Reviewerly/exHarmony huggingface dataset:
Simple authentication methods with pseudo IDs and derivative passwords

Nowadays, the endless stream of unauthorized login incidents that were thought to use a list of user IDs and passwords continued to leak in ID incidents. As a result, there is a growing awareness of the dangers of authentication methods that use only IDs and passwords, and new identity management technologies that incorporate other authentication methods are drawing attention [1]. Consequently, there is a wide variety of specifications related to the management and coordination of IDs used on the Internet. Because each is developed based on different concepts, it is necessary to select the one most appropriate for the environment it will be used in. It also encompasses an extremely diverse range of approaches, such as user authentication, issuing, and usage of credentials, management and circulation of attribute information, access control for various resources [2], and delegation of authority [3], as well as technology for coordinating this information between different entities. This paper discusses simple authentication methods with pseudo IDs and derivative passwords and we also organize the Identity technology, propose a new concept called derivative Tokens and IDs.
Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis

Diagonalization is an “age old” technique from Linear Algebra, and it has had significant applications in Pattern Recognition (PR) and data pre-processing. By using the eigenvectors of the covariance matrix of a single class as the basis vectors describing the feature space, the transformed data can be rendered to have a diagonal covariance matrix. If the covariance matrices of two classes are utilized, the covariance matrix of transformed data of the first class can be the made the Identity, while that of the second can be diagonal, implying independence in the case of Normally distributed data (Similar diagonalizing schemes form the basis of the Principal Component Analysis (PCA) and some feature selection/reduction etc. schemes.). In all of the cases reported in the literature, the entire covariance matrix is diagonalized, which is, computationally, a very tedious and cumbersome process. In this paper, we propose a radically different paradigm where we opt to render the transformed data to be block diagonalized. In other words, the covariance of the transformed data is made up of a predetermined number of block matrices, implying that these corresponding features are assumed to be correlated, while the others are assumed independent. Regression is now done by getting the best value based on each of these sub-blocks and averaging between them. This is essentially an ensemble machine, where the sub-blocks lead to their own respective regression values, which are then averaged to obtain the overall solution. This technique has been used to analyze the survival rate of cancer patients depends on the type of cancer, the treatments that the patient has undergone, and the severity of the cancer when the treatment was initiated. In our prima facie study, we consider adenocarcinoma, a type of lung cancer detected in chest Computed Tomography (CT) scans on the entire lung, and images that are “sliced” versions of the scans as one progresses along the thoracic region. The results that we have obtained using such a block diagonalization are quite amazing. Indeed, they surpass the results obtained from some of the well-established feature selection/reduction strategies.
Cartoon image colorization based on emotion recognition and superpixel color resolution

With the development of artificial intelligence technology, it is possible to automatically colorize the hand drawn sketch by machine. Researchers have conducted in-depth and meticulous study on hand-drawn manuscript recognition, generation, and retrieval. For the emotion-based line art colorization, facial expression should be also extracted from the image itself. To solve the recognition and colorization problem, this paper has proposed an algorithm with the DenseNet network for emotional recognition of anime faces and performed a two-stage interactive coloring method in view of superpixel color analysis features. Among them, the superpixel color analysis technology used a simple linear iterative clustering of SLIC algorithm. According to the experiment, the prompt color information of corresponding position could be generated through the emotion recognition result. After the prediction of superpixel color analysis by GAN (generative adversarial network), the original cartoon image could be rendered with suitable color scheme. The visualization results proved that our algorithm proposed would effectively realize the emotion-based line art colorization of high interactivity and reasonable color distribution.
A traffic recognition based on artificial neural networks in SDN systems

This paper presents a traffic recognition, the basis of QoS, based on artificial neural networks in SDN systems. To the design methodology of systems, this paper applied successive semi-supervised learning and based upon Convolutional Neural Network (CNN) to enabling packet identification and determine the traffic type of a flow. The resulting reveals up to ninety-five percent of correctness of given/labelled flow recognition and a ninety-two percent of correctness for unknown flows.
Development of Student Character Achievement Index Web using Combination Method of Rational Unified Process and Goal Directed Design

Student Character Achievement Index (SCAI) is an indicator used as a standard assessment of student character while being a student of the Politeknik Statistika STIS. In addition, “Leadership Perspective” is used as an alternative assessment aimed at students who have participated in the organization. SCAI Web was built to facilitate SCAI and a leadership perspective management. SCAI Web is expected to be a good management system for student activities. In its implementation, the problems found include that the SCAI database has not been integrated with the SIPADU-STIS database (i.e. database of the Integrated Information System in Politeknik Statistika STIS); the activation of dimensions, categories and subcategories cannot handle dynamic changes; SCAI application processing is semi - manual; there is no organizational structure adjustment in the assessment of the leadership perspective; SCAI results presented during monitoring were not presented in detail; as well as the need for an assessment of the SCAI web user interface. This study aims to analyze the interface with the Goal Directed Design method and the development of the proposed system using the Rational Unified Process method. The results of the proposed system test show that the proposed web meets the needs and deserves to be recommended as the SCAI website.
Measurement-Aided Cubature Weight Correction GM-PHD algorithm for Muti-target tracking

So as to improve the accurate of multi-target tracking, this paper proposes a Measurement-Aided Cubature Weight Correction GM-PHD (CKF-MCGMPHD) algorithm. The algorithm introduces the measurement auxiliary weight correction into the GM-PHD algorithm, and uses the estimated value to correct the updated Gaussian weights to fix the Gaussian weights, so as to further improve the accuracy of the filtering algorithm, and reduce the calculation at the same time; The CKF method is introduced into the GM-PHD algorithm, and uses cubature point to predict and update the GM-PHD, so as to improve the nonlinearity and stability of the filtering algorithm. The simulation results show that compared with the traditional algorithm, CKF-MCGMPHD can obtain more accurate target tracking results under different clutter environments, and the computational complexity is smaller.
Design of Autonomous Navigation System for Quadrotor in Subway Tunnel

Aiming at the subway tunnel environment with weak light and no GPS signal, we build a hardware platform of quadrotor based on lidar, airborne upper computer and bottom flight control board. Combined with HectorSLAM technology, we also design a route planning algorithm based on the improved Rapidly-exploring Random Tree (RRT) and Artificial Potential Field Method (APF), in order to realize intelligent inspection of the subway tunnel. Firstly, for the path planning of large scene map, an improved algorithm Rapidly-exploring Random Tree with Regression Filtering Mechanism and Trunk Constraint (ReT-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) is proposed to reduce the scope of invalid search space, avoid excessive search of explored areas and effectively save path planning time. Secondly, we use cubic B-spline to optimize the planned route generated by ReT-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and improve tracking accuracy. Thirdly, we introduce `route point' gravity to improve traditional APF, in order to realize online obstacle avoidance combined with the planned route. Finally, we carry out experiments to verify the effectiveness of the improved methods and the feasibility of the design of autonomous navigation system.
Expert System in Clustering the Damage of a Motorcycle Matic with the K-Means Algorithm

Abstract Research on Expert Systems to Diagnose Matic Motorcycle Engine Damage by applying Algortima K-means. Research to detect damage to an automatic motorcycle by observing the symptoms of an automatic motorcycle. This study aims to help Matic Motorcycle users to find out about damage to the automatic motorcycle based on the results of the application of the K-Means algorithm without having to meet with experts directly. Research by applying Algortima k-Means by forming groupings based on electrical damage, compression and engine performance, continuous variable transmission timing and automatic sensors so that the damage can be grouped. The results of the expert system research with the K-Means Algorithm can help matic motorcycle users to find out the type of damage based on the grouping that has been determined by the K-Means algorithm.
Multi-label Image Classification Optimization Model Based on Deep Learning

In order to meet the needs of diversified image information retrieval in the real world, to solve the “semantic gap” problem of image and natural language conversion, and to optimize the accuracy and efficiency of multi-label classification method, this paper proposes a deep learning and multi-label BR algorithm. An image classification method uses a residual neural network with better overall performance to extract image depth learning features, and takes the extraction result as an input, and generates a result vector through spatial regularization of the image space and the label, and the result is obtained. The elements are added as the final prediction result by directly using the residual network prediction result. The whole network is trained by the softmax loss function. Compared with other traditional models, the spatial relationship results of the labels provide a good regularization effect for multi-label image classification, which improves the accuracy on the NUS-WIDE dataset and its recall rate, etc.
Blockchain-Based E-Health Sharing and Management via Mobile Application

Information of any form is a valuable asset, and therefore its protection is one of the most important factors in order to avoid and combat misuse of such information. Consequently, personal health information is considered to be a vital resource. Personal health information are in the form of electronic health records (EHRs) and personal health records (PHRs). Therefore, applying the functionalities of blockchain can help protect the patient’s EHR, PHR and provide permission to access the patient’s information. Blockchain can provide improved data protection through the provision of a decentralized, distributed public record. The World Health Organization (WHO) defines healthcare to be one of the fundamental and basic human rights (World Health Organization (WHO): https://www.who.int/mediacentre/news/statements/fundamental-human-right/en/ ). It states that “the right to health for all people means that everyone should have access to the health services they need, when and where they need them, without suffering financial hardship.” However, it has been reported there has been a rise in deaths due to human error, inadequate patient details and inaccurate medical information. These calamities can be reduced through ensuring that the patient’s medical data are organized and protected such that it is available whenever required. Therefore, the aim of this paper is to provide the functionalities of blockchain using R3 Corda to protect the patient’s personal health information by providing the patient authorization to manage who can access their health records and accordingly provide permission based on the patient’s discretion.
Research and application of improved K-means based on MapReduce

Abstract With the development of big data, the traditional data mining clustering algorithm K-Means is inefficient and has poor scalability in dealing with massive data. MapReduce on the Hadoop platform was used to realize the parallel processing of the K-Means algorithm, the performance of the algorithm was tested by experiments. The results show that the improved K-Means algorithm has good parallel expansion capability, high efficiency, and great potential when processing big data mining. The algorithm is applied to the big data processing of customer consumption in a restaurant chain, and the effectiveness of the algorithm is verified, which can better serve the decision of restaurant.
Fingertip-Specific Mobile Interaction Through Camera-Based Fingertip Identification

Abstract In this paper, we propose to utilize camera-based fingertip identification to enable fingertip-specific mobile interaction without hand occlusion. The core idea of the proposed fingertip identification method is to utilize features, whether innate or got-up, on a fingertip to recognize its identity rather than utilize hand information to infer the fingertip’s identity. A fighter game prototype is presented to demonstrate the interaction idea. Possible ways to realize fingertip identification and how to utilize fingertip identification to enable richer interactions on mobile devices are also discussed.
Effects of Volatility and Trend Indicator for Improving Price Prediction of Cryptocurrency

Abstract The purpose of this research is to identify how effective the determinants of the improved price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then we treated them as time series data to run dynamic trend indicator and first-differencing volatility regression model. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are predictable. It is easy to say that major stakeholders (Apple, Amazon, Facebook, Google, Tesla) affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is strong. We have evaluated all the major cryptocurrency prices with exact accuracy of 95.38% using the volatility regression model effectively. All the cryptocurrencies are evaluated against US dollars in regard of different cryptocurrency like Bitcoin, Ethereum, Litecoin and Ripple digital currency. Cryptocurrencies shouldn’t be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.
Provenance-based Classification Policy based on Encrypted Search

As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as “public accessible”, which can be accessed by the public.
Compilation and Application of Graphics Textbook Based on AR Technology

In recent years, with the continuous development of information technology, information-based hybrid teaching method is accelerating into the classroom. As a link between two-dimensional (2D) books and three-dimensional (3D) entities, 3D models and 3D animations, Augmented Reality (AR) technology is playing an unparalleled advantage in the construction of informatization teaching materials. Because of the need for abstract spatial imagination to understand spatial structure and geometry knowledge, graphics course has always been difficult for some students, and some students’ interest in learning is relatively low. The application of AR technology in the creation and compilation of graphics teaching materials can solve this problem. The creation and compilation of Graphics Textbook Based on AR technology needs the cooperation of cartographers, software technicians and publishing editors. The content creation team composed by the teachers of cartography course is the knowledge-based main body of the drawing textbook production based on AR technology; the technology company, as the software support team, is the technical main body of the drawing textbook production based on AR technology; the publishing editor is responsible for the organization and implementation of the new form of teaching material, and is the organizational main body of drawing textbook production based on AR technology. AR technology enhances the 3D presentation of the content of the teaching material, interesting animation demonstration, the deconstruction reappearance and rotation change of the 3D structure. It also strengthens the knowledge of the graphics textbook, improves the ease of learning, enhances the interest of learning, and helps students understand. The technical team develops the supporting app and presents the resources in the terminal equipment. The updating and maintenance of resource data need to be completed in the background database, and the sharing of resources plays an important role in promoting the integration of media, promotion and use of graphics teaching materials.
A Learning Based Factorization Approach for Uniform Distribution of Data in M2M Communication

Now-a-days, Machine-to-Machine (M2M) communication can be accomplished with execution of Internet-of-Things (IoT) that is carried out in wireless networks. Extensive M2M access in leads to BS congestion that causes access probability and delay deterioration. Class bar can directly influences flow of machine based communication with controlling factors to eliminate overload. Here, randomly accessibly resources are shared between H2H or M2M communication devices, various investigation based on this controlling scheme considers only limited preambles allocated to M2M traffic. Moreover, whenever communication lacks in massive accessibility in M2M communications, it is appropriate to quickly fulfil requests from devices with accessible preambles, specifically time based IoT environment. To overcome this, a novel Factorization function (FF) with transformation is introduced to Learning automata which leads to enhanced LA algorithm that is specified as FF-LA in this work. Simulation outcomes depict that FF-LA scheme can work effectually to control traffic and can attain theoretical optimality. BS implementation with FF can significantly control M2M traffic by dynamically handling class bar with H2H traffic interference and offer quality of service to both H2H and M2M traffic.
Design of a Commercial Vehicle Monitoring System Based on Multiple Sensors

The paper describes a design of commercial vehicle monitoring system (CVSM) which can help to promote transportation safety and improve traffic efficiency. To meet the demand of practical application, we analyze functional requirements and set the general objective and basic routes of system design. The structure design consists of three main parts, namely, on-board terminal design, mobile application development and abnormal driving behavior detection. The real-time performance, reliability, and stability of the system are verified through experiments.
VarSem: declarative expression and automated inference of variable usage semantics

Programmers declare variables to serve specific implementation purposes that we refer to as variable usage semantics (VUS). Understanding VUS is required for various software engineering tasks, including program comprehension, code audits, and vulnerability detection. To help programmers understand VUS, we present a new program analysis that infers a variable's usage semantics from its textual and context information (e.g., symbolic name, type, scope, information flow). To support this analysis, we introduce VarSem, a domain-specific language, in which a variable's semantic category is expressed as a set of declarative rules. VarSem's execution determines which program variables belong to a given semantic category. VarSem translates high-level declarative rules into low-level program analysis techniques, including natural language processing and data flow, and provides a highly extensible architecture for specifying new rules and analysis techniques. We evaluate VarSem with eight real-world systems to identify their personally identifiable information variables. The evaluation results show that VarSem infers variable semantics with satisfying accuracy/precision and passable recall, thus potentially benefiting both software and security engineers.