Reviewerly/exHarmony huggingface dataset:
Software Cost Estimation for Python Projects Using Genetic Algorithm

Software cost estimation is a process of planning, risk analysis, and decision making for project management in software development. Cost of project development encompasses a software project’s effort and development time. One popular model of software cost estimation is constructive cost model (COCOMO) model, which is a mathematical model proposed by Boehm, used for estimate the software effort and development time. The objective of this paper is to improve the basic COCOMO model’s coefficients for modern programming languages like Python, R, C++, etc. Many techniques were presented in the past for effort and time estimation using machine learning. But all these techniques were trained and tested for older programming languages. In order to improve the accuracy of COCOMO for modern programming languages, six Python projects have been considered and genetic algorithm (GA) is applied in these projects to define new values for basic COCOMO coefficients and the development time is calculated for Python projects. The time estimated using GA coefficients is compared with the original COCOMO and actual time. Using mean magnitude relative error, the error from the original COCOMO time is 54.49% and error from GA time is 21.23%.
Software Defect Prediction on Unlabelled Dataset with Machine Learning Techniques

In this work, we are going to explore how to conduct defect software prediction on unlabelled datasets by exploiting unsupervised machine learning techniques. In previous literature, various approaches have been proposed over time with the aim of labelling an unlabelled dataset (i.e. classifying dataset instances/modules in terms of their defectiveness), they can usually rely on other software datasets, software experts or metrics' thresholds. In this study, we intend to show the results obtained by exploiting CLAMI and CLAMI+, two approaches that overcome the various limitation of the previous ones adopted by researches, since they are independent on metrics' threshold, do not need experts software knowledge and can be easily automated.Our prediction model takes as input a set of unlabelled datasets and machine learning techniques. Its output is composed of the values of several performance indicators on training datasets and predictions on test datasets. The latter can be employed to deduce information on the status of software code and, consequently, concentrate the software developers' effort only where necessary.
Methods of emotional self-regulation in shaping emotional expression of a future vocalist

Training of future vocalists in higher education institutions involves the use of new approaches and methods, which is associated with changes in modern society and the image of the future specialist. The future vocalist, as a specialist, not only has knowledge and skills in his profession but also readiness to go beyond his capabilities. He uses interesting techniques, new approaches, looks for the ways to improve his activities every day. During the training of future vocalists at a higher education institution, the foundations of understanding the importance of professional self-improvement are laid. In the article, the author examines the issue of the formation of emotional expressiveness as one of the sources of successful professional development of a future vocalist at solo singing classes. To achieve this goal, the author conducts a theoretical analysis of previous pedagogical, psychological, methodological and art studies on this subject. It allows to determine the specifics of emotional expressiveness and the relevance of its introduction to the training of future vocalists. The peculiarities of the emotional and evaluative components of the structure of emotional expressiveness are determined. For the successful implementation of this component into solo singing lessons, a set of methods, most successfully affecting the formation of emotional expressiveness of a future vocalist, is outlined. In the article, the author focuses on the methods of emotional self-regulation. The author emphasizes the need to control the emotional state of a student during a class, his emotional settings before a concert performance, as well as the creation of a positive atmosphere during work. Methods and exercises of psychophysical self-regulation of emotional state and development of students’ empathy are characterized. It includes psychological techniques, breathing exercises, autosuggestion, the use ofimagination etc.
Recent Advances in Generative Adversarial Networks: An Analysis along with its outlook

From the past few years, Generative adversarial networks (GANs) have gained more and more interest of researchers of Artificial Intelligence and this is only due to the reliability on huge amount of data, well designed network architectures and smart training techniques because of which they produce highly realistic pieces of content of images, texts and sounds. The inspirational idea of working in GANs has been derived from game theory, named as the zero-sum game. GANs consist of two components-a generator as well as a discriminator both of which act like two players of the game playing in opposition with each other. This paper focuses on the basic theory and principle mechanism of GANs. Next, the paper discusses few variants based on architecture as well as loss functions of some kinds. Finally, the last section of paper presents few other variants of GANs which are implemented in the field of computer vision and other real world problems. It is found that this area has a wider scope in terms of virtual real interaction and integration along with parallel learning. So it is considered as new implementation area for GANs in the coming future.
Anytime Minibatch with Delayed Gradients: System Performance and Convergence Analysis

We present convergence analysis of Anytime Minibatch with Delayed Gradients (AMB-DG) algorithm. In AMB-DG, workers compute gradients in epochs of fixed duration while the master uses stale gradients to update the optimization parameters. We analyze AMB-DG in terms of its regret bound and convergence rate. We present results for convex smooth objective functions which show that AMB-DG achieves the optimal regret bound and convergence rate. To complement our theoretical contribution, we deploy AMB-DG on SciNet, an academic high performance cloud computing platform, and compare its performance with that of the K-batch async scheme. K-batch async provides a baseline for schemes that exploit works completed by all workers while using stale gradients. In our experiments, for MNIST AMB-DG converges 2.45 times faster than K-batch async.
Box Particle Fast Labeled Multi-Bernoulli Filter

This paper presents a box particle fast labeled multi-Bernoulli (BP-FLMB) filter to reduce the computational burden of the sequential Monte Carlo fast generalized labeled multi-Bernoulli (SMC-FGLMB) filter. Firstly, the joint prediction and update formulas based on standard LMB filter are derived. Then, Gibbs sampling is used to truncate the hypothesis components. Finally, the fast LMB (FLMB) filter is implemented by box particles. The simulation results show that the proposed BP-FLMB filter has better real-time performance.
An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia

HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules.
Reference Images Development for Evaluation of Tone Mapping Operators

High Dynamic Range (HDR) imaging has been deployed in many multimedia devices recently. Various tone mapping operators (TMOs) have been developed which transform HDR radiance to the display devices. The objective of this study is to develop reference images for subjective evaluation of TMOs and full reference image quality metrics. For this purpose two psychophysical experiments were conducted. In the first experiment, high quality reference images were obtained containing the right features of colourfulness, sharpness and contrast. The reference images were used to evaluate the TMOs in subjective and objective assessments. In the second experiment, five popular TMOs were evaluated subjectively. It was found that Reinhard’s photographic tone reproduction based local TMO performed best among the five TMOs. Three full reference image quality metrics SSIM, CIELAB (2:1) and S-CIELAB were used to evaluate structural similarity and colour image quality of those TMOs. Their results showed that the three metrics agreed well with the visual results.
Silhouettes Based-3D Object Reconstruction Using Hybrid Sparse 3D Reconstruction and Volumetric Methods

This paper presents a hybrid approach for 3D object reconstruction from multiple images taken from different viewpoints. The proposed method allows to obtain a complete and automatic reconstruction from limited number of images. It begins with a sparse 3D reconstruction based on camera self-calibration and interest point matching between images. The integration of sparse approach allows us to automatically estimate the projection matrices without using a turn-table (controlled environment) often used in the Shape from Silhouette approach. In addition, it offers the possibility of an accurate estimation of the initial bounding box of the object. This bounding box is discretized into voxels afterward. Then, the reconstruction process consists in using the image Silhouettes and the photo-consistency test to finally have a volumetric textured model that can be transformed into surface model by applying the marching cube algorithm. The experiments on real data are performed to validate the proposed approach; the results indicate that our method can achieve a very satisfactory reconstruction quality.
Mathematical Model of Transposition Chaotic Encryption System Based on Field-Programmable Gate Arrays for Multimedia Data

In the paper, the mathematical model of memory consumption and through-put parameter estimation of a cryptographic system that employs transposition chaotic maps is investigated. The target platform for the system implementation is Field-Programmable Gate Array (FPGA) devices. The proposed mathematical model takes into account limitations of FPGA circuits and can be used to assess usage of FPGA memory resources preliminary and to evaluate throughput efficiency of the encryption system. First, expressions for memory consumption are introduced. The second part is devoted to analysis of the system throughput. Different architectures for memory organization have been considered in the paper as well as all stages of the cipher pipeline. The architectures are characterized by the different level of parallelism on the level of message processing that results in variations in throughput value. The work presents generalized expression for throughput estimation for all the considered cipher architectures. It allows the designer of the system to select an FPGA die that fulfills the basic requirements for cryptographic system implementation.
FEDet: Feature Enhancement Single Shot Detector

SSD (Single Shot MultiBox Detector) is one of the out-performing object detection algorithm. Due to the slightly week feature representation obtained by the convolutional neural networks, which the detection results are acquired by these feature maps separately, causing the detection performance less than desirable. In this paper, we propose a semantic supervised module to enhance the representation ability of the shallow feature maps, and a feature fusion method was worked out to boost the disparate feature maps ' detection power . More over, we design a receptive field module to fit in with the various size of objects after the fused feature map. We call our detection algorithm FEDet. The experiments demonstrate that our method can get superior performance on Pascal VOC and MS COCO detection dataset than original SSD algorithm and even some state-of-the-art detectors.
Multilabel Classification Using Low-Rank Decomposition

In the multilabel learning framework, each instance is no longer associated with a single semantic, but rather with concept ambiguity. Specifically, the ambiguity of an instance in the input space means that there are multiple corresponding labels in the output space. In most of the existing multilabel classification methods, a binary annotation vector is used to denote the multiple semantic concepts. That is, +1 denotes that the instance has a relevant label, while −1 means the opposite. However, the label representation contains too little semantic information to truly express the differences among multiple different labels. Therefore, we propose a new approach to transform binary label into a real-valued label. We adopt the low-rank decomposition to get latent label information and then incorporate the information and original features to generate new features. Then, using the sparse representation to reconstruct the new instance, the reconstruction error can also be applied in the label space. In this way, we finally achieve the purpose of label conversion. Extensive experiments validate that the proposed method can achieve comparable to or even better results than other state-of-the-art algorithms.
Motion Based Masking of a Moving Vehicle's Environment

The problem of autonomous vehicle navigation requires the use of high-definition and well-maintained maps. Such a map can be constructed using the method developed for the so-called CrowdMapping architecture. This paper proposes a method for constructing masks for such map creation purposes via segmenting dynamic and static regions of an image sequence. The segmentation is performed by comparing a calculated and a predicted optical flow field. The proposed segmentation algorithm contains a single image depth estimation part for predicting the expected optical flow field. The comparison method of the two flow fields is also presented in this paper. The proposed method has been evaluated both qualitatively and quantitatively using the KITTI vision dataset, and achieved a filtering error of 7...12% compared to manually prepared ground truth images.
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images, which are required to model datasets and a large number of topics, e.g., tens of thousands of topics for industry scale applications. Although distributed CPU systems have been used to address this problem, they are slow and resource inefficient. GPU-based systems have emerged as a promising alternative because of their high computational power and memory bandwidth. However, existing GPU-based LDA systems can only learn thousands of topics, because they use dense data structures, and have linear time complexity to the number of topics. In this article, we propose SaberLDA, a GPU-based LDA system that implements a sparsity-aware algorithm to achieve sublinear time complexity to learn a large number of topics. To address the challenges introduced by sparsity, we propose a novel data layout, a warp-based sampling kernel, an efficient sparse matrix counting method, and a fine-grained load balancing strategy. SaberLDA achieves linear speedup on 4 GPUs and is 6-10 times faster than existing GPU systems in thousands of topics. It can learn 40,000 topics from a dataset of billions of tokens in two hours, which was previously only achievable using clusters of tens of CPU servers.
Upgrading CRFS to JRFS and its Benefits to Sequence Modeling and Labeling

Two important sequence tasks are sequence modeling and labeling. Sequence modeling involves determining the probabilities of sequences, e.g. language modeling. It is still difficult to improve language modeling with additional relevant tags, e.g. part-of-speech (POS) tags. For sequence labeling, it is worthwhile to explore task-dependent semi-supervised learning to leverage a mix of labeled and unlabeled data, besides pre-training. In this paper, we propose to upgrade conditional random fields (CRFs) and obtain a joint generative model of observation and label sequences, called joint random fields (JRFs). Specifically, we propose to use the potential function in the original CRF as the potential function that defines the joint distribution. This development from CRFs to JRFs benefits both modeling and labeling of sequence data, as shown in our experiments. For example, the JRF model (using POS tags) outperforms traditional language models and avoids the need to produce hypothesized labels by a standalone POS tagger. For sequence labeling, task-dependent semi-supervised learning by JRFs consistently outperform the CRF baseline and self-training, on POS tagging, chunking and NER.
HSOP: A Hybrid Service Orchestration Platform for Internet-Telephony Networks

Nowadays Telecom service providers are seeking new paradigms of service creation and execution platform to reduce new services' time to market and increase profitability. However, the existing static services orchestration approaches cannot meet the dynamic complicated business demands. This paper proposes a hybrid service orchestration platform for Internet-Telephony networks. Firstly, designs a hybrid service orchestration language for developers to achieve dynamic and rapid orchestration of new hybrid services over the Internet-Telephony networks. Secondly, proposes an event-driven and component-based hybrid service orchestration container to meet the asynchronous dynamic interactions between hybrid services. Thirdly, proposes a cost-aware auto-scaling approach, including the pre-scaling and real-time scaling stages, to dynamically scale the required resources at different levels. Finally, illustrates the hybrid voice chatting services orchestration scenario, and also the effectiveness and practicability of the proposed platform are validated through extensive experiments.
Region of Interest Extraction Based on Convolution Neural Networks for Image Linear Distortion Correction

Image linear distortion, which causes tortuosity and texture loss in the image, is a fundamental problem in computer vision. In the correction processing, extracting the region of interest is the core technology. The commonly used methods include several steps which require the complicated interactive operation before perspective transformation. In this paper, we propose an accurate and fast region of interest extraction method based on convolution neural networks(ROI Net), which directly exploits and extracts the region of interest after mass dataset training without manual interactions. As a result, we can simplify the steps and provide more accurate parameters to complete the follow-up perspective transformation work, contributing to the image linear distortion correction. The experimental results can demonstrate that the algorithm proposed in the paper is state-of-the-art and accurate.
Hidden Message Detection in MS-Word File by Analyzing Abnormal File Structure

Messages can be concealed by exploiting the internal structural features of MS-Word (docx) files, which are saved as ZIP files. In the MS-Word file which is stored based on ZIP structure, messages can be hidden through structural forgery and modification process. This can also be applied to JPG and PNG images included in OOXML-based internal files. Therefore, it is necessary to analyze forgery and alteration of digital document through internal structure analysis of MS-Word file. In this paper, we examine the vulnerability of OOXML-based MS-Word file structure, and analyze how data can be concealed in MS-Word file. As a detection mechanism, we implemented a software to identify hidden message on MS-Word file efficiently for responding forgery attacks.
Exploring the spatial reasoning ability of neural models in human IQ tests

Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. We hope that our work can encourage further research into human-level spatial reasoning and provide a new direction for future work.
A novel mechanism for dynamic multifarious and disturbed human face recognition using advanced stance coalition (ASC)

Multifarious face recognition is a significant and challenging issue in human face recognition area. All those existing mechanisms for this issue has the least significant features with less expected outcome in the present technical scenario. In this paper, a novel scheme named Advanced Stance coalition to recognize human faces in the frame is propounded. The significant feature in ASC is to reduce the calculation time and to improve the result accuracy. Initially, images collected undergo feature extraction to identify the human face. Then, the median filtering is applied to remove noise and extract its feature. In the output section, MD5 hashing scheme is employed to prevent originality using block dioptry distribution with naive Bayes. The propounded ASC has significant features and suggests many details at training period which offers better results in the examination phase. From the result, it's clear that the proposed scheme is more persistent to noise and other major disturbances.
Theoretical and experimental analysis on the generalizability of distribution regression network

There is emerging interest in performing regression between distributions. In contrast to prediction on single instances, these machine learning methods can be useful for population-based studies or on problems that are inherently statistical in nature. The recently proposed distribution regression network (DRN) [13] has shown superior performance for the distribution-to-distribution regression task compared to conventional neural networks. However, in Kou et al. [13] and some other works on distribution regression, there is a lack of comprehensive comparative study on both the theoretical basis and generalization abilities of the methods. We derive some mathematical properties of DRN and compare it to conventional neural networks. We also perform comprehensive experiments to study the generalizability of distribution regression models, by studying their performance with limited training data, data sampling noise and varying task difficulty. DRN consistently outperforms conventional neural networks, requiring fewer training data and maintaining strong performance with noise. Furthermore, the theoretical properties of DRN can be used to provide some explanations on the ability of DRN to achieve better generalization performance than conventional neural networks.