasmodium falciparum-infected red blood cells. Our predictive model produced reports at a rate 15 times faster than manual counting without compromising on accuracy. Another unique feature of our model is its ability to generate segmentation masks on top of bounding box classifications for immediate visualization, making it superior to existing models. Furthermore, with greater standardization, it holds much potential to reduce errors arising from manual counting and save a significant amount of human resources, time, and cost.
Numerical analysis for iterative filtering with new efficient implementations based on FFT

The development of methods able to extract hidden features from non-stationary and non-linear signals in a fast and reliable way is of high importance in many research fields. In this work we tackle the problem of further analyzing the convergence of the Iterative Filtering method both in a continuous and a discrete setting in order to provide a comprehensive analysis of its behavior. Based on these results we provide a new efficient implementation of Iterative Filtering algorithm, called Fast Iterative Filtering, which reduces the original iterative algorithm computational complexity by utilizing, in a nontrivial way, Fast Fourier Transform in the computations.
AdvFaces: Adversarial Face Synthesis

Face recognition systems have been shown to be vulnerable to adversarial faces resulting from adding small perturbations to probe images. Such adversarial images can lead state-of-the-art face matchers to falsely reject a genuine subject (obfuscation attack) or falsely match to an impostor (impersonation attack). Current approaches to crafting adversarial faces lack perceptual quality and take an unreasonable amount of time to generate them. We propose, AdvFaces, an automated adversarial face synthesis method that learns to generate minimal perturbations in the salient facial regions via Generative Adversarial Networks. Once AdvFaces is trained, a hacker can automatically generate imperceptible face perturbations that can evade four black-box state-of-the-art face matchers with attack success rates as high as 97.22% and 24.30% at 0.1 % False Accept Rate, for obfuscation and impersonation attacks, respectively.
MedHypChain: A patient-centered interoperability hyperledger-based medical healthcare system: Regulation in COVID-19 pandemic

Recently, an infectious disease, coronavirus disease 2019 (COVID-19), has been reported in Wuhan, China, and spread worldwide within a couple of months. There have been seen an outbreak of COVID-19 in many countries, where the infected patients' rate overwhelmed the inadequate medical services. The push of patient-centered interoperability (PCI) from medical institution-centered interoperability may defeat the current and post resultant disease of the COVID-19 pandemic. This paper proposes a state-of-the-art privacy-preserving medical data sharing system based on Hyperledger Fabric (MedHypChain), where each transaction is secured via an Identity-based broadcast group signcryption scheme. We proved that MedHypChain achieves confidentiality, anonymity, traceability, and unforgeability. Besides, we regularize the MedHypChain to implement the PCI healthcare system, where the patient manages its health-related information in the blockchain that can be accessible to the authorized entity. We also use the Hyperledger caliber as a benchmark tool to analyze the performance of MedHypChain in three metrics (latency time, execution time, and throughput) for up to 20 permissioned nodes. Finally, we compare MedHypChain with related blockchain-based healthcare systems and found that the proposed scheme needs the least computation cost and communication cost and achieves all security features, such as authenticity, scalability, and access control.
Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios

In this article, we propose a nonlinear receding horizon game-theoretic planner for autonomous cars in competitive scenarios with other cars. The online planner is specifically formulated for a multiple-car autonomous racing game, in which each car tries to advance along a given track as far as possible with respect to the other cars. The algorithm extends previous work on game-theoretic planning for single-integrator agents to be suitable for autonomous cars in the following ways: 1) by representing the trajectory as a piecewise polynomial; 2) incorporating bicycle kinematics into the trajectory; and 3) enforcing constraints on path curvature and acceleration. The game-theoretic planner iteratively plans a trajectory for the ego vehicle and then the other vehicles in sequence until convergence. Crucially, the trajectory optimization includes a sensitivity term that allows the ego vehicle to reason about how much the other vehicles will yield to the ego vehicle to avoid collisions. The resulting trajectories for the ego vehicle exhibit rich game strategies such as blocking, faking, and opportunistic overtaking. The game-theoretic planner is shown to significantly outperform a baseline planner using model-predictive control, which does not take interaction into account. The performance is validated in high-fidelity numerical simulations with three cars, in experiments with two small-scale autonomous cars, and in experiments with a full-scale autonomous car racing against a simulated vehicle (video is available at https://youtube.com/playlist?list=PLmIcLEh8KMje4rYBqRANDuKvqFvj7LCRp).
A new lightweight cryptographic algorithm for enhancing data security in cloud computing

Data has been pivotal to all facets of human life in the last decades. In recent years, the massive growth of data as a result of the development of various applications. This data needs to be secured and stored in secure sites. Cloud computing is the technology can be used to store those massive amounts of data. . The rapid development of this technology makes it more critical. Therefore, it has become urgent to secure data from attackers to preserve its integrity, confidentiality, protection, privacy and procedures required for handling it. This paper proposed a New Lightweight Cryptographic Algorithm for Enhancing Data Security that can be used to secure applications on cloud computing. The algorithm is a 16 bytes (128-bit) block cipher and wants 16 bytes (128-bit) key to encrypt the data. It is inspired by feistal and substitution permutation architectural methods to improve the complexity of the encryption. The algorithm achieves Shannon's theory of diffusion and confusion by the involvement of logical operations, such as (XOR, XNOR, shifting, swapping). It also features flexibility in the length of the secret key and the number of turns. The experimental results of the proposed algorithm presented a strong security level and an apparent enhancement in measures of cipher execution time and security forces compared to the cryptographic systems widely used in cloud computing.
A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments

Bots are frequently used in Github repositories to automate repetitive activities that are part of the distributed software development process. They communicate with human actors through comments. While detecting their presence is important for many reasons, no large and representative ground-truth dataset is available, nor are classification models to detect and validate bots on the basis of such a dataset. This paper proposes a ground-truth dataset, based on a manual analysis with high interrater agreement, of pull request and issue comments in 5,000 distinct Github accounts of which 527 have been identified as bots. Using this dataset we propose an automated classification model to detect bots, taking as main features the number of empty and non-empty comments of each account, the number of comment patterns, and the inequality between comments within comment patterns. We obtained a very high weighted average precision, recall and F1-score of 0.98 on a test set containing 40% of the data. We integrated the classification model into an open source command-line tool to allow practitioners to detect which accounts in a given Github repository actually correspond to bots.
A Storage Optimization Scheme for Blockchain Transaction Databases

As the typical peer-to-peer distributed networks, blockchain systems require each node to copy a complete transaction database, so as to ensure new transactions can by verified independently. In a blockchain system (e.g., bitcoin system), the node does not rely on any central organization, and every node keeps an entire copy of the transaction database. However, this feature determines that the size of blockchain transaction database is growing rapidly. Therefore, with the continuous system operations, the node memory also needs to be expanded to support the system running. Especially in the big data era, the increasing network traffic will lead to faster transaction growth rate. This paper analyzes blockchain transaction databases and proposes a storage optimization scheme. The proposed scheme divides blockchain transaction database into cold zone and hot zone using expiration recognition method based on Least Recently Used (LRU) algorithm. It can achieve storage optimization by moving unspent transaction outputs outside the in-memory transaction databases. We present the theoretical analysis on the optimization method to validate the effectiveness. Extensive experiments show our proposed method outperforms the current mechanism for the blockchain transaction databases.
Scalable multi-channel dilated CNN–BiLSTM model with attention mechanism for Chinese textual sentiment analysis

Due to the complex semantics of natural language, the multi-sentiment polarity of words, and the long-dependence of sentiments between words, the existing sentiment analysis methods (especially Chinese textual sentiment analysis) still face severe challenges. Aware of these issues, this paper proposes a scalable multi-channel dilated joint architecture of convolutional neural network and bidirectional long short-term memory (CNN–BiLSTM) model with an attention mechanism to analyze the sentiment tendency of Chinese texts. Through the multi-channel structure, this model can extract both the original context features and the multiscale high-level context features. Importantly, the number of the model channel can be optimally expanded according to the actual corpus. Furthermore, the attention mechanism including local attention and global attention is adopted to further distinguish the difference of features. The former is employed to weight the output features of each channel, and the latter is used to weight the fused features of all channels. Besides, an adaptive weighted loss function is designed to effectively avoid the imbalance of classes in training data. Finally, several experiments are performed to demonstrate the superior performance of the proposed model on two public datasets. Compared with word-level methods, the accuracy and Macro-F1 are respectively increased by over 1.19% and 0.9% on NLPCC2017-ECGC corpus, the accuracy and F1 are respectively increased by more than 1.7% and 1.214% on ChnSentiCorp-Htl-unba-10000 corpus. Compared with char-level pre-training methods, the accuracy and Macro-F1 also respectively achieve an improvement of over 3.416% and 4.324% on the NLPCC2017-ECGC corpus, the accuracy and F1 are respectively increased by more than 0.14% and 3% on the ChnSentiCorp-Htl-unba-10000 corpus.
A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute

Vehicle path planning is a key issue for car navigation systems. When path planning, considering the time spent at intersections is more in line with the actual situation, so it is of practical significance to study the path planning problem take account intersection attributes. In this article, we study the problem in a deterministic network, taking the minimization of travel time from the origin to the destination as the optimization goal. For this purpose, we construct a mathematical model for the problem. This paper proposes a reverse labeling Dijkstra algorithm (RLDA) based on traditional Dijkstra algorithm to solve the problem, it is proved that the correctness of the RLDA algorithm theoretically, and analyze that the RLDA algorithm has a lower polynomial time complexity. Finally, we selected the actual road network as the simulation experiment object to verify the effectiveness of the algorithm searching for the optimal path. And select 10 groups of networks of different sizes and conduct extensive experiments to compare the convergence efficiency and calculation speed between RLDA and PSO, GA, ACO, NNA, OPABRL. The statistical results show that the convergence rate of the RLDA algorithm is better than that of ACO, NNA, and GA. When the number of network nodes is less than 350, the algorithm has the smallest running time.
Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.
Multi-Meteorological-Factor-Based Graph Modeling for Photovoltaic Power Forecasting

Solar energy is a strongly intermittent renewable energy source, which is affected by varied meteorological conditions, and thus produces arbitrary power outputs in photovoltaic (PV) power generation. Complex weather variations make it challenging to develop an efficient PV power forecasting method. In this study, a graph modeling method is proposed for short-term PV power prediction. Unlike many conventional machine-learning models, the proposed model is capable of evaluating interconnections among various meteorological input factors. This study details the design and operation of graph modeling, including graph construction, node feature construction, message transfer, and readout. An entire model is established consisting of spectral graph convolution, multiple graphical edges and a hierarchical output manner. The testing results suggest that the proposed multi-graph model outperforms other benchmark models in terms of accuracy under day-ahead forecasting cases. Besides, the single-graph model achieves a reduced cost of training time comparing to deep-learning benchmark models.
A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

This article presents the second Part of a two-Part survey that reviews evolutionary dynamic optimization (EDO) for single-objective unconstrained continuous problems over the last two decades. While in the first part, we reviewed the components of dynamic optimization algorithms (DOAs); in this part, we present an in-depth review of the most commonly used benchmark problems, performance analysis methods, static optimization methods used in the framework of DOAs, and real-world applications. Compared to the previous works, this article provides a new taxonomy for the benchmark problems used in the field based on their baseline functions and dynamics. In addition, this survey classifies the commonly used performance indicators into fitness/error-based and efficiency-based ones. Different types of plots used in the literature for analyzing the performance and behavior of algorithms are also reviewed. Furthermore, the static optimization algorithms that are modified and utilized in the framework of DOAs as the optimization components are covered. We then comprehensively review some real-world dynamic problems that are optimized by EDO methods. Finally, some challenges and opportunities are pointed out for future directions.
A Systematic Literature Review of A* Pathfinding

A* is a search algorithm that has long been used in the pathfinding research community. Its efficiency, simplicity, and modularity are often highlighted as its strengths compared to other tools. Due to its ubiquity and widespread usage, A* has become a common option for researchers attempting to solve pathfinding problems. However, the sheer amount of research done on the topic makes it difficult to know where to start looking. With this paper, we hope to create an accessible, up to date reference on the current state of the A* search algorithm for future pathfinding projects to consider. This paper examines A-Star’s current usage in the field of pathfinding, comparing A* to other search algorithms. It also analyzes potential future developments for A-Star’s development. A* cannot keep up with the demands of current pathfinding problems. Other algorithms can maintain the same performance while also demanding less overhead and this problem only grows worse as grid size increases. However, use of innovative modifications such as different heuristic types or secondary components to the algorithm allow A* to achieve very fast times with good accuracy when dealing with large maps, while only having slightly increased overhead costs for the modifications. While beginning to show its age, improved algorithms based on the classic A* algorithm are more than capable of keeping up with modern pathfinding demands. These derivative search algorithms such as HPA* is used to overcome the limitations of A*. HPA* can compete with and even surpass its competitors depending on the challenge faced.
Lime: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks

Understanding the interconnected relationships of large-scale information networks like social, scholar and Internet of Things networks is vital for tasks like recommendation and fraud detection. The vast majority of the real-world networks are inherently heterogeneous and dynamic, containing many different types of nodes and edges and can change drastically over time. The dynamicity and heterogeneity make it extremely challenging to reason about the network structure. Unfortunately, existing approaches are inadequate in modeling real-life dynamical networks as they either have strong assumption of a given stochastic process or fail to capture the heterogeneity of network structure, and they all require extensive computational resources. We introduce <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lime</small> , a better approach for modeling dynamic and heterogeneous information networks. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lime</small> is designed to extract high-quality network representation with significantly lower memory resources and computational time over the state-of-the-arts. Unlike prior work that uses a vector to encode each network node, we exploit the semantic relationships among network nodes to encode multiple nodes with similar semantics in shared vectors. By using many fewer node vectors, our approach significantly reduces the required memory space for encoding large-scale networks. To effectively trade information sharing for reduced memory footprint, we employ the recursive neural network (RsNN) with carefully designed optimization strategies to explore the node semantics in a novel cuboid space. We then go further by showing, for the first time, how an effective incremental learning approach can be developed – with the help of RsNN, our cuboid structure, and a set of novel optimization techniques – to allow a learning framework to quickly and efficiently adapt to a constantly evolving network. We evaluate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lime</small> by applying it to three representative network-based tasks, node classification, node clustering and anomaly detection, performing on three large-scale datasets. We compare <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lime</small> against eleven prior state-of-the-art approaches for learning network representation. Our extensive experiments demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lime</small> not only reduces the memory footprint by over 80 percent and the processing time over 2x when learning network representation but also delivers comparable performance for downstream processing tasks. We show that our incremental learning method can boost the learning time by up to 20x without compromising the quality of the learned network representation.
Investigating managers' understanding of chatbots in the Korean financial industry

There is growing interest in chatbots as a leading technology for digital transformation in the financial industry. However, studies have largely investigated chatbots from the perspective of customers and developers, and very few examine chatbot services from the perspective of managers, who play a central role in the organizational adoption of chatbots. This paper aims to explore managers' perception of chatbots to help understand the initiation of financial chatbots and forecast the future of their service in South Korea, which has seen active use of chatbots by financial firms. We conducted semi-structured interviews with managers in charge of chatbot services in Korean financial firms and employed a core–periphery analysis of social representations. Our findings will enhance the understanding of the status of chatbot services in the Korean financial industry and provide theoretical implications and managerial insights.