Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach to Analyze Classification Result for Twitter Sentiment

Monitoring of social media has been growing day by day and analyzing social data helps in identifying behavior of people. News and media play a biased role in conveying a particular incident or policies. Thus the analysis of social media data such as twitter comments uses sentiment analysis that examines the opinion of people on certain government policy declared by the central government. In this paper, twitter sentiment analysis using SVM and NN has been proposed that analyzing the twitter dataset of particular policies and finding its polarity of sentiment. With a quick increase in the use of the Internet, people can raise out their opinion on social media about a particular topic. This work compares the accuracy between the two classification algorithms namely Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). These two algorithms are supervised algorithms, which mean that the model trains itself with the use of a training dataset and performs testing with the help of the testing dataset. This work proposes the ANN and SVM classification algorithm which is used to classify the emotion behind the text, which can achieve acceptable accuracy in classifying results. It is trained over five hundred epochs. The resulting confusion matrix is obtained by comparing predicted with actual labels. Experiments demonstrate that Support Vector Machine outperforms the accuracy when compared with BPNN.
Sentiment Analysis of Hotel Reviews Using Machine Learning Techniques

Sentiment analysis is the task of identifying opinions expressed in any form of text. With the widespread usage of social media in our daily lives, social media websites became a vital and major source of data about user reviews in various fields. The domain of tourism extended activity online in the most recent decade. In this paper, an approach is introduced that automatically perform sentiment detection using Fuzzy C-means clustering algorithm, and classify hotel reviews provided by customers from one of the leading travel sites. Hotel reviews have been analyzed using various techniques like Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Random Forest. An ensemble learning model was also proposed that combines the five classifiers, and results were compared.
Decisi-o-rama: An open-source Python library for multi-attribute value/utility decision analysis

Environmental decisions are complex as they are multi-dimensional, highly interdisciplinary and not only involve multiple stakeholders with conflicting objectives, but also many possible alternatives with uncertain consequences. The difficulty lies in making trade-offs between tough value trade-offs on the one hand while appreciating uncertain impacts of alternatives on the other. To support decisions tackling such problems, a combination of multi-criteria decision analysis (MCDA) and environmental models is promising yet limited by the available MCDA software. Here, we present Decisi-o-rama, an open-source Python MCDA library for single and sets (portfolios) of alternatives in the context of multi-attribute value/utility theory (MAUT/MAVT). Its development was driven by four aspirations that are crucial for usability in the context of environmental decision-making: (1) interoperability, (2) uncertainty-awareness, (3) computational efficiency, and (4) integration with portfolio decisions. The results indicate that these aspirations are met, thus facilitating the adoption of MCDA methods by environmental researchers and practitioners.
A Geometrical Approach based on 4D Grids for Conflict Management of Multiple UAVs operating in U-space

This paper addresses the problem of conflict management from the tactical point of view for multiple UAVs operating in a common U-space. The proposed solution is based on the representation of the trajectories into a 4D grid to optimize the conflict searching tool and an iterative geometric approach to resolve the conflicts, splitting the multi-conflict problem into several simpler sub-problems, and provide an alternative set of trajectories free of conflicts. The method is assessed through two different metrics: the processing time to find the solution and the deviation from initial trajectories; getting a significantly better performance in comparison with other traditional method.
Continuous Facial Emotion Recognition Method Based on Deep Learning of Academic Emotions

It is important to comprehend students' academic emotions in interactive teaching environments.Academic emotions refer to facial expressions that students display along with their academic performance in a learning process.By noting students' academic emotions, teachers can provide the most suitable teaching material according to the emotions to improve their academic performance and motivation.The results can also be subsequently applied to adaptive learning.Recently, some researchers have attempted to study academic emotions with the aid of facial and emotion recognition technologies.However, most studies focused on the analysis and recognition of a single image.It was not considered that academic emotions are a continuous expression in response to the learning situation over a period of time.To address this problem, a continuous facial emotional pattern recognition method based on deep learning is proposed in this study to analyze academic emotions.This method combines the convolutional neural network (CNN) and the long short-term memory (LSTM) network for deep learning to recognize and analyze the continuous facial academic emotional pattern of students and thus recognize academic emotions.Through this method, the e-learning system can understand the learning progress of students quickly and accurately, and offer the students appropriate teaching materials to enhance their academic performance and motivation.The experimental results showed that the recognition accuracies of the CNN model and CNN plus LSTM were 72.47 and 84.33%, respectively.The combination of two neural networks improved the accuracy by approximately 12% compared with that for the CNN alone.
Hermes: Guidance-enriched Visual Analytics for economic network exploration

The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other. By analyzing the investment flows, it is possible to reconstruct the supply chain for the production of most goods, whose understanding is important to analysts and public officials interested in creating and evaluating strategies for informed and strategic decision making, for instance, adjusting tax policies. Those networks of players and investments, however, tend to be complex and very dense, which leads to over-plotted visualizations that obfuscate precious information such as the dependencies between productive sectors and regions. In this paper, we propose Hermes, a guidance-enriched Visual Analytics environment (named after the Greek God of Commerce) for the exploration of complex economic networks, to uncover supply chains, regions' productivity, and sector-to-sector relationships. With practical knowledge regarding guidance, we designed and implemented a visual sub-graph querying approach to extract patterns from such complex investment graphs obtained from real-world data. We present a three-fold evaluation of the system: we perform a qualitative evaluation of our approach with three domain experts, a separate assessment of the proposed guidance features with an expert researcher in this field, and a case study of Hermes using a bank account network dataset to demonstrate the generalizability of our approach.
Dynamic Context Modeling for Cyber-Physical Systems Applied to a Pill Dispenser

Networked and intelligent cyber-physical systems are being increasingly applied in automation technology. Surrounding parameters representing the ambient environment and the users interacting with these systems can greatly influence their operation and tend to be heterogeneous and dynamic. There is insufficient consideration for these parameters and how they relate to the system and can contribute to optimizing its analysis and operation. Enabling context-awareness for cyber-physical systems presents an approach to consider surrounding and situational factors. In this contribution, the design of context-aware cyber-physical systems is addressed with the goal to first introduce a fitting definition for context-aware cyber-physical systems as well as propose an approach to dynamically model the system's context in relation to its user and environment. The concept is shown for a user-centric pill dispenser, based on which the role as well as applicability of the context model is presented.
Incremental multi-agent path finding

Existing multi-agent path finding (MAPF) algorithms are offline methods that aim at finding conflict-free paths for more than one agent. In many real-life applications it is possible that a multi-agent plan cannot be fully executed due to some changes in the environment (represented as a graph), or in missions in which the agents are involved. Even in the case of a minor change, the offline planning algorithm must be re-started from scratch to generate a new plan, and this often requires a substantial amount of time. Motivated by this real-life requirement, we introduced the Incremental Multi-Agent Path Finding (I-MAPF) problem. Any location (node) in the initial environment (graph) can become unavailable for some time and then become available again. Agents can be informed about these changes before they occur and some agents have to update their plans if they planned to use that location. The Conflict Based Search (CBS) is one of most the successful algorithms in solving MAPF problems. To our best knowledge, there are no currently existing studies that attempt at solving the I-MAPF problem. In this paper, we propose a new method to solve the I-MAPF problem, called CBS-D*-lite. CBS-D*-lite is built upon CBS and avoids re-planning for agents that are not affected by the environmental changes. To achieve this, CBS-D*-lite employs D*-lite, an incremental single-agent path-finding algorithm as the lower-level search method in CBS. We show that the number of time-steps required to solve a problem is generally lower than with regular CBS. Empirically, we show that the CBS-D*-lite provided faster results than regular CBS, and the total cost provided CBS-D*-lite is generally close to the total cost values provided by the regular CBS when there are environmental changes.
Ontology-Based Skill Description Learning for Flexible Production Systems

The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industrial ontologies through inductive logic programming is introduced and benefits and drawbacks of the proposed solution are evaluated.
All but one

Serverless computing through Function-as-a-Service (FaaS) platforms is a popular new cloud computing model, offering high elasticity while being fully-managed. However, FaaS platforms do not quantify elasticity; thus, application developers are unaware of how elastic FaaS platforms are. This paper is an extended version of [18]. The original paper proposes an experiment design and corresponding toolkit for observing elasticity and its associated trade-offs with latency, reliability, and execution costs. Results for evaluating four popular FaaS platforms by AWS, Google, IBM, and Microsoft indicate significant short-comings in terms of elasticity for all but AWS's service offer. One year later, we reproduced all experiments and revisited our discussions. We report significant improvements by Google and IBM. Our results indicate that (currently) all but Microsoft's FaaS platform are elastic with differences in latency and execution costs.
Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound

Breast cancer is the most common invasive cancer in women. Besides the primary B-mode ultrasound screening, sonographers have explored the inclusion of Doppler, strain and shear-wave elasticity imaging to advance the diagnosis. However, recognizing useful patterns in all types of images and weighing up the significance of each modality can elude less-experienced clinicians. In this paper, we explore, for the first time, an automatic way to combine the four types of ultrasonography to discriminate between benign and malignant breast nodules. A novel multimodal network is proposed, along with promising learnability and simplicity to improve classification accuracy. The key is using a weight-sharing strategy to encourage interactions between modalities and adopting an additional cross-modalities objective to integrate global information. In contrast to hardcoding the weights of each modality in the model, we embed it in a Reinforcement Learning framework to learn this weighting in an end-to-end manner. Thus the model is trained to seek the optimal multimodal combination without handcrafted heuristics. The proposed framework is evaluated on a dataset contains 1616 sets of multimodal images. Results showed that the model scored a high classification accuracy of 95.4%, which indicates the efficiency of the proposed method.
Two-Stage Model-Agnostic Meta-Learning With Noise Mechanism for One-Shot Imitation

Given that humans and animals can learn new behaviors in a short time by observing others, the question we need to consider is how to make robots behave like humans or animals, that is, through effective demonstration, robots can quickly understand and learn a new ability. One possible solution is imitation based meta-learning, but most of the related approaches are limited in a particular network structure or a specific task. Particularly, meta-learning methods based on gradient-update are prone to overfit. In this article, we propose a generic meta-learning algorithm that divides the learning process into two independent stages (skill cloning and skill transfer) with a noise mechanism which is compatible with any model. The skill cloning stage enables a good understanding of the demonstration, which helps the skill transfer stage when the robot applies the learned experience into new tasks. The experimental results show that our algorithm can alleviate the phenomenon of overfitting by introducing a noise mechanism. Our method not only performs well on the regression task but is significantly better than the existing state-of-the-art one-shot imitation learning methods in the same simulation environments (i.e., simulated pushing and simulated reaching).
Big data for time series and trend analysis of poly waste management in India

Big data indicate data in huge amount that is increasing in volume with the time duration, it may be about anything. Initially, it was not in use, but now this data can be utilized because it has meaning and directs to open new dimensions of thinking. It may have information of interest for engineering applications, for environmental applications, for security applications or for healthcare applications. This paper has effort to draw the attention of its use to see the trend analysis of polymer waste management. Management of polymer waste has importance to its effective management that may be a problem or may be an achievement according to the effectiveness of its management. To perform this analysis, rich no of papers were investigated to find out a rich amount of quality data. These papers and studies were selected from reputed databases including Web of Science, etc. The study gives an analytical view of the research literature output in plastic waste management in India by Time series and trend analysis.
Quantum temporal imaging of antibunching

We consider transformation of quantum intensity correlations of light, observed by direct photodetection, by a single-lens temporal imaging scheme with magnification factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>M</mml:mi> </mml:math> . We prove that such an imaging scheme performs noiseless stretching or contraction of the second-order intensity correlation function without deteriorating such nonclassical features as antibunching and sub-Poissonian statistics of the photons. We also evaluate the photocurrent fluctuation spectrum for the photodetection of nonclassical light and show that this temporal imaging scheme performs its noiseless scaling by magnification factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>M</mml:mi> </mml:math> without adding any noise. We illustrate our results by two examples of nonclassical light sources: sub-Poissonian laser as a continuous-wave macroscopic nonclassical light source exhibiting antibunching and sub-Poissonian statistics of photons and single-photon source as its pulsed counterpart possessing similar nonclassical features. Our results open interesting possibilities for noiseless control of temporal and spectral characteristics of nonclassical light and can find numerous practical applications in quantum optics and quantum information.
Multi-layer Joint Learning of Chinese Nested Named Entity Recognition Based on Self-attention Mechanism

Nested named entity recognition attracts increasingly attentions due to their pervasiveness in general domain as well as in other specific domains. This paper proposes a multi-layer joint learning model for Chinese named entities recognition based on self-attention aggregation mechanism where a series of multi-layered sequence labeling sub-models are joined to recognize named entities in a bottom-up fashion. In order to capture entity semantic information in a lower layer, hidden units in an entity are aggregated using self-attention mechanism and further fed into the higher layer. We conduct extensive experiments using various entity aggregation methods. The results on the Chinese nested entity corpus transformed from the People's Daily show that our model performs best among other competitive methods, implying that self-attention mechanism can effectively aggregate important semantic information in an entity.
Multi-robot Path Planning and Trajectory Smoothing

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.
Solidarity cryptocurrencies as digital community platforms

We investigate the phenomenon of solidarity cryptocurrencies by combining the societal and communitarian aspects of the traditional community currencies with the the blockchain architecture chosen in a cryptocurrency project. We classify and analyze 20 selected solidarity cryptocurrencies from different countries according to three critical aspects of solidarity cryptocurrencies: scale, territorial scope and price stability. Analysis of these aspects is critical to understand the solidarity cryptocurrency concept that connects the originally separated universes of traditional community currencies and cryptocurrencies. This paper contributes to explain the emergent solidarity cryptocurrencies phenomenon by describing particular types captured by our classification. The main findings about solidarity cryptocurrencies in this study are, first, the alignment between type of governance and the respective architecture platform; second, those with closed governance are more likely to be pegged on fiat currencies or other stable measures; third, those focused on local issues tend to have a higher level of adoption.
Online supervised global path planning for AMRs with human-obstacle avoidance

In smart factories, the performance of the production lines is improved thanks to the wide application of mobile robots. In workspaces where human operators and mobile robots coexist, safety is a fundamental factor to be considered. In this context, the motion planning of Autonomous Mobile Robots is a challenging task, since it must take into account the human factor. In this paper, an implementation of a three-level online path planning is proposed, in which a set of waypoints belonging to a safe path is computed by a supervisory planner. Depending on the nature of the detected obstacles during the robot motion, the re-computation of the safe path may be enabled, after the collision avoidance action provided by the local planner is initiated. Particular attention is devoted to the detection and avoidance of human operators. The supervisory planner is triggered as the detected human gets sufficiently close to the mobile robot, allowing it to follow a new safe virtual path while conservatively circumnavigating the operator. The proposed algorithm has been experimentally validated in a laboratory environment emulating industrial scenarios.
Computer-Aided Diagnosis of Thyroid Nodule from Ultrasound Images Using Transfer Learning from Deep Convolutional Neural Network Models

Nowadays, thyroid cancer is considered as one of the most common endocrine cancer in the human body. Ultrasonography is the primary imaging modality for the diagnosis of thyroid cancer. Computer-Aided assessment of ultrasound images for differentiating malignant nodules from benign nodule may help the clinicians for their decision making, and it leads to early diagnosis and on-time treatment. The important problem is difficulty in capturing features appropriate for differentiating malignant nodules from benign nodules. In this study, we extensively investigated the feasibility of transfer learning technique for the extraction of high-level features from thyroid ultrasound images. Images are preprocessed to adjust the skewed distribution using a cluster-based sampling technique. Pre-trained convolutional neural network models are fine-tuned with these preprocessed Images for the extraction of high-level semantic features from Images. Then the extracted features are fed into several supervised learning algorithms, and the performance of each model is evaluated. The experimental results recommend the viability of the Inception-v3 network and Xception network for efficiently differentiating malignant thyroid nodules from benign nodules.