Reviewerly/exHarmony huggingface dataset:
Predicting the June 2019 Istanbul Mayoral Election with Twitter

In this study, it is aimed to predict the Istanbul Mayoral election which was held on 23 June 2019 with the data collected through the APIs provided by Twitter. Machine learning models were built on a sample data which was labeled manually. Then, these ML models were applied on the whole tweet data set to predict sentiments of tweets regarding candidates. Among applied ML methods, Logistic Regression provided best result for this study. Using predicted sentiments, voting rates for candidates are estimated following different approaches. 2.2% MAE for the voting rates was achieved with the best prediction.
Spatial Data and Remote Sensing Techniques Integration to Detection and Slicing of Bavanat Red Bed Copper Deposits (NE Shiraz, Iran)

Bavanat red bed copper deposits (Jolani area) are located in south Sanandaj-Sirjan metamorphic belt and approximately 15 km NW of Bavanat. In terms of lithology, these deposits include purple to red siltstone(PSS) which are also seen among the layers of green sandstone(GS). Copper mineralization such as malachite is observed in the GS unit at the surface. Both PSS and GS units have Jurassic age. The current study, has used Landsat 8 and SPOT 5 images for RS processing. This study indicated that RGB=432 color composite in SPOT 5 image has the best contrast for enhancement and detection of PSS and GS units. Only the Landsat 8 fused image has been able to enhance and detect the GS unit. Also, based on band ratio technique, RGB=(b6/b2), (b5/b3), (b7/b1) color composite for Landsat 8 data, has the best contrast for PSS and GS units. PCA method shows that RGB=PC4, PC2, PC1 for Spot 5 dataand RGB=PC1, PC2, PC3 for Landsat 8 data have the best contrast for enhancement and detection of the Bavanat red bed copper deposits. In this study, different methods of supervised classification such as SAM, SID and SVM were reviewed. Among these methods, SVM technique has the best layout for SPOT 5 image. This important layout as a basic geological map, can be very useful in additional exploration studies on the Bavanat red bed deposits.
Study of circular recognition algorithm in statistics of microbubbles

In view of the results of large-scale preparation of microbubbles, as well as the digital requirements for microbubble size and quality control, this paper uses the algorithm based on the Hough circular transformation, assisted by the related pre-processing and the selection constraints setting of the results to realize the identification, detection and statistics of large-volume microbubbles. On the basis of the theoretical analysis and the experimental method, the algorithm proposed in this paper uses the computer vision library (OpenCV) to realize the program and achieves the purpose of judging the quality control of the preparation results by measuring and comparing the particle size distribution of the microbubbles obtained from a large number of preparations.
WIKLIN PLATFORM SOLUTIF DAN INOVATIF SEBAGAI UPAYA PENGELOLAAN SAMPAH DI PROVINSI DAERAH ISTIMEWA YOGYAKARTA

Tempat Pembuangan Sampah Terpadu (TPST) located in Bantul Regency is overwhelmed to receive hundreds of tons of garbage that come every day from three large regions: Yogyakarta City, Bantul Regency, and Sleman Regency (Kartamantul). So that the entire dredging process that has been carried out to increase the capacity of the TPST cannot be helped because it is not proportional to the rate of garbage coming. The rubbish is dominated by plastic waste. The rubbish certainly accumulates and pollutes the surrounding environment, reducing the green land where people play and livestock foraging. Now livestock are forced to find food among the rubbish, which of course can threaten the health of the community and its livestock. To overcome this problem, an information system design method is used to create a platform that is able to accommodate the plastic waste management needs that are a problem in the region. Created Wiklin platform that is expected to be able to help overcome these problems, as well as help the surrounding community by creating jobs that can improve their lives.
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation

In recognition-based action interaction, robots’ responses to human actions are often pre-designed according to recognized categories and thus stiff. In this paper, we specify a new Interactive Action Translation (IAT) task which aims to learn end-to-end action interaction from unlabeled interactive pairs, removing explicit action recognition. To enable learning on small-scale data, we propose a Paired-Embedding (PE) method for effective and reliable data augmentation. Specifically, our method first utilizes paired relationships to cluster individual actions in an embedding space. Then two actions originally paired can be replaced with other actions in their respective neighborhood, assembling into new pairs. An Act2Act network based on conditional GAN follows to learn from augmented data. Besides, IAT-test and IAT-train scores are specifically proposed for evaluating methods on our task. Experimental results on two datasets show impressive effects and broad application prospects of our method.
Scrum with eXtreme Programming: An Agile Alternative in Software Development

The growing demand for software products worldwide, in various areas of knowledge, means that software development teams are forced to use agile development methodologies that optimize time and resources. However, using SCRUM in software projects contributes to management, but it does not describe the development process; Due to the agile eXtreme Programming (XP) methodology, this process is provided in detail. This article focuses on the analysis of a large amount of research related to these approaches, to propose to the software development teams a framework validated by experts that combines these two powerful strategies, showing in detail the minimum technical deliverables in each phase of software development; complementing with other suggestions obtained from the expertise of the evaluators of the proposal. The first section details the fundamental concepts of SCRUM; Next, the XP methodology is described, then, it focused on analyzing previous studies of the combination of these strategies and then theoretically generating a detailed three-stage framework that combines SCRUM strategies with XP fortitude, which is employable in any type of software project; along with the minimum documentation required to expedite development work. Then the results obtained from the evaluation rubric are concluded and, finally, the open research directions on this topic are defined.
Application of Improved AprioriSome Algorithm in Supermarket O2O Marketing

Sequential pattern mining is the key technology for analyzing data. Using Python language and its IDE tool PyCharm can effectively mine the transaction data set generated by supermarket O2O marketing. In this paper, the existing AprioriSome algorithm is improved, and the constraints such as time interval and time window are added, and it is applied to the real transaction data set of a large supermarket chain in Henan. The results show that the running time of the improved AprioriSome algorithm is reduced, and the number of frequent sequences excavated is obviously increased and more practical.
Edge disjoint caterpillar realizations

Edge disjoint realization problems have connections for example to discrete tomography. In this paper, we consider the edge disjoint caterpillar realizations of tree degree sequences. We give necessary and sufficient conditions when two tree degree sequences have edge disjoint caterpillar realizations. We conjecture that an arbitrary number of tree degree sequences have edge disjoint realizations if every vertex is a leaf in at most one tree. We prove that the conjecture is true if the number of tree degree sequences is at most four. We also prove that the conjecture is true if n≥max{22k−11,396}, where n is the number of vertices and k is the number of tree degree sequences.
Research on Digital Image Watermarking Technology

With the continuous development of network technology, people are becoming more and more dependent on digital images. At the same time, some problems have emerged about digital copyright, so the protection of digital images has become a hot issue in research. This paper makes a detailed analysis on the main attributes, classification and system model of digital image watermarking, and systematically introduces several typical image watermarking algorithms and common attack methods. Finally, it proposed the main problems of current digital image watermarking technology, and summarized current situation and looked forward to future research directions.
Mixture Variational Adaptive Filter with Uncertain and Non-Gaussian State Propagation

In this paper, a novel mixture variational adaptive filter (MVAF) is proposed to deal with the nonlinear state estimation problem with uncertain and non-Gaussian state propagation. The main idea of MVAF is to describe the state propagation by Gaussian mixture model, different from traditional one, the mean of which is composed of certain and uncertain two parts. In the certain part, the nonlinear state function is used directly without approximation so that the error caused by linearization can be avoided. In the uncertain part, variational parameters are introduced, which can capture the feature of unknown process noise. Then, based on the variational Bayesian framework, through iteratively and alternatively achieving the fitting of the state propagation and the approximation of process noise, the estimation accuracy and adaptiveness can be enhanced gradually. The superior performance of MVAF is demonstrated in two simulations.
Porting the Software Product Line Refinement Theory to the Coq Proof Assistant

Software product lines are an engineering approach to systematically build similar software products from a common asset base. When evolving such systems, it is important to have assurance that we are not introducing errors or changing the behavior of existing products. The product line refinement theory establishes the necessary conditions for such assurance. This theory has been specified and proved using the PVS proof assistant. However, the Coq proof assistant is increasingly popular among researchers and practitioners, and, given that some programming languages are already formalized into such tool, the refinement theory might benefit from the potential integration. Therefore, in this work we present a case study on porting the PVS specification of the refinement theory to Coq. We compare the proof assistants based on the noted differences between the specifications and proofs of this theory, providing some reflections on the tactics and strategies used to compose the proofs. According to our study, PVS provided more succinct definitions than Coq, in several cases, as well as a greater number of successful automatic commands that resulted in shorter proofs. Despite that, Coq also brought facilities in definitions such as enumerated and recursive types, and features that support developers in their proofs.
Research on Query Term Expansion based on RankSVM and LDA Model

Abstract The lack of semantic association will lead to poor performance of retrieval system. This paper proposed a query terms extension method based on RankSVM and LDA model. Firstly, with the aid of RankSVM’s excellent performance of sorting, high-quality initial retrieved results will be obtained. Then utilizing the LDA model to select the documents related to query term and to generate topic model for sequenced initial retrieval results.in the post-processing step, using threshold to select the highest probability among each topic and regards them as query terms set. Experiments show that the query terms expansion method proposed in this paper is superior to the current query expansion methods based on pseudo-relevance feedback in the five evaluation index, therefore this is a feasible method.
Construction of entanglement-assisted quantum MDS codes

Abstract Entanglement-assisted quantum error correction codes (EAQEECs) can improve the reliability of transmission, which make use of pre-existing entanglement between two parties. In this paper, we construct two classes of entanglement-assisted quantum maximum distance separable (EAQMDS) codes via generalized Reed-Solomon (GRS) codes. Consequently, the results show that some of these EAQMDS codes have much larger minimum distance than ones available in the literature. Meanwhile, many of these EAQMDS codes are new in the sense that the parameters of these codes are not covered by the previously known ones. (Abstract)
SoK: Comparison of the Security of Real World RSA Hash-and-Sign Signatures

In this modern day and age, where the majority of our communication occurs online, digital signatures are more important than ever before. Of the utmost importance are the standardised signatures that are deployed not only across the Internet, but also in everyday devices, such as debit and credit cards. The development of these signatures began in the 1990s and is still an ongoing process to this day. We will focus on RSA-based hash-and-sign signatures, specifically deterministic hash-and-sign signatures. We will give a survey of all standardised deterministic RSA hash-and-signatures, where we explore the history of each one, from inception, to attacks and finally proofs of security. As the security proofs have also appeared over the span of two decades, their statements are not always compatible with one another. To ensure this, we will consider only deterministic standardised signature schemes included in PKCS, ISO, and ANSI standards, as well as the non-standardised Full-Domain Hash, to provide a complete picture.
Murphree's Numerical Term Logic Tableaux

Murphree's Numerical Term Logic is a logic capable of representing and performing inference with numerical quantifiers by modifying Sommers' Term Functor Logic: in this contribution we offer a tableaux method for it.
Dead time estimation in generalized Laguerre functions basis

The dead time estimation problem is analyzed. The usage of generalized Laguerre functions on dead time estimation is studied. The free parameters for optimal approximation with generalized Laguerre functions are computed in time space. The better quality dead time estimation is achieved with generalized Laguerre functions than with simple Laguerre functions.
An Improved Term Weighting Method for Content Analysis on Chinese Internet Media Contents

The term frequency-inverse document frequency (TF-IDF) is a term weighting method that is widely used for textual content analysis. However, when using TF-IDF for classification on unbalanced, distributed Chinese Internet media content, it is unreasonable for IDF to equate the inverse text high frequency having strong distinguishing ability. In this paper, the computational formula for calculating collection frequency is optimized by combining it with the average value of document frequency (D̅F̅). This combination helps to reduce the influence caused by any unbalanced distribution of content on the Internet. A novel computational formula based on (D̅F̅) is designed as a collection frequency factor, combining with term frequency (TF) to form a new term weighting method named TF-ISDF. Experiments on support vector machine (SVM) and Random Forests (RF) algorithms are demonstrated to validate the two different term weighting schemes (TWS). The results show that the proposed TF-ISDF approach exhibits better performance than TF-IDF in both classification algorithms and analysis of Chinese Internet media content.
HPSGD: Hierarchical Parallel SGD with Stale Gradients Featuring

While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers. To alleviate this problem, a novel Hierarchical Parallel SGD (HPSGD) strategy is proposed based on the observation that the data synchronization phase can be paralleled with the local training phase (i.e., Feed-forward and back-propagation). Furthermore, an improved model updating method is unitized to remedy the introduced stale gradients problem, which commits updates to the replica (i.e., a temporary model that has the same parameters as the global model) and then merges the average changes to the global model. Extensive experiments are conducted to demonstrate that the proposed HPSGD approach substantially boosts the distributed DNN training, reduces the disturbance of the stale gradients and achieves better accuracy in given fixed wall-time.
Computer Vision and Image Analysis with the Vision Framework

This chapter introduces the Vision Framework. It describes the image analysis workflow used in the Vision Framework, explains how Core ML models can be integrated into the workflow, and offers practical recommendations for developing your own solutions using the Vision Framework.
Stage acoustics and parametric design: The development of an integrated early design tool

Traditional ray tracing software tools (e.g. Odeon, CATT-Acoustic, EASE) enable detailed analysis of stage acoustics; however, they are typically undertaken in later design stages and lack the flexibility required for early design development. This paper, which follows from a poster presentation at ISRA 2019, investigates the use of a three-dimensional modelling platform (Rhinoceros/Grasshopper) to quickly assess the influence of architectural changes on reflections that support orchestral ensemble. This approach enables immediate feedback, a more creative design process and better integration of architecture and acoustics. Early reflections have been found to be vital for effective orchestral ensemble. Therefore, the study focused on the investigation of early energy distribution on stage with ray tracing analysis using a parametric tool. This tool also considers cross-stage shielding effects from the orchestra and the directivity of instruments. The results of the tool have been compared to an existing acoustic modelling software to determine its accuracy and reliability. Additionally, the expansion of the tool with an evolutionary solver has also been explored. The development of a Rhinoceros/Grasshopper design tool has been found to be beneficial in the analysis of stage conditions and enhances the design collaboration during early design phases.
Inpainting via Sparse Representation Based on a Phaseless Quality Metric

An inpainting method via sparse representation based on a new phaseless quality metric is presented in this paper. Since power spectra, phaseless features, of local regions within images enable more successful representation of their texture characteristics compared to their pixel values, a new quality metric based on these phaseless features is newly derived for image representation. Specifically, the proposed method enables spare representation of target signals, i.e., target patches, including missing intensities by monitoring errors converged by phase retrieval as the novel phaseless quality metric. This is the main contribution of our study. In this approach, the phase retrieval algorithm used in our method has the following two important roles: (1) derivation of the new quality metric that can be derived even for images including missing intensities and (2) conversion of phaseless features, i.e., power spectra, to pixel values, i.e., intensities. Therefore, the above novel approach solves the existing problem of not being able to use better features or better quality metrics for inpainting. Results of experiments showed that the proposed method using sparse representation based on the new phaseless quality metric outperforms previously reported methods that directly use pixel values for inpainting.
Brief review of classification algorithm on breast cancer

Breast cancer is a malignant neoplasm disease which is an abnormal growth of breast tissue that is different from surrounding tissue. It became the second most case of death that caused by cancer after lung cancer. Several studies have been done to diagnose patients of breast cancer using various type of algorithm. This paper presents a brief review of breast cancer detection using datamining classification algorithm. C4.5, Naive Bayes and K-Nearest Neighbour algorithm are applied on breast cancer data set. Outputs are decision tree, models and computations. This study aims to evaluate performance of those three-data mining classification algorithm.
Radar Emitter Type Identification Effect Based On Different Structural Deep Feedforward Networks

A radar emitter identification method based on depth network model is studied in this paper. Based on analysing the matching correlation of radar emitter data and the characteristics of deep network model, the estimated parameters sample diagram (EPSD) is used as the input form of parameter data, and the deep feedforward network (DFN) is used to fit the abstract function of mapping input data to types. After further detailed analysis of the network model, the conclusion of network parameter setting is given, and the fine adjustment of network is realized. The simulation results show that the identification rate of radar emitter can be greatly improved by using the deep network model. It is obviously there is information existing in the characteristics of radar emitter that can be mined by deep feedforward network.
Sensitiveness Based Strategy for Network Compression

The success of convolutional neural networks has contributed great improvement on various tasks. Typically, larger and deeper networks are designed to increase performance on classification works, such as AlexNet, VGGNets, GoogleNet and ResNets, which are composed by enormous convolutional filters. However, these complicated structures constrain models to be deployed into real application due to limited computational resources. In this paper, we propose a simple method to sort the importance of each convolutional layer, as well as compressing network by removing redundant filters. There are two implications in our work: 1) Downsizing the width of unimportant layers will lead to better performance on classification tasks. 2) The random-pruning in each convolutional layer can present similar results as weight-searching algorithm, which means that structure of the network dominates its ability of representation. As a result, we reveal the property of different convolutional layers for VGG-16, customized VGG-4 and ResNet-18 on different datasets. Consequently, the importance of different convolutional layers is described as sensitiveness to compress these networks greatly.
A Two-Phase Approach Using LDA for Effective Domain-Specific Tweets Conveying Sentiments

Bhagat, Pradnya Pawar, Jyoti D.Twitter is a free social networking platform where people can post and interact with short messages known as “Tweets”. The freedom of being able to reach out to the world in a fraction of seconds has made Twitter an effective medium for the general public to express their opinion on a global scale. Since Tweets have the potential to make a global impact, companies too have started using the service to reach out to their customers. Moreover, in spite of this service being immensely effective, it is found challenging by many users to express their views through a Tweet due to the restriction imposed of minimum 280 characters. The proposed work is aimed at helping people compose better quality Tweets belonging to a specific domain in the restricted character limit. The system is designed to mine important features/topics about a domain using Latent Dirichlet Allocation (LDA) algorithm and to compute the polarity of the sentiment words associated with them with respect to the domain using a two-phase approach on an Amazon review corpus. The discovered topics/features and sentiments are recommended as suggestions to Twitter users while composing new Tweets. The paper describes and presents initial results of the system on cell phones and related accessories domain.