Reviewerly/exHarmony huggingface dataset:
A Lightweight Network for Outdoor Illumination Estimation on Mobile Devices

Outdoor illumination estimation is particularly essential for augmented reality applications. Traditionally, handcraft illumination features are used to recover outdoor illumination such as shadows and highlight areas of the scenes. These features are so dependent on human experience and auxiliary equipments that the performances of these approaches are limited. This paper presents a lightweight network based on MobileNet V3 to estimate outdoor illumination from a single outdoor image. A physically-based illumination model is fitted to the sky regions of outdoor panoramas to generate illumination parameters, and different field of view images are extracted from outdoor panoramas. The images with illumination parameters annotated are then used to train our lightweight network. Experiments show that our method can perform well and is suitable for mobile-based applications.
Serialized Local Feature Representation Learning for Infrared-Visible Person Re-identification

Infrared-Visible person re-identification is a kind of cross-modality person re-identification. The purpose of the task is that given a person image we need to find another image on the same person from gallery. The query images and gallery images are not only in RGB modality but in infrared modality as well. The cross-modality person ReID task can deal with the limitation of single modality because we usually can get images in more than one modality. In our work, we take advantage of both global feature and local feature. We use a dual-path structure to extract features from RGB images and infrared images respectively. Besides, we add the LSTM structure in each path to learn the serialized local features. The loss function consists of cross-entropy loss and hetero-center loss so that the model can bridge the cross-modality and intra-modality gaps to capture the modality-shared features and improve the cross-modality similarity. Finally, we do experiments on two datasets including SYSU-MM01 and RegDB, then compare with other methods in recent studies.
Wireless Speed Control of Vehicles with Detection of Person &amp; Zebra Crossing

Pedestrians crossing zebra lines are one of the major concerns for road accidents. Nowadays, the number of road accidents increases due to careless driving and pedestrian motions at crosswalks. It is necessary to detect both person and zebra crossings properly and control vehicle speed accordingly. Here in this paper, a suitable solution that improves both detections can be introducing. Here used the TensorFlow Single Shot Detection (SSD) model is the best and most convenient trained model for Zebra line and person detection. A database is taking for the analysis. The input image could process as a crosswalk detection, which has more used for zebra crossing identification via the SSD model. Suppose detected the person and zebra crossings were at the same time. In that case, it will perform commands such as run, slow down, stop, horn, etc., with the help of wireless serial communication Universal Asynchronous Receiver-Transmitter. A Bluetooth command signal matches UART, which provides the vehicle with the necessary control inputs to execute the prescribed topology properly. Simultaneous detection of pedestrians at zebra crossings is a critical factor. It results most efficiently and to identify the person detection.
Challenges and Solutions Faced by Blockchain in Education Informatization

Blockchain technology relies on its advantages such as decentralization, consensus mechanism, traceability and automatic execution, it makes educational practitioners and researchers more and more engaged in this aspect of research. In scope, the research directions are quite different at home and abroad, and in depth, most of the research is at the stage of theoretical knowledge. In order to block chain technology in the process of education informatization can effectively build a talent training system and promote education equity, we analyze and discuss the problems existing in the practical application of blockchain in learning methods, learning outcomes, digital education resources, education evaluation and education smart contract management, etc. Therefore, we propose solutions and provide examples and theoretical basis for the implementation of blockchain applications.
Hydrological Study of the Nakayasu Hydrograph Method for Design of Water Retention in the JIIPE Gresik Industrial Estate

Abstract The development area of the JIIPE industrial estate could change the land use around the development site. The research area mostly has clay lithology. This development area must be protected from flood. This study aims to plan a reservoir that can process the drainage system. The data needed are geographic data, rainfall data, meteorological data, area data, and land function data. This study used the Nakayasu Hydrograph method. The Nakayasu hydrograph method parameters are time interval from the rain surface to the top of the hydrograph, period time from the center of gravity of the rain to the center of gravity of the hydrograph, period time for the hydrograph, the area of the river basin, and the length of the longest main river channel. The calculation result from this method is the peak discharge value and this calculated based on the existing and planned conditions. In this study, the calculation of the probability of discharge in the return period of 25 years is carried out. From this calculation, a hydrograph graph of the flood discharge from the existing and planned conditions is generated. The calculation for the difference between the maximum debit between of existing and planned conditions is to determine the debit that must be held every second in the storage. The generated discharge value is 18.7 m 3 / s. The calculation of storage volume the reservoir to control the volume need is 168,300 m 3 . The calculation of the storage volume based on the area of the reservoir is used as the volume of availability, which is 169,956 m 3 , so that the recommended water retention dimensions are obtained with an area value of 36,947 m 2 and depth of water retention of 4,5 m.
Your Eyes Show What Your Eyes See (Y-EYES)

As the need for contactless biometric authentication becomes more significant during COVID-19, and beyond, the popular biometric authentication method for mobile devices, iris detection, and facial recognition confronts various usability, security, and privacy concerns, including mask-wearing and various Presentation Attacks (PA). Specifically, liveness detection against spoofed artifacts is one of the most challenging tasks as many existing methods cannot conclusively assess the user's physical presence in unsupervised environments. Even though several methods have been proposed for tackling PA with motion challenges and 3D mapping, most of them require expensive depth sensors and fail to detect sophisticated 3D reconstruction attacks.
Deep Data Mining And Hadoop Simulation In Computerized Systems

In this investigation, he main aim was to determine how effective and feasible it would be to exploit idle computational storage. Architecturally, the proposed model was that which relied on HDFS (Hadoop Distributed File System). Also, CPN tools were used during model implementation. Hence, the tools constituted CPN ML programming language and Colored Petri Dish Nets. To ensure that the availability of the workstations was characterized within the model, the data collection process occurred in a computer lab for about 40 days. In the findings, it was established that when three tests in the form of a physical test, a cloud test, and a simulation base test are applied, the deep data locality approach yields a significant improvement in the Hadoop performance. Particularly, the use of the deep data locality technique led to a 34 percent improvement in the Hadoop system. Thus, it was concluded that the superiority of the proposed approach arises from its ability to yield a reduction in the HDFS data movement.
Video quality assessment with deep architecture

Abnormal video quality judgment is an important part of monitoring system maintenance. However, it is still challenging to accurately judge possible anomalies. A novel deep model here is proposed to improve exception detection by considering remote context information in the automatic encoder framework. Specifically, C3D and recurrent neural network (RNN) with long short term memory (LSTM) unit will be used to explore the specified features of video sequence and extract advanced spatiotemporal features. In addition, an automatic encoder is used to represent each video frame to explore the hidden details. In addition, high-frequency structure details in gradient images are explored by the dual stream scheme. Consequently, it enhances its performances. Compared with other advanced methods, the experimental results show the effectiveness of our model.
A Parallel Optimization Method for KCF based on Inter-core Communication of Multi-core DSP

Target tracking is an important branch in the field of computer vision. In recent years, as target tracking application have increasingly higher requirements for realtime performance, higher requirements have been put forward for the optimization and application of embedded platforms. Therefore, in a multi-core DSP platform, parallel computing efficiency becomes the key to improving realtime performance. This paper proposes the concept of execution parallelism based on inter-core communication, and optimizes KCF for the parallelism parameters. This paper aims at the multi-core parallel development of KCF algorithm based on inter-core interrupt and shared memory on TMS320C6678 platform. By connecting the FPGA-based video board for physical verification, the parallel scheme based on inter-core communication can reach a tracking speed of 60 fps, which has certain engineering practical significance.
Coevolution of AI and Level Generators for Super Mario Game

Procedural content generation (PCG) is now used in many games to generate a wide variety of content. One way of evaluating this content is by artificial intelligence (AI) controlled players. Inversely, PCG content can also be used when training AI players to ensure generalization. Evolutionary algorithms are employed in both AI and PCG fields, but rarely simultaneously. In this work, we use evolutionary algorithms for both AI players and level generation in the platformer game Super Mario. We further combine them into a coevolution, where the AI players are evaluated by adapting level generators, and vice versa, level generators are evaluated by adapting AI players. This yields an AI player trained on gradually more difficult levels and a sequence of level generators with gradually increasing difficulty. Such sequence of generators might be useful for human game playing in commercial games.
MESH ENABLED IOT BASED SMART DEVICE FOR ATHLETES

Athletes are persons who are skilled in sports and other exercises. They take part in field tracks and competitive events. Athletes compete in various parameters like speed, physical strength, Positioning, etc. Every athlete must stay fit and healthy. Constant monitoring is done through this device. Consistent health status and progress in Game is essential. This device provides real-time data of the player like acceleration, angle, directions, and their health status during games and training. It also helps the audience understand the game better and to learn the performance of the player. This device can support both Wi-Fi and Bluetooth mesh networks. This project deals with real-time data of a game collected from various devices, sent and stored in the cloud, and can be accessed from anywhere anytime. Through this device, Trainees can be able to track their player's status every day so that they need not be with the player all day. Particle argon is used to mesh Wi-Fi and Bluetooth.
A method of dental model segmentation based on shortest path

To accurately extract the gum line of teeth from the three-dimensional dental model, a new method based on the traditional Dijkstra shortest path algorithm with Sigmoid curvature transformation is proposed in this paper. The curvature of each vertex of the dental model is transformed based on the sigmoid function, and each edge of the model is assigned with corresponding weight, then the shortest path representing the gum line of teeth is calculated. Also, a queue optimization and storage structure optimization strategy is used to make the algorithm implementation efficient. The experimental results show that the proposed tooth segmentation method can extract the gum line of tooth boundary accurately.
Cloud computing Privacy and Security

A big concern in information technology has consistently been data protection. It is especially serious in the cloud computing setting because the data is stored in various locations, even across the globe. The two key reasons for consumer concerns about cloud technology are data security and privacy protection. Although many techniques have been investigated in both academia and industries on cloud computing topics, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. In the cloud architecture, data security and privacy protection problems are relevant to both hardware and software. This study explores various security approaches and challenges for data protection in the cloud, from both software and hardware perspectives, and aims to enhance data security and privacy protection for the trusted cloud environment. In this paper, we compare the current research work on data security and privacy protection strategies used in cloud computing with a comparative research study.
Trust Mechanism and Key Management in IoT

In the IoT security architecture described in Chap. 3, there is a supporting pillar called trust and key management. This chapter discusses what “trust” is in the sense of network environment, and how such “trust” can be established and extended/transferred. Based on the “trust”, secret keys can be shared securely and reliably, i.e., some attacks based on identity forgery can be identified, such as man-in-the-middle attacks. Different types of identities are introduced (e.g. account as an ID, address as an ID, tag as an ID), and some mechanisms of identity authentication are introduced. The “lightweight” feature of identity authentication is addressed. Such feature is to accommodate the security requirements of resource-constrained IoT devices.
Relating Entropies of Quantum Channels

In this work, we study two different approaches to defining the entropy of a quantum channel. One of these is based on the von Neumann entropy of the corresponding Choi-Jamio{\l}kowski state. The second one is based on the relative entropy of the output of the extended channel relative to the output of the extended completely depolarizing channel. This entropy then needs to be optimized over all possible input states. Our results first show that the former entropy provides an upper bound on the latter. Next, we show that for unital qubit channels, this bound is saturated. Finally, we conjecture and provide numerical intuitions that the bound can also be saturated for random channels as their dimension tends to infinity.
High Density Multi-Chip Module for Photonic Reservoir Computing

Simulation and experimental results of a time-delay photonic reservoir computer based on VCSEL and photodiode chips with multi-mode waveguide splitter and combiner is presented. The design is based on technology developed for a high bandwidth density optical mulit-chip module with polymer waveguides for optical interconnect applications. The number of virtual nodes in the reservoir can be adjusted by varying the feedback delay time and the input signal rate. Results on distorted signal recovery and non-linear waveform generation tasks are presented and are promising for the development of a platform for board-integrated photonic neural network architecture.
Person Re-identification Based on Hash

Person re-identificaton aims to retrieve interested person objects from the person image database in cross camera scene, which has a wide range of application value in the field of video surveillance and security. With the generation of massive monitoring data, the retrieval speed of person re-identificaton is required to be higher. Facing the problem of the slow retrieval speed of person re-identification in large-scale monitoring data, we propose a person re-identification method based on hash. When training hash mapping function, we innovatively adds a batch hash code learning (BHL) module in the network to generate hash code as supervision information, which contributes greatly in retaining similarity information between person image pairs. Although the retrieval speed gets improved due to the concise binary hash features from hash mapping function above, we still need to retain the high accuracy at the same time and a coarse-to-fine (CF) retrieval strategy is proposed. Experiments on two public person re-identification datasets, Market-1501 and DukeMTMC-ReID, show the effectiveness of the proposed method. Compared with the benchmark model, the performance of our method is only 0.5% lower in mAP, but higher by 0.3% in Rank-1 on Market-1501 dataset. And on DukeMTMC-ReID dataset, Rank-1 and mAP are only 0.3% and 0.4% lower. However, the retrieval speed of the two datasets is increased by 12 times and 16 times respectively.
Distant Supervised Relation Extraction based on Dense Neural Network with 2-Level Attention Mechanism

Dense connected convolutional neural network (Dense Net) is a new architecture of deep convolutional neural network, which ensures the maximum information transmission between network layers by establishing the connection relationship between different layers. In the task of text remote supervised relation extraction, aiming at the limitation of existing neural network methods using shallow network to extract features, a deep convolution neural network model with dense connection based on attention mechanism is designed. In this model, the dense connection module and maximum pooling layer composed of 5 layer convolutional neural network are used as the sentence encoder. By combining the lexical, syntactic and semantic features of different levels, it helps the network to learn the features, so as to obtain more abundant semantic information of the input sentence. At the same time, it reduces the gradient vanishing phenomenon of deep neural network, and makes the network more capable of representing natural language Strong. Secondly, the word level attention is introduced to calculate the relevance between two entities and context words, so as to fully capture the semantic information of the entity context in the sentence; then the sentence level attention is constructed on multiple instances to reduce the problem of label error labeling; finally, the dependency inclusion relationship between different relationships is automatically learned through the attention of the relationship level. The average accuracy of the model on the NYT freebase dataset is 83.5%. Experimental results show that the model can effectively use features and improve the accuracy of remote supervised relationship extraction.
Suspicious Mass Detection Algorithms In Mammograms

In the recent years the death rate due to breast cancer among women has increased significantly and now it is a recognized world health problem. Early detection and treatment can reduce the death rate of breast cancer effectively. Presently numbers of imaging techniques are available for detection of breast cancer. Mammography test is the most efficient and reliable to find the breast cancer early. But finding and detecting breast cancer on mammogram is repetitive, tiring and fatigue obligation to radiologist; hence sometimes it may be overlooked. Therefore, smart Computer-Aided Detection system require to be extended and combined in new way in order to provide automatic detection of suspicious mass that meets the needs of medical application to point out the occurrence of breast cancer. Suspicious mass detection accuracy can be improved, which will assist the radiologist to classify the breast cancer. This paper presents algorithms to detect the suspicious mass in mammogram image, and also extract GLCM features of suspicious mass. These extracted GLCM features are graphically represented and based on the variation of these features; the mammogram is analyzed and classified as malignant and non-malignant.
An Effective Analysis of Data Clustering using Distance-based K- Means Algorithm

Abstract Real-world data sets are regularly provides different and complementary features of information in an unsupervised way. Different types of algorithms have been proposed recently in the genre of cluster analysis. It is arduous to the user to determine well in advance which algorithm would be the most suitable for a given dataset. Techniques with respect to graphs are provides excellent results for this task. However, the existing techniques are easily vulnerable to outliers and noises with limited idea about edges comprised in the tree to divide a dataset. Thus, in some fields, the necessacity for better clustering algorithms it uses robust and dynamic methods to improve and simplify the entire process of data clustering has become an important research field. In this paper, a novel distance-based clustering algorithm called the entropic distance based K-means clustering algorithm (EDBK) is proposed to eradicate the outliers in effective way. This algorithm depends on the entropic distance between attributes of data points and some basic mathematical statistics operations. In this work, experiments are carry out using UCI datasets showed that EDBK method which outperforms the existing methods such as Artificial Bee Colony (ABC), k-means.
Credit Risk Prediction of Telecom Users based on Model Fusion

With the development of information technology and communication industry, there is a phenomenon of malicious arrears of some telecom users, which brings economic losses to telecom operators. To solve the above problems, this paper proposes a credit risk prediction method for telecom users based on model fusion. We utilized three popular and diverse classifiers as the individual learner, including k-nearest neighbor (KNN), random forest and extreme gradient boosting (XGBoost), the results of the three classifiers were then fitted using logistic regression. To verify this model, real data from Chinese telecom users were used in a test. According to the experimental results, the proposed fusion model significantly outperforms the compared methods in terms of recall rate, F1 value and AUC value, which can effectively predict the credit risk of telecom users.
ARTIFACTS INFORMATION MANAGEMENT SYSTEM (AIMS) FORDIGITAL FORENSICS (AIMS for Digital4N6)

Digital Forensics is a systematic approach for recovering the contents from the digital sources and analyzing their evidence contents for detecting the cyber crimes. Presently cyber security issues are increasing around the globe, for this reason, it is essential to have digital forensic experts to handle the situation both technically and legally. During Investigation process, different types of evidences are collected from various digital source in the network environment. Artifacts are the areas to hold information about the digital activities performed on the computer system, networking environment or in any digital infrastructure set-up. The Artifacts Information Management System (AIMS) is intended for use by the Digital Investigators and their team members for the digital evidences analyses during the investigation process. The main idea of this system is to store and retrieve the digital forensics analysis information from a simple database which can be viewed and to understand the artifacts from digital sources.The present paper discusses briefly about thedesigning aspects of Artifacts Information Management System for Digital Forensics (AIMS) and its advantages.