Reviewerly/exHarmony huggingface dataset:
Robots Versus Speakers: What Type of Central Smart Home Interface Consumers Prefer?

In smart home environments, central interfaces that take commands from users and give orders to each relevant device appropriately are increasingly important. We investigated the type of central interface that consumers are more willing to adopt and whether these interfaces enhance the evaluation of services provided by smart home devices. This study confirms that speaker interfaces are preferred over social robots, speaker interfaces are perceived by users as more persuasive, and the adoption of central interfaces increases the overall service evaluation.
Application of Computer Technology in Administrative Management

Abstract In recent years, computer technology is developing at a rapid rate, and its development direction is no longer limited to the technology industry, and it has been widely used in various fields. Computer technology has also been introduced into the administrative management of colleges and universities, which not only provides a new working mode for the implementation of the administrative management of colleges and universities, but also helps to improve the level of administrative management of colleges and universities, so that it can provide better services for the development of colleges and universities. Therefore, the article analyzes the application of computer technology in administrative management, aiming to optimize the quality of university administrative management and promote the development of university education.
Secure data sharing for mobile cloud computing using RSA

Abstract Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of information.However, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.
Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs' vast view scope. However, its limitations on flight altitude prevent the UAVs from observing the global map. Thus motion planning in the local map becomes a Partially Observable Markov Decision Process (POMDP) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned systems under partial observation from UAV without reconstruction of global maps. Our algorithm consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV's perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of the memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing it with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate.
<i>Things for thought</i> – a creative toolkit to explore belonging

This paper describes the development of a creative toolkit - Things for thought – designed as a research probe that engages participants through a guided object-based exploration to help them better understand what belonging might mean to them. The toolkit draws on the Māori (indigenous people of Aotearoa New Zealand) creation narrative and uses engagement (via haptic interaction with physical objects), connection (with others by working collaboratively to subjectively identify similarities/differences), and discussion (using abstract physical objects, questions, and inclusive discussion techniques). Things for thought was designed to facilitate engagement between people from a wide range of communities. It uses Māori narrative and cultural values to help people understand and enhance connection to place and community. By using an inclusive cultural framework and a shared language (touch), an opportunity for innovative discussion around the topic of belonging, with the ultimate purpose of designing with socially inclusive communities was created. Plans are to test this toolkit in a range of settings including facilitating community discussions. We imagine that the toolkit could supplement co-creation workshops, workplace interactions, and be used in any context in which an in-depth understanding of oneself and others is useful.
Implementation of Machine Learning for Sentiment Analysis of Social and Political Orientation in Pekanbaru City

Abstract Nowadays, people are free to express their opinions regarding a problem in online social networks. One of most popular social network used to express opinions is Twitter. Public opinion in online social network has become a new source of big data that is interesting to be investigated. Opinion expressed by the public through social media is valuable data that can be further processed by using natural language processing (NLP). This research is expected to explain the social, economic, and political orientation of the people of Pekanbaru city by utilizing NLP algorithm. In addition, in terms of data sources, similar research is dominated by national studies, a little local. This research used Sentiment Analysis of Natural Language Processing (NLP) algorithm to analyze Pekanbaru citizen’s views and perceptions about social and political issues. The methods consist of: (i) data collection, (ii) data preprocessing, and (iii) sentiment classification. Thousands of tweets were extracted from Twitter API platform as research samples. As a result, the research has obtained 833 tweets about social orientation and 156 tweets about political trends. Overall, our tweets mined data were dominated with positive sentiments (53%). Education was the topic with most positive sentiments (42%) while political figure was the topic with most neutral sentiments (65%) and environment was the topic with most negative sentiments (56%). Regarding the discourses, “sampah” (garbage, waste, trash, etc) was the most posted and discussed in Twitter along with floods and air pollution topics.
Movement Strategies in Virtual Reality: The Influence of Effort Costs and Target Depth

Kinematic analysis of users’ movement trajectories can reveal strategic movement biases; however, it is unclear if these strategic behaviors also occur in virtual reality (VR). To examine this question, we adapted a task from previous laboratory studies to examine strategic biases in movement behavior in VR. Twelve participants moved to targets positioned in one of two directions along the x-, y-, or z-axis in a VR inter-face, and trajectory profiles were derived and parsed to identify kinematic milestones. Results showed that users achieved similar task completion times across all conditions but used different strategies to select targets at different locations. These findings confirm that users can adapt their movement behaviors to optimize movement performance in VR and highlight how the constraints imposed by VR environments alter users’ movement strategies.
AMQAN: Adaptive Multi-Attention Question-Answer Networks for Answer Selection

Community Question Answering (CQA) provides platforms for users with various background to obtain information and share knowledge. In the recent years, with the rapid development of such online platforms, an enormous amount of archive data has accumulated which makes it more and more difficult for users to identify desirable answers. Therefore, answer selection becomes a very important subtask in Community Question Answering. A posted question often consists of two parts: a question subject with summarization of users' intention, and a question body clarifying the subject with more details. Most of the existing answer selection techniques often roughly concatenate these two parts, so that they cause excessive noises besides useful information to questions, inevitably reducing the performance of answer selection approaches. In this paper, we propose AMQAN, an adaptive multi-attention question-answer network with embeddings at different levels, which makes comprehensive use of semantic information in questions and answers, and alleviates the noise issue at the same time. To evaluate our proposed approach, we implement experiments on two datasets, SemEval 2015 and SemEval 2017. Experiment results show that AMQAN outperforms all existing models on two standard CQA datasets.
$k$-Anonymity Based Location Data Query Privacy Protection Method in Mobile Social Networks

With continuous development of mobile social network application services, researches on query privacy protection in location-based services has captured more and more interests from scholars. Aiming at the problem that privacy disclosure of location data and query content information of users in mobile social network applications, a privacy query method based on k-anonymity model is proposed in paper, which protects the user's location information on data processor side and location services provider respectively. On the privacy protection processor side, an anonymous frame construction algorithm based on k-anonymity location information is proposed, which randomly selects a point near the longitude and latitude grid as the center, and selects a straight line with the farthest distance between the center and the four vertices on the longitude and latitude grid as the initial radius R, and then constructs the user's location data anonymous frame according to the user-defined privacy configuration to protect the location information when the user queries. On the location service providers side, a location information query algorithm based on dynamic planning is proposed, which takes the center of location information anonymous frame of the query request user as the center, then obtains an area of range and uses the dynamic planning query algorithm to carry out the Top-k ranking to obtain the optimal answer tree, finally feeds back the query results of the Top-k answer tree to the privacy protection processor to protect the user's location query content. Performance analysis and experimental results show that our method can protect privacy and security of location data query with low loss of location data and acceptable efficiency and accuracy of location information query.
A Graph-based Multi-Label Feature Selection using ant Colony Optimization

Multi-label classification aims at considering multiple labels for each sample. The main issue of multi-label classifiers is reducing their performances while tackling with high dimensional tasks. Feature selection is a general approach to overcome this issue. This paper proposes a multi-label feature selection approach using ant colony optimization (ACO). The proposed method is a multivariate and filter approach, which integrates the mutual information with ACO to rank features. In the early step of the proposed method, the solution space is converted to a graph where nodes represent features, and each shows the relevancy between its corresponding features. In this paper, the relevancy between features is calculated by using the mutual information theory. Then, a graph of features is generated, and then ants are moved through the graph to weight the features based on their importance. In this paper, a novel measure based on mutual information is used to evaluate solutions chosen by the ants. This means that in this paper there is no need to use any learning model to evaluate the solutions and thus the proposed method is classified as a filter approach. The quality of each solution is added to its corresponding features as their pheromone values. Finally, the final feature set including those features which take high pheromone values will be created. The performance of our method is evaluated by performing a set of experiments on five datasets. The obtained results show the better performance of our method in comparison with the other methods.
Using simulation to handle implicit likelihoods in a Bayesian analysis

KEY POINTSThis article presents a Bayesian inferential method where the likelihood for a model is unknown, i.e., an implicit likelihood, but where data can easily be simulated from the data model. We use simulated data to estimate the implicit likelihood in a Bayesian analysis employing a Markov chain Monte Carlo algorithm. Two examples are presented.
End-to-end Relation Extraction Using Graph Convolutional Network with a Novel Entity Attention

There are more and more researches on joint relation extraction, however, the current popular joint extraction method has more or less limitations, either the training time is too long or the effect is not very good. In this paper, we propose an end-to-end relation extraction model, without using handcraft features, and propose a novel graph convolutional neural network based on entity attention mechanism which can perform better feature extraction on tree nodes. In addition, for preserving relevant information on the dependency tree to the greatest extent, we use a path-centric pruning strategy to remove irrelevant content, it makes the model more robust. Our model consists of five parts: Bert layer for vector representation, BiGRU layer, CRF layer for sequence labeling, GCN layer and Predict layer. To evaluate our method, we conduct experiments on the public dataset NYT and ACE05. Our model achieve the state of the art on the task of entity and relation extraction.
Regularizing conjunctive features for classification

We consider the feature-generation task wherein we are given a database with entities labeled as positive and negative examples, and we want to find feature queries that linearly separate the two sets of examples. We focus on conjunctive feature queries, and explore two problems: (a) deciding if separating feature queries exist (separability), and (b) generating such queries when they exist. To restrict the complexity of the generated classifiers, we explore various ways of regularizing them by limiting their dimension, the number of joins in feature queries, and their generalized hypertreewidth (ghw). We show that the separability problem is tractable for bounded ghw; yet, the generation problem is not because feature queries might be too large. So, we explore a third problem: classifying new entities without necessarily generating the feature queries. Interestingly, in the case of bounded ghw we can efficiently classify without explicitly generating such queries.
The Light-On Project: Design and Construction of a Sun-Tracking System Using Image Processing

Abstract Solar energy is one of the fastest growing energy industries around the world. One of the main problems faced by PV installations is the diversity of solar energy supply during the day. To increase the efficiency of providing this energy, use solar tracking - A solar tracking system is a specific device intended to move the PV modules in such a way that they continuously face the sun with the aim of maximizing the irradiation received by the PV array. The use of such a solution always increases efficiency, regardless of location. This is crucial in the case of India, which increased its energy production from solar panels by 8 times in 4 years. In this article we present the results of joint work of students from Poland and India. Based on the work, they designed and constructed a solar tracking system based uniquely on image processing. Instead of the most common photoresistors, to determine the position of the sun in the sky, the image from the camera was used, which was then subjected to appropriate filtration to quickly and efficiently determine the brightest point of the image - assuming that this is the position of the sun. Then, based on the coordinates of the brightest point, servo motors position the panel at the right angle, perpendicular to the sun, on the vertical and horizontal axis. The control unit is Raspberry Pi Zero, to which both the servos and the camera are connected. Research in laboratory conditions, under artificial light, confirmed the correct operation of the system. The system should be able to follow the sun also under various weather conditions which are occurring in Nagpur where system is located. It can be used in practical application and operate in real time.
Adaptive Resource Management Utilizing Reinforcement Learning Technique in Inter-Cloud Environments

Abstract In cloud computing, the cloud provider agent offers the quality of service (QoS) for different categories of cloud consumer agents. In general, the inter-cloud environment provides resources as a virtual machine (VM) instance representing processing power, Memory allocated in RAM, and secondary storage for consumer agents with QoS guarantees. A service level agreement framework with a reinforcement learning mechanism is considered for provisioning VM’s for all categories of client classes. The parameters like cost of service, availability, and service demand are considered while provisioning VM’s in the inter-cloud environment. QoS violation happens because another set of cloud consumer agents receives the less no of VMs. In our approach, the adaptive resource provisioning is integrated with reinforcement learning mechanism during the service admission process and ensures the collaborated cloud providers will gain more profits without violation of SLA.
Random Forest Model and Sample Explainer for Non-experts in Machine Learning – Two Case Studies

Machine Learning (ML) is becoming an increasingly critical technology in many areas such as health, business but also in everyday applications of significant societal importance. However, the lack of explainability or ability of ML systems to offer explanation on how they work, which refers to the model (related to the whole data) and sample explainability (related to specific samples) poses significant challenges in their adoption, verification, and in ensuring the trust among users and general public. We present novel integrated Random Forest Model and Sample Explainer – RFEX. RFEX is specifically designed for important class of users who are non-ML experts but are often the domain experts and key decision makers. RFEX provides easy to analyze one-page Model and Sample explainability summaries in tabular format with wealth of explainability information including classification confidence, tradeoff between accuracy and features used, as well as ability to identify potential outlier samples and features. We demonstrate RFEX on two case studies: mortality prediction for COVID-19 patients from the data obtained from Huazhong University of Science and Technology, Wuhan, China, and classification of cell type clusters for human nervous system based on the data from J. Craig Venter Institute. We show that RFEX offers simple yet powerful means of explaining RF classification at model, sample and feature levels, as well as providing guidance for testing and developing explainable and cost-effective operational prediction models.
Efficient Approaches to Agile Cost Estimation in Software Industries: A Project-Based Case Study

Agile was invented to improve and overcome the traditional deficiencies of software development. At present, the agile model is used in software development very vastly due to its support to developers and clients. Agile methodology increases the interaction between the developer-client, and it makes software product defects free. The agile model is fast and becoming more popular because of its features and flexibility. The study shows that the agile software development model is an efficient and effective software development strategy that easily accommodates user changes, but it is not free from errors or shortcomings. The study shows that COCOMO and Planning Poker are famous cost estimation procedures, but are not ingenious for agile development. We conduct a study on real-time projects from multinational software industries using different estimation approaches to estimate the project’s cost and time. We thoroughly explain these projects with the limitations of the techniques. The study has proven that the traditional and modern estimation approaches still have limitations to accurate estimation of projects.
A predictive replication for multi‐tenant databases using deep learning

Abstract The service level agreement (SLA) is an agreement between clients and the service provider, which defines the minimum performance and availability requirements for software. The service provider should have effective strategies for placement, migration, and replication of the tenants to reduce their operational costs, maximize the utilization of their hardware and software resources and accordingly meet the SLA requirements with slight SLA violations. In this research, a clustered‐based multi‐tenant database management system (CB‐MT DBMS) is proposed. Additionally, a dynamic proactive provisioning technique is built using three different prediction models: The Recursive Window Forecasting Autoregressive Integrated Moving Average (ARIMA) model, Exponential Moving Average (EMA) model, and the proposed Recurrent Neural Network (RNN) with Long Short‐Term Memory (LSTM) cells model. Various experimental scenarios with different datasets have been conducted to prove that the RNN model with LSTM cells is a promising solution in multi‐tenant environments, where the tenants have irregular workload patterns. Experimental results firstly show that the RNN model accuracy is superior to their counterparts (i.e., ARIMA and EMA models) when applied to multi‐tenant database workloads generated using TPC benchmarks, as it reduces the prediction error value which is computed using the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) metrics. Secondly, Experimental results prove that the RNN prediction model accuracy is superior to their counterparts for detecting SLA violation values and windows using different SLA values.
COAL: Convolutional Online Adaptation Learning for Opinion Mining

Thanks to recent advances in machine learning, some say AI is the new engine and data is the new coal. Mining this ‘coal’ from the ever-growing Social Web, however, can be a formidable task. In this work, we address this problem in the context of sentiment analysis using convolutional online adaptation learning (COAL). In particular, we consider semi-supervised learning of convolutional features, which we use to train an online model. Such a model, which can be trained in one domain but also used to predict sentiment in other domains, outperforms the baseline in the range of 5-20%.
Introduction to a Support Model Based on Cryptocurrencies for Social Inclusion Projects

Eradication of poverty and social exclusion has been one of the main goals of the international community for several years now. With the implementation of specific policies, frequently directly aligned with the Sustainable Development Goals (SDGs), governments and international organizations adopted measures to assist vulnerable groups worldwide. As data suggests, while existing problems and challenges are far from being solved, new challenges arise. Against this backdrop, in this work, by focusing on specific issues and challenges, and by showcasing developments in the European Union (EU), we propose a model based on cryptocurrency technologies to suggest how certain of these challenges can be addressed. In this view, the findings of research presented here add to the discussion on how to attain SDG 10, i.e. “Reduce inequalities within and among countries”.