Reviewerly/exHarmony huggingface dataset:
Low Resolution Recognition of Aerial Images

Remote sensing for classification has been widely studied and is useful for a lot of applications like precision agriculture, surveillance, and military applications. Recently, due to tremendous results achieved by deep learning using Convolutional Neural Networks (CNN) for Imagenet dataset, there have been a large number of works which use deep learning for aerial image classification. Most of the works concentrate on original resolution and there are no works on low-resolution recognition of aerial images. This work is critical because aerial images are taken from a very high distance from the ground and the cost of installing high definition cameras is high, so it is hard to get a high resolution of the image. In this paper, we explore how we can do the better classification of aerial images for original spatial resolution and low spatial resolution in deep learning by using texture information. In our framework, we use YUV color space which is generally used for video coding and we also use Laplacian of Gaussian (LOG) information to exploit the texture information. We decouple RGB information into luminance information (Y channel), color information (UV) and texture information (LOG) and we train a separate CNN for each feature and combine them using autoencoder and with our results, we show that we do better than RGB images in original resolution and low resolution.
A Cooperative Method of Task Scheduling based on FPGA Cloud Platform

A cloud platform based on FPGA acceleration cards cluster and servers cluster is proposed in this paper. Multi-type boards in the cloud platform could provide multi-type application acceleration services to meet the needs of different tenants. Therefore, we propose to design an optimal task scheduling and service scheme to maximize tenants satisfaction and minimize operators benefits loss. Firstly, the problem of task scheduling is considered as a ‘max-min’ joint optimization problem. Then it is transformed into a bi-level optimization problem with the lower as tenants' satisfaction maximization problem and the upper as FPGA cloud platform operators benefits loss minimization problem. Finally, because the lower problem is convex, the MATSuMoTo algorithm is used to obtain the best task scheduling strategy of the cloud platform.
Single view based nonlinear vision pose estimation from coplanar points

In this paper, an iterative algorithm is proposed to determine the pose of a planar target. The algorithm relies on the planar target whose size is known, and the pose of the target can be recovered by using coplanar points from only a single image. The iterative optimization algorithm includes two steps: first, the rough pose parameters are calculated by using the least square method based on the 2D-to-3D point correspondences, then, the rough pose is used as the initial guess for the optimal iterative algorithm which minimizes the reprojection errors for coplanar image points to determine the exact pose solution. Moreover, the corresponding image processing algorithm is addressed. The pose determination algorithm includes edge detection, line extraction, pose computing, etc. Simulation results concerning the effectiveness and accuracy of the method are presented; furthermore, experiments carried out on a variable geometry truss robot manipulator with eye-in-hand camera configuration for the autonomous capturing task demonstrate the robustness of the proposed method in the real environments.
Decentralized Authorization and Authentication Based on Consortium Blockchain

With the development of digital society, the number of Internet platforms increases rapidly and a huge amount of personal information is stored online. It is convenient for users to log in to all platforms with a common account. Third-party authorization protocols like OAuth 2.0 allow the delegation of access control to dedicated service providers. However, OAuth protocol follows the centralized approach to manage authorization and authentication information, which relies on a centralized party and makes it a target under attack. In practice, it is vulnerable to attacks like replay attack, cross-site request forgery (CSRF) attack, and so on. Also, the centralized party cannot provide customized access control for other platforms. To solve these problems, the paper proposes a consortium blockchain architecture and designs protocols for account management and distributed consensus. The paper discusses the potentials of the proposed approach to effectively address certain vulnerabilities in current OAuth-like authorization and authentication services with tolerable performance.
SAR image processing based on similarity measures and discriminant feature learning

Synthetic aperture radar (SAR) images have attracted much attention due to their all-weather operation, high spatial resolution, and recent improvements in processing these images. Earth Observation (EO) satellites produce a large number of SAR images every day. It is a difficult task to find useful information from these SAR images quickly and accurately. Several satellite systems in orbit collect a large number of SAR images daily. Analytical methods are needed to effectively use them to retrieve information needed by various users. Traditional remote sensing image classification schemes need to describe metadata of remote sensing images, such as radar parameters and image description parameters (size, date, sensor, time, etc.). However, these metadata are insufficient to fully process SAR images to provide useful information. One solution is content-based image retrieval (CBIR) programs.
Best (and Worst) Practices for Organizing a Challenge on Cardiac Biophysical Models During AI Summer: The CRT-EPiggy19 Challenge

During the last years tens of challenges have been organized to benchmark computational techniques with shared data. Historically, most challenges in conferences such as MICCAI have been devoted to medical image processing, especially on object recognition or segmentation tasks. Due to the increasing popularity and easy access to machine (deep) learning methods, as part of our current Artificial Intellingence (AI) summer, the number of AI-related challenges has exploded. In parallel, the community of biophysical models also has a valuable history of organizing challenges, including synthetic and experimental data, to assess the accuracy of the resulting simulations. In this paper, the similarities and differences in computational challenges organized by these communities are reviewed, suggesting best practices and what to avoid when organizing a challenge on biophysical models. Specifically, details will be given about the preparation of the CRT-EPiggy19 challenge.
Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection

Pedestrian detection is an indispensable technology for designing autonomous driving systems. This paper proposes a cascaded sliding window based real-time approach to generate 3D pedestrian proposals on point clouds only. After rasterizing the raw point cloud, a 3D sliding window is adopted to extract pedestrian candidates. Two features are proposed to improve the proposal performance. One is the central points density feature, which acts as a filter to speed up the process and reduce false alarms. The other is the location feature, including the density distribution and height difference distribution of the point cloud, which describes the profile and location of an object in a sliding window. The scores generated by this feature are then applied to non-maximum suppression (NMS) to remove sub-optimal boxes. Experiments on the KITTI 3D object detection benchmark show that our approach achieves state-of-the-art results among comparable methods, while maintaining the speed and accuracy trade-off.
College Students’ Portrait Technology Based on Hybrid Neural Network

Students have produced a large number of data in the teaching life of colleges and universities. At present, the development trend of university data is to gradually form a high-dimensional data storage system composed of student status information, educational administration information, behavior information, etc. It is of great significance to make use of the existing data of students in Colleges and universities to carry out deep-seated and personalized data mining for college education decision-making, implementation of education and teaching programs, and evaluation of education and teaching. Student portrait is the extension of user portrait in the application of education data mining. According to the data of students’ behavior in school, a labeled student model is abstracted. To address above problems, a hybrid neural network model is designed and implemented to mine the data of college students and build their portraits, so as to help students’ academic development and improve the quality of college teaching. In this paper, experiments are carried out on real datasets (the basic data of a college’s students in Beijing and the behavior data in the second half of 2018–2019 academic year). The results show that the hybrid neural network model is effective.
A Speckle Reduction Method Based on Coupled Partial Differential Equation for Synthetic Aperture Sonar

Aiming to the problem of losing detailed information of Guass pre-filter in traditional partial differential equation (PDE) method, a speckle reduction method based on coupled PDE for synthetic aperture sonar (SAS) images is proposed in this paper. The proposed method introduce edge strength function in filter processing, which can keep the edge as same as initial image in filter. The evaluation has been carried out using the equivalent number of looks (ENL), edge preserve index(EPI) and image variance as the criteria. Through speckle reduction experiment of real SAS image, comparing the result of proposed method with traditional PDE method and classical Lee filter method, the efficiency of the proposed method is verified.
Strengthened PAKE Protocols Secure Against Malicious Private Key Generator

At WISA 2015, Choi et al. [9] proposed an identity-based password-authenticated key exchange (iPAKE) protocol using the Boneh-Franklin IBE scheme. In this paper, we revisit the iPAKE protocol [9] (and its generic construction) that has been standardized in the international standard committee ISO/IEC JTC 1/SC 27. First, we show that the iPAKE protocol is insecure against passive/active attacks by a malicious PKG (Private Key Generator) where the malicious PKG can find out all clients’ passwords by just eavesdropping the communications, and the PKG can share a session key with any client by impersonating the server. Then, we propose two strengthened PAKE (SPI and SPI-S) protocols that prevents such malicious PKG’s passive/active attacks. Also, we discuss security of the SPI and SPI-S protocols, and compare relevant protocols in terms of efficiency and security.
Optimization of giving employee craft assessment using artificial neural network with Hebb algorithm

Abstract One of the most important things in human resource management is employee discipline that can be seen in employee presence. It is very important for companies to set up employee attendance systems properly. In general, benefits - allowances are always associated with employee precession data, so that the results of the calculations became accurate and balanced with employee performance. As is the case with craft benefits, this craft allowance is held for companies to improve employee discipline, especially in presence or presence. Based on the background of the above problems, a problem can be formulated, namely the determination of the model in the process of determining the recipients of employee craft benefits so that the monthly income is optimal and significant. Based on the problem, the authors are interested in redeveloping the final project with the optimization of giving employee craft assessment using the hebb rule neural network method.
A New Robust and Reversible Watermarking Technique Based on Erasure Code

With the growth of Information and Communication Technology, data sharing plays a pivotal role in all parts of the Internet. A major issue that needs to be tackled urgently is to protect the ownership of data during the sharing process. Digital watermarking technology is a major solution to ownership protection. Many reversible watermarking approaches are proposed to achieve the function of ownership protection, but most methods can only resist malicious attacks to a certain extent and can not regenerate the complete watermarks in case part of watermarks are destroyed. In this paper, a robust and reversible watermarking technique (RRWEC) applied in relational database has been proposed, which utilizes the Erasure Codes Technique and watermarking in groups. The watermarks embedded into data come from meaningful characters of identification information of a database owner. The grouping method based on clustering, without dependent primary keys, is utilized to group the data, which can resist attacks on the data structure. The erasure code technique is devised to regenerate the complete watermarking information when some sub-watermarks embedded into data are maliciously modified or removed. The results of experiments verify the effectiveness and robustness of RRWEC against malicious attacks, such as data structure attack, subset addition attack, subset alteration and deletion attack, and show that when half of the watermarks are destroyed, the watermark regeneration algorithm can still recover the complete watermarking information.
Learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot

Deep neural networks facilitate visuosensory inputs for robotic systems. However, the features encoded in a network without specific constraints have little physical meaning. In this research, we add constraints on the network so that the trained features are forced to represent the actual twist coordinates of interactive objects in a scene. The trained coordinates describe 6d-pose of the objects, and SE(3) transformation is applied to change the coordinate system. This algorithm is developed for a mobile service robot that imitates an object-oriented task by watching human demonstrations. As the robot has mobility, the video demonstrations are collected from different viewpoints. Our feature trajectories of twist coordinates are synthesized in the global coordinate after SE(3) transformation is applied according to robot localization. Then, the trajectories are trained as probabilistic model and imitated by the robot with geometric dynamics of se(3). Our main contribution is to develop a trainable robot with visually demonstrated human performances. Additionally, our algorithmic contribution is to design a scene interpretation network where se(3) constraints are incorporated to estimate 6d-pose of objects.
The Transformation from Traditional Application to Blockchain-Based Application

Traditional application uses centralized database and the data is managed and maintained by a single institution. Blockchain is a decentralized, non-tamperable, traceable, multi-party distributed database, which can greatly improve the security of data. It is crucial to transfer traditional application to blockchain-based application for many application scenarios. However, as a new field, most developers are not familiar with blockchain technology. Therefore we propose a method to transfer the traditional application to application based on Hyperledger Fabric. First, we finish the secondary development of Fabric-sdk-java and define a set of API mapping rules. Secondly, we compare the storage model between Mysql and CouchDB, and implement a SQL-Fabric transformation engine through the smart contract, which can parse SQL and automatically transform into read and write operations on the distributed ledger.
Exact Algorithm for One Cardinality-Weighted 2-Partitioning Problem of a Sequence

We consider a problem of 2-partitioning a finite sequence of points in Euclidean space into two clusters of the given sizes with some additional constraints. The solution criterion is the minimum of the sum (over both clusters) of weighted intracluster sums of squared distances between the elements of each cluster and its center. The weights of the intracluster sums are equal to the cardinalities of the desired clusters. The center of one cluster is given as input, while the center of the other one is unknown and is determined as a geometric center, i.e. as a point of space equal to the mean of the cluster elements. The following constraints hold: the difference between the indices of two subsequent points included in the first cluster is bounded from above and below by given some constants. It is shown that the considered problem is the strongly NP-hard one. An exact algorithm is proposed for the case of integer-valued input of the problem. This algorithm has a pseudopolynomial running time if the space dimension is fixed.
The Cryptocurrency Simulation using Elliptic Curve Cryptography Algorithm in Mining Process from Normal, Failed, and Fake Bitcoin Transactions

On each cryptocurrency transaction, a high-level security is needed to protect user data as well as data on the transaction. At this stage, it takes the appropriate algorithm in securing transactions with more efficient processing time. The Elliptic Curve Cryptography (ECC) is one of the cryptography algorithms which has high-level security, and ECC is often compared with the Rivest, Shamir, and Adleman (RSA) algorithm because it has a security level that is almost the same but has some differences that make ECC is superior compared to the RSA algorithm, so that the ECC algorithm can optimize cryptocurrency security in the transaction process. The purpose of this study is to simulate the bitcoin transactions using cryptography algorithms. This study uses the ECC algorithm as the algorithm ECDH and ECDSA key exchange as the algorithm for signing and verifying. The comparison results of ECC and RSA processing time is 1:25, so the ECC is more efficient. The total processing time of ECC is 0,006 seconds and RSA is 0,152 seconds. The researcher succeeded to implement the ECC algorithm as securing algorithms in mining process of 3 scenarios, normal, failed, and fake bitcoin transactions.
Matching User Accounts Across Social Networks Based on LDA Model

Identifying users across social networks has received more and more attention in recent years. In this paper we propose a model that combines ATM and DTM. The model uses the ATM model to study the user's potential behavior information and extract user's topic preferences. Then it analyzes the trend of user topic preferences changing with time through the DTM. The purpose of this paper is to improve the accuracy of account identification across social networks. The experimental results show that the model performs well on Chinese text or mixed text in Chinese and English because it obtains higher accuracy and F1 scores.
A Proposed Blockchain Model to Enhance Student Knowledge

As the education becomes more competitive in nature, students need to learn many other subjects or courses by interest or need. We identified the multidisciplinary knowledge of the students and created interested groups based on the grades or points obtained through questionnaire. But this learning happens through online, in order to maintain security of the student information, we have proposed a blockchain model SIBC(Student Interest Blockchain). This model is useful to identify the student interests and creates a platform to fulfill the student interest along with his regular course. It was proposed to maintain separate blocks for each specialization area, and the student can join any one of the block based on the points got in the questionnaire. Each block consist of students, faculty experts, industry experts, alumni of that institution and all other resources available are helping the students to enhance his knowledge in that particular area.
Data democracy for psychology

Psychology is a discipline with a long past, but short history. Psychological influences are found in every discipline from fine art to abstract mathematics because psychological forces are at play within the human behavior that necessitated the inception of those disciplines. Likewise, most disciplines' understanding of psychology is fundamentally similar, though the language used to describe the same psychological considerations may vary between an engineer and a writer. In psychology, we look at how individuals develop a pattern of behavior over the different developmental stages and then scaffold in new information to fit into the evolving schema over time. This chapter takes a similar approach to investigating why and how contextual understanding is important in developing more efficient artificial intelligence.
Manual Audit for BitUnits Contracts

In the blockchain 2.0 era, smart contracts based on blockchain technology have been widely used in many fields such as sharing economy, digital payment, and financial asset disposal because of its dispersion, observability, verifiability and automatic execution. With the widespread application of smart contracts, the researchers gradually found many types of security problems, so the audit of smart contracts has become the vital way to ensure its security. This paper introduces the implementation mechanism of smart contract model, and summarizes 11 kinds of high frequency smart contract vulnerabilities, such as transaction order dependence, constructor out of control, denial of service, etc. Then, this paper selects the newly released BitUnits contract for auditing, find out its security hole and give the solution.
An Approach to Online Fuzzy Clustering Based on the Mahalanobis Distance Measure

The manuscript gives consideration to the problem of fuzzy clustering data streams. The offered approach incorporates the concepts of the probabilistic fuzzy clustering based on the specific sort of distance metrics. The main emphasis of the study was put on the application of Mahalanobis measures in the fuzzy clustering algorithms that let design classes of a hyperellipsoidal shape which can change the orientation of their axes in a feature space. The substantial hallmark of the presented fuzzy clustering scheme is its aptitude to group data in a sequential style on the assumption of the fact that groups have an arbitrary shape (which cannot typically be classified linearly) and to be mutually intersecting.
Analysis of the formation of a dynamic brief key algorithm RC4+ for file security

Abstract The development of technology, makes it easy for us to communicate and exchange information and can also have an impact on the development of cryptographic algorithms. An algorithm that is used to do encryption and decryption must not only be correct, but also must be used efficiently. An algorithm is said to be efficient if the algorithm uses relatively smaller memory and has a fast processing time. So far, many studies have discussed the efficiency of an algorithm. Regarding the measurement of time and memory space needed is known as algorithm complexity. The complexity of the algorithm itself can be interpreted as a number of computational steps carried out by an algorithm to solve problems. The RC4 + algorithm is a symmetric algorithm that is used to encrypt and decrypt messages, and use the same key in the encryption-decryption process. The key used is the RC4 + session key key. In the result, the amount of memory needed by the modified RC4 + Algorithm is more. Execution time or runtime The modified RC4 + algorithm for encryption and decryption is much faster than the original algorithm.