Reviewerly/exHarmony huggingface dataset:
The Impact of Cloud Computing on Organizational Performance

The use of cloud computing has remarkable advantages in business performance. It is related especially in the portion of the organizational environment, such as organizational culture and organizational agility. Organizational agility provides an easier process to search and retrieve knowledge and allow the businesses to utilize and apply this knowledge to get high-quality services. Agility and culture factors can help organizations to enhance their cloud computing adoption. The achievement of any organization is dependent upon human resources. With human resources, the organization can develop its employees by sharing knowledge, skill, and experience of personnel. Expert cloud has a significant impact on and direct relation with human resources as it facilitates the communication among human resources better, more efficiently, and reduces the cost of the service. In this paper, the authors discuss the relationship between expert cloud and human resources to enhance the organizational performance through the assistance of organizational agility and culture.
AI BASED FACIAL RECOGNITION TECHNOLOGY AND CRIMINAL JUSTICE: ISSUES AND CHALLENGES

Law enforcement agencies in many countries have initiated the use of AFRS (Automated FacialRecognition System),it’s an artificial intelligence system that compares face traits automatically,to identify unknown suspects using images and other social media platforms. Its capabilities arefast developing in conjunction with artificial intelligence, and it has a lot of potential for solvingcrimes. However, it also has a negative connotation. There are substantial privacy and ethicalconsiderations that necessitate legislation and regulation. The Ministry of Home Affairs of Indiahas released a new plan for AFRS aimed at upgrading law-enforcement agencies for the purposeof detection of criminals, proper data exchange among law enforcement agencies throughout theglobe. It is indeed evident to say, India is third largest country in technological development andthe citizens are coping up with these changes. The author proposes to examine the emergence ofAutomated Facial Recognition System, contemporary uses, and legislative developments invarious countries like US, UK and Australia, as well as doing an ethical examination of theconcerns that may arise during its implementation. The author will also implicate the authenticityof FIR registered against unknown person through the use of AFRS, and legislations related to it.Ethical considerations are used to mediate conflicting issues with reference to modern innovationvis-a-vis public safety mechanism, privacy and democratic responsibility.
Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applied several pre-processing techniques. Secondly, we have used the fastText method for vectoring the analysed tweets. Thirdly, we have implemented the CNN for extracting and selecting the pertinent features from the tweets. Fourthly, we have fuzzified the CNN output using the Gaussian Fuzzification (GF) method for coping with vague data. Then we have applied fuzziness C4.5 for creating the fuzziness rules. Finally, we have used the General Fuzziness Reasoning (GFR) approach for classifying the new tweets. In summary, our method integrates the advantages of CNN and C4.5 techniques and overcomes the shortcomings of ambiguous data in the tweets using FRBS, which is consists of three-phase: fuzzification phase using GF, inference mechanism using fuzziness C4.5, and defuzzification phase using GFR. Also, to give our approach the ability to deal with the massive data, we have implemented it on the Hadoop framework of five computers. The experiential findings confirmed that our model operates excellently compared to other chosen models form the literature.
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers

This paper investigates continual learning for semantic parsing.In this setting, a neural semantic parser learns tasks sequentially without accessing full training data from previous tasks.Direct application of the SOTA continual learning algorithms to this problem fails to achieve comparable performance with retraining models with all seen tasks, because they have not considered the special properties of structured outputs, yielded by semantic parsers.Therefore, we propose TO-TAL RECALL, a continual learning method designed for neural semantic parsers from two aspects: i) a sampling method for memory replay that diversifies logical form templates and balances distributions of parse actions in a memory; ii) a two-stage training method that significantly improves generalization capability of the parsers across tasks.We conduct extensive experiments to study the research problems involved in continual semantic parsing, and demonstrate that a neural semantic parser trained with TOTAL RECALL achieves superior performance than the one trained directly with the SOTA continual learning algorithms, and achieve a 3-6 times speedup compared to retraining from scratch.Code and datasets are available at:
Adaptive scene-aware deep attention network for remote sensing image compression

Deep neural networks have been successfully used in image processing and have benefitted image compression technology. On this basis, deeper and more extensive use of compression algorithms in remote sensing applications was investigated in our study. As a general residual block does not effectively utilize the connections between channels and spatial regions in the reconstruction process, an attention mechanism codec framework based on the channel-spatial attention residual block was introduced. Consequently, the network can account for the interdependence between channels and enhance specific spatial areas. Simultaneously, the feature importance map makes the network focus on valuable information, especially for small objects and remote sensing images with many tiny details, which is crucial to describe their contours. Introducing the attention mechanism commonly used in target detection can effectively recover the missing detailed information for remote sensing compressed images. Moreover, to utilize the underlying information of the various scene categories and characterize the feature differences between scenes in remote sensing images, an adaptive scene-aware module was introduced to enhance the feature representation in remote sensing images of different scenes. The experimental results show that the proposed method has superior subjective and objective effects in remote sensing image compression tasks.
Prediction of Chaotic Time Series Based on BEN-AGA Model

Aiming at the prediction problem of chaotic time series, this paper proposes a brain emotional network combined with an adaptive genetic algorithm (BEN-AGA) model to predict chaotic time series. First, we improve the emotional brain learning (BEL) model using the activation function to change the two linear structures the amygdala and the orbitofrontal cortex into the nonlinear structure, and then we establish the brain emotional network (BEN) model. The brain emotional network model has stronger nonlinear calculation ability and generalization ability. Next, we use the adaptive genetic algorithm to optimize the parameters of the brain emotional network model. The weights to be optimized in the model are coded as chromosomes. We design the dynamic crossover probability and mutation probability to control the crossover process and the mutation process, and the optimal parameters are selected through the fitness function to evaluate the chromosome. In this way, we increase the approximation capability of the model and increase the calculation speed of the model. Finally, we reconstruct the phase space of the observation sequence based on the short-term predictability of the chaotic time series; then we establish a brain emotional network model and optimize its parameters with an adaptive genetic algorithm and perform a single-step prediction on the optimized model to obtain the prediction error. The model proposed in this paper is applied to the prediction of Rossler chaotic time series and sunspot chaotic time series. The experimental results verify the effectiveness of the BEN-AGA model and show that this model has higher prediction accuracy and more stability than other methods.
TheraTrousers: Reactive wearable for playful therapeutic self-empowerment

This paper introduces a smart garment for motivating children and teens to perform gait therapy exercises, while enabling them to do so at home, outdoors or otherwise outside a practitioner's facilities. Taking the shape of a pair of trousers that look and behave like sci-fi attire, the wearable connects with a similarly themed game that turns physiotherapy sessions into exciting explorations in a light-hearted science-fiction scenario. The garment includes embedded sensors and actuators, connected via embroidered conductive yarn tracks, and its overall construction was achieved using standardized industry techniques and machinery, aimed at large-scale automated production. While measuring the user's activity to detect the performance of specific movements, the wearable also reacts to these, as well as other gameplay events, as a form of feedback and positive reinforcement. The overall aim was both to increase enjoyment during and compliance to therapy routines, and to foster autonomy, self-empowerment and a positive outlook for user.
The Practice of Mapping-based Navigation System for Indoor Robot with RPLIDAR and Raspberry Pi

This paper presents the prototyped design implementation of a mapping mobile robot for an indoor environment. The system design in this paper includes a Raspberry Pi connected to RPLIDAR A1 and other devices like Arduino, encoder. The low-cost mapping mobile robot emerges with features like SLAM which has the capability to form the Map of the environment using Lidar scans and robotic operating system software package to communicate with ROS in the Raspberry Pi using ROS network configurations. Moreover, the indoor map was built by using an open-source algorithm with hector SLAM software package for indoor SLAM, which can get the indoor grid maps in ROS graphical tool RVIZ. The experimental results of the open-source algorithms were instructed to explore the corridor inside a building and do mapping in real-time show that the mobile robot for SLAM is feasible and high-precision grid maps can be constructed.
Photons and qubits get a better connection

Quantum networks will require versatile quantum interconnects.
Similar Questions Correspond to Similar SQL Queries: A Case-Based Reasoning Approach for Text-to-SQL Translation

Based on the natural truth that similar questions correspond to similar SQL queries, a CBR-based approach is proposed to deal with the Text-to-SQL task in this paper. We follow the traditional CBR processes: similarity assessment, case retrieval, and case reuse. First, we introduce a neural classifier in the similarity assessment stage and comprehensively uses classification probability and literal cosine similarity to measure similarity. Then, based on the results of the similarity assessment, our model retrieves a case template. Finally, our model fills the columns and values generated by the Ranker module and Question Answering (QA) module into the solution template. At this point, a SQL query suitable for the new case is generated. We evaluate our models on a large-scale Text-to-SQL dataset—WikiSQL. Experimentally, our model has a competitive performance compared with the baseline and significantly improves the accuracy of the aggregation function prediction.
Secure Data Hiding Technique Using Video Steganography

Moving information through the web has become simple and quick due to the rapid technological innovation. However, it should be highlighted that because of the hazardous nature of the internet, any individual may gain access to and alter the data through hacking. Therefore, it is crucial to protect any personal data against being accessed by unapproved individuals. Cryptography and steganography are the two most common methods available to protect data. Steganography is a method used to cover hidden data in some other media without leaving any proof of the information adjustment, while cryptography changes the plain information to incomprehensible information, which is called scrambled data. This paper presented video steganography as an effective and robust tool to conceal data. We accepted a video as a spread media and used it to cover a document (.pdf) and voice message in such a way that the information is not outwardly conspicuous using LSB Method.
SageDy: A Novel Sampling and Aggregating Based Representation Learning Approach for Dynamic Networks

Served as an important role in numerous machine learning models, network representation learning, also known as graph embedding, aims to represent large-scale networks by mapping nodes into a low-dimensional space. However, transitional approaches mainly focus on the learning on static graphs instead of dynamic situation. Consider the broad existence of dynamic network in real world, this paper proposes a novel framework SageDy (sampling and aggregating on dynamic networks) to address the challenge in dynamic network for representation learning. In SageDy, we first propose a sampling method and a novel aggregator function to achieve high-quality representation; Then we developed two influence factors to measure the time interval influence and information upheaval influence. Finally, a temporal attention network is well introduced to model temporal information. The extensive experiments on four real-world network datasets demonstrate that SageDy could well fit the demand of dynamic network representation and significantly outperform other state-of-the-art methods.
Revisiting the Quantum Open System Dynamics of Central Spin Model

In this work, we revisit the theory of open quantum systems from the perspective of fermionic baths. Specifically, we concentrate on the dynamics of a central spin half particle interacting with a spin bath. We have calculated the exact reduced dynamics of the central spin and constructed the Kraus operators in relation to that. Further, the exact Lindblad type canonical master equation corresponding to the reduced dynamics is constructed. We have also briefly touched upon the aspect of non-Markovianity from the backdrop of the reduced dynamics of the central spin.Quanta 2021; 10: 55–64.
Research on the Application of New Media Technology in The Teaching of Film and Television Aesthetics

This paper introduces the design and implementation of new media film and television aesthetics teaching information management system based on Web. The system adopts JSP platform. At the same time, combined with its database management system SQL Server 2016. The system uses network multimedia technology and distributed object technology to realize multi-level interaction between students and content, students and students, students and teachers. It achieves a friendly interaction effect, and provides a complete set of solutions for the informatization construction of teaching resource management. Finally, the system is successfully applied to the teaching resource management business of a university, which can effectively improve the efficiency of resource management and standardize the business process of resource management. The system is proved to have practical reference and guiding significance.
Off-Policy Differentiable Logic Reinforcement Learning

In this paper, we proposed an Off-Policy Differentiable Logic Reinforcement Learning (OPDLRL) framework to inherit the benefits of interpretability and generalization ability in Differentiable Inductive Logic Programming (DILP) and also resolves its weakness of execution efficiency, stability, and scalability. The key contributions include the use of approximate inference to significantly reduce the number of logic rules in the deduction process, an off-policy training method to enable approximate inference, and a distributed and hierarchical training framework. Extensive experiments, specifically playing real-time video games in Rabbids against human players, show that OPDLRL has better or similar performance as other DILP-based methods but far more practical in terms of sample efficiency and execution efficiency, making it applicable to complex and (near) real-time domains.
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach

This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
A ROS Package for Human-In-the-Loop Planning and Control under Linear Temporal Logic Tasks

In this paper, we propose a ROS software package for planning and control of robotic systems with a human-in-the-Ioop focus. The software uses temporal logic specifications, specifically Linear Temporal Logic, for a language-based method to develop correct-by-design high level robot plans. The approach is structured to allow a human to adjust the high-level plan online. A human may also take control of the robot (in a low-level control fashion), but the software prevents the human from implementing dangerous behaviour that would violate the high-level task specification. Finally, the planner is able to learn human-preferred high-level tasks by tracking human low-level control inputs in an inverse learning framework. The proposed approach is demonstrated in a warehouse setting with multiple robot agents to showcase the efficacy of the proposed solution.
The potential of chatbots in travel and tourism services in the context of social distancing

As many other sectors, the travel and tourism industries faced great challenges following the 2019/2020 health pandemic, with confinements and border closures bringing travel to a halt. Although research exploring the impact of the health crisis is increasing, prior work investigating the potential of emerging technologies to mitigate the negative consequences remains scarce. The purpose of the current paper is to examine tourists' chatbot usage intentions in service encounters within the context of a future international travel, assuming continued social distancing. Our results indicate that automation, habit, social presence and health consciousness all contributed positively to chatbot usage intentions. Further variations were observed as a function of experiencing government-imposed lockdown. The role of social presence and human qualities in chatbots was weakened when controlling for lockdown and during the trip experience. Finally, the impact of health consciousness on use intentions was most pronounced during the travel stage.
Theoretical analyses of univariate estimation-of-distribution algorithms

Optimization is a core part of technological advancement and is usually heavily aided by computers. However, since many optimization problems are hard, it is unrealistic to expect an optimal solution within reasonable time. Hence, heuristics are employed, that is, computer programs that try to produce solutions of high quality quickly. One special class are estimation-of-distribution algorithms (EDAs), which are characterized by maintaining a probabilistic model over the problem domain, which they evolve over time. In an iterative fashion, an EDA uses its model in order to generate a set of solutions, which it then uses to refine the model such that the probability of producing good solutions is increased.In this thesis, we theoretically analyze the class of univariate EDAs over the Boolean domain, that is, over the space of all length-n bit strings. In this setting, the probabilistic model of a univariate EDA consists of an n-dimensional probability vector where each component denotes the probability to sample a 1 for that position in order to generate a bit string.My contribution follows two main directions: first, we analyze general inherent properties of univariate EDAs. Second, we determine the expected run times of specific EDAs on benchmark functions from theory. In the first part, we characterize when EDAs are unbiased with respect to the problem encoding. We then consider a setting where all solutions look equally good to an EDA, and we show that the probabilistic model of an EDA quickly evolves into an incorrect model if it is always updated such that it does not change in expectation.In the second part, we first show that the algorithms cGA and MMAS-fp are able to efficiently optimize a noisy version of the classical benchmark function OneMax. We perturb the function by adding Gaussian noise with a variance of σ², and we prove that the algorithms are able to generate the true optimum in a time polynomial in σ² and the problem size n. For the MMAS-fp, we generalize this result to linear functions. Further, we prove a run time of Ω(n log(n)) for the algorithm UMDA on (unnoisy) OneMax. Last, we introduce a new algorithm that is able to optimize the benchmark functions OneMax and LeadingOnes both in O(n log(n)), which is a novelty for heuristics in the domain we consider.
Securing Healthcare Information Using Blockchain Technology: A Deep Insight

Today’s world has turned its attention toward blockchain technology due to its vast characteristics and essential features, making it apt for securing healthcare information. Apart from healthcare industry, even finance industry makes use of blockchain technology. The attackers may try to hack the system to get the records of the patients to gain useful information. It can also be breached when data is shared across environments such as sharing of data as well as images. The paper addresses blockchain technology as well as issues related to information in healthcare along with the comparison of works, making a deep insight into the issues and solutions. The paper also provides a survey on existing solutions along with good research ideas for future researchers in the field. Finally, the advantages along with the disadvantages of the works available so far have been discussed.
Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer

Recently, a large pre-trained language model called T5 (A Unified Text-to-Text Transfer Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no study has been found using this pre-trained model on Text Simplification. Therefore in this paper, we explore the use of T5 fine-tuning on Text Simplification combining with a controllable mechanism to regulate the system outputs that can help generate adapted text for different target audiences. Our experiments show that our model achieves remarkable results with gains of between +0.69 and +1.41 over the current state-of-the-art (BART+ACCESS). We argue that using a pre-trained model such as T5, trained on several tasks with large amounts of data, can help improve Text Simplification.