Reviewerly/exHarmony huggingface dataset:
Classification of Mammogram Masses Using GLCM on LBP and Non-overlapping Blocks of Varying Sizes

Classification of benign and malignant masses in mammograms is a challenging task in the development of computer-aided diagnosis (CAD) system. Feature extraction particularly is the most important step while designing those systems as it contributes positively to the overall performance when features are discriminative. This paper presents texture-based features extraction technique that divides images into non-overlapping blocks of varying sizes prior to extraction. The images are first converted to local binary pattern (LBP) format using LBP operators, and then second-order statistics are derived from these blocks with gray level co-occurrence matrix (GLCM). Support vector machine (SVM) with radial basis function (RBF) kernel and k-nearest neighbors (KNN) were used for classification. The proposed approach was tested on complete and divided images to evaluate the performance using multiple metrics. Our results showed that our approach enhances the classification by 17, 38, 3, and 21% with respect to accuracy, sensitivity, specificity, and AUC.
Privacy-Utility Trade-off with the Stratonovich’s Value of Information

We consider the problem of publishing data with utility and privacy guarantees in a statistical decision-theoretical framework. In this framework, we introduce a statistical decision-theoretic quantity called average gain for measuring not only privacy but also utility. We also show a relationship between the average gain and the $\alpha$-leakage, a tunable leakage measure proposed by Liao et at. Moreover, we formulate the privacyutility trade-off (PUT) problem using Stratonovich’s value of information (VoI) and present an analysis of the PUT.
Dynamic Binary Translation Cache Optimization Algorithm in Cloud Computing Environment

In the dynamic translation process, some static information is needed in order to optimize the Japanese translation process. When there is too much static information, the benefits of optimization are far less than the overhead caused by processing this part of the information. To solve this problem, the typical library function processing of this kind of optimization method is taken as an example to propose a dynamic binary Japanese translation cache optimization algorithm in cloud computing environment. By analyzing the characteristics of dynamic and static fusion of such methods, the data that needs to be queried in the Japanese translation process is optimized, and the implementation of the query is optimized according to the characteristics of the data. By using the optimized query data and query method, the problem that the optimization effect is degraded when the function size is increased when using the library function processing mechanism is solved. The experimental results show that the algorithm is correct and effective.
Efficient Sentiment-Aware Web Crawling Methods for Constructing Sentiment Dictionary

In traditional web crawling, all web pages crawled are first stored to databases.As a result, this approach can store unnecessary web pages and requires additional running time for the construction of a sentiment dictionary in a particular domain because sentiment words should be identified by scanning all web pages in the database.To address these problems, we first define the sentiment-aware web crawling problem and then propose two hash-based methods for the implementation.One is based on hash join and the other is bucket-sorted hash join.In particular, we propose a novel bucket-sorted hash join for the efficient sentiment-aware web crawling method.Our experimental results show that the proposed web crawling method using bucket-sorted hash join outperforms existing web crawling methods by significantly reducing the running time and storage space.In the proposed method, the time taken to execute the sentiment-aware task per web page is 0.016 seconds and the database space can be saved by 59% compared to the existing web crawling methods.
Blockchain-Based Software Architecture Development for Service Requirements With Smart Contracts

We review the background, classification, specifications, and related aspects of using smart contracts in blockchain services and introduce a framework for generating a general software architecture in the context of contract-oriented programming.
Efficient Visual Sentiment Prediction Approaches Using Deep Learning Models

Sentiment analysis of images is becoming very important with the increase in the use of social media by people. Many existing works in the literature focus on textual sentiment analysis with the text being extracted from the social media sites like Twitter, Facebook, Amazon and Movie reviews, etc. But sharing of images and videos through social media is increasing compared to text. Images reflect the sentiment in a much better way compared to the text and thus are preferred in analyzing the sentiment. So, there is a need to develop a robust model to carry out image sentiment prediction. In this paper, we employ a transfer learning model based on VGG-16 architecture to carry out the image sentiment analysis. The dataset employed is the Crowdflower database that has more than 15,000 images (Twitter) URLs with its polarity label (Positive, Negative). The proposed model can handle a large set of data as it is based on deep learning and there is no need for explicit feature extraction. The results show the effectiveness of our proposed model in performing sentiment prediction on images.
Optimal Policies for Distributed Quantum Computing with Quantum Walk Control Plane Protocol

Distributed quantum computing is a promising application of quantum networks as it leverages the power of interconnected small quantum computers to perform generic quantum operations that cannot be tackled by single computers alone. The key question in distributed quantum computing using quantum networks is applying a quantum gate on qubits that are geographically separated. In this work, we propose an optimization framework to describe distributed control policies that implement a quantum circuit with gates acting on qubits located in distinct nodes. The distributed control operations throughout the quantum network can be described by a logical control plane quantum walk protocol. We then investigate the problem of assigning logical qubits in a circuit description to physical qubits in the network, with the objective of minimizing the amount of quantum control information exchanged between nodes. We present an integer programming formulation for the problem that determines both qubit assignment and network paths for the transmission of quantum control information. Our formulation restricts to circuits with 2-qubit controlled gates and highlights the difficulty of the problem in terms of complexity. Our formulation can be used to analyze the demand for network resources in terms of the number of channel uses with respect to node capacity and circuit width.
Toward Measuring the Resemblance of Embedding Models for Evolving Ontologies

Updates on ontologies affect the operations built on top of them. But not all changes are equal: some updates drastically change the result of operations; others lead to minor variations, if any. Hence, estimating the impact of a change ex-ante is highly important, as it might make ontology engineers aware of the consequences of their action during editing. However, in order to estimate the impact of changes, we need to understand how to measure them.
Scalable Programming Workflows for Validation of Quantum Computers

Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.
Privacy-Enhanced Crowdsourcing Data Trading based on Blockchain and Stackelberg Game

Crowdsourcing Data Trading is a novel paradigm, where a platform can aggregate data collected by a group of mobile users. Traditional crowdsourcing data trading systems rely on trusted data trading brokers, which increase costs and cannot prevent collusion. What’s more, they don’t take of data quality and truthfulness into consideration con-currently. The emergence of blockchain and smart contracts brings in a decentralized scheme. However, the selection of the most effective providers remains challenging. To tackle these problems, we propose a dynamic-game-with-complete-information-and-Blockchain-based Crowdsourcing data Trading system (CBCT), which mainly includes a smart contracts called CBCToken. Firstly, we replace the data trading broker and adopt the Stackelberg Game, a Dynamic Game with complete information (DGC), to manage the selection of providers. Moreover, homomorphic watermarking technology is applied to protect the data copyright. Lastly, we deploy BCDToken on an Ethereum test network to demonstrate its practicability and significant performances.
Exploring Pupil Position as An Eye-Tracking Feature for Four-Class Emotion Classification In VR

Abstract This paper presented a preliminary investigation of a novel approach on emotion recognition using pupil position in Virtual Reality (VR). We explore pupil position as an eye-tracking feature for four-class emotion classification according to the four-quadrant model of emotions via a presentation of 360° videos in VR. A total of ten subjects participated in this emotional experiment. A 360° video with four sessions of stimulation of emotions will be presented in VR to evoke the user’s emotions. The eye data were recorded and collected using Pupil Labs eye-tracker and the emotion classification was done by using pupil position solely. The classifier used in this investigation is the Support Vector Machine (SVM) machine learning algorithm. The results showed that the best accuracy achieved from this four-class random classification was 59.19%.
ROLE of cloud computing and its impact on consumer behavior in financial sector

Cloud computing is one of the most potent innovations that has piqued the interest of technologists worldwide. Cloud computing offers several advantages, but it also poses numerous security threats that no business can afford to ignore. Cloud computing has significantly influenced consumer behavior and therefore the sales and marketing outlook of companies. In Cloud Computing, customer relationship management (CRM) has been transformed in ways that benefit both consumers and the business model. Due to cloud computing capabilities and developments, the customer have swiftly acclimated to convenience, quick turnarounds and easy alternatives for purchases. The behavior of consumers was severely influenced by cloud computing. In order to keep up with other competitors on the market, marketers have been forced to respond even more to consumer needs, since companies are in the middle of developing cloud computing systems to make consumers shopping even faster and convenient while simultaneously monitoring consumer behavior. The management of customer relationships is becoming increasingly efficient, with dependable and highly comprehensive data showing consumer behaviors. Storing and assessing that date is essential for sales and marketing because it helps with direct sales efforts and can also reflect emerging market trends, new market forces and ways for businesses to maintain a competitive edge while other companies struggle to develop ways to get a head start on using cloud computing systems.
Incremental Feature Learning Using Constructive Neural Networks

Data-driven applications often change over time by considering new features to improve predictive accuracy. Re-training a model from scratch for every change loses the learned knowledge and is very time-consuming. To fill the big literature gap, we devise an incremental feature learning algorithm using constructive neural networks to include new groups of features gradually and without re-learning from scratch. The algorithm dynamically constructs the final model by determining the optimal network topology leading to the best performance. We demonstrate our algorithm’s efficacy through a regression problem by evaluating the sequential models obtained after extending the feature space incrementally, using different feature rankings. We also assess our algorithm without feature grouping and with the non-incremental learning version.
Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System

Recommender systems are extensively used today to ease out the problem of information overload and facilitate the product selection by users in e-commerce market. Both privacy and security are two major concerns of the user in these systems. For the protection of the user’s rating, there are several existing works on the basis of encryption or randomization methodologies. This paper proposes a methodology that not only protects the privacy of ratings but also provides better accuracy. After applying fuzzification on the user ratings, random rotation and perturbation methods are used before being fed to the collaborative filtering system. In this process, similar users are grouped into clusters by which recommendation is made. By considering different cluster size on four different datasets, the proposed fuzzified k-Mode clustering method provides less MAE and RMSE value as compared to other k-Means and k-Mode clustering approach and also achieves the better privacy than randomized perturbation method by obtaining IVDM value i.e. 0.67, 0.61, 0.55 and 0.7.
Art in the Age of Machine Learning by Sofian Audry

December 07 2021 Art in the Age of Machine Learning by Sofian Audry Art in the Age of Machine Learning by Sofian Audry. Foreword, Yoshua Bengio. ( 2021, The MIT Press.) Hardcover, xix, 214 Pages. Simon Penny Simon Penny * University of California, Irvine, Claire Trevor School of the Arts, Department of Art, Music, and Informatics. penny@uci.edu * Corresponding Author. Search for other works by this author on: This Site Google Scholar Author and Article Information Simon Penny * University of California, Irvine, Claire Trevor School of the Arts, Department of Art, Music, and Informatics. penny@uci.edu * Corresponding Author. Online Issn: 1530-9185 Print Issn: 1064-5462 © 2021 Massachusetts Institute of Technology2021Massachusetts Institute of Technology Artificial Life 1–3. https://doi.org/10.1162/artl_r_00352 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Simon Penny; Art in the Age of Machine Learning by Sofian Audry. Artif Life 2021; doi: https://doi.org/10.1162/artl_r_00352 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2021 Massachusetts Institute of Technology2021Massachusetts Institute of Technology Article PDF first page preview Close Modal You do not currently have access to this content.
Extreme wave height detection based on the meteorological data, using hybrid NOF-ELM method

The realization of accurate extreme wave height occurrence prediction is essential for offshore and onshore structures. In this paper, a new hybrid Natural Outlier Factor-Extreme Learning Machine (NOF-ELM) method is proposed to predict the extreme wave height occurrence based on the meteorological data. Four major hurricanes in the Gulf of Mexico are used to evaluate the performance of the proposed model. Moreover, the effect of metrological parameters on the occurrence of extreme wave height is investigated. The results of the ELM classifier are then compared with traditional classification techniques, including Logistic Regression, C4.5 Decision Trees, Discriminant Analysis, k-Nearest Neighbours, classic Multi-Layer Perceptron neural networks, and Support Vector Machines. The results show that the proposed method performs well in extreme wave height detection based on metrological parameters by mean accuracy higher than 99%. Furthermore, the results indicate that radial basis ELM has the best performance in extreme wave detection.
Multi-Modal Remote Sensing Image Matching Method Based on Deep Learning Technology

Abstract Remote sensing is a scientific technology that uses sensors to detect the reflection, radiation or scattering of electromagnetic wave signals from ground objects in a non-contact and long-distance manner. The images are classified by the extracted image feature information Recognition is a further study of obtaining target feature information, which is of great significance to urban planning, disaster monitoring, and ecological environment evaluation. The image matching framework proposed in this paper matches the depth feature maps, and reversely pushes the geometric deformation between the depth feature maps to between the original reference image and the target image, and eliminates the geometric deformation between the original images. Finally, through feature extraction of the corrected image, the extracted local feature image blocks are input into the trained multi-modal feature matching network to complete the entire matching process. Experiments show that the negative sample set construction strategy that takes into account the sample distance proposed in this experiment can effectively deal with the problem of neighboring point interference in RSI matching, and improve the matching performance of the network model.
A Theoretical Review of Cloud Computing Services in Big Data Analytics

The global cloud market has recently increased dramatically due to the Covid-19 pandemic. In such situation, people are looking for flexibility to work from home without going to the office physically and hence many organizations have started to look for a cloud-based solution. Clearly, Cloud not only offers flexibility, but also provides scalability and availability to an organization especially for startups and SMEs which have not established a firm and stable architecture yet. Hence, this paper aims to provide a better understanding on the model, features, and services to select the most suitable cloud platform based on the objectives or requirements of an organization. Finally, potential challenges of cloud development are also described to assist organizations in deploying cloud as a service in order to make a wise decision beforehand as it would be a long-run processes.
Simpler and Enhanced Multifactorial Evolutionary Algorithms for Continuous Optimization Tasks

Multifactorial optimization has become one of the most promising paradigms for evolutionary multitasking within the field of computational intelligence. This methodology can improve the performance results for multiple, simultaneous optimization problems by exploiting the transfer of genetic information between them. In this paper, we present an indepth analysis of this approach by considering several variations of the standard multifactorial evolutionary algorithm (MFEA). By using a simpler structure together with some enhanced operators, two new multifactorial evolutionary algorithms are proposed. We demonstrate that, compared with the traditional MFEA, our approach produces better results on a set of continuous optimization benchmark problems.
Digital Fabrication: Machine Learning-based Immersive Experiencing for the Virtual Space in a Future Museum

In this paper, “Digital Fabrication” artwork is developed to be exhibited in a future museum. According to a built-in RGB camera, we adopt “Teachable Machine” (Google's AI tool) to train pose classifiers. A game-like sci-fi story script is created for providing immersive user experience. The avatar in the virtual space is interactively fabricated based on the recognized pose belonging to a user. The proposed on-going “Digital Fabrication” artwork is online exhibited on a computer screen.
ORCHID: Optimisation of Robotic Control and Hardware In Design using Reinforcement Learning

The successful performance of any system is dependant on the hardware of the agent, which is typically immutable during RL training. In this work, we present ORCHID (Optimisation of Robotic Control and Hardware In Design) which allows for truly simultaneous optimisation of hardware and control parameters in an RL pipeline. We show that by forming a complex differential path through a trajectory rollout we can leverage a vast amount of information from the system that was previously lost in the ‘black-box’ environment. Combining this with a novel hardware-conditioned critic network minimises variance during training and ensures stable updates are made. This allows for refinements to be made to both the morphology and control parameters simultaneously. The result is an efficient and versatile approach to holistic robot design, that brings the final system nearer to true optimality. We show improvements in performance across 4 different test environments with two different control algorithms - in all experiments the maximum performance achieved with ORCHID is shown to be unattainable using only policy updates with the default design. We also show how re-designing a robot using ORCHID in simulation, transfers to a vast improvement in the performance of a real-world robot.
Facemasks Block Lower Visual Field in Youth Ice Hockey

Wearing a facemask (FM) reduces the spread of COVID-19, but it also blocks a person's lower visual field. Many new public safety rules were created in response to COVID-19, including mandated FM wearing in some youth sports like youth ice hockey. We hypothesized that FM wearing in youth hockey players obstructs the lower field of view and may impact safety. Youth hockey players (n = 33) aged 12.03 (1.6) years button press when they saw an LED on the floor turn on in two conditions (wearing FM or no FM) in random order. An interleaved one-up/one-down two-alternative-forced-choice adaptive staircase design was used. Visual thresholds were calculated for each condition and participant. The visual angle threshold (VAT) was determined using standing eye height and the linear distance from the tip of the skates to the visual threshold. Paired t-tests determined whether mask wearing changed the VAT. We modeled the probability a player could see the puck on their stick in four distinct scenarios to estimate the potential impact of FM wearing during hockey play. The average unmasked VAT (11.4 degrees) was significantly closer to the skates than the masked VAT (20.3 degrees) (p < 0.001). Our model indicated a significant reduction in ability to visualize the puck using peripheral vision when more upright while wearing a FM. FM wearing compromised their lower visual field, suggesting a downward head tilt may be necessary to see the puck. Playing ice hockey while wearing a FM may lead to unsafe on-ice playing conditions due to downward head tilt to see the puck.