Reviewerly/exHarmony huggingface dataset:
Optimal Management of Virtual Infrastructures Under Flexible Cloud Service Agreements

Cloud-service agreements entail the provisioning of virtual infrastructures at specified availability levels. The agreements specify the infrastructure prices and penalties borne by providers when assured availabilities are not met. Hence, backup resources should be optimally provisioned by balancing the provisioning costs with penalties. We develop stochastic dynamic optimization models of the backup provisioning problem by integrating methods from Operations Management, Statistics and Machine Learning, and Computer Science. We present two sets of dynamic provisioning strategies: periodic policies where resources are adjusted at regular intervals and aperiodic policies that allow flexible timing of such interventions. A closed-loop optimization model under conservative resource control and a certainty-equivalent optimization model under aggressive resource control are developed for periodic management. Similarly, we propose two strategies for aperiodic management: single intervention with single look-ahead and multiple interventions with single look-ahead. We develop computationally efficient online algorithms for both periodic and aperiodic models. We evaluate the models through computational experiments and validate with Amazon EC2 use cases. We provide cloud service providers practical guidelines for efficiently formulating flexible service agreements and cost-effectively managing the virtual infrastructure resources. The proposed resource optimization framework is most suitable to IaaS and can easily be extended to PaaS environments.
Comparative study of phase unwrapping algorithms based on solving the Poisson equation

Phase unwrapping is a crucial process to obtain the absolute phase profile in many optical phase measurement techniques such as interferometry, holography, profilometry, etc. In this paper, we have studied several phase unwrapping algorithms based on solving the discrete Poisson equation. The differences among those algorithms lie in two aspects: one is the way to calculate the input for the Poisson equation using the wrapped phase data and the other is the way to compute the output (unwrapped phase data) using the corresponding input. Firstly, the method to compute the input for the Poisson equation was investigated using the finite difference (FD) and fast Fourier transform(FFT) method. Then different methods, based on FFT or discrete cosine transform (DCT), were employed to calculate the unwrapped phase, and their performances were compared in terms of accuracy and efficiency. To enhance the precision of those algorithms, an iteration strategy was introduced and its performance was investigated under different noise conditions. Finally, several real phase data were tested by using the direct and iterative methods. The detailed software package can be found in https://www.mathworks.com/matlabcentral/fileexchange/71810-phase- unwrapping-algorithms-by-solving-the-poisson-equation.
Multilingual Cyber Abuse Detection using Advanced Transformer Architecture

The rise in the number of active online users has subsequently increased the number of cyber abuse incidents being reported as well. Such events pose a harm to the privacy and liberty of users in the digital space. Conventionally, manual moderation and reporting mechanisms have been used to ensure that no such text is present online. However, there have been some flaws in this method including dependency on humans, increased delays and reduced data privacy. Previous approaches to automate this process have involved using supervised machine learning and traditional recurrent sequence models which tend to perform poorly on non-English text. Given the rising diversity of users being a part of the cyberspace, a flexible solution able to accommodate multilingual text is the need of the hour. Furthermore, text in colloquial languages often hold pertinent context and emotion that is lost after translation. In this paper, we propose a generative deep-learning based approach which involves the use of bidirectional transformer-based BERT architecture for cyber abuse detection across English, Hindi and code-mixed Hindi English(Hinglish) text. The proposed architecture can achieve state-of-the-art results on the code-mixed Hindi dataset in the TRAC-1 standard aggression identification task while being able to achieve very good results on the English task leaderboard as well. The results achieved are without using any ensemble-based methods or multiple models and thus prove to be a better alternative to the existing approaches. Deep learning based models which perform well on multilingual text will be able to handle a broader range of inputs and thus can prove to be crucial in cracking down on such social evils.
A new approach to fuzzy control using the distending function

Here, we develop a fuzzy controller using fuzzy arithmetics and a new type of membership function. The proposed new fuzzy control technique is simple, fast and computationally efficient, compared to the classical techniques (Mamdani, Takagi Sugeno) and it can also adapt to the process dynamics. The unique features are: 1) A new class of parametric membership function called the Distending Function (DF) is introduced; 2) A general parametric operator system is used. It utilizes most of the fuzzy operator systems for evaluating the knowledge base; 3) Inference is based on fuzzy arithmetic operations; 4) This leads to a computationally efficient single-step defuzzification. With these concepts, the paradigm of fuzzy control design changes radically. Using this technique with an optimization method, an adaptive fuzzy controller is designed. This adaptive controller adjusts to the changing dynamics of the non-linear processes by tuning our new type of membership function. The effectiveness of the proposed methodology is demonstrated on two industrial processes (a water tank system and continuously stirred tank reactor system).
Dual Adversarial Inference for Text-to-Image Synthesis

Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color, composition, etc.), and the style, which is usually not well described in the text (e.g., location, quantity, size, etc.). However, in previous works, it is typically treated as a process of generating images only from the content, i.e., without considering learning meaningful style representations. In this paper, we aim to learn two variables that are disentangled in the latent space, representing content and style respectively. We achieve this by augmenting current text-to-image synthesis frameworks with a dual adversarial inference mechanism. Through extensive experiments, we show that our model learns, in an unsupervised manner, style representations corresponding to certain meaningful information present in the image that are not well described in the text. The new framework also improves the quality of synthesized images when evaluated on Oxford-102, CUB and COCO datasets.
Study on the intelligent system of sports culture centers by combining machine learning with big data

Abstract With the vigorous development of sports, people’s awareness of engaging in sports has gradually increased, and the requirements for a sports culture center have been higher. However, the service system of traditional sports cultures center is single, which cannot meet people’s growing experience needs. Therefore, it is urgent for the service system of sports culture centers to move towards intellectualization. Firstly, this paper discusses the service system of traditional sports culture centers and finds that there are some problems, such as slow transmission of information, poor sharing of resources, and weak flexibility of response, which seriously affect the consumer experience of users and restrict the development of sports culture centers. Then, with the help of computer network technology, the design of intelligent system architecture of sports culture centers is completed, which makes many intelligent subsystems interconnected and interoperable, integrates information, realizes the integration of data application network, and achieves the goal of resource sharing and function upgrading. Then, based on the intelligent system, the big data platform is built with the help of big data technology, and the support vector machine-back propagation (SVM-BP) neural network composite model is used to realize the prediction of the passenger flow in the cultural center, which provides guidance for adjusting the service plan in advance, effectively coping with the peak passenger flow and improving the user experience. Finally, through empirical analysis, we know that the design of an intelligent system greatly improves the service quality of cultural centers. The research results not only achieve a significant increase in passenger flow but also provide an effective way for the service of sports culture centers to move towards intellectualization.
Domain Adaptation for Car Accident Detection in Videos

In this paper, we implement a deep learning model for car accident detection using synthetic videos while adapting the model, using domain adaptation (DA), to real videos from CCTV traffic cameras. The synthetic data are rendered using a video game. The reason to use such data is the lack of real videos of car crashes from CCTV. Though a video game may allow us to generate car crashes in a variety of scenarios, the distinction in synthetic and real videos can negatively affect the model's performance. Accordingly, our aim is three-fold: render numerous synthetic videos having significant variations, train a 3D CNN based deep model on the collected videos, and use DA to adapt the model from synthetic to real videos. Our experimental results, obtained under a variety of experimental setups, demonstrate the feasibility of using our approach for car accident detection in real videos.
Exposure-Based Energy Curve Equalization for Enhancement of Contrast Distorted Images

This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.
First Arrival Time Identification Using Transfer Learning With Continuous Wavelet Transform Feature Images

In our work, the deep learning technique has been used to develop an automatic method for identifying the first arrival times of seismic waves. This method introduces transfer learning to train a deep neural network, given a limited number of continuous wavelet transform (CWT) feature images as input. The application of the CWT for feature extraction, aimed at detecting abrupt changes in the amplitude, phase, and frequency produced by first arrivals as a whole rather than any single characteristic, provides the most informative images. First, we apply the CWT to each seismic trace to obtain the CWT feature images and split them into a set of subimages. Then, a pretrained convolutional neural network (CNN) is fine-tuned with limited labeled subimages. The resulting model can be used to predict probability distributions of noise, first-break, and post first-break. Finally, the first arrival times are extracted from the peaks of the probability distributions. We have tested the performance of the method using vibroseis, dynamite, and air gun shot records, which include various types of seismic waves and noise. More accurate and robust results can be obtained with the proposed method compared with the short-time and long-time average (STA/LTA) algorithm and the adaptive multiband picking algorithm (AMPA).
Logistic Sine Map Based Image Encryption

In this paper, an image encryption algorithm is proposed in which first the image pixels are shuffled using the Arnold map. Second, a 256-bit key is generated using a 2D logistic Sine map and a congruential generator. Then, the pixel values of the shuffled image will be changed by XORing them with the key, producing the encrypted image. The security of the resulting encrypted image is tested using multiple metrics which exhibit the quality of the proposed image encryption algorithm.
Spatial Attention for Multi-Scale Feature Refinement for Object Detection

Scale variation is one of the primary challenges in the object detection, existing in both inter-class and intra-class instances, especially on the drone platform. The latest methods focus on feature pyramid for detecting objects at different scales. In this work, we propose two techniques to refine multi-scale features for detecting various-scale instances in FPN-based Network. A Receptive Field Expansion Block (RFEB) is designed to increase the receptive field size for high-level semantic features, then the generated features are passed through a Spatial-Refinement Module (SRM) to repair the spatial details of multi-scale objects in images before summation by the lateral connection. To evaluate its effectiveness, we conduct experiments on VisDrone2019 benchmark dataset and achieve impressive improvement. Meanwhile, results on PASCAL VOC and MS COCO datasets show that our model is able to reach the competitive performance.
Digital touch for remote personal communication: An emergent sociotechnical imaginary

This article makes legible emergent social imaginaries of digital touch for remote communication in personal relationships, with attention to digital touch interfaces. It draws on data from rapid prototyping research workshops with apprentice professionals embedded within digital communication. Touch is discussed with respect to four analytical themes: materiality, body, emplacement and temporality. We illustrate how participants’ past and present experiences and future visions of remote digital touch thread through these themes and weave together to form a hegemonic, emergent sociotechnical imaginary of digital touch. The article contributes to social debates within digital personal remote communication by foregrounding touch, the material and the sensorial. The article’s novel interdisciplinary framework (combining design-based rapid prototyping with a multimodal and multi-sensorial analysis within the frame of the sociotechnical imaginary) also contributes to methodology around future-facing phenomena, prior to the process of their solidification into material, political formations.
Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification

Biomedical image classification often deals with limited training sample due to the cost of labeling data. In this paper, we propose to combine deep learning, transfer learning and generative adversarial network to improve the classification performance. Fine-tuning on VGG16 and VGG19 network are used to extract the good discriminated cancer features from histopathological image before feeding into neuron network for classification. Experimental results show that the proposed approaches outperform the previous works in the state-of-the-art on breast cancer images dataset (BreaKHis).
Learning to Caption Images Through a Lifetime by Asking Questions

In order to bring artificial agents into our lives, we will need to go beyond supervised learning on closed datasets to having the ability to continuously expand knowledge. Inspired by a student learning in a classroom, we present an agent that can continuously learn by posing natural language questions to humans. Our agent is composed of three interacting modules, one that performs captioning, another that generates questions and a decision maker that learns when to ask questions by implicitly reasoning about the uncertainty of the agent and expertise of the teacher. As compared to current active learning methods which query images for full captions, our agent is able to ask pointed questions to improve the generated captions. The agent trains on the improved captions, expanding its knowledge. We show that our approach achieves better performance using less human supervision than the baselines on the challenging MSCOCO dataset.
Generative music in video games: State of the art, challenges, and prospects

Music is a common element in almost all video games. Most music in games is written by a human composer, and played as a linear piece behind gameplay. Adaptive and/or Generative music systems can be used to extend the musical content or create new musical content using algorithms and AI. While there is research into these systems, there has yet to be an organized examination of their architecture and use. We present a taxonomy of generative music in games, to allow for examination and discussion of generative music systems. In doing so, we also present a survey of the current state of the art of generative music systems in games, and discuss challenges and prospects of generative music for games.
TKG: Efficient Mining of Top-K Frequent Subgraphs

Frequent subgraph mining is a popular data mining task, which consists of finding all subgraphs that appear in at least minsup graphs of a graph database. An important limitation of traditional frequent subgraph mining algorithms is that the minsup parameter is hard to set. If set too high, few patterns are found and useful information may be missed. But if set too low, runtimes can become very long and a huge number of patterns may be found. Finding an appropriate minsup value to find just enough patterns can thus be very time-consuming. This paper addresses this limitation by proposing an efficient algorithm named TKG to find the top-k frequent subgraphs, where the only parameter is k, the number of patterns to be found. The algorithm utilizes a dynamic search procedure to always explore the most promising patterns first. An extensive experimental evaluation shows that TKG has excellent performance and that it provides a valuable alternative to traditional frequent subgraph mining algorithms.
Robust Image Hashing With Singular Values Of Quaternion SVD

Abstract Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance.
Non-Functional Requirements Classification with Feature Extraction and Machine Learning: An Empirical Study

Non-Functional Requirements (NFR) describe a set of quality attributes required for a software such as security, reliability, performance, etc. Extracting and considering NFR from software requirement specification can help developers to deliver quality software which meets users expectations completely. Since, the functional and non-functional requirements are mixed together within the same SRS, it requires a lot of human effort for distinguishing them. This paper proposed automatic NFR classification approach for quality software development by combining machine learning feature extraction and classification techniques. An empirical study with seven machine learning algorithms and four feature selection approaches have been applied to automatically classify NFR for finding out the best pair. The experiments were measured with statistical analysis including precision, recall, F1-score, and accuracy of the classification results through all the combinations of the techniques and algorithms. It is found that, SGD SVM classifier achieves best results where precision, recall, F1-score, and accuracy reported as 0.66, 0.61, 0.61, and 0.76 respectively. Additionally, TF-IDF (character level) feature extraction technique illustrated higher average score than others.
Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning

Dogs are one of the most common domestic animals. Due to a large number of dogs, there are several issues such as population control, decrease outbreak such as Rabies, vaccination control, and legal ownership. At present, there are over 180 dog breeds. Each dog breed has specific characteristics and health conditions. In order to provide appropriate treatments and training, it is essential to identify individuals and their breeds. The paper presents the classification methods for dog breed classification using two image processing approaches 1) conventional based approaches by Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) 2) the deep learning based approach by using convolutional neural networks (CNN) with transfer learning. The result shows that our retrained CNN model performs better in classifying a dog breeds. It achieves 96.75% accuracy compared with 79.25% using the HOG descriptor.
A Clustering Method of Asset Cybersecurity Classification

A formal method for asset classifying by cyber security is provided. The problem is solved in the frame of a clustering problem. The data structure and content are specified. A classification algorithm for systems with arbitrary number of subsystems is provided.
Fighting African capital flight: trajectories, dynamics, and tendencies

Abstract This study provides a harmonization framework for common capital flight policies in Africa. It builds on evidence of persistent extreme poverty in the continent to assess how common measures can be adopted by sampled countries on one cause of extreme poverty: capital flight. The dataset is sub-divided into fundamental characteristics of African capital flight based on income levels, legal foundations, natural resources, political stability, regional proximity, and religious domination. The main finding shows that from a projection date of 2010, a feasible timeframe for harmonizing policies is between 2016 and 2023. This timeframe coincides with the beginning of the post-2015 agenda on sustainable development goals.