Reviewerly/exHarmony huggingface dataset:
Sentiment-Aware Neural Recommendation with Opinion-Based Explanations

Explainable recommendation systems are crucial for complex decision making, which provide users with the recommendation results as well as the reasons why such items are recommended. However, most existing explainable recommendation methods only consider one aspect of user sentiment, such as ratings or reviews, which fails to capture the fine-grained user sentiment. In this paper, we propose a novel sentiment-aware neural recommendation model, named SNROE, which jointly performs a rating prediction task and an explanation generation task, to guarantee both the accuracy of recommendation and the personalization of explanations. For the rating prediction task, we adopt MLP to learn user/item representations. For the explanation generation task, we propose a sentiment-aware explanation generation method, which utilizes pretrained Transformer to generate opinion-based explanations by fusing users’ rating-level sentiment, aspect-level sentiment and review-level sentiment. We also propose a joint training algorithm to jointly optimize the above two tasks. The experiments demonstrate the effectiveness and the efficiency of our proposed model compared to the baseline models.
Nested Polynomials to Increase the Plasticity of Artificial Neural Networks

In this work, a nested polynomial structure is proposed as an alternative neuron model approach to the layers of traditional artificial neural networks, allowing increased its plasticity. This structure is employed to create a scalar field that allows generating new outputs in the supervised learning architecture while preserving the same network structure and operation. The proposed structure was implemented to classify non-linear functions and compared with other orthodox techniques, proving to have superior performance on the different proposed tasks.
CollageNet: Face Super-Resolution Using Reference Images

A novel approach to face super-resolution (SR) using reference images, called CollageNet, is described in this paper. First, we extract features and reorganize them into patches. Next, we compute the similarity between input and reference patches and select the most similar reference patch to each input patch. We then compose a collaged feature using such selected reference patches. Finally, we blend both input and collaged features to obtain an SR image. Experimental results demonstrate that CollageNet provides excellent performance.
A Sequence-Aware Recommendation Method based on Complex Networks

Online stores and service providers rely heavily on recommendation software to guide users through the vast number of available products.Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges.For instance, most existing work models the recommendation problem as a matrix completion problem to predict the user preference for an item.This abstraction prevents the system from utilizing the rich information from the ordered sequence of user actions logged in online sessions.To address this limitation, researchers have recently developed a promising new breed of algorithms called sequence-aware recommender systems to predict the user's next action by utilizing the time series composed of the sequence of actions in an ongoing user session.This paper proposes a novel sequence-aware recommendation approach based on a complex network generated by the hidden metric space model, which combines node similarity and popularity to generate links.We build a network model from data and then use it to predict the user's subsequent actions.The network model provides an additional information source that improves the recommendations' accuracy.The proposed method is implemented and tested experimentally on a large dataset.The results prove that the proposed approach performs better than state-of-the-art recommendation methods.
TBAC: Transformers Based Attention Consensus for Human Activity Recognition

Human Activity Recognition is an important task in Computer Vision that involves the utilization of spatio-temporal features of videos to classify human actions. The temporal portion of videos contains vital information needed for accurate classification. However, common Deep Learning methods simply average the temporal features, thereby giving all frames equal importance irrespective of their relevance, which negatively impacts the accuracy of the model. To combat this adverse effect, this paper proposes a novel Transformer Based Attention Consensus (TBAC) module. The TBAC module can be used in a plug-and-play manner as an alternate to the conventional consensus meth-ods of any existing video action recognition network. The TBAC module contains four components: (i) Query Sampling Unit, (ii) Attention Extraction Unit, (iii) Softening Unit, and (iv) Attention Consensus Unit. Our experiments demonstrate that the use of the TBAC module in place of classical consensus can improve the performance of the CNN-based action recognition models, such as Channel Separated Convolutional Network (CSN), Temporal Shift Module (TSM), and Temporal Segment Network (TSN). We also propose the Decision Consensus (DC) algorithm that utilizes multiple independent but related action recognizer models in order to improve upon the performance of most of these constituent models, using a novel fusion algorithm. Results have been obtained on two benchmark human action recognition datasets, HMDB51 and HAA500. The use of the proposed TBAC module along with Decision Consensus achieves state-of-the-art performances, with 85.23% and 83.73% classification accuracies on the two databases HMDB51 and HAA500, respectively. The code will be made publicly available.
PENERAPAN METODE MIND MAPING UNTUK MENINGKATKAN KEMAMPUAN MENULIS NARASI SISWA KELAS IV SD NEGERI 33 PAGARALAM

The purpose of this study was to describe the improvement of students' narrative writing skills using the mind map method in grade IV SD Neger 33 Pagar Alam for the 2020/2021 academic year. This research is a classroom action research (PTK) which consists of 4 stages, namely planning, acting, observing, and reflecting. The research subjects were 21 students of grade IV SD Negeri 33 Pagar Alam. Based on the results of the study, it was found that the application of the mind map method could improve the writing skills of the sixth grade students of SD Negeri 33 Pagar Alam, this can be seen from the average value in cycle I and cycle II and classically it has increased in cycle II.
Study on Value Extraction and exchange and sharing Mode of historical Retained information

Among the many retained historical information, The CPC’s archives show their unique value because of their original and authentic nature. The CPC’s history archives are used to tell the red story, spread the red culture and highlight the unique advantages of retained historical information. In the one hundred birthday of CPC, test training base in the camp salute an archives exhibition tributing to CPC centennial. The archives exhibition expands the scope of publicity, restores historical information, and highlights the role of archives in education, communication and sharing.
Path planning method of automatic driving for directional navigation based on particle swarm optimisation

In order to overcome the poor planning efficiency of the automatic driving trajectory planning method for directional navigation, a Particle Swarm Optimisation (PSO) based trajectory planning method is proposed. The kinematic characteristics of the vehicle are analysed and the vehicle dynamic equation is constructed. The position coordinates, speed and other motion parameters of the directional navigation vehicle are transformed into a Frenet coordinate system. The trajectory quality evaluation model of automatic driving vehicle for directional navigation is constructed. The trajectory quality evaluation index is taken as the constraint, and each variable is iteratively optimised by particle swarm optimisation algorithm, so as to effectively realise the trajectory planning of automatic driving of directional navigation. Simulation results show that the proposed method can effectively improve the efficiency of autopilot trajectory planning and enhance the safety of the whole method.
Pragmatic Variant of the Methodology of Designing Local Learning Systems

The article completes a series of articles devoted to the development of the pragmatic approach to the pedagogical designing of local learning systems. It describes the methodology of designing the educational process, taking into account both the requirements imposed on the projected process and the specifics of the conditions in which this process will be implemented. The described design process is focused on implementation within the framework of the methodical work of teachers. Therefore, it does not involve the development of original teaching technologies, but provides for an analysis of the existing arsenal of pedagogical technologies for the selection of those options that most fully correspond to the project situation under consideration. As a source of information about pedagogical technologies, it is proposed to use the encyclopedia of educational technologies by G.K. Selevko, in which data on the absolute majority of existing educational technologies are presented in a single format. At the same time, attention is focused on the peculiarities of the interpretation in the proposed source of those individual concepts and provisions of pedagogy and psychology that determine the nature of the project decisions made.
MLSmellHound

Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure maintainability, developers use code analysis tools to aid them in identifying defects and maintaining standards. With the inclusion of machine learning, tools must account for the cultural differences within the teams which manifests as multiple programming languages, and conflicting definitions and objectives. Existing tools fail to identify these cultural differences and are geared towards software engineering which reduces their adoption in ML projects. In our approach we attempt to resolve this problem by exploring the use of context which includes i) purpose of the source code, ii) technical domain, iii) problem domain, iv) team norms, v) operational environment, and vi) development lifecycle stage to provide contextualised error reporting for code analysis. To demonstrate our approach, we adapt Pylint as an example and apply a set of contextual transformations to the linting results based on the domain of individual project files under analysis. This allows for contextualised and meaningful error reporting for the end user.
PERANCANGAN SISTEM INFORMASI PEMESANAN TIKET PESAWAT DOMESTIK BERBASIS WEB PADA CV JENIKA GROUP DEPOK

Based on the Tiket.com survey in the third quarter of 2020, there was a three-fold increase in transactions that occurred in hotel booking services and flight bookings. This increase is considered very fast in the midst of the Covid-19 pandemic due to the encouragement of people to take vacations in the midst of the pandemic. To be able to compensate for the increase that occurred, tour and travel companies are required to be able to optimize services by computerizing the flight ticket booking system. Therefore, the author intends to create a website for booking domestic airline tickets for CV JENIKA GROUP Depok. The author uses a method that has proven its effectiveness in designing an information system, namely the Waterfall method. Based on the results of the study, it can be concluded that the design of this web-based domestic flight ticket booking program can provide convenience and comfort for admins and customers of CV JENIKA GROUP and can further expand marketing reach.
Determinants Of Economic Growth in Iraq: A Standard Study for the Period (1970-2016)

The subject of economic growth and development has taken a great space of importance in recent decades, level in terms of economic theory, scientific and academic research or the level of international institutions, and the level of countries and their economic orientations.Economic growth as a general phenomenon is a means of achieving various purposes.Growth rate or at least improve it by introducing all the conditions imposed by economic development.Economic growth remains the main concern of the various systems on the one hand and individuals on the other.It is at the top of the objectives of economic policies as it represents the material conclusion of economic and non-economic efforts in society.
Development of instruction support system using RFID and application to mechanical design and drafting class

Mechanical design and drafting are fundamental subjects related to mechanical engineering. In this subject, students are required to acquire various knowledge and skills, and the progress of each student is different. Therefore, it is important to provide guidance from teachers suitable for each individual. In order to improve instruction suitable for individuals, it is effective to accumulate examples of personalized instruction as videos and share them among teachers. However, in mechanical design and drafting, it is difficult to review because the content of instruction is diverse and the videos are enormous. The system proposed in this paper for this task reduces the input burden during personalized instruction and speeds up the reference of student information by attaching RFID labels to learning outcomes. Furthermore, it is possible to search for personalized instruction videos from personalized instruction information. Therefore, the teacher can review their instruction content from the video. This paper describes the outline of the developed system and the effect of applying this system to the class.
Strategi Pelayanan Perpustakaan di Era Pandemi Covid-19 pada Dinas Perpustakaan dan Kearsipan Provinsi NTB

This study aims to determine the strategy of library services in the era of the covid-19 pandemic and to determine the supporting factors and inhibiting factors of library services in the era of the covid-19 pandemic at the Library and Archives Service of West Nusa Tenggara Province. The research method used in this research is descriptive qualitative research method. Sources of data in this study are primary data sources and secondary data. The technique of determining the informants used purposive sampling. Data collection techniques used in this study were observation, interviews, and documentation. The data analysis used in this research is an interactive analysis model. The results of research conducted at the Office of the Library and Archives of West Nusa Tenggara Province can be seen that in general the Office of the Library and Archives of West Nusa Tenggara Province has implemented a library service strategy. The library service strategy in the Covid-19 pandemic era at the West Nusa Tenggara Provincial Library and Archives Service is based on the West Nusa Tenggara Provincial Regulation Number 10 of 2019 concerning Regional Innovation which includes: the NTBelib application, iDepositNTB and Mobile Literacy Cafe Car (Feeling). Based on the results of the study, it can also be seen that the strategy carried out by the Office of the Library and Archives of West Nusa Tenggara Province has not provided optimal library services to the user community because it is influenced by inhibiting factors.
All-fiber Dynamically Tunable Beamsplitter for Quantum Random Number Generators

In this work we demonstrate an all-fiber dynamically tunable beamsplitter based on a Sagnac interferometer capable of realizing measurement-device independent protocols for certifying the privacy of the generated sequence.
Experiencing psychological trauma as a source of value self-determination of adolescents

Th e value self-determination of adolescents is a process of self-organization, balancing on the edge of chaos fi lled with attractors. Pedagogical support of value self-determination is a resonance excitation of the system in a state of chaos (psychological trauma). Pedagogical infl uence at the point of bifurcation causes the “attraction” of positive attractors, the formation of new value structures.
Detecting Age-Related Linguistic Patterns in Dialogue: Toward Adaptive Conversational Systems

This work explores an important dimension of variation in the language used by dialogue participants: their age. While previous work showed differences at various linguistic levels between age groups when experimenting with written discourse data (e.g., blog posts), previous work on dialogue has largely been limited to acoustic information related to voice and prosody. Detecting fine-grained linguistic properties of human dialogues is of crucial importance for developing AI-based conversational systems which are able to adapt to their human interlocutors. We therefore investigate whether, and to what extent, current text-based NLP models can detect such linguistic differences, and what the features driving their predictions are. We show that models achieve a fairly good performance on age-group prediction, though the task appears to be more challenging compared to discourse. Through in-depth analysis of the best models’ errors and the most predictive cues, we show that, in dialogue, differences among age groups mostly concern stylistic and lexical choices. We believe these findings can inform future work on developing controlled generation models for adaptive conversational systems.
Point-and-Shoot All-in-Focus Photo Synthesis From Smartphone Camera Pair

All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendly to ordinary people. To achieve point-and-shoot AIF photography with a smartphone, we expect that an AIF photo can be generated from one shot of the scene, instead of from multiple photos captured by the same camera. Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras. The goal is to recover sharp details from defocused regions in the main-camera photo with the help of the ultra-wide-camera one. The camera setting poses new challenges such as parallax-induced occlusions and inconsistent color between cameras. To overcome the challenges, we introduce a predict-and-refine network to mitigate occlusions and propose dynamic frequency-domain alignment for color correction. To enable effective training and evaluation, we also build an AIF dataset with 2686 unique scenes. Each scene includes two photos captured by the main camera, one photo captured by the ultra-wide camera, and a synthesized AIF photo. Results show that our solution, termed EasyAIF, can produce high-quality AIF photos and outperforms strong baselines quantitatively and qualitatively. For the first time, we demonstrate point-and-shoot AIF photo synthesis successfully from main and ultra-wide cameras.
Single Image Depth Estimation Using Improved U-Net and Edge-Guide Loss

Monocular depth estimation is regarded as a critical link in context-aware scene comprehension, which typically uses image data from a single point of view as the input to directly predict the depth value corresponding to each pixel in the image. However, predicting accurate object borders without replicating texture is difficult, resulting in missing tiny objects and blurry object edge in predicted depth images. In this study, we propose a monocular depth estimation approach by utilizing an improved U-Net-based encoder-decoder network structure. Specifically, we propose a novel training loss term named edge-guide loss that enforces the network focusing on object edges, leading to improved accuracy of the depth of tiny objects and edges.In the network, we build the encoder using DenseNet-169 and the decoder using 2Ã— bilinear up-sampling, skip-connections and hybrid dilated convolution. And skip-connections are used to send multi-scale feature maps from encoder to decoder. We specifically create a new loss function, edge-guide loss and three basic loss terms. We test our algorithm on the NYU Depth V2 dataset. The results of the experiments show that the proposed network can create depth images from a single RGB image with unambiguous borders and more tiny object depth. In the meantime, compared with state-of-the-art approaches, our proposed network outperforms for both visual quality and objective measurement.
ANALYSIS OF INFORMATION SERVICES FOR TEACHERS AND STUDENTS DENGAN MODEL Delone and McLean

In the era of globalization. Improving information for teachers and students is one of the must and one of the requirements in facing the rapid development of digital information, especially services, especially in school environments such as teachers and students, absolutely carried out by the school to improve the quality and useful information services. This researcher analyzes the quality of service for teachers and students at SMKN5 Bekasi City whether the services have been properly implemented and in accordance with the initial goal of establishing a school website system, namely making the center and improving information services, In this analysis using the DeLone and McLean model for teachers and students. in managing the analysis using the Sem PLS method with SmartPls tools in data management. research, measuring the quality of a website based on the de lone mc lean instrument, namely system quality, information quality, service quality, usage, level of satisfaction and benefits. which is positive and significant for all variables, namely system quality, service quality, information quality, user satisfaction level and net benefits and service quality have no effect on user satisfaction and from the research results it is found that the Smkn 5 Bekasi City Website service is successful and in accordance with the information provided. used by teachers and students. Keywords: SemPls, Delone Mclean ,SmartPls
Mathematics Student Worksheet Based on Guided Discovery for Concept Understanding and Curiosity

This study aims to develop and produce a mathematics student worksheet based on guided discovery to facilitate the understanding concept and the curiosity of eighth grade students on prism and pyramid material. The type of research is research and development using the Richey and Klein development procedure, which consists of the planning, production, and evaluation. Mathematics student worksheet assessment for material and media experts. Material and media experts fill out the mathematics student worksheet assessment. The research data analysis technique is product quality analysis. According to the evaluation of material experts and media experts, the mathematics student worksheet is very good. Based on the results of the study, it found that the quality of the mathematics student worksheet based on guided discovery was classified as very good with the specification of the average score of the material expert validator assessment of 194.5 from the ideal maximum score of 208 and classified in the very good category with the specification of the average score of the media expert validator assessment of 57 from the ideal maximum score of 72. Based on this, the product of the guided discovery-based math worksheets is declared valid and can use in the learning process.