Reviewerly/exHarmony huggingface dataset:
Joint Pixel-Level and Feature-Level Unsupervised Domain Adaptation for Surveillance Face Recognition

Face recognition (FR) is one of the most successful image analysis and understanding applications, which has recently received significant attentions. However, despite the remarkable progress in face recognition-related technologies, dependably recognizing surveillance faces is still a big challenge due to different data distributions between commonly used faces and surveillance faces. Although collecting labeled surveillance faces could be direct and helpful, it is infeasible due to privacy and labor cost. In comparison, it is practical to use the unsupervised domain adaptation (UDA) method to regularize the learning of surveillance face representations by utilizing small-scale unlabeled data. In this paper, a joint pixel-level and feature-level UDA framework has been proposed for tacking the above problems. Specifically, we introduce a training method of domain adversarial to align the domain gap in feature space. Additionally, we propose to use a CycleGAN-based image style transformer to capture pixel-level domain shifts. Moreover, we adopt feature identity-consistency loss on the original CycleGAN to alleviate the identity attribute shift caused by unstable image style transfer. The experimental results on the self-built SFace-450 dataset demonstrate that our method consistently outperforms the state-of-the-art FR and UDA methods.
Deep Learning-Based Named Entity Recognition and Knowledge Graph for Accidents of Commercial Bank

With the diversified development of business, the construction of the banking system has become increasingly complex, which is prone to accidents. Since system accidents are the result of the combined action of various risk factors, accident management requires comprehensive knowledge support. Although bank accident management has accumulated a large amount of data, there is still a lack of effective solutions to obtain the required knowledge from big data quickly and accurately when faced with a specific accident. To solve the above problems, we developed bank accident management from the perspective of knowledge support to introduce relevant methods and technologies in the field of artificial intelligence. Then, accident management based on named entity recognition and knowledge graph can be developed. The entity annotation corpus in banking accidents is constructed. For the context of each bank accident, key information (four types of entities: time, accident name, loss amount, and reason) is automatically extracted by the BERT-BiLSTM-CRF model. Various entities and relational knowledge elements in the knowledge graph are retained in the graph database Neo4j to form a knowledge graph in the field of banking accidents. We provide important references for the bank's accident analysis, cause investigation, resource allocation, and management decision-making.
Multi-Curve Translator for High-Resolution Photorealistic Image Translation

The dominant image-to-image translation methods are based on fully convolutional networks, which extract and translate an image’s features and then reconstruct the image. However, they have unacceptable computational costs when working with high-resolution images. To this end, we present the Multi-Curve Translator (MCT), which not only predicts the translated pixels for the corresponding input pixels but also for their neighboring pixels. And if a high-resolution image is downsampled to its low-resolution version, the lost pixels are the remaining pixels’ neighboring pixels. So MCT makes it possible to feed the network only the downsampled image to perform the mapping for the full-resolution image, which can dramatically lower the computational cost. Besides, MCT is a plug-in approach that utilizes existing base models and requires only replacing their output layers. Experiments demonstrate that the MCT variants can process 4K images in real-time and achieve comparable or even better performance than the base models on various photorealistic image-to-image translation tasks.
Analysis of the Effect of Flow Rate on the Performance of Solar Water Heater Trickle System

Every country is faced with the problem of limited fossil fuels hence the need for renewable energy sources as alternative solutions to this problem. Among the numerous types of renewable energy, solar can produce 834.1 GW of energy, the highest in the world. It can be used as a heater by utilizing a solar collector, which is part of the Solar Water Heater (SWH). Therefore, this research aims to examine the performance of the SWH type trickle by determining the total efficiency with the fluid flow rate of 2, 3, and 4 lpm. The results showed that the total efficiency produced with 4 lpm higher than the 3 and 2 lpm, with an efficiency value of 58.3%, 49.9%, and 49.7%, respectively. The higher the flow rate, the lower the temperature, with a rise in the accumulated heat absorbed by the water hence the total efficiency value produced increases. In conclusion, the higher the total efficiency value, the better the SWH performance.
Artificial Intelligence Promises to Public Organizations and Smart Cities

Artificial Intelligence (AI) is influencing almost every area of modern life from entertainment, commerce, and healthcare to organizations internal processes operations. Netflix knows what movies and series people want to watch, Amazon knows when and where people like to shop, and Google knows what users are looking for. All this information may be utilized to create very comprehensive personal profiles, which can be useful not just for behavioral analysis and targeting, but also for forecasting economic trends, political changes and for understanding people’s attitude towards different topics. There is a lot of hope that AI could lead to significant breakthroughs in all aspects of life. It can help in organizing public administration institutions’ internal processes as well as urban development and/or transformation. In this article, the author considers both of those directions similarly important to citizens and provides a list of political, economic and administrative benefits in using AI applications in public organizations (at local, regional or national levels). Several ethical considerations regarding the use of AI and its potential impact on the current labor market are included in the discussion section.
A coordinated assignment method for multi-UAV area search tasks

This paper mainly studies the coordination task assignment problem faced by multi-UAV systems when performing area search tasks. First, for the coverage search task, by analyzing the task load of each UAV, this paper proposes and establishes a dynamic model of UAV task load based on the biological competition mechanism, and proves that the model can realize the area under the condition of weighted threat probability. Even distribution of search tasks. Secondly, on the basis of solving the priority traveling salesman problem, through simulated annealing algorithm, the established priority traveling salesman problem is solved, and the path planning method of UAV is obtained. Finally, through simulation experiments, the effectiveness and engineering practical significance of the area search task assignment method proposed in this paper are verified.
Multi-Column Convolutional Neural Network for Vehicle-Type Classification

Vehicle type classification is one of the major important applications in field of road security. In this paper, we present a deep learning-based approach to classify vehicles into six categories using Multi-Column Convolutional Neural Network (MCCNN). Instead of using fixed scale convolutional layer, the model we used is able to extract features from different scale of an image using variant filters to improve classification performance especially in vehicles with similar appearance like Suv and Sedan. The feasibility of the proposed approach is checked through an experimental investigation conducted on BIT dataset which includes 9850 frontal-view images of different types and high-resolution. The MCCNN model achieves a classification accuracy rate of 95.48% using the whole unbalanced dataset outperforming previous studies. An additional evaluation using 200 images from each class is performed yielding to an average accuracy rate of 88.37%.
A Scalable Approach of Practical Byzantine Fault Tolerance Algorithms for IoMT Blockchains

Blockchain-based solutions for Internet of Things (IoT) networks constitutes a current trend in cybersecurity and brings significant benefits into current centralized IoT-based health monitoring systems by addressing security challenges. Complex and power intense blockchain solutions do not perform satisfactory in the resource-constrained IoT, and especially Internet of Medical Things (IoMT), devices of these systems due to the latter's limited processing power, storage capacity, and battery life. Therefore, in this paper, we propose a scalable Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for IoMT blockchains to: i) enhance scalability in IoMT blockchains, ii) reduce communication overhead, iii) enhance security while reducing the computational cost for suitability to the resource constraint nature of IoMT devices, iv) facilitate decentralized accountability, and v) eliminate single point of failure.
Parkinson’s Disease Stage Classification with Gait Analysis using Machine Learning Techniques and SMOTE-based Approach for Class Imbalance Problem

High variability in symptom severity and progression rate roots the need for a diverse training dataset, to build an efficient Parkinson’s Disease (PD) severity prediction model. The Physionet database comprises gait signals of PD subjects belonging to various H&Y score-based severity levels but forms an imbalanced dataset. A dataset is said to be imbalanced if the representation of the classification categories within a dataset is not equal. The severity of misclassifying abnormal cases as normal is high and thus is a matter of concern. This paper shows how a technique called Synthetic Minority Oversampling Technique (SMOTE) deals with the class imbalance problem in PD stage-wise classification by improving minority class recognition. The method is validated by quantifying the dissimilarity among samples generated showing the non-existence of overlapping or replication. Spatiotemporal gait parameters along with their regularity and symmetry features are the attributes considered. Classifiers are trained with balanced & imbalanced datasets and their predictive accuracy attributes are compared. Results show an improvement in determining the minority class by the model trained with the balanced dataset, thus improving the generalizability of the model.
Conflict-free and energy-efficient path planning for multi-robots based on priority free ant colony optimization

&lt;abstract&gt;&lt;p&gt;With the background of limited energy storage of robots and considering the high coupling problem of multi-agent path finding (MAPF), we propose a priority-free ant colony optimization (PFACO) to plan conflict-free and energy-efficient paths, reducing multi-robots motion cost in the rough ground environment. First, a dual-resolution grid map considering obstacles and ground friction factors is designed to model the unstructured rough terrain. Second, an energy-constrained ant colony optimization (ECACO) is proposed to achieve energy-optimal path planning for a single robot, in which we improve the heuristic function based on the combined effects of path length, path smoothness, ground friction coefficient and energy consumption, and consider multiple energy consumption metrics during robot motion to improved pheromone update strategy. Finally, considering multiple collision conflict cases among multiple robots, we incorporate a prioritized conflict-free strategy (PCS) and a route conflict-free strategy (RCS) based on ECACO to achieve MAPF with low-energy and conflict-free in a rough environment. Simulation and experimental results show that ECACO can achieve better energy saving for single robot motion under all three common neighborhood search strategies. PFACO achieves both the conflict-free path and energy-saving planning for robots in complex scenarios, and the study has some reference value for solving practical problems.&lt;/p&gt;&lt;/abstract&gt;
Varangian

The complexity and scale of modern software programs often lead to overlooked programming errors and security vulnerabilities. Developers often rely on automatic tools, like static analysis tools, to look for bugs and vulnerabilities. Static analysis tools are widely used because they can understand nontrivial program behaviors, scale to millions of lines of code, and detect subtle bugs. However, they are known to generate an excess of false alarms which hinder their utilization as it is counterproductive for developers to go through a long list of reported issues, only to find a few true positives. One of the ways proposed to suppress false positives is to use machine learning to identify them. However, training machine learning models requires good quality labeled datasets. For this purpose, we developed D2A [3], a differential analysis based approach that uses the commit history of a code repository to create a labeled dataset of Infer [2] static analysis output.
Enhancement and detection of objects in underwater images using image super-resolution and effective object detection model

It is imperative to build an automatic underwater object recognition system in place to reduce the costs of underwater inspections as well as the associated risks. An effective method of detecting underwater objects from underwater images of aquatic after enhancing them using the Image Super-resolution technique is proposed in this study. The proposed approach comprises of two major sections, Underwater Image Enhancement, and Object detection. To enhance the underwater images, a lightweight Reduced Cascading Residual Network (RCARN) is proposed that imposes the Image Super-resolution technique. Later, the enhanced images generated by the RCARN model are supplied for the object detection process, where a significant object detection model, YOLOv3 is employed in this study. To improve its performance, this YOLOv3 is trained on one of the largest datasets, the COCO data, followed by being fine-tuned using enhanced Underwater images. The dataset utilized in this work contains 6 classes of underwater objects namely dolphin, jellyfish, octopus, seahorse, starfish, and turtle. All these images are actual real field images collected from various sources. With this proposed approach, a better overall ACS and mAP of 95.44% and 75.33% are achieved here, which are improved by ~8.75% and ~15%, respectively when compared to actual collected low-resolution images.
Hands-on Infrastructure as Code with Hashicorp Terraform

Cloud-native systems are characterized by increased speed and agility because of the utilization of microservices, containers, and a contemporary system design. They automate the build and release stages so that they can guarantee the quality and integrity of the code. Cloud technologies allow for quick and scalable deployment by layering abstractions on top of complex infrastructure services like Amazon Elastic Compute Cloud (EC2), Amazon Simple Storage Service (S3), and Kubernetes. With Terraform’s user-friendly interface, automating the rollout of these services is a breeze. But there is more to the story than meets the eye. You will now understand how the virtual cloud environments with these technologies are deployed and provisioned.
Educational Quality in Virtuality During the Covid 19 Pandemic in Colombia

This study is developed in order to determine the educational quality in Colombia during the covid 19 pandemic from the perspective of the student experience. The methodology used is framed towards the quantitative field with a correlational level and a field-non-experimental design. For data collection, the database “Cuarentena COVID 19 Jóvenes” is taken as a reference, which is supplied by the mayor of Medellin. The sample under study consisted of a total of 2058 subjects to whom a questionnaire is applied with 12 questions related to their sociodemographic characteristics, access, and availability for classes in virtual modality, and their perception of the experience in the learning processes during virtuality. For the statistical processing of the information, a descriptive analysis of the data is developed in the first instance, based on the frequency and percentage statistics, to subsequently generate cross tables between the elements related to educational quality in Colombia during the covid 19 pandemic from the perspective of the student experience, and finally an analysis of the correlations between the elements is presented through the Chi-squared test. The whole process is developed through the SPSS statistical software. The results allowed to identify in the first instance a high level of students who took classes in their virtual modality, which had access to the internet (74.9%) and an electronic device (78.6%) that allowed them to access these classes in virtual modality. In turn, students considered in a high level of proportion that virtual classes had impaired the learning experience by 69%.
Ten Challenges and Explainable Analogs of growth functions and distributions for statistical literacy and fluency

Personalized learning, science education, and public understanding of science are linked in many ways. Studies of public understanding of science suggest that citizen science literacy is not just about reforming school science curricula. Understanding statistics is a challenge for many people, and customized learning methods are required. Statistics are useful for keeping records, calculating probabilities, and providing knowledge. Basically, they help us understand the world a little better through numbers and other quantitative information. Motivated by the overall goal of promoting understanding of statistical concepts and minimizing misinformation, thirteen gamification and data physicalization initiatives help us to offer ten challenges. They allow players to record, update, and explore the characteristics and differences between different statistical concepts. This work combines dynamic physical visualization and gamification to explain linear and exponential functions and a set of distributions using analog explicable cubes as a means of representation.
Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word

In this study, we propose a head-to-head type (H2H-type) inter-personal multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM, which is a probabilistic generative model that represents the symbol emergence between two agents as multiagent multimodal categorization. A Metropolis-Hastings method-based naming game based on the Inter-MDM enables two agents to collaboratively perform multimodal categorization and share signs with a solid mathematical foundation of convergence. However, the conventional Inter-MDM presumes a tail-to-tail connection across a latent word variable, causing inflexibility of the further extension of Inter-MDM for modeling a more complex symbol emergence. Therefore, we propose herein a head-to-head type (H2H-type) Inter-MDM that treats a latent word variable as a child node of an internal variable of each agent in the same way as many prior studies of multimodal categorization. On the basis of the H2H-type Inter-MDM, we propose a naming game in the same way as the conventional Inter-MDM. The experimental results show that the H2H-type Inter-MDM yields almost the same performance as the conventional Inter-MDM from the viewpoint of multimodal categorization and sign sharing.
SecSharp: Towards Efficient Trusted Execution in Managed Languages (Work in Progress)

Trusted execution environments (TEEs) gained significant traction in recent years. They have become the foundation of Confidential Computing in cloud services, where certain security properties can be guaranteed on untrusted servers. Despite this adoption, writing code to target TEEs remains challenging. The SDKs for popular TEE implementations, like Intel SGX, are aimed at low-level languages like C/C++. Previous research has introduced support for developing and running programs written in managed programming languages in a TEE environment. However, in these works, the language runtime is embedded into the TEE, increasing the Trusted Computing Base (TCB) and thus inherently reducing trust into the TEE itself. To solve this problem, we propose a new approach to integrate the development of TEE code in managed languages, without the need to embed the full language runtime inside the TEE. It allows developers to write the TEE logic as part of their program in a managed programming language. Using the existing compiler infrastructure, the TEE logic is extracted and passed to a source-to-source compiler that transforms it into a low-level unmanaged equivalent. The resulting low-level code is then compiled by the compiler toolchain targeting the TEE platform. This paper reports on the design and the first results of our work-in-progress implementation of SecSharp, a tool to enable TEE development in C#.
Navigation by Scenarios Using Topological Map

We propose a passage classification method using omnidirectional camera images and object detection by machine learning. In this method, image data of 360 degrees horizontally is firstly acquired using an omnidirectional camera. Next, objects such as passages and doors are detected from the acquired image data using the YOLO detector. Finally, it is determined which passage feature is classified according to the type and position of the detected object. We conducted an experiment to classify passage features from images in order to verify the effectiveness of the proposed method. In addition, the proposed method is applied and used for our scenario-based navigation method. We conduct navigation experiments and verify the effectiveness of the applied method.
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation

Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical deployment. In this paper, we present an effective teacher-student learning approach to train privacy-preserving deep learning models via differentially private data-free distillation. The main idea is generating synthetic data to learn a student that can mimic the ability of a teacher well-trained on private data. In the approach, a generator is first pretrained in a data-free manner by incorporating the teacher as a fixed discriminator. With the generator, massive synthetic data can be generated for model training without exposing data privacy. Then, the synthetic data is fed into the teacher to generate private labels. Towards this end, we propose a label differential privacy algorithm termed selective randomized response to protect the label information. Finally, a student is trained on the synthetic data with the supervision of private labels. In this way, both data privacy and label privacy are well protected in a unified framework, leading to privacy-preserving models. Extensive experiments and analysis clearly demonstrate the effectiveness of our approach.