Reviewerly/exHarmony huggingface dataset:
Hand motion tracking based on gesture understanding using leap gesture for virtual 3D batik gallery

Abstract Virtual batik 3D gallery is designed as one of the efforts utilizations of digital-based on media for Industrial Revolution 4.0 where the flow of globalization is becoming sophisticated which emphasizes the digital economical pattern, artificial intelligence, big data, robotic. This research emphasizes on hand motion tracking based scientific knowledge on Gesture Understanding using Leap gesture for the 3-Dimensional Virtual Batik Gallery. Gesture-understanding based on Hand Motion Tracking which advocates Leap Gesture for a 3D Virtual Batik Gallery functions as an interactive virtual media replacing part of the functions Keyboard and Mouse process. This is aimed at varying the media packaging, especially for information providers in terms of how they can relay the information quickly to others, designing application, or documenting data. The method used in this research is Design and Development (D&amp;D) and required three steps involving the process of modelling the initial design (3D modelling) using Normal Map technique, the process of making gesture scenario in the form of FSM in every room, and gesture design using hand motion tracking in Leap Motion controller. The result of the study was in the forms of responses to hand motion tracking in a 3D batik gallery, which was grouped into 3 hand-gesture-understanding concepts: Tap Gesture with motion response description for GPU (16 mm/s 60 FPS), (10 mm / s 100 FPS), (5 ms/200 FPS), CPU (33 ms 30FPS), and (16 ms / 60 FPS); for describing and detecting Flyhand Gesture on GPU (16 mm/s) (60 FPS10 mm / s) (100 FPS5 m/s 200 FPS) on CPU (33 ms/30FPS) (16 ms / 60 FPS); for Trusther in (GPU = 16 mm/s) (60 FPS33mm / s) (100 FPS 16 ms / 60 FPS) CPU (33 ms 30 FPS 10 ms) (60 FPS 33 ms 5 ms 200 FPS); and for Hold Hand Gesture on CPU (33 ms 30 FPS) (66 ms 60 FPS) and the response on GPU (33 ms 30 FPS) (66 ms 60 FPS).
PR-based SAR Reconstruction Algorithm for Phase Noise Mitigation with Block-Processing

In practice, the imagery of synthetic aperture radar (SAR) can be seriously contaminate by the random phase noise caused by the frequency jitter of transmit signal and atmospheric turbulence. Through the traditional phase retrieval based SAR autofocus algorithms, the computation time will increase sharply by the raising size of scene. In this paper, we introduce block-processing to the problem of phase retrieval based SAR imagery to simplify the reconstruction. A new quick SAR imaging algorithm is proposed to reconstruct the SAR image with phase-corrupted data in high quality and can greatly reduce the running time. Experiments shows the significant performance in recovery of the SAR image of proposed approach and the ability of reducing the computational complexity.
Computational Secure ORAM (COMP SE-ORAM) with [omega](log n) Overhead: Amazon S3 Case Study – Random Access Location

Oblivious RAM (ORAM) is a strong-provable secured approach to accessing cloud stored objects. Many ORAM models have been proposed to improve storage, communication and privacy efficiency of the ORAM constructions with no much attention paid to a holistic approach to the oblivious cloud storage security. Statically secure ORAM models generally have the security overhead of ω̃(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) where n is the memory size. This paper proposes an ORAM model with computational security overhead of ω̃(log n) using Amazon Simple Storage (Amazon S3) as a storage model. Another benefit of this proposed model is its conceptual simplicity at implementation. The construction of this model is based on the ORAM model proposed by Kai-Min Chung, Zhenming Liu and Rafael Pass. The crucial ramification of this model is to reduce the overhead from ω̃(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) to ω̃(log n). Using the Amazon S3, security analysis was conducted with the result incurring ω̃(log n) security overhead. This is achieved by randomizing the location of the storage node.
A Hierarchy of Algebras for Boolean Subsets

We present a collection of axiom systems for the construction of Boolean subalgebras of larger overall algebras. The subalgebras are defined as the range of a complement-like operation on a semilattice. This technique has been used, for example, with the antidomain operation, dynamic negation and Stone algebras. We present a common ground for these constructions based on a new equational axiomatisation of Boolean algebras. All results are formally proved in Isabelle/HOL.
Construction of Correlation Analysis Model between Students' Body Measurement Index and Skeletal Muscle Quality Index

In order to quantitatively analyze the correlation between students' body measurement index and skeletal muscle quality index, to analyze the correlation between students' body measurement index and skeletal muscle quality index, and to improve students' skeletal muscle quality index. A correlation analysis model between students' body measurement index and skeletal muscle quality index based on quantitative feature analysis is proposed. The promoting factors and related efficacy of students' body measurement index to skeletal muscle quality index are analyzed by statistical feature analysis method, and the metabolic feedback characteristic coefficient of human body is tested. The quantitative analysis value of skeletal muscle quality index is constructed by grouping sample test method. Combined with mass spectrometry (ESI-MS) and infrared spectrum (IR) analysis method, the quantitative evaluation of skeletal muscle quality index is carried out. Big data information fusion method is used to evaluate the correlation between students' body measurement index and human health literacy, and fuzzy decision-making method is used to model the correlation relationship, and the correlation quantitative analysis of skeletal muscle quality index is realized. The results showed that the correlation between students' body measurement index and skeletal muscle quality index is significant (P > 0.5). The results of correlation analysis between students' body measurement index and skeletal muscle quality index are accurate and had good quantitative analysis ability.
The Research on Multi-angle Face Tracking Based on Multi-feature Fusion and KCF

A method for multi-angle face tracking based on multi-feature fusion and KCF algorithm is proposed in this paper. Firstly, Haar-like, MB-LBP and HOG features are combined to detect the face region, which is regarded as the initial tracking search window. Secondly, training, updating and adjusting search window size. Finally, if the tracking target is lost, face region is relocated through the multi-angle detection algorithm which can realize real-time and automatic tracking. To verify the effect, our method is compared with the traditional KCF algorithm. The results of simulation experiments show that, the method proposed can track multi-angle face of video sequences from video database in real time, the center distance error is 8.49, the overlap rate is 58.78%, and the tracking time is 55.41ms.
Attitude Survey of the Technical College (KOSEN) Student Concerned with University-industry Research Collaboration Local Brands Products Development

This report describes the results of two times of questionnaires for the attitude survey of the student concerned with local brands products development. Intended to make use for future university-industry research collaboration product development by examining a result. The local brands products development may contribute to regional activation. In addition, it is hoped that it is useful for the carrier formation of the student by having you participate in a high school student and a student for university-industry research collaboration. As a result, it is necessary for a student to know a variety of “local brands” and “success examples”. If a student gets “knowledge” and “a technique” and “confidence” by learning a success example of the development about local brands products, a student is more likely to be interested. Because a student participates eagerly, the development of local brands products may be promoted.
On Constrained Modified Cramér-Rao Lower Bounds for Non-Standard Deterministic Estimation

In deterministic parameter estimation, the constrained Cramér-Rao bound (CCRB) is of great interest for analysis and design of a system of measurement in the asymptotic region. However in many problems, the probability density function (p.d.f.) parameterized by the unknown deterministic parameters is expressed as the integral of a joint p.d.f. depending on random variables as well, which prevents from using the CCRB. Based on a framework lately introduced to tackle theses problems, various possible forms of a constrained modified CRB (CMCRB) are introduced and compared with the existing litterature.
Research on the Safety Prediction Method of Long Jump in Big Data

The long jump has a great influence on sports events, but the long jump athletes are vulnerable in training and their safety cannot be guaranteed. Therefore, this paper puts forward the research on the safety prediction method of long jump movement under big data. Through the research on the sport form characteristics of long jump athletes, this paper analyzes the athletes' physical quality and training mode, so as to develop the method to guarantee the safety prediction of long jump athletes. This paper makes a comparative analysis on the safety prediction methods of big data long jumpers. The experimental data shows that the safety prediction methods of big data long jumpers are 16.8% higher than the traditional methods. The prediction accuracy is better, the prediction time is shortened, and the safety of athletes can be better guaranteed(Abstract).
Modelling the Integration of Costs and Benefits During Decision Making

In this paper a computational cognitive model for decision making based on cost-benefit comparison is presented. The brain weighs costs against benefits by combining reward and loss signals into a single, difference-based neural representation of net value. The presented model integrates such findings of the literature and is able to explain a person’s decision making behavior through several scenarios.
Chinese Lexical Based Sentiment Analysis Framework in Meteorology

Evaluating the performance of meteorological service not only relies on physical measurements but also requires public feedback. As a significant part of the evaluation, assessing the sentiment of these feedbacks gives an insightful view which can help to establish reliable decision support in policy making and emergency responding. However, climate and weather are always accompanying phenomena in the evolutionary process of human beings, making the climate and weather-related words themselves contain a large number of extend meanings in people's daily expression. These polysemy phenomena have led to a decline in the performance of traditional general sentiment calculations in the vertical field of meteorology. As far as we know, there is no Chinese sentiment calculation tool in the meteorological field, due to the different expressions between different languages. Facing with the above problems and issues, this paper proposes and establishes a sentiment analysis based on lexicon-based framework of Chinese text in the meteorological field. The framework fuses two major sentiment assessment method consists of two approaches, namely machine learning-based and knowledge based one. First, word embedding similarity and word co-occurrence methods are utilized to construct the domain sentiment dictionary which includes 8,796 negative words and 4,751 positive words. Then, by using the extend sentiment dictionary to select theme words from original text as feature, machine learning models are introduced to assess the sentiment tendency. Experiment showed that the performance of the proposed framework was improved up to 85.79% in terms of overall accuracy compared with traditional methods.
An Improved Convolutional Neural Network for Monocular Depth Estimation

Depth estimation from monocular image plays an essential role in artificial intelligence, which is one of the important ways for sensing the operating environment in automatic-driving system or advanced driving assistant system. The most recent approaches have gained significant improvement for depth prediction based on convolutional neural networks (CNNs). In this paper, a novel framework of CNNs is proposed for monocular depth estimation based on deep ordinal regression network (DORN) and a U-net structure. The new model is trained, verified in process and tested on 5000 images from a simulation experiment platform provide by “Grand Theft Auto”. To eliminate or at least largely reduce the impact from ground truth with no depth values, three different training strategies were employed for network optimization. We developed an effective weighted training strategy for depth prediction to improve the estimation accuracy. The comparison of evaluations over our results and DORN demonstrated the effectiveness of our method. The results showed that the proposed method achieved state-of-the-art performances.
A Framework for Managing an Elastic Redis Cache

In this demonstration we present ESCAPe, a framework for automatically monitoring and managing an elastic Redis cache. System administrators can configure ESCAPe in concert with their Redis cluster, to meet application real-time objectives while minimizing the cost associated with scaling the applications. ESCAPe comprises Agents that are deployed at each node in the Redis cluster in order to implement the scaling decisions and apply the proposed eviction policy, and a ESCAPe Manager that communicates with all other nodes and makes the scaling decisions in response to the monitored response times and costs. Finally ESCAPe's Web User Interface visualizes realtime statistics related to response time and the distribution of object types in the cluster nodes as well as historical statistics. The system administrators can further fine-tune the cluster parameters and monitor the impact of their decisions on the application performance and cost.
Pattern-Based Knowledge Graph Embedding for Non-functional Requirements

Under the big data environment, knowledge of software non-functional requirements is connected with and distributed in large-scale, multi-sources, heterogeneous, multimodels and continuously growing data sources. In order to organize the information and establish a relationship between concepts so as to make knowledge available, we focus on building a knowledge graph of non-functional requirements. We found that the non-functional requirement knowledge is valid in specific fields. Considering the fact that traditional knowledge embedding model is not pleasant when dealing with one-to-many and manyto- one complex relationships in non-functional knowledge graphs, we will classify the domain of knowledge in non-functional knowledge graph by introducing patterns. Moreover, we are going to introduce pattern information into traditional knowledge embedding model, and propose a pattern-based non-functional knowledge embedding model-PNFE. The experimental results imply that PNFE model is superior to other traditional models in dealing with link prediction of non-functional knowledge graphs and quadruple classification tasks.
BinderAnn: Automated Reification of Source Annotations for Monadic EDSLs

Embedded Domain-Specific Languages (EDSLs) are an alternative to quickly implement specialized languages without the need to write compilers or interpreters from scratch. In this territory, Haskell is a prime choice as the host language. EDSLs in Haskell, however, are often incapable of reifying useful static information from the source code, namely variable binding names and source locations. Not having access to variable names directly affects EDSLs designed to generate low-level code, where the variables names in the generated code do not match those found in the source code—thus broadening the semantic gap among source and target code. Similarly, many existing EDSLs produce poor error messages due to the lack of knowledge of source locations where errors are generated. In this work, we propose a simple technique for enhancing monadic EDSLs expressed using $$\mathbf {do}$$ notation. This technique employs source-to-source plugins, a relatively new feature of GHC, to annotate every $$\mathbf {do}$$ statement of our EDSLs with relevant information extracted from the source code at compile time. We show how these annotations can be incorporated into EDSL designs either directly inside values or as monadic effects. We provide BinderAnn, a GHC source plugin implementing our ideas, and evaluate it by enhancing existing real-world EDSLs with relatively minor modification efforts to contemplate the source-level static information related to variables names and source locations.
Machine Learning Applications for Classification Emergency and Non-Emergency Patients

Emergency departments of hospitals are busy. In recent years, patient arrivals have significantly risen at emergency departments in Turkey like other countries in the world. The main important features of emergency services are uninterrupted service, providing services in a short time, and priority to emergency patients. However, patients who do not need immediate treatment can sometimes apply to this department due to several reasons like working time and short waiting time. This situation can reduce efficiency and effectiveness at emergency departments. On the other hand, computers solve complex classification problems by using machine learning methods. The methods have a wide range of applications, such as computational biology and computer vision. Therefore, classification of emergency and non-emergency patients is vital to increase productivity of the department. This chapter tries to find the best classifier for detection of emergency patients by utilizing a data set.
Preventing the Vehicle Accidents on Highways and Implementing Safety and Automation

In this paper, a mechanism for vehicle control and accident avoiding system is proposed. The salient features of this project are prevention of forward collision mechanism with alert, braking, lane changing and speed governing, i.e., when the vehicle attains high speed limit, it takes over the speed governing mechanism without the permission of the driver. The vehicle-to-vehicle communication is implemented to send the alert signals to the nearby vehicles whenever an accident occurs. The vehicle working environment for the driver is to avoid the forward collision and conditioning, collision avoidance and condition which are main factors to improve the safe traffic conditions. To implement this system, ARM7 is used. This microcontroller provides more efficient, reliable and effective working. This system estimates the speed and distance of the existing vehicles around, and the targeted vehicle measures these values using ultrasonic sensors. This results the speed of that particular vehicle to avoid collisions. MEMS provide facility for accident detection system. GPS is used to transmit the location of the accident and nearby other vehicles or static things like. ZigBee is used as an alternate to the network areas.
A Novel Feature Selection Model for JPEG Image Steganalysis

Image steganalysis is a very important research topic in the field of information security. The existing feature based image steganalysis methods have achieved the appealing performance. The performance of them greatly depends on the quality of the hand-crafted steganalysis feature vectors, such as Cartesian Calibration PEV (CC-PEV), DCT Residuals (DCTR), and so on. However, these feature vectors may contain some redundant elements that will reduce the discrimination power and increase the computation cost. In this paper, a novel feature selection model is proposed for JPEG image steganalysis. Specifically, the proposed model imposes an $$l_{2,1}$$-structural constraint on the projection matrix for feature selection. Further, to make the model insensitive to noises and outliers, a capped $$l_2$$-norm based loss function is adopted. Moreover, a graph-based manifold regularization term which exploits the intrinsic local geometric structure of the data is added into the objective function to select the effective feature elements. Finally, an alternately iterative optimization algorithm with proven convergence is given to solve the proposed model. The extensive experiments on three state-of-the-art JPEG steganographic algorithms with 0.1 and 0.2 embedding rates and two JPEG quality factors show that the proposed model can effectively remove some irrelevant and redundant elements meanwhile retaining high detection accuracy.
Blockchain-Based Metadata Protection for Archival Systems

Long-term archival storage systems must protect data from powerful attackers that might try to corrupt or censor (part of) the documents. They must also protect the corresponding metadata information, which is essential to maintain and rebuild the stored data. In this practical experience report, we present metablock, a metadata protection system leveraging the Ethereum distributed ledger. We combine metablock with an existing secure long-term data archival system to provide a scalable design that allows external auditing, data validation and efficient data repair. We reflect on our experiences in using a blockchain for metadata protection, with the goal of providing valuable insights and lessons for developers of such secure systems, by highlighting the potential and limitations of the approach. Our prototype is available at https://github.com/ArnaudLhutereau/mb.
Student candidates’ literacy analysis using an ICT-based tool

Abstract This study aims to determine the readiness of schools in implementing the policy of school literacy movement as an effort to form literate generations and to analyze complex readiness in terms of facilities and school culture. This research was conducted in elementary schools in Pekalongan city and regency totaling 39 schools. This research uses qualitative research method. Data analysis uses the Miles and Huberman model steps as for steps (1) data reduction, (2) data presentation, and (3) drawing conclusions / verification. Data is collected through observation and interviews. The results of the study show that there are five principles of school literacy, namely 1). It should be in accordance with the developmental stages of student characteristics. 2) it uses a variety of texts and paying attention to the needs of students. 3) It must be in an integrated and sustainable manner. 4). It involves oral communication skills. 5) It should consider diversity. Of these five principles, in average, some schools only prepared the materials for schools, not the culture of school literacy. It has not yet been built in an integrated and sustainable manner considering that preparing literate generation should focus on sustainable processes that will benefit in the long time. Therefore, results of this study are important to be used as a reference for schools and policy makers in preparing schools so that they are ready to implement the school literacy movement for literate generations.