Reviewerly/exHarmony huggingface dataset:
User Context-Aware Attention Networks for Answer Selection

Answer selection aims to find the most appropriate answer from a set of candidate answers, playing an increasingly important role in Community-based Question Answering. However, existing studies overlook the correlation among historical answers of users and simply summarize user contexts by concatenation or max-pooling when modeling user representations. In this paper, we propose a novel User Context-aware Attention Network (UCAN) for the answer selection task. Specifically, we apply the BERT model to encode representations of questions, answers, and user contexts. Then we use the CNN model to extract the n-gram features. Next, we model the user context as a graph and utilize the graph attention mechanism to capture the correlation among answers in the user context. We further use the Bi-LSTM to enhance the contextual representations. Finally, we adopt a multi-view attention mechanism to learn the context-based semantic representations. We conduct experiments on two widely used datasets, and the experimental results show that the UCAN outperforms all baselines.
A Semi-automatic Framework Towards Building Electricity Grid Infrastructure Management Ontology: A Case Study and Retrospective

Thanks to their extensive use in Internet-based applications, ontologies have gained significant popularity and recognition within the semantic web domain. They are widely regarded as valuable sources of semantics and interoperability in artificial intelligence systems. With the exponential growth of unstructured data on the web, there is a pressing need for automated acquisition of ontologies from unstructured text. This research area has seen the emergence of various methodologies that leverage techniques from machine learning, text mining, knowledge representation and reasoning, information retrieval, and high level natural language processing. These new techniques represent an opportunity to introduce automation into the process of ontology acquisition from unstructured text. To this end, this contribution offers a semi-automatic framework with a concrete usage of a tooled NLP-based approach to design an application ontology in a real-world industrial context. We discuss the state of the art analysis, the challenges met and the technological choices for the realization of this approach. Specifically, we explore its application in the real-world scenario of RTE’s power grid event management.
BOLOGNA SYSTEM: WHY IS IT NOT SUITABLE AND WHAT CAN REPLACE IT?

The article looks into the reasons of why the Bologna system in Russian higher education failed to take off. The main reason is argued to be the lack of a parallel additional system of professional education to complement the general higher education system. This complementarity principle is part of the Bologna Declaration, and it is implemented in all countries where the Bologna system is in use. The article draws attention to important differences in educational technologies applied in Russian or foreign universities, which complicate the graduates’ adaptation to the professional work in the unstable job market environment. The article proposes some principles which can underlie the efficient national system of higher education.
Executive summary

Egypt is implementing ambitious reforms to stabilise its economy, attract investors, and spur durable economic growth. These efforts are having a positive effect. Since 2013, GDP growth has doubled, reaching 5.5% in 2019, the highest level in almost a decade. Foreign investment inflows have been steadily increasing since 2011, and despite more modest inflows over the past two years, in 2018 Egypt was the largest recipient of FDI in Africa and the second highest in the MENA region. Both the budget and current account deficit have narrowed in the past couple of years, and unemployment is decreasing. These positive gains will be tested in the near- to medium-term. Initial projections on the economic effects of the coronavirus pandemic portend declines in FDI in all economies for the coming year at least, along with disruptions to global value chains. A global recession appears likely. The steps the Egyptian government has taken to address its economic challenges, in recent years and in response to the pandemic, may help it weather some of the negative impacts of the current crisis. The new outlook makes continuing to advance Egypt’s reform agenda all the more imperative.
(In)aceitabilidade do padrão de colocação dos pronomes clíticos no português brasileiro por falantes escolarizados

Brazilian Portuguese prioritizes pre-verbal placement (proclisis) of its clitic pronouns. However, post-verbal placement (enclisis) has not been completely excluded from this variety, being acquired by means of schooling. This study aimed at evaluating the acceptability of both these possible clitic placements in sentences with and without proclisis triggers – elements that, according to the normative tradition (based on European Portuguese, whose clitic placement pattern is syntacticly restrained), give rise to proclisis (whereas enclisis obtains at their absence) – by means of an acceptability judgement test with a six-point scale carried out by 79 Brazilian Portuguese native speakers who had completed, at least, Secondary School. The results showed that, even though the items with proclisis have been better evaluated than the ones with enclisis, the enclitic placement was evaluated above the central point of the scale used in this study, indicating that it is, as well, an acceptable placement for those speakers. It is concluded that, although the classes regarding clitic placement revolve around teaching the students similar rules to those found in the European Portuguese syntax, it does not seem to allow them to develop the syntactic knowledge specific to proclisis and enclisis, probably because Brazilian Portuguese is the oral variety used in the classrooms and the clitic placement rules (and their application) are restricted to the written modality.
Implementation of the Classical Learning Model in Kindergarten

Purpose of the study: The aim of this research is to describe the implementation of the classical learning model in kindergarten.&#x0D; Methodology: The method used in this research is descriptive quantitative and qualitative using questionnaires, observations and interviews as data collection techniques. Analysis of questionnaire and observation data uses statistical techniques, while interviews use interactive analysis.&#x0D; Main Findings: The implementation of the classical learning model in kindergarten is of "good" quality. The results of the questionnaire showed a percentage of 81% for general implementation, with a percentage of 75% for class setting indicators, 77% for teaching preparation indicators, and 93% for implementation process indicators. The observation results also showed "good" quality with a percentage of 83% in general, with a percentage of 64% for class setting indicators, 32% for teaching preparation indicators, and 70% for implementation process indicators. The interview results show that the school has implemented the classical learning model well, but there are shortcomings in using the latest curriculum and curriculum standards.&#x0D; Novelty/Originality of this study: The novelty of this research lies in the description of the implementation of the classical learning model in kindergarten and the analysis of its quality based on questionnaires, observations and interviews. This research provides a better understanding of the challenges and advantages of implementing classical learning models in early childhood education institutions with minimal teaching staff.
Experience of flexibility and educational interaction in a technological high school in the context of emergency remote teaching. Learned lessons

The objective of this document is to visualize the experience of educational flexibility lived during a period of emergency remote teaching through the graphic representation of the relationships between the actors and the teaching-learning processes, the determination of criteria and academic-administrative categories of the structure in the institution of El Grullo Technological High School that allows the implementation of a procedure of a multimodal system itself which facilitates the adaptation of learning processes mediated by technology with educational quality by identifying the interactions in pedagogical mediations that favor learning and give way to the choice of appropriate technological mediations that develop educational innovation as a significant change in their interaction internally and with the environment, understanding education as a relational process. These relationships are presented as a result of the collection of information and data obtained through interviews, sample survey and analysis of documents of the institution, interaction that is shown in the schematization, and visual representation of the educational processes of the case study in the community education of the El Grullo High School as learned lessons.
Comprehensive quantitative analysis of erythrocytes and leukocytes using trace volume of human blood using microfluidic-image cytometry and machine learning

A diagnostic test based on microfluidic image cytometry and machine learning has been designed and applied for accurate classification of erythrocytes and leukocytes, including a unique fully-automated 5-part quantitative differentiation into neutrophils, lymphocytes, monocytes, eosinophils, and basophils, using minute amounts of whole blood in a single counting chamber. A low-cost disposable multilayer microdevice for microfluidic image cytometry was developed that comprises a 1 mm × 22 mm × 70 μm (w × l × h) rectangular microchannel, allowing the analysis of trace volume of blood (20 μL) for each assay. Automated analysis of digitized binary images applying a border following algorithm was performed allowing the qualitative analysis of erythrocytes. Bright-field imaging was used for the detection of erythrocytes and fluorescence imaging for 5-part differentiation of leukocytes after acridine orange staining, applying a convolutional neural network enabling unparalleled speed for identification and automated morphology classification yielding 98.57% accuracy. Blood samples were obtained from 30 volunteers and count values did not significantly differ from data obtained using a commercial automated hematology analyzer.
On Neuroevolution of Multi-Input Compositional Pattern Producing Networks: A Case of Entertainment Computing, Edge Devices, and Smart Cities

This work presents a novel approach by utilizing Heterogeneous Activation Neural Networks (HA-NNs) to evolve the weights of Artificial Neural Networks (ANNs) for reinforcement learning in console and arcade computer games like Atari's Breakout and Sonic the Hedgehog. It is the first study to explore the potential of HA-NNs as potent ANNs in solving gaming-related reinforcement learning problems. Additionally, the proposed solution optimizes data transmission over networks for edge devices, marking a novel application of HA-NNs. The study achieved outstanding results, outperforming recent works in benchmark environments like CartPole-v1, Lunar Lander Continuous, and MountainCar-Continuous, with HA-NNs and ANNs evolved using the Neuroevolution of Augmenting Topologies (NEAT) algorithm. Notably, the key advancements include exceptional scores of 500 in CartPole-v1 and 98.2 in Mountain Car Continuous, demonstrating the efficacy of HA-NNs in reinforcement learning tasks. Beyond gaming, the research addresses the challenge of efficient data communication between edge devices, which has the potential to enhance performance in smart cities while reducing the load on edge devices and supporting seamless entertainment experiences with minimal commuting. This work pioneers the application of HA-NNs in reinforcement learning for computer games and introduces a novel approach for optimizing edge device communication, promising significant advancements in the fields of AI, neural networks, and smart city technologies.
H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation

Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each session consists of a series of items, and each user owns a series of sessions. However, the existing PSBR methods can merely capture the pairwise relation information within items and users. To effectively capture the hierarchical information, we propose a novel hierarchical hypergraph neural network to model the hierarchical architecture. Moreover, considering that the items in sessions are sequentially ordered, while the hypergraph can only model the set relation, we propose a directed graph aggregator (DGA) to aggregate the sequential information from the directed global item graph. By attentively combining the embeddings of the above two modules, we propose a framework dubbed H3GNN (Hybrid Hierarchical HyperGraph Neural Network). Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed model compared to the state-of-the-art methods, and ablation experiment results validate the effectiveness of all the proposed components.
The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges due to the fundamental difference in the nature and the construct of general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural language description can be a promising technique to accelerate ML programming tasks. In recent years, although many deep learning-based neural code generation models have been proposed with high accuracy, the fact that most of them are mainly evaluated on general programming tasks calls into question their effectiveness and usefulness in ML programming tasks. In this article, we set out to investigate the effectiveness of existing neural code generation models on ML programming tasks. For our analysis, we select six state-of-the-art neural code generation models and evaluate their performance on four widely used ML libraries, with newly created 83K pairs of natural-language described ML programming tasks. Our empirical study reveals some good, bad, and missing aspects of neural code generation models on ML tasks, with a few major ones listed below. ( Good ) Neural code generation models perform significantly better on ML tasks than on non-ML tasks with an average difference of 10.6 points in BLEU-4 scores. ( Bad ) More than 80% of the generated code is semantically incorrect. ( Bad ) Code generation models do not have significance in improving developers’ completion time. ( Good ) The generated code can help developers write correct code by providing developers with clues for using correct APIs. ( Missing ) The observation from our user study reveals the missing aspects of code generation for ML tasks, e.g., decomposing code generation for divide-and-conquer into API sequence identification and API usage generation.
Teach and Explore: A Multiplex Information-guided Effective and Efficient Reinforcement Learning for Sequential Recommendation

Casting sequential recommendation (SR) as a reinforcement learning (RL) problem is promising and some RL-based methods have been proposed for SR. However, these models are sub-optimal due to the following limitations: a) they fail to leverage the supervision signals in the RL training to capture users’ explicit preferences, leading to slow convergence; and b) they do not utilize auxiliary information (e.g., knowledge graph) to avoid blindness when exploring users’ potential interests. To address the above-mentioned limitations, we propose a multiplex information-guided RL model (MELOD), which employs a novel RL training framework with Teach and Explore components for SR. We adopt a Teach component to accurately capture users’ explicit preferences and speed up RL convergence. Meanwhile, we design a dynamic intent induction network (DIIN) as a policy function to generate diverse predictions. We utilize the DIIN for the Explore component to mine users’ potential interests by conducting a sequential and knowledge information joint-guided exploration. Moreover, a sequential and knowledge-aware reward function is designed to achieve stable RL training. These components significantly improve MELOD’s performance and convergence against existing RL algorithms to achieve effectiveness and efficiency. Experimental results on seven real-world datasets show that our model significantly outperforms state-of-the-art methods.
Human Pose Estimation Using Parallel Architecture

Human posture recognition is extensively researched for identifying human activities and various applications. Even though it might be tough because of a number of things, like complex backgrounds, moving body positions, occlusions of the self and objects, poor image resolutions, and varying lighting, certain obstacles might occur. To address the restrictions and improve performance, a model for assessing human posture is developed that makes use of deep convolutional neural networks. The proposed method utilizes a bottom-up parsing technique to identify crucial locations in the human body. Moreover, it employs a non-parametric method to describe the vector field of key point associations, allowing for the grouping of anatomical key points for each individual. The accuracy of localized key points is further improved through multiple stages of enhanced prediction. The proposed method is trained and tested on the MPII Human Pose dataset, and it demonstrates superior performance in terms of accuracy compared to the latest state-of-the-art technique. Moreover, by including an occlusion network in the feature representation process, it efficiently discovers occluded key points, resulting in a mean average precision of 90.4%.
A Configurable Protocol for Quantum Entanglement Distribution to End Nodes

The primary task of a quantum repeater network is to deliver entanglement among end nodes.Most of existing entanglement distribution protocols do not consider purification, which is thus delegated to an upper layer.This is a major drawback since, once an end-to-end entangled connection (or a portion thereof) is established it cannot be purified if its fidelity (F) does not fall within an interval bounded by Fmin (greater than 0.5) and Fmax (less than 1).In this paper, we propose the Ranked Entanglement Distribution Protocol (REDiP), a connection-oriented protocol that overcomes the above drawback.This result was achieved by including in our protocol two mechanisms for carrying out jointly purification and entanglement swapping.We use simulations to investigate the impact of these mechanisms on the performance of a repeater network, in terms of throughput and fidelity.Moreover, we show how REDiP can easily be configured to implement custom entanglement swapping and purification strategies, including (but not restricted to) those adopted in two recent works.
Cubature Kalman Optimizer: A Novel Metaheuristic Algorithm for Solving Numerical Optimization Problems

This study introduces a new single-agent metaheuristic algorithm, named cubature Kalman optimizer (CKO). The CKO is inspired by the estimation ability of the cubature Kalman filter (CKF). In control system, the CKF algorithm is used to estimate the true value of a hidden quantity from an observation signal that contain an uncertainty. As an optimizer, the CKO agent works as individual CKF to estimate an optimal or a near-optimal solution. The agent performs four main tasks: solution prediction, measurement prediction, and solution update phases, which are adopted from the CKF. The proposed CKO is validated on CEC 2014 test suite on 30 benchmark functions. To further validate the performance, the proposed CKO is compared with well-known algorithms, including single-agent finite impulse response optimizer (SAFIRO), single-solution simulated Kalman filter (ssSKF), simulated Kalman filter (SKF), asynchronous simulated Kalman filter (ASKF), particle swarm optimization algorithm (PSO), genetic algorithm (GA), grey wolf optimization algorithm (GWO), and black hole algorithm (BH). Friedman's test for multiple algorithm comparison with 5% of significant level shows that the CKO offers better performance than the benchmark algorithms.
Directional Antenna for High Density Access Point Stadion Coverage

The existence of a Wi-Fi network in the stadium is an important issue because of the many requests for communication access from spectators. Implementation of HD-AP Wi-Fi using directional antennas is one solution to avoid severe interference. This study simulates the coverage of the AP installed on the roof of the tribune station. The simulator is designed using HPBW Elevations 200, 400 and 600. Testing also considers the use of the appropriate transmit power, to obtain the appropriate HPBW combination to avoid interference. The analysis is carried out by comparing the reception of the strongest signal with reception at the sector border. The results of the study show that Antenna directional with HPBW Elevation 400 is more effective in serving users in three service sectors.
PERANCANGAN PEMBANGKIT LISTRIK TENAGA SURYA ATAP DI HOTEL MEGA DANA

The utilization of renewable energy is one of the solutions to overcome the increasing electricity demand. One of these solutions is to install Solar Power systems on the roofs of offices, industries, or households. This study targets to meet electricity demand by utilizing the roof of the Mega Dana Hotel as a place for the installation of solar panels, the solar panel will later produce electrical energy to meet the electricity needs of the hotel bars. The simulation results of the production of electric energy at Hotel Mega Dana's solar panel system using an application called Helioscope. This study analyzes the economics of designing a rooftop solar power system at Hotel Mega Dana. The feasibility of this research investment is assessed from the value of NPV, PI, and DPP. According to the data obtained, the Hotel Mega Dana building has a roof area of 655.92 m 2. The study used 39 solar panel modules, 1 20kW inverter, and 5 48V 100 Ah TLO batteries, the system can produce 17,6 kWp of power, with a total annual energy production of 21.83 MWh to supply electricity for the bar at Hotel Mega Dana. This research was planned with an initial capital of IDR 254,578,500, a PI value of 1.09, and a DPP value of 21.9 years. The results of this research analysis can be considered feasible because the design has a positive NPV value and is expected to return investment at 21.9 years out of 25-years project life.