Reviewerly/exHarmony huggingface dataset:
The Theory of Construct and Instrument Development Process: Supplementing Human Judgment with Natural Language Processing Techniques

The construct and instrument development process relies significantly on human judgment in the initial stages of the process, specifically in developing construct definition statements, and in developing instruments with high construct and content validity. Natural language processing techniques can be employed to support human judgment and improve the quality of constructs and instruments employed in research. The paper describes the use of such techniques and presents illustrative results from the use of those techniques. The illustrations support our premise that the use of those techniques can improve the rigor of the process and improve the quality of constructs and instruments employed in research.
Forensic Analysis of Binary Structures of Video Files

As technology advances, multimedia files such as videos are susceptible to manipulation. This has led to serious concerns that images and videos are not trustworthy evidence as the files can be manipulated easily. As a result, forensic analysis of electronic multimedia files plays an important role in verifying the authenticity of video files. This paper provides comprehensive details of a binary file forensic analysis technique for different media file containers, mostly focused on AVI and MP4/MOV container format. We also provide a considerable number of details to identify a forgery among video files. We present pivotal parameters which need to be tested to authenticate a video file. By analyzing the binary data structures and metadata, we can detect the use of editing tools, verify the purported source of a video file, and identify the true acquisition device model.
Mobile Music with the Faust Programming Language

The Faust programming language has been playing a role in the mobile music landscape for the past ten years. Multiple tools to facilitate the development of musical smartphone applications for live performance such as faust2ios, faust2android, faust2api, and faust2smartkeyb have been implemented and used in the context of a wide range of large scale musical projects. Similarly, various digital musical instruments leveraging these tools and based on the concept of augmenting mobile devices have been created. This paper gives an overview of the work done on these topics and provide directions for future developments.
Neighbourhood Based Bi-Level Contrast Adjustment for Underwater Image Enhancement Using Modified Particle Swarm Optimization

This paper presents a neighbourhood based bi-level contrast adjustment algorithm for underwater image enhancement. In this algorithm, at the outset, the histogram of the images is divided into two equal parts. A Modified Particle Swarm Optimization (MPSO) is introduced in the proposed algorithm to find two different points in each part of the histogram such that each part of the histogram can be separately stretched on the basis of these points. The quality of the output images (enhanced images) is visually and quantitatively judged with reference to the best fitness, mean fitness, Peak Signal to Noise Ratio (PSNR), Underwater Image Quality Measure (UIQM), average PSNR and average UIQM values of all test images and Friedman test. The acquired results proves that there is a substantial improvement of the proposed algorithm compared to others.
A Semantic Demand-Service Matching Method based on OWL-S for Cloud Testing Service Platform

Traditional testing service incurs high cost and low efficiency because of the expenditure on testing tools and the geographical location. Cloud testing service platform (CTSP) uses cloud infrastructure for testing service, which leads to a more cost-effective testing solution. However, how to realize the intelligent matching among the various testing services and the testing demands is one of the common issues and aims for CTSP. This paper investigates a semantic demand-service matching method for CTSP. Considering the diverse, heterogeneous and dynamic characteristics of cloud testing services, an Input, Output, Precondition, Effect (IOPE) matching model based on Web Ontology Language for Service (OWL-S) is proposed, and a three-phase matching process is developed consisting of parameter matching, attribute matching and global matching. To compute the matching degree between a testing service and a testing demand during the matching process, a quantitative matching method is put forward. At last, the effectiveness and feasibility of the proposed method is tested by a case study.
Lexicographic Environments in Natural Language Processing (NLP)

In this paper, a literature review is presented in reference to the most important lexicographical environments that have been developed in the last decades, with the aim of utilizing them in various knowledge management applications, in the wider area of ​​the Semantic Web (SW). This paper focuses on the semantic networks of lexical information, in order to highlight their value but also the urgent need to design and implement an electronic conceptual dictionary with ontological structure for the modern Greek language, according to international dictionary standards.
Just look at to open it up:

Abstract Electronic documents constitute specific units of information, and protecting them against unauthorized access is a challenging task. This is because a password protected document may be stolen from its host computer or intercepted while on transfer and exposed to unlimited offline attacks. The key issue is, therefore, making document passwords hard to crack. We propose to augment a common text password authentication interface to encrypted documents with a biometric facial identity verification providing highly personalized security mechanism based on pseudo-identities. In consequence the encrypted document can be unlocked with the legitimate user’s face, while for everyone else stays encrypted with a hard to crack text password. This paper makes two contributions: (1) The proposed scheme enables password autofill without referring to any external service, which significantly limits the possibilities of an attack by adversaries when opening, reading and editing the protected document, (2) By the adoption of biometric verification techniques enabling fine-tuning of false acceptance and false rejection rates, it provides for responsible adaptation to users.
Extracting Facial Features in Filter Modified Images using Neural Networks

Abstract Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam’s razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.
Grid Traversal Path Planning for Robot Foraging

The autonomous mobile robot is a system which must perform the desired task by navigating around the terrain either indoors or outdoors. A novel algorithm is proposed for mobile robots for the implementation in applications that require autonomous path planning and not just following a programmed path in an unknown and dynamic environment. It proceeds by following an inward spiral path rather than line by line. The algorithm studied below can be implemented for a wide variety of applications that involves mobile robots in foraging tasks. Along with its key merits and demerits and how it performs when compared to other existing algorithms and few applications where the algorithm comes handy are mentioned in this paper.
Multi-person Collaborative Hoisting Training System Based on Mixed Reality

In the early stage of hoisting training, virtual hoisting training equipment is often used to avoid safety accidents caused by lack of experience. However, there are some problems in the existing virtual training equipment, such as single interaction mode, limited viewing angle, and the system that is not portable. To solve the problems, a multi-person cooperative hoisting training system based on mixed reality is proposed in this paper. To ensure the accuracy of the virtual model, on the basis of collecting a large number of actual data, the system employs Pro/E software to carry out 3D accurate modeling of the box hoisting cart. In an effort to ensure the positioning accuracy of the holographic virtual model, the system firstly adopts image recognition method for initial positioning, SLAM algorithm for real-time positioning, and spatial anchor point for accurate positioning. In order to solve the problem of multi-person cooperative training, the system adopts the hardware composition of a server, a plurality of mixed reality intelligent glasses, a wireless router and so on, and uses gesture interaction and wireless analog controller interaction to realize efficient human-machine interaction. Via comparative experiments, it is proved that the system can greatly improve the operation level of operators and is of use value.
Study Analisis Metode Analisis Sentimen pada YouTube

It cannot be denied that YouTube is now the most popular video sharing website. Opinions written from viewers could be an important asset for the development of a company or YouTubers. In this report, the technique used to analyze those comments is called sentiment analysis or opinion mining techniques. Although this knowledge or technique is not new, it is still important to continue studying this, given the importance of the viewer comments. This paper is the result of a literature review of several studies that have been conducted using various methods in sentiment analysis. The purpose of this analytical study is to obtain a theoretical basis that can support further research by studying the working methods, advantages, and disadvantages of each method. For the reason of having no knowledge on how the method works will affect the results and will be a waste of time. The results of comparisons from research that have been done, showing that the Naïve bayes algorithm has a higher accuracy, then SVM then DT. But this is a preliminary result as no study has used all methods at once in a case.
IoT Environment for Monitoring Human Movements: Hip Fracture Rehabilitation Case

Hip fracture incidence increases with age and possess a serious long-term devastating impact on the physical functionality of the older population and on their ability to live independently. Increased risk in mortality rate, movement restriction, person’s well-being and loss of independence are some of the significant problems associated with the injury. Rehabilitation plays a significant role in recovery of the muscle strength, boosting the quality of life and improving the physical functionality in all stages of care. The rehabilitation program has activity movements relevant to an injury that needs to be performed under predefined supervised or unsupervised environment. Continual monitoring of these activity movements in any environment can significantly help in follow up the correct implementation of a rehabilitation program. The ever-growing technology like Internet of Things (IoT) and advancements in digital health revolutionizing all industries and markets could be leveraged upon in advancing and converting the conventional rehabilitation care into a smart rehabilitation care. This paper proposes an IoT environment for long-term monitoring of hip fracture rehabilitation related activity movements. Functionality of the attributes involved in the environment have been discussed pragmatically through experimental analysis. Results reflect that the environment can offer flexibility for different movement monitoring, analysis and visualisation applications.
Table Motion Detection in Interventional Coronary Angiography

The most common method for detecting coronary artery stenosis is interventional coronary angiography (ICA). However, 2D angiography has limitations because it displays complex 3D structures of arteries as 2D X-ray projections. To overcome these limitations, 3D models or tomographic images of the arterial tree can be reconstructed from 2D projections. The 3D modeling process of the arterial tree requires accurate acquisition geometry since in many ICA acquisitions the patient table is translated to cover the entire area of interest, the original calibrated geometry is no longer valid for the 3D reconstruction process. This study presents methods for identifying the frames acquired during table translation in an angiographic scene. Spatio-temporal methods based on deep learning were used to identify translated frames. Three different architectures3D convolutional neural network (CNN), bidirectional convolutional long short term memory (CONVLSTM), and fusion of bi-directional CONVLSTM and 3D CNNwere trained and tested. The combination of CONVLSTM and 3D CNN surpasses the other two methods and achieves a macro f1-score (mean f1-scores of two classes) of 93%.
Independent Finite Approximations for Bayesian Nonparametric Inference

Completely random measures (CRMs) and their normalizations (NCRMs) offer flexible models in Bayesian nonparametrics. But their infinite dimensionality presents challenges for inference. Two popular finite approximations are truncated finite approximations (TFAs) and independent finite approximations (IFAs). While the former have been well-studied, IFAs lack similarly general bounds on approximation error, and there has been no systematic comparison between the two options. In the present work, we propose a general recipe to construct practical finite-dimensional approximations for homogeneous CRMs and NCRMs, in the presence or absence of power laws. We call our construction the automated independent finite approximation (AIFA). Relative to TFAs, we show that AIFAs facilitate more straightforward derivations and use of parallel computing in approximate inference. We upper bound the approximation error of AIFAs for a wide class of common CRMs and NCRMs — and thereby develop guidelines for choosing the approximation level. Our lower bounds in key cases suggest that our upper bounds are tight. We prove that, for worst-case choices of observation likelihoods, TFAs are more efficient than AIFAs. Conversely, we find that in real-data experiments with standard likelihoods, AIFAs and TFAs perform similarly. Moreover, we demonstrate that AIFAs can be used for hyperparameter estimation even when other potential IFA options struggle or do not apply.
Research on anomaly data mining method of new energy field stations based on improved Adaboost algorithm

Abstract Traditional anomalous data mining methods require a lot of prior knowledge, which leads to low data mining integrity and efficiency. For this reason, a new energy field abnormal data mining method based on improved Adaboost algorithm is proposed. After pre-processing the new energy field data, the algorithm is improved by introducing dynamic weight parameters to address the shortcomings of the Adaboost algorithm. After calculating the degree of data anomaly using the direct push belief machine, the neural network is used to reduce the error value of the Adaboost algorithm, and finally the output of the Adaboost algorithm is used to realize abnormal data mining. The simulation experiment proves that the researched abnormal data mining method has high data integrity and high efficiency.
Atomistic Mathematical Theory for Metaheuristic Structures of Global Optimization Algorithms in Evolutionary Machine Learning for Power Systems

Global Optimization in the 4D nonlinear landscape generates kinds and types of particles, waves and extremals of power sets and singletons. In this chapter these are demonstrated for ranges of optimal problem-solving solution algorithms. Here, onts, particles, or atoms, of the ontological blueprint are generated inherently from the fractional optimization algorithms in Metaheuristic structures of computational evolutionary development. These stigmergetics are applicable to incremental machine learning regimes for computational power generation and relay, and information management systems.
Research on the Talent Training Mode of “Integration of Work, Study and Business” in Chin’s Higher Vocational Education Under the Background of Industry 4.0

Industry 4.0 has become a new driven force of global economic development which fundamentally changed the demand for the technical talents. Due to the lack of motivation of enterprises and failure of cooperation between colleges and enterprises, it is difficult for China's talent training mode of “work-integrated learning” of higher vocational education to cultivate inter-disciplinary and talents with comprehensive skills that are needed under the background of Industry 4.0. This paper aims at constructing the talent education mode of “integration of work, learning, and business and implementing the teaching reform of multi-level projects to develop abilities gradually Schools and enterprises can closely cooperate to cultivate students' comprehensive abilities, whole industry chain thinking and market awareness. This paper provides reference for the talent cultivation of China's higher vocational education under the background of Industry 4.0.
Heuristic-Based Clustering Approach for Discovering Colossal Patterns from High-Dimensional Databases

Since its introduction, frequent pattern mining in data mining has been extensively studied to discover relationships among variables in large databases. Majority of the frequent pattern mining algorithms have been developed based on the Apriori property which traverse iteratively over the item lattice in a level-wise approach. This phenomenon works well with small- and medium-size pattern sequences from the databases and does not contain any useful information for data analysts. However, for large patterns the runtime complexity will increase exponentially as the pattern sequence length increases. In this paper, a new Heuristic Clustering based Colossal Pattern mining from high dimensional datasets (HCCP). This paper proposes a strategy which avoids exhaustive level-wise pattern tree traversal and quickly mines colossal patterns. Our method performs binary clustering over sub-pattern. In our method, we used a lattice array to construct sub-pattern sequences along with support values. Further, these sub-patterns are explored as conditional patterns by estimating core patterns. Finally, colossal cluster is constructed and colossal patterns are discovered. Our experimental result on various input datasets shows that our HCCP achieves very high mining results. In fact, our analysis of results reveals that this algorithm outperforms with core fusion and colossal pattern miner (CPM) in diverse aspects.
Seismic Event Classification Using Spectrograms and Deep Neural Nets

In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using eight different deep learning architectures. The developed method used three deep convolutional neural networks named DCNN1, DCNN2, and DCNN3, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3, and two autoencoder neural networks named AE1 and AE2, to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images. The three deep recurrent neural network-based models reached the worst results due to the overfitting produced by the small number of samples in the training sets. The DCNN1 overcame the remaining models by obtaining an area under the curve of the receiver operating characteristic and accuracy scores of 0.98 and \(95\%\), respectively. Although these values were not the highest values per metric, they did not represent statistical differences against other results obtained by more algorithmically complex models. The proposed DCNN1 model showed similar or superior performance compared to the majority of the state of the art methods in terms of accuracy. Therefore it can be considered a successful scheme to classify LP and VT seismic events based on their spectrogram images.
F3N: Full Feature Fusion Network for Object Detection

This paper is mainly aimed at proposing a powerful feature fusion method for object detection. An exceptionally significant accuracy improvement is achieved by augmenting all multi-scale features by adding a finite amount of computation. Hence, we created our detector based on a fast detector on SSD [1] and called it Full Feature Fusion Network (F3N). Using several Feature Fusion modules, we fused low-level and high-level features by parallel low-high level sub-network with repeated information exchange across multi-scale features. We fused all the multi-scale features using concatenate and interpolate methods within several feature fusion modules. F3N achieves the new state of the art result for one-stage object detection. F3N with 512x512 input achieves 82.5% mAP (mean Average Precision) and 320x320 input yields 80.3% on the VOC2007 test, with 512x512 input achieving 81.1% and 320x320 input yielding 77.3% on the VOC2012 test. In MS COCO data set, 512x512 input obtains 33.9% and 320x320 input yields 30.4%. The accuracies are significantly enhanced compared to the current mainstream approaches such as SSD [1], DSSD [8], FPN [11], YOLO [6].
A virtual reality system for dissecting vertebrates with an observation function

Giving chances of hands-on experience is important for K-12 education, in order to make students obtain deep understanding. However, there is less opportunities of such experience in science education in Japan, due to some reasons. Therefore, in this paper, a virtual reality system is proposed with which the students can interactively experiment the dissection of vertebrate animals. Through a series of four stages in VR space, the proposed system provides a learner to learn the inner structure of the animal and the procedure of the dissection. Also giving the haptic sense in use of the vibration on the hand controllers, the system represents difference between alive and dead, so that the learner has a chance to think of the animal ethics.