Reviewerly/exHarmony huggingface dataset:
A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

With the increasing for function, scale, hierarchy and complexity of software project, the software life cycle and development stage show a trend of cross-cutting and fuzzy boundary.The non-technical factors, such as poor management and control during the implementation of software projects, are the major reason for causing the low success rate of software projects recently.Therefore, the software quality evaluation under complex environment should take the cross-influence between different stages of software life cycle and different quality evaluation standards into consideration.Our research is to construct a new software quality evaluation model by using the influence relationship and the influence intensity index between project management domain and project quality evaluation criteria including scope, cost, and time.First, we came up with the definition of software project management domain in the process of software project development and management.Second, we proposed a mathematical method for extracting the direct or indirect influence relation between them, and give a definition for the quantitative evaluation index and its calculation formula.At last we proposed to construct a neural network training model which includes evaluation model logic relationships and software quality quantitative evaluation index.Through study and training by simulated software project management data, we can discover some key data, such as normal threshold range of influence, factor weights, etc.Therefore, a complete evaluation system is built, and the scientific nature and accuracy of the proposal evaluation system will be improved.
Blockchain-Based Contract Signing Protocol for Confidential Contracts

Contract signing is a trust service that requires a fair exchange of elements: each signer's signature on the contract. This kind of service usually relies heavily on the use of trusted third parties. These entities are an obstacle to extend the use of this service. In this paper we use blockchain-based technologies to provide a contract signing protocol that removes completely the involvement of trusted third parties while satisfying the requirements of this kind of service. We present a protocol that can be used for the signature of public contracts but also for the signature of private and confidential contracts. The protocol has been designed taking into account its possible extension to multiparty contracts.
A Stacked Deep MEMC Network for Frame Rate Up Conversion and its Application to HEVC

Optical flows and video frame interpolation are considered as a chicken-egg problem such that one problem affects the other and vice versa. This paper presents a stack of deep networks to estimate intermediate optical flows from the very first intermediate synthesized frame and later generate the very end interpolated frame by combining the very first one and two learned intermediate optical flows based warped frames. The primary benefit is that it glues two problems into a single comprehensive framework that learns altogether by using both an analysis-by-synthesis technique for optical flow estimation and Convolutional Neural Networks (CNN) kernels-based frame synthesis. The proposed network is the first attempt to merge two previous branches of previous approaches, optical flow-based synthesis and CNN kernels-based synthesis into a comprehensive network. Experiments are carried out with various challenging datasets, all showing that the proposed network outperforms the state-of-the-art methods with significant margins for video frame interpolation and the estimated optical flows are more accurate for challenging movements. Furthermore, the proposed Motion Estimation Motion Compensation (MEMC) network shows its outstanding enhancement of the quality of compressed videos.
SDRS: A new lossless dimensionality reduction for text corpora

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.
Graph Wavelet-Based Multilevel Graph Coarsening and Its Application in Graph-CNN for Alzheimer’s Disease Detection

Along with the classical applications like graph partitioning, graph visualization, etc., graph coarsening has been recently applied in graph convolutional neural network (GCNN) architectures to perform the pooling operation in the graph domain. In this paper, we propose a novel two-stage graph coarsening method rooted on the graph signal processing with its application in the GCNN architecture. In the first stage of coarsening, the graph wavelet transform (GWT) based features are used to obtain a coarsened graph which preserves the topological characteristics of the original graph. In the second stage, the coarsening problem is formulated as an optimization problem where the reduced Laplacian operator at each level is obtained as a restriction of the original Laplacian operator to a specified subspace that also maximizes the topological similarity. The performance of the proposed coarsening algorithm is quantified in the general coarsening context using different graph coarsening quality measures. Its effectiveness as a pooling operator in GCNN is validated by applying it for the graph coarsening operation in the GCNN architecture. This modified GCNN architecture is then used as a graph signal classifier for the early detection of Alzheimer's disease. The results show that the proposed coarsening method outperforms state-of-the-art methods, both in the general coarsening context and as a pooling operator in the GCNN architecture.
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability

Nowadays, the use of machine learning models is becoming a utility in many applications. Companies deliver pre-trained models encapsulated as application programming interfaces (APIs) that developers combine with third-party components and their own models and data to create complex data products to solve specific problems. The complexity of such products and the lack of control and knowledge of the internals of each component used unavoidable cause effects, such as lack of transparency, difficulty in auditability, and the emergence of potential uncontrolled risks. They are effectively black-boxes. Accountability of such solutions is a challenge for the auditors and the machine learning community.
Reversible data hiding in halftone images based on minimizing the visual distortion of pixels flipping

With the rapid development of multimedia technology, reversible data hiding (RDH) has been proposed as a technique to protect the hidden information and recover the original image. However, the visual quality of the existing RDH methods in halftone images is not typically satisfactory. In this paper, to reduce the visual distortion, a novel RDH method in halftone images based on minimizing the visual distortion of pixels flipping is proposed. First, based on the human visual system, the suitable marked blocks are selected by the designed visual quality score. Then, by analyzing the block structure of halftone images, the optimal flippable pixels in the suitable marked blocks are selected to embed data. One of the advantages of the proposed method is that the distortion caused by pixels flipping is minimal. The other is that to achieve reversibility, the positions of these optimal flippable pixels can be directly determined by calculating the minimal visual distortion without recording the location map. Experimental results demonstrate that the proposed method ensures high visual quality for marked images and achieves high embedding payload.
Deep Neural Network Based Ambient Airflow Control through Spatial Learning

As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than ± 5 ° . In addition, the target temperature can be reached 19.8% faster and the power consumption can be saved up to 20.5% by the time the target temperature is achieved.
A multi-label learning model for psychotic diseases in Nigeria

The goal of Multi-Label Classification (MLC) is to allot an instance to a set of different labels. This task is usually addressed by either transforming the problem into several binary problems, adapting machine learning models to fit multi-label data or create an ensemble of models that can classify multi-label datasets. The communal relationship between Bipolar, Insomnia, Schizophrenia, Vascular Dementia (VD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in the Psychotic Disorder Diseases (PDD) motivate the research for a diagnostic method that classifies and evaluates each psychotic disorder simultaneously. This study experimentally evaluates 15 MLC methods using 10 evaluation measures over a new PDD dataset. The performance of these methods is measured with four ranking - based, three example-based and three label-based measures. Also, the efficiency of these methods is measured by their 90-10 Train-Test split with the 10 evaluation measures. The results show that the Label Powerset (LC) and Pruned Sets (PS), MLC methods with Naïve Bayes (NB) and Naïve Bayes Tree (NBTree) consistently performed best in terms of the evaluation measures on the PDD dataset. Schizophrenia has the highest classification accuracy with Bipolar the lowest in the data split of 90-10. Logistic model tree (LMT) is the best algorithm for Insomnia and Bipolar while Naïve Bayes (NB) is the best for Schizophrenia, VD and MBD. Support vector machines (SVM) with ensemble learning and classification (ELC) and Ensemble of Pruned Set (EPS) are the best classifiers for Bipolar while SVM with regression and threshold (RT) is the least. The classifiers are statistically significantly different for Insomnia, VD and ADHD only.
Improving LPCNET-Based Text-to-Speech with Linear Prediction-Structured Mixture Density Network

In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully achieved high-quality and lightweight speech synthesis systems by combining a vocal tract LP filter with a WaveRNN-based vocal source (i.e., excitation) generator. However, the quality of synthesized speech is often unstable because the vocal source component is insufficiently represented by the μ-law quantization method, and the model is trained without considering the entire speech production mechanism. To address this problem, we first introduce LP-MDN, which enables the autoregressive neural vocoder to structurally represent the interactions between the vocal tract and vocal source components. Then, we propose to incorporate the LP-MDN to the LPCNet vocoder by replacing the conventional discretized output with continuous density distribution. The experimental results verify that the proposed system provides high quality synthetic speech by achieving a mean opinion score of 4.41 within a text-to-speech framework.
The Development of 'JEGER' Application Using Android Platform as History Learning Media and Model

This study aims to describe the development of the 'JEGER' application using android platform to develop a history learning media and model based on historical potential of General Soedirman's guerrilla in Pacitan, Indonesia. This development used three main steps adopted from Borg and Gall's model. The development scheme of the application takes a place as a media and model in history learning. The draft or prototype was validated by experts of learning material and media. The revised results of the validator's suggestion are tested to examine the effectiveness and feasibility. The average evaluation of the five assessment components showed an average of 92%. It means that the application of ‘‘JEGER’’ is very effective and feasible as an innovative and creative media and model for historical learning
A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage

In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than 80 %), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.
Cross-VAE: Towards Disentangling Expression from Identity For Human Faces

Facial expression and identity are two independent yet intertwined components for representing a face. For facial expression recognition, identity can contaminate the training procedure by providing tangled but irrelevant information. In this paper, we propose to learn clearly disentangled and discriminative features that are invariant of identities for expression recognition. However, such disentanglement normally requires annotations of both expression and identity on one large dataset, which is often unavailable. Our solution is to extend conditional VAE to a crossed version named Cross-VAE, which is able to use partially labeled data to disentangle expression from identity. We emphasis the following novel characteristics of our Cross-VAE: (1) It is based on an independent assumption that the two latent representations' distributions are orthogonal. This ensures both encoded representations to be disentangled and expressive. (2) It utilizes a symmetric training procedure where the output of each encoder is fed as the condition of the other. Thus two partially labeled sets can be jointly used. Extensive experiments show that our proposed method is capable of encoding expressive and disentangled features for facial expression. Compared with the baseline methods, our model shows an improvement of 3.56% on average in terms of accuracy.
Handwriting identification using deep convolutional neural network method

Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. This paper proposes a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Research Data Management Services and Strategic Planning in Libraries Today: A Longitudinal Study

INTRODUCTION Research data services have been adopted by many academic libraries. This study tracked the changes in research data management services and staffing among Association of American Universities (AAU) libraries over the past 5 years and compared them to the libraries’ goals for research data management (RDM) in their strategic plan. METHODS This quantitative study examined libraries at the 60 U.S. AAU institutions. In order to examine longitudinal changes, portions of Briney et.al. (2015a) were used as a basis for measuring data librarian staffing and services. These trends were compared to the contemporary strategic priorities of libraries interviewed by Meier (2016), as well as against strategic plans of 2014 and 2019 available online. RESULTS &amp;amp; DISCUSSION While there have been modest increases in libraries in the sample population offering data services, most of those gains have been among the libraries that did not consider RDM a priority in 2014. Interestingly, some of the libraries that mentioned RDM as a priority in 2014 have lost data librarian positions. Over half of the libraries in this study now provide or support a data repository. Many library strategic plans that mentioned RDM as an explicit goal 5 years ago now no longer mention it. CONCLUSION Data librarian positions, data services, and data repositories have now become common features of large research university libraries. However, research data services are no longer as prominent in many library strategic plans at institutions where such services are more established, and libraries instead seem to be moving on to the work of rethinking the nature of the services or expanding them. Data: https://scholarsphere.psu.edu/concern/generic_works/t1g05ff42r
Development of Learning Management System-Based Blended Learning Model using Claroline in Higher Education

&lt;p class="0abstract"&gt;Learning Management System (LMS) is a learning process that utilizes computer information technology equipped with internet and multimedia telecommunications facilities (graphics, audio, video) in delivering material and interaction between instructors and learners. The purpose of this study is to develop blended learning Using Claroline as a learning tool that facilitates students in learning. To achieve the objective of the study the research uses a quantitative approach to collect data using pre and post tests and questionnaires. The sample in the study were students of economic Islamic consisting of two classes, with a total of 50 students enrolled in this study. The results show that the difference in the average pre-test and post-test score was -29.43720. While the t-test that tests Ho: pre-test = post-test gives a value of t = -37.43720 with a degree of freedom of 49. While the p-value for the two-sided test of 0,000 is smaller than α = 0.05. This data approves that the statistical hypothesis Ho: pre-test = post-test is rejected, meaning that the average pre-test and post-test scores are significantly different. The findings of this study can be used to recommend effective ways of learning and teaching using e-learning that can improve student learning outcomes in higher education. The implications of this research is to encourage teachers to use e-learning technology and facilitate students with the technology in improving academic learning outcomes.&lt;/p&gt;
The Rhetorical Structure of the Lubuklinggau Mayor's Speech in Building Community Trust

This study aims to describe the rhetorical structure of the Lubuklinggau Mayor's speech in building public trust. The research method uses the content analysis method. Data collection techniques using documentation techniques in the form of video recordings of Lubuklinggau Mayor's speeches. Text data analyzed were taken from speeches delivered by the Mayor of Lubuklinggau. Text data were analyzed based on the generic structure of narrative texts by Labov and the rhetorical structure theory developed by Mann and Thompson. The results of the study show that each text has an abstract part, orientation, problem complexity, resolution, and data and fact based codes so as to build public trust. The interesting thing from the speech by the Mayor of Lubuklinggau is that the delivery of speech is dominated by the Palembang regional language and begins with small talk that implies a specific purpose and is accompanied by expressions that contain humor and appreciation.
Prior Art Search Using Multi-modal Embedding of Patent Documents

Due to the limitations of the existing prior art search methods, a new patent search paradigm can be innovated by the concepts based on a precise patent document embedding, and a real-time feedback. These concepts can be achieved by the following ideas. The latest language model BERT can be incorporated with the description drawing embedding so that the explorable user interactive model can be adopted to the patent domain for “Building an artificial intelligent patent search system." Therefore, these methodologies mainly with the help of deep learning can solve the traditional labor-intensive and time-consuming prior art search.