Reviewerly/exHarmony huggingface dataset:
Designing for liberation

research-article Designing for liberation: our lives, mobility, and technology Share on Authors: Margaret Jack Cornell Tech and Syracuse University Cornell Tech and Syracuse UniversityView Profile , Anupriya Tuli IIIT-Delhi IIIT-DelhiView Profile Authors Info & Affiliations InteractionsVolume 28Issue 2March - April 2021 pp 34–41https://doi.org/10.1145/3448020Published:03 March 2021 0citation2,828DownloadsMetricsTotal Citations0Total Downloads2,828Last 12 Months2,828Last 6 weeks57 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Sentiment-based Similarity Model for Recommendation Systems

Recommendation Systems are tools that interpret the users' preferences in an attempt to generate fitting suggestions. Studies in this domain of research tend to conclude that the numerical user ratings are not powerful enough to truly express the users' preferences. The best way to overcome this is by extending the analysis to other elements provided by the user, such as text-based reviews of items. This data is believed to reveal a deeper understanding of the user's sentiment regarding a certain item. The goal of the proposed paper is to exploit the valuable information offered by the textual reviews, by mixing Sentiment Analysis techniques into the recommendation process. The contributions of this paper bring two major improvements to the traditional k Nearest Neighbors collaborative filtering algorithm. As a first step, a sentiment rating approach is developed based on calculated sentiment scores for each item. The resulting sentiment ratings replace the numerical ones in the recommendation process. Next, a sentiment based user similarity measure is defined taking into account three factors of similitude: the attractiveness, relevance, and popularity of reviews and users. Several experimental setups using two different datasets demonstrate that the newly proposed similarity measure outperforms some of the traditional ones and can be successfully used in the recommendation process.
Holonic Architecture for a Table Grape Production Management System

The management of table grape production is complex, since it must adhere to strict production requirements, facilitate numerous decisions by various experts, and integrate many human workers. This paper discusses the challenges experienced with a current production management system, as well as the potential benefits that could be gained from implementing an improved system. The paper suggests the use of a holonic approach to develop such a table grape production management system – using the ARTI reference architecture to implement the holonic system. The implementation of the ARTI reference architecture is also discussed to show that the architecture can be a feasible solution.
Site Assessment for Wet and Dry Concentrated Solar Power Plant Integration Using an Analytical Hierarchy Process–Geographic Information System Approach: A Case Study in the Marrakesh‐Safi Region

Herein, a combination of a multicriteria decision‐making (MCDM) technique and geographic information system (GIS) is established to identify the most appropriate sites for the installation of large‐scale concentrated solar power (CSP) plants in the Marrakech‐Safi region located in mid‐south Morocco. For this purpose, numerous criteria affecting its deployment, such as climate, orography, location, and water resources, are taken into account. As a CSP plant requires cooling for its power block cycle, wet and dry cooling options are explored to assess the suitability of the field of study to host CSP plants with both scenarios. Results reveal that 62.4% of the study area is deemed appropriate for CSP plant hosting. Moreover, 16.1% and 7.6% of the suitable surface area is evaluated as highly suitable for wet and dry cooling systems, respectively. Nevertheless, the excellent areas represent 4.5% for both scenarios.
Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study

In the context of smart cities, there is a general benefit from monitoring close encounters among pedestrians. For instance, for the access control to office buildings, subway, commercial malls, etc., where a high amount of users may be present simultaneously, and keeping a strict record on each individual may be challenging. GPS tracking may not be available in many indoor cases; video surveillance may require expensive deployment (mainly due to the high-quality cameras and face recognition algorithms) and can be restrictive in case of low budget applications; RFID systems can be cumbersome and limited in the detection range. This information can later be used in many different scenarios. For instance, in case of earthquakes, fires, and accidents in general, the administration of the buildings can have a clear record of the people inside for victim searching activities. However, in the pandemic derived from the COVID-19 outbreak, a tracking that allows detecting of pedestrians in close range (a few meters) can be particularly useful to control the virus propagation. Hence, we propose a mobile clustering scheme where only a selected number of pedestrians (Cluster Heads) collect the information of the people around them (Cluster Members) in their trajectory inside the area of interest. Hence, a small number of transmissions are made to a control post, effectively limiting the collision probability and increasing the successful registration of people in close contact. Our proposal shows an increased success packet transmission probability and a reduced collision and idle slot probability, effectively improving the performance of the system compared to the case of direct transmissions from each node.
A Speech Recognized Dynamic Word Cloud Visualization for Text Summarization

A word cloud is a vivid graphic depiction of the words delimited in a section of text engendered by web-oriented tools and it is a straightforward and visually appealing visualization method for text. It has a versatile utility to provide an overview by fractionating text down to those words with higher frequency. Usually, a word cloud can be built by pure text summarization where monotonous data is turned into a stunning visualization format to highlight important textual content and immediately convey crucial information. It is easy to understand visual data rather than any form of data as they are informal to comprehend and memorize. Word clouds can be exercised to analyze data from social network websites and stream service platforms for identifying the trending updates. This approach is an effective technique used to solve text analysis tasks and evaluate it as a qualitative behavior. The input data may consist of articles and pronouns like `a', `the', `and', `he', etc which have to be ignored and the frequency of significant words should be considered to produce a word cloud. Depending on the frequency of the word, the size of the word will vary while producing a word cloud. In addition to the pre-existing static text word cloud, our method proposes an improvement for the dynamic data input from the user, with speech recognition enabled with guaranteed semantic coherence and spatial stability of the words analyzed. Our work is personified in an interactive visual study system that aids users to perform text analysis and derive insights from a huge collection of documents and voice commands.
WITHDRAWN: An improved harmony search based extreme learning machine for intrusion detection system

Intrusion Detection System (IDS) is one of the best ways to combat several cybercrimes, both at the edge of the network and inside the segments of the internal network. This article proposes a hybrid learning approach namely Improved Harmony Search Extreme Learning Machine based IDS (IHSELMIDS) to classify NSL KDD dataset. This dataset is a polished version of its predecessor i.e., Knowledge Discovery in Databases (KDD). Since 1999, many researchers have relied on the dataset of KDD for the evaluation of anomaly detection system. Thus, it is popularly known as KDD"99 dataset. Improved Harmony Search has been used to boost the weights of input and latent biases for a more robust and stable Extreme Learning Machine (ELM). In addition, the generalized inverse Moore – Penrose is used to systematically evaluate the weights of output. Additionally, to address the curse of high dimensionality in the dataset, correlation-based feature selection with greedy hill climbing is proposed which reduces time complexity while increases computational efficiency. A series of performance evaluation measures such as training and testing accuracy, True Positive Rate (TPR), True Negative Rate (TNR), G-mean, F-score, False Alarm Rate (FAR), Receiver Operating Characteristic curve (ROC) and Confusion Matrix is into consideration to contrast and examine the efficiency, flexibility and reliability of the proposed model. The experimental result showed that IHSELMIDS outperforms all the benchmark models considered in this study.
Cluster State-based Quantum Computing

This chapter is devoted to cluster state-based quantum computing. The chapter starts with a definition of cluster states, followed by a description of their relationship to graph states and stabilizer formalism. We also discuss the relationship between cluster states and qubit teleportation circuits. We further formulate the conditions to be met for a corresponding set of unitaries to be able to generate the cluster state. We then relate the problem of generating the cluster state to the Ising model. The focus is then moved to the universality of cluster state-based quantum computing. We prove that a 1D cluster state is sufficient to implement an arbitrary single-qubit gate, while a 2D cluster state is needed for an arbitrary two-qubit gate by applying the proper sequence of measurements. We demonstrate that a five-qubit linear chain cluster is enough to implement arbitrary single-qubit rotation by employing the Euler decomposition theorem. We also demonstrate that T-shape and H-shape cluster states can be used to implement the CNOT gate. We then discuss cluster state processing by first describing the roles of X-, Z-, and Y-measurements. We further provide a generic 2D cluster state to be used in one-way quantum computation (1WQC) and provide details of the corresponding quantum computational model. During one-way quantum computation, the random Pauli by-product operator naturally arises, and we show that the performance of 1WQC is not affected by it. After that we discuss various physical implementations suitable for 1WQC with special focus being devoted to photonic 1WQC implementations, including resource-efficient linear optics implementation. We describe how the Bell states can be used to form linear chain, T-shape, H-shape, and arbitrary 2D cluster states through type I and type II fusion processes. We describe how the implementation of cluster states and 1WQC can be experimentally demonstrated. Furthermore, we briefly describe the basic concepts of continuous variable cluster state-based quantum computing. Finally, after the chapter summary, we provide a set of problems for self-study.
..Dis-AE-LSTM: Generative Adversarial Networks for Anomaly Detection of Time Series Data

Anomalies in time series data contain valuable information for engineers, scientists and entrepreneurs. However, the anomaly detection in time series is facing with the complexity caused by the large volume and high dimensions of data. To mitigate the problem, we propose an unsupervised anomaly detection method for time series data based on generative adversarial networks (GANs) with the long-short term memory recurrent neural networks (LSTM-RNN) as base modules. The nature of generative models is used to capture the distributions of high dimensional data, where the LSTM module is used to learn the temporal relationships. The proposed framework is designed with the capability to reconstruct input data after training, and the reconstruction errors based anomaly scores are assigned to each time step in the time series, which determines the anomalies. Experimental results show the superiorities and efficiency of the proposed method.
A Genetic Programming Framework for Novel Behaviour Discovery in Air Combat Scenarios

Behaviour trees offer a means to systematically decompose a behaviour into a set of steps within a tree structure. Genetic programming, which has at its core the evolution of tree-like structures, thus presents an ideal tool to identify novel behaviour patterns that emerge when the algorithm is guided by a set fitness function. In this paper, we present our framework for novel behaviour discovery using evolved behaviour trees, with some examples from the beyond-visual range air combat domain where distinct strategies emerge in response to modelling the effects of electronic warfare.
Blockchain-Based Solution for Multiple Operator Spectrum Sharing (MOSS) in 5G Networks

The fifth-generation (5G) and beyond networks will benefit billions of endpoint devices, machines, and users by enabling the wide-area data collection and transmission. This massive growth in these endpoints will require sharing spectrum resources to enable ultra-reliable low-latency communication (URLCC) and to use enhanced mobile broadband (eMBB), which is connectable via over-the-air (OTA) interfaces. These requirements create opportunities for carrier-service providers (CSPs) to dynamically share their bandwidth among on-network and cross-network endpoints. To date, the existing centralized dynamic spectrum sharing solutions do not involve the endpoints. Moreover, these solutions are monopolized by CSPs, and hence raise the users' concerns over the security and privacy of their endpoint data. In this paper, we propose a blockchain-based smart contract (SC) solution to enable secure and dynamic spectrum sharing among endpoint users. We design a multiple operators spectrum sharing (MOSS) SC which enables and facilitates dynamic spectrum sharing among cooperating endpoint users (e.g. autonomous cars in smart cities), in a trusted, decentralized, and secure manner. The proposed SC penalizes the non-cooperating endpoint users. We implement the proposed SC considering the permissionless blockchain requirements, implemented it on the Ethereum network, and tested our proposed solution in terms of gas consumption, security, and reliability. The proposed MOSS SC code has been made publicly available on GitHub.
Research on Name Entity Recognition Method in Civil Aviation Text

Named entity recognition technology has been applied to various fields in recent years, obtaining business entities in civil aviation passenger reviews can quickly locate pain points in the service process. The benchmark BiLSTM model performs well for general text, but there are a large number of composite entities in civil aviation public opinion, which needs to improve the model's ability to recognize local text. The paper propose an ODCNN-BiLSTM-CRF model integrated with convolutional neural network. We obtain the standard data set through manual annotation and semisupervised learning firstly. Then learn the word embedding vector from the dataset. We use one-dimensional convolutional neural network (ODCNN) to obtain local context features of text data, and use long and short-term (LSTM) neural network to obtain context features that depend on long text, the two features were fused to obtain the predictive labels through conditional random field (CRF). Experimental results show that our three proposed models improved the recognition performance compared with the previous benchmark model, with the F value reaching 74.01%, 73.72% and 68.06%.
Data Sharing for Multiple Groups with Privacy Preservation in the Cloud

With almost unlimited storage capacity and low maintenance cost, cloud storage becomes a convenient and efficient way for data sharing among cloud users. However, this introduces the challenges of access control and privacy protection when data sharing for multiple groups, as each group usually has its own encryption and access control mechanism to protect data confidentiality. In this paper, we propose a multiple-group data sharing scheme with privacy preservation in the cloud. This scheme constructs a flexible access control framework by using group signature, ciphertext-policy attribute-based encryption and broadcast encryption, which supports both intra-group and cross-group data sharing with anonymous access. Furthermore, our scheme supports efficient user revocation. The security and efficiency of the scheme are proved thorough analysis and experiments.
SIFT and HOG features for the retrieval of ancient Kannada epigraphs

In this work, the authors presented the indexing method for efficient retrieval of Kannada characters. Here the characters were segmented by the connected component method. Then features were extracted by scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) methods. These features were condensed by principal component analysis. They presented the indexing approach using K-dimensional tree (K-D tree) to improve the identification process. For the experiment, they used their own database. The results of the experiment show that the indexing prior is faster than conventional identification approaches in terms of time to script. From experimentation, they observe that fusion features achieve the maximum accuracy of 90% with varying principal component analysis features.
Multi-Technique Redirected Walking Method

The Room-Scale locomotion method is one of the most realistic locomotion methods used in virtual reality technologies. This is due to the natural interaction obtained through the tracking of its controllers and the head-mounted display with six degrees of freedom. However, its mapping by position between the physical and the virtual world limits the user’s movement to the physical workspace provided by the corresponding device. Redirected Walking methods use gain algorithms that add modifications to the virtual movement of the user, optimizing the virtual workspace in the same physical workspace. In this article, we develop a new Redirected Walking method, which combines the modification of three gain algorithms (curvature gain, rotation gain, and translation gain), a new algorithm (deviation gain), a path predictive method of the user’s locomotion, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proportional distance to the center</i> function, and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user direction smoothing</i> function that softens the effect of the algorithms. Complementing the new method, we offer an automatic calibration system that allows the user to use our method in a personalized way.
A Quantitative and Qualitative Review on the Main Research Streams Regarding Blockchain Technology in Healthcare

(1) Background: Blockchain technology has been gaining high popularity in the healthcare domain. This has brought about a spate of recent studies regarding blockchain technology in healthcare, creating high demand for quantitative or qualitative reviews on the main research streams thereof. In order to contribute to satisfying the high demand, this research presents a quantitative and qualitative review on studies regarding blockchain technology in healthcare. (2) Methods: A quantitative review was performed by searching the Web of Science database for articles published until 10 March in 2020, and a qualitative review was conducted by using the content analysis based on the integrative view of Leavitt’s diamond model. (3) Results: The quantitative review identified five research streams. The number of articles about blockchain technology in healthcare has dramatically increased since 2016, with a compound annual growth rate of 254.4%. English is the most dominant language used in the articles, and the USA and China are the top two countries of origin of the articles, representing overwhelming portions. The IEEE Access, Journal of Medical Systems, Journal of Medical Internet Research, Applied Sciences Basel, and Sensors are the top five journals in terms of publication. The articles showed an L-shaped distribution in terms of their annual average numbers of citations. The qualitative review revealed two research streams. Most of the top 10 articles ranked by their annual average numbers of citations concentrated on developing or proposing new technological solutions using blockchain technology to effectively revolutionize the current methods of managing data in the healthcare domain. The majority of the top 10 articles pursued the convergence of blockchain technology with cloud technology or IoT. (4) Conclusions: This article illuminates the main research streams about blockchain technology in healthcare through a quantitative and qualitative review, providing implications for future research on blockchain technology.
Determination of the relationship pattern of association topic on Al-Qur’an using FP-Growth Algorithms

Abstract The Qur’an is an Islam holy book used as life guidance. Since its function as human life guidance, some strategies and ways are necessary in learning al Qur’an. Many strategies are usable in learning Qur’an, one of them is by learning munasabah science and the relationship between the topic and verses of Qur’an. The development of Data Mining that is expanded with text mining makes it easy to divide and find out the relationship of the topic of Qur’an especially in its translation. FP – Growth Algorithm is one of algorithm with many excellences in obtaining association pattern, in this case by determining topic relationship on Qur’an. The experiment in this research was carried out with 4 models where the best parameter is at minimum support 80% and its minimum confidence is 40%. Rule that is produced by algorithm is 159 with lift ratio 1.203. The best rule is implemented on android programming language as Qur’an learning media.
Predictable and Adaptive Goal-oriented Dialog Policy Generation

Most existing commercial goal-oriented chatbots are diagram-based; i.e., they follow a rigid dialog flow to fill the slot values needed to achieve a user's goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot's logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in several domains show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.