Reviewerly/exHarmony huggingface dataset:
Extracting feature requests from online reviews of travel industry

Before product development, Requirement Engineering (RE) is the fundamental need to know customer preferences for any product. Traditionally, RE is carried out in several ways, particularly by conducting interviews, questionnaires, surveys etc. but these methods provide limited amount of data. As user’s preferences vary from country to country for any type of application, it is very hectic and time consuming to collect user requirements from different countries manually. As the internet is widely used now a days, a large number of customer’s reviews are available online that can be used to obtain the requirements for any product without manual work. Online customer reviews can be divided into three types: user experience, bugs and feature requests. Among these 3 categories, feature requests can be very useful for stakeholders (analysts/ requirements engineers) to acquire the requirements of each application. So, the approach is proposed for feature requests extraction from mobile application reviews of travel industry. In this paper, 4 categories of mobile apps of travel industry belonging to 5 countries have been extracted from Google Play Store and Apple Store. For each category, data from 5 different mobile applications have been considered. Since, the review of users from different countries is in their respective language, those reviews are translated into a standard language i.e. English, and then feature requests from these reviews have been extracted. After that, features are retrieved from user reviews and topic modeling is performed on extracted features since one or more features can be modelled under one topic. To know the opinions of users for any feature request, sentiment analysis is also performed on feature request sentences. These feature requests are also classified as Functional and Non-functional Requirements since it is very useful for application developers to improve or maintain the product to better facilitate the application users
Post-hoc recommendation explanations through an efficient exploitation of the DBpedia category hierarchy

Leveraging knowledge graphs for post-hoc recommendation explanations has been investigated in recent years. Existing approaches rely mainly on the overlap properties (encoded by knowledge graphs) that characterize both user liked items and the recommended ones. These approaches, however, do not fully leverage the property hierarchy of knowledge graphs which may lead to flawed explanations. In this paper we introduce an approach that takes the whole property hierarchy into account. This is done with a limited computation time overhead thanks to efficient algorithmic optimizations relying on sub-ontology extraction. The hierarchical relationships among properties are also considered to avoid redundant properties for explanation. We carried out a user study of 155 participants in the movie recommendation domain and used both offline and online metrics to assess the proposed approach. Significant improvements, in terms of informativeness (by 39%), persuasiveness (by 22%), engagement (by 29%) and user trust (by 26%), are suggested by the obtained results, as compared to the state-of-the-art property-based explanation model. Our findings indicate the superiority of accounting for the whole property hierarchy when dealing with post-hoc recommendation explanations.
Information Literacy and Metacognitive Abilities of Teachers: Case of a South African Rural School

The objective of this qualitative study was to assess the information literacy and metacognitive abilities of teachers in a rural school in South Africa. Data were collected through semi-structured interviews. Due to COVID-19 safety considerations, primary data was acquired from 10 teachers via semi-structured telephonic interviews. All participants’ views were transcribed and analysed using thematic analysis to determine the similarities and differences in terms of their responses. The findings of the study amongst others revealed that despite the giant strides made by the South African government to digitize teaching and learning activities in schools, large proportion of rural teachers have phobia to use computer applications unassisted. Also, low self-evaluation, goal-setting and help-seeking/self-reflection were found to be metacognitive abilities which hamper their effective teaching skills via usability of ICT. The study concludes by recommending programmes such as cognitive development, emotional intelligence, computer-digital programming for all teachers in rural schools for optimal performance.
Digital Technologies and Professional Learning of Science Teachers

The application of digital technologies in science education is pervasive and has gained traction in the recent two decades. Research about the adoption of technologies in science education has progressed from examining science teaching and learning through technologies to with technologies, reflecting the evolving conceptualizations of digital technologies in science education. Teachers play critical roles in the purposeful engagement of technologies in the classroom, but there are still gaps in teacher’s knowledge and practices in using technologies in science classrooms. The effectiveness of technology-centric approaches of teacher professional development has also been questioned. In this regard, the emergence of technological pedagogical content knowledge (TPACK) has been used as a framework for more holistic professional development of teachers. This chapter discusses research related to technologies and professional learning of science teachers, including the rationales for the use of TPACK to frame science teachers’ professional learning, the theoretical underpinning of TPACK, the epistemological approaches of professional learning of science teachers from the TPACK perspective, as well as the methodological foci and key research outcomes. Suggestions on areas to build on the field of study on TPACK in science education are offered.
Neural Network-Based Activity Recognition System

The pose recognition system points out the correct posture and position of the individual. This system helps the user identify the different postures of humans, and this type of recognition helps to monitor the movement in healthcare, defense, and sensitive areas of interest. The pose estimation algorithm with long short-term memory and convolution neural network is presented. Additionally, the results are compared.
Positive Adult Education, Learned Helplessness and the Pygmalion Effect

Positive education is seen as a transformative methodological approach capable of improving the act of teaching and learning and, above all, essential for the development of students' personal skills and competences. However, few studies have been carried out on this topic in the field of adult and continuing education; instead, they have been published mainly in the field of formal education and at school age. This study works with a sample of 399 people over 16 years of age and students of the Universidad Popular de Dos Hermanas in order to show the relationship between the Pygmalion effect and learned helplessness in the process of acquiring knowledge in adulthood. In this way, three tools were used: one questionnaire that showed the teachers' perceptions of the students' qualities and behaviour and two that provided information on self-concept, self-esteem, personal and social skills and other variables directly related to emotional intelligence and positive education. It shows how exposure to negative operational constraints hinders the psychosocial and socio-educational development of learners in all possible ways, while, on the other hand, it indicates the importance of positive education to compensate for this phenomenon by enhancing the development and growth of those who study and participate in non-formal education through positive reinforcement. Likewise, the factorial interrelation of both positive and negative conditioning factors and their incidence on learning is shown; the importance of neutralising the negative components and strengthening the positive reinforcement and the role played by the community and education professionals as catalysts and behavioural modulators at any stage of learning and age group for the achievement of the objectives of the student and of education itself in a broad sense.
Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure

Person re-identification is the task of recognizing a subject across different non-overlapping cameras across different views and times. Most state-of-the-art datasets and proposed solutions tend to address the problem of short-term re-identification. Those models can re-identify a person as long as they are wearing the same clothes. The work presented in this paper addresses the task of long-term re-identification. Therefore, the proposed model is trained on a dataset that incorporates clothes variation. This paper proposes a multi-modal person re-identification model. The first modality includes soft bio-metrics: hair, face, neck, shoulders, and part of the chest. The second modality is the remaining body figure that mainly focuses on clothes. The proposed model is composed of two separate neural networks, one for each modality. For the first modality, a two-stream Siamese network with pre-trained FaceNet as a feature extractor for the first modality is utilized. Part-based Convolutional Baseline classifier with a feature extractor network OSNet for the second modality. Experiments confirm that the proposed model can outperform several state-of-the-art models achieving 81.4 % accuracy on Rank-1, 82.3% accuracy on Rank-5, 83.1% accuracy on Rank-10, and 83.7% accuracy on Rank-20.
Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

The search range and search position of the particle are closely related to the shrinkage-expansion factor of the particle and the value of the center of the potential well. As the number of iterations increases, its search will gradually fall near the center of the potential well, and the search range will gradually decrease. Therefore, when the center of the potential well and the search range gradually approach the global optimum, the final result of the algorithm can be guaranteed to be the global optimum. Therefore, we need to optimize the shrinkage-expansion factor and the potential well center, improve the individual development ability of the group in the later stage of the algorithm and the expansion ability of the group, so that the potential well center is easier to fall near the global optimal point. Based on the chaotic strategy, a particle swarm initialization method is proposed. Using the unique ergodicity of the chaotic system, the method can make the particles spread well in the solution space of the search problem. Based on the antecedent normal cloud model, an adaptive method for determining control parameters and the center of the potential well is proposed. Based on the consequent normal cloud model, a particle adaptive mutation method is proposed. The comprehensive application of these improvement measures to the improved QPSO effectively improves the performance of the original algorithm. In this paper, the principle and method of multioptical axis parallelism measurement are studied. Based on the hardware equipment of the parallelism measurement system based on the large-diameter off-axis parabolic mirror collimator, the measurement accuracy and automation of the system are improved through image processing technology. The degree and anti-interference ability of the system are analyzed in detail. According to the characteristics of the system, an optimized focus evaluation function and a center positioning algorithm are proposed to improve the measurement speed and accuracy of the system. Through the improvement of the system DCT focus evaluation coefficient method and the optimization of the least squares ellipse fitting process, the measurement accuracy and anti-interference ability of the system are improved, the calculation time is shortened, and the real-time performance and automation of the system are enhanced.
Intellectual property in digital agriculture

This article seeks to address the impact of digitisation of the agricultural market in assessing the market definitions in the context of potential harm to competition in this sector. In doing so, the emphasis will be on the role of trust, frontier technologies, and intellectual property in this market and the article asks the question how the transfer, processing, and analytics of data can be made broadly accessible to all stakeholders, while assessing regulation on the basis of competition law and the law against unfair competition.
Disentangling the Toing and Froing of Professional Learning Community Implementation by Reconnecting Educational Policy with School Culture

Professional Learning Community (PLC) is embedded in one of the Malaysian educational policies with the aim of improving teachers' effectiveness.However, the robust empirical evidence upon this matter is scarce, especially in terms of the relationship between school culture and PLC.Therefore, a correlational design study conducted to examine the relationships between the school culture dimensions (Collaboration, Collegiality, Emphasis on Learning, Professional Value, Sharing Planning, Transformational Leadership) and PLC.A randomized controlled trial cluster involving 612 teachers has been conducted.The Partial Least Squares-Structural Equation Modeling (PLS-SEM) using SmartPLS version 3 was applied to analyze the data.The result from SEM path analysis shows that only three constructs have significant influences on PLC, which are Professional Value (β:0.045);Sharing Planning (β:0.071), and Transformational Leadership (β:0.040).This study has gone some way towards enhancing the understanding of the importance of school culture in promoting teachers' professional development through PLC.
Face Mask Detection Using Deep Learning

COVID-19 is a newly identified corona virus that causes an infectious sickness. Masks are a vital tool for preventing the spread of Covid-19 and saving lives. Because the wearing of mask has become one of the non-pharmaceutical intervention approaches that could be used to prevent SARS-CoV2 droplets from covering infected patients’ noses and mouths and being released in public, it should be used. In tests, wearing a face mask has been proven to significantly reduce viral transmission and provide a sensation of protection. The proposed technique uses Deep Convolutional Neural Networks (DCNN) with layers to test the suggested technique for binary classification with average pooling layer, flattening layer, dense ReLU layer and decisive layer with a soft max activation function using three models such as ResNet50, AlexNet and Mobilenet. The proposed method can be integrated into Facial landmarks with face mask using Multi-Task Cascaded Convolutional Neural Network, which is the most accurate face detection approach (MTCNN). Here, InceptionV3 and VGG 16 are two pre-trained models that are used to detect the face mask. The two models are compared based on the performance and it is found from the results that VGG 16 offers better accuracy than InceptionV3.
Exocentric Control Scheme for Robot Applications: An Immersive Virtual Reality Approach

Unmanned Aerial Vehicles (UAVs) exhibit great agility but usually require an experienced pilot to operate them in certain applications such as inspection for disaster scenarios or buildings. The reduction of cognitive overload when driving this kind of aerial robot becomes a challenge and several solutions can be found in the literature. A new virtual control scheme for reducing this cognitive overload when controlling an aerial robot is proposed in this paper. The architecture is based on a novel interaction Drone Exocentric Advanced Metaphor (DrEAM) located in a Cave Automated Virtual Environment (CAVE) and a real robot containing an embedded controller based on quaternion formulation. The testing room, where real robots are evolving, is located away from the CAVE and they are connected via UDP in a ground station. The user controls manually a virtual drone through the DrEAM interaction metaphor, and the real robot imitates autonomously in real time the trajectory imposed by the user in the virtual environment. Experimental results illustrate the easy implementation and feasibility of the proposed scheme in two different scenarios. Results from these tests show that the mental effort when controlling a drone using the proposed virtual control scheme is lower than when controlling it in direct view. Moreover, the easy maneuverability and controllability of the real drone is also demonstrated in real time experiments.
Accurate Calibration of Multi-Perspective Cameras from a Generalization of the Hand-Eye Constraint

Multi-perspective cameras are quickly gaining importance in many applications such as smart vehicles and virtual or augmented reality. However, a large system size or absence of overlap in neighbouring fields-of-view often complicate their calibration. We present a novel solution which relies on the availability of an external motion capture system. Our core contribution consists of an extension to the hand-eye calibration problem which jointly solves multi-eye-to-base problems in closed form. We furthermore demonstrate its equivalence to the multi-eye-in-hand problem. The practical validity of our approach is supported by our experiments, indicating that the method is highly efficient and accurate, and outperforms existing closed-form alternatives.
Continual Sequence Generation with Adaptive Compositional Modules

Continual learning is essential for real-world deployment when there is a need to quickly adapt the model to new tasks without forgetting knowledge of old tasks.Existing work on continual sequence generation either always reuses existing parameters to learn new tasks, which is vulnerable to catastrophic forgetting on dissimilar tasks, or blindly adds new parameters for every new task, which could prevent knowledge sharing between similar tasks.To get the best of both worlds, in this work, we propose continual sequence generation with adaptive compositional modules to adaptively add modules in transformer architectures and compose both old and new modules for new tasks.We also incorporate pseudo experience replay to facilitate knowledge transfer in those shared modules.Experiment results on various sequences of generation tasks show that our framework can adaptively add modules or reuse modules based on task similarity, outperforming state-of-the-art baselines in terms of both performance and parameter efficiency.We make our code public at https://github.com/GT-SALT/
DataLab: A Platform for Data Analysis and Intervention

Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.In this paper, we propose DATALAB, a unified dataoriented platform that not only allows users to interactively analyze the characteristics of data, but also provides a standardized interface for different data processing operations.Additionally, in view of the ongoing proliferation of datasets, DATALAB has features for dataset recommendation and global vision analysis that help researchers form a better view of the data ecosystem.So far, DATALAB covers 1,715 datasets and 3,583 of its transformed version (e.g., hyponyms replacement ) , where 728 datasets support various analyses (e.g., with respect to gender bias) with the help of 140M samples annotated by 318 feature functions. 1DATALAB is under active development and has been recently upgraded based on reviewers' constructive suggestions. 2We have released a wealth of resources to meet the diverse needs of researchers: web platform, 3 open-sourced code of web platform, 4 web API, open-sourced SDK, 5 PyPI published package, 6 and online documentation.
Exploring Global Diversity and Local Context for Video Summarization

Video summarization aims to automatically generate a diverse and concise summary which is useful in large-scale video processing.Most of the methods tend to adopt self-attention mechanism across video frames, which fails to model the diversity of video frames.To alleviate this problem, we revisit the pairwise similarity measurement in self-attention mechanism and find that the existing inner-product affinity leads to discriminative features rather than diversified features.In light of this phenomenon, we propose global diverse attention which uses the squared Euclidean distance instead to compute the affinities.Moreover, we model the local contextual information by novel local contextual attention to remove the redundancy in the video.By combining these two attention mechanisms, a video SUMmarization model with Diversified Contextual Attention scheme is developed, namely SUM-DCA.Extensive experiments are conducted on benchmark data sets to verify the effectiveness and the superiority of SUM-DCA in terms of F-score and rank-based evaluation without any bells and whistles.
Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing

Rapid detection and mitigation of issues that impact performance and reliability are paramount for large-scale online services. For real-time detection of such issues, datacenter operators use a stream processor and analyze streams of monitoring data collected from servers (referred to as data source nodes) and their hosted services. The timely processing of incoming streams requires the network to transfer massive amounts of data, and significant compute resources to process it. These factors often create bottlenecks for stream analytics. To help overcome these bottlenecks, current monitoring systems employ near-data processing by either computing an optimal query partition based on a cost model or using model-agnostic heuristics. Optimal partitioning is computationally expensive, while model-agnostic heuristics are iterative and search over a large solution space. We combine these approaches by using model-agnostic heuristics to improve the partitioning solution from a model-based heuristic. Moreover, current systems use operator-level partitioning: if a data source does not have sufficient resources to execute an operator on all records, the operator is executed only on the stream processor. Instead, we perform data-level partitioning—i.e., we allow an operator to be executed both on a stream processor and data sources. We implement our algorithm in a system called Jarvis, which enables quick adaptation to dynamic resource conditions. Our evaluation on a diverse set of monitoring workloads suggests that Jarvis converges to a stable query partition within seconds of a change in node resource conditions. Compared to current partitioning strategies, Jarvis handles up to 75% more data sources while improving throughput in resource-constrained scenarios by 1.2-4.4×.