Reviewerly/exHarmony huggingface dataset:
Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews

Tourism is one of the fastest-growing industries. Many travelers book hotels and share their experiences using travel e-commerce sites. To improve the quality of products and services, we can take advantage by analyzing their reviews. We can see the good and the bad thing reviews in every aspect of the hotel. However, research to analyze sentiment in every aspect using Indonesian hotel reviews is still relatively new. In this work, we propose to create an Aspect-based Sentiment Analysis (ABSA) using Indonesian hotel reviews to solve the problem. This research consists of four steps: collecting data, preprocessing, aspect classification, and sentiment classification. Our classification process compares with eight deep learning methods (RNN, LSTM, GRU, BiLSTM, Attention BiLSTM, CNN, CNN-LSTM, and CNN-BiLSTM). In aspect classification, we have six classes of aspects which are harga (price), hotel, kamar (room), lokasi (location), pelayanan (service), and restoran (restaurant). In sentiment analysis, we compared two scenarios to classify sentiments as positive or negative. The first one is to classify sentiment in all aspects, and the second one is to classify sentiment in every aspect. The results showed that LSTM achieved the best model for aspect classification with an accuracy value of 0.926. For sentiment classification, our experiments showed that classify sentiment in every aspect achieved a better result than classify sentiment in all aspects. The result showed that the CNN model gets an average accuracy score of 0.904.
A Haar Wavelet Series Solution of Heat Equation with Involution

It is well known that the wavelets have widely applied to solve mathematical problems connected with the differential and integral equations. The application of the wavelets possess several important properties, such as orthogonality, compact support, exact representation of polynomials at certain degree and the ability to represent functions on different levels of resolution. In this paper, new methods based on wavelet expansion are considered to solve problems arising in approximation of the solution of heat equation with involution. We have developed new numerical techniques to solve heat equation with involution and obtained new approximative representation for solution of heat equations.
Strategy for Realizing Regional Rural Water Security on Tropical Peatland

Fulfilling the need for clean water and proper sanitation is, globally, a basic human requirement, and Indonesia is no exception. Clean water and adequate sanitation are the sixth goal of the Sustainable Development Goals (SDGs), and targets include to ensure the availability of clean water and sustainable sanitation for all by 2030. The achievement of targets in water supply and sanitation in Indonesia is still lagging behind other fields. There are differences in the ease of obtaining access to clean water sources in urban and rural areas, especially for rural communities living on peatlands who experience issues in being provided with clean water. The difficulty is that, even though the amount of available water is relatively large, its quality is low. Barriers to the equitable distribution of services by the government to the entire community are caused by the limited availability of funding, the geographical conditions of scattered settlements, and the limited capacity of human resources. As a result of this problem, it is necessary to formulate a management strategy for providing access to clean water and sanitation for rural communities on peatlands. This research uses the case-study method. The management strategy was formulated on the basis of the environmental and socioeconomic conditions of the community. This method was used to test the effectiveness of the formulated strategy in realizing water security on peatlands in the village of Wajok Hilir. The resulting management strategy can be applied to other villages that have the same land characteristics. The strategy to realize water security for rural communities on peatlands is a self-service strategy. The implementation of the self-service strategy needs to be supported by infrastructure in the form of peat water treatment plant buildings, wells drilled to aquifer depths, and canal blocking to control the groundwater level in peatlands. The management strategy is carried out with community participation, considering that peatlands are a potential water source to meet clean water needs and achieve sustainable water security targets. Efforts to implement the self-service strategy are carried out with these methods: (1) socialization carried out in an integrated manner, (2) infrastructure development as a pilot project, (3) providing knowledge about infrastructure operations, (4) providing temporary assistance to the community, and (5) providing material supplies so that rural communities that are partners can become pioneers of similar movements.
On the Correlation among Edge, Pose and Parsing

Semantic parsing, edge detection, and pose estimation of human are three closely-related tasks. They present human characteristics from three complementary aspects. Compared to learning them individually, solving these tasks jointly can explore the interaction of their contextual cues. However, prior works usually study the fusion of two of them, e.g., parsing and pose, parsing and edge. In this paper, we explore how pixel-level semantics, human boundaries and joint locations can be effectively learned in a unified model. Specifically, we propose an end-to-end trainable Human Task Correlation Machine (HTCorrM) to implement the three tasks. It is asymmetric in that it supports a main task using the other two as auxiliary tasks. We also introduce a Heterogeneous Non-Local module (HNL) to discover the correlations of the three heterogeneous domains. HNL fully explores the global dependency among tasks between any two positions in the feature map. Experimental results on human parsing, pose estimation and body edge detection demonstrate that HTCorrM achieves competitive performance. We show that when designated as the main task, the accuracy of each of the three tasks is improved. Importantly, comparative studies confirm the advantages of our proposed feature correlation strategy over feature concatenation or post processing.
A Sociolinguistic Perspective of WhatsApp Statuses Used by Jordanians

The study aimed to examine the use level of the WhatsApp among Jordanians, and also to identify differences among Jordanians in WhatsApp status uses in light of their gender, age, marital status, and socioeconomic level. The population of the study included all students who are enrolled at the English department of Jadara and Irbid national Universities in Irbid, Jordan. The sample of the study consisted of (262) students whose ages are between 18-40 and were selected randomly. To achieve the aims of the study, a (23) item questionnaire was developed based on previous literature and used for data collection. The results showed that the level of using WhatsApp application among university students was high. The result showed that there are statistically significant differences at (a= 0.05) due to gender in favor of females, due to age in favor of the age group 18-21, and in Socioeconomic level in favor of more than 500. In light of results, the study recommend to work on motivating students for more use of this important application for academic purposes.
A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

In high dimensional data, Principal Component Analysis (PCA)-based Pearson correlation remains broadly employed to reduce the data dimensions and to improve the effectiveness of the clustering partitions.Besides being prone to sensitivity on non-Gaussian distributed data, in a high dimensional data analysis, this algorithm may influence the partitions of cluster as well as generate exceptionally imbalanced clusters due to its assigned equal weight to each observation pairs.To solve the unbalanced clusters in hydrological study caused by skewed character of the dataset, this study came out with a robust method of PCA in term of the correlation.This study will explain a RPCA to be proposed as an alternative to classical PCA in reducing high dimensional dataset to a lower form as well as obtain balance clustering result.This study improved where RPCA managed to downweigh the far-from-center outliers and develop the cluster partitions.The results for both methods are compared in term of number of components and clusters obtained as well as the clustering validity.Regarding the internal and stability validation criteria, this study focuses on the cluster's quality in order to validate the results of clusters obtained for both methods.From the findings, the amount of clusters had improved significantly by using RPCA compared to classical PCA.This proved that the proposed approach are outliers resistant than classical PCA as the proposed approach made a thorough observation assessment and downweigh the ones which were distant from the data center.
Facial Expression Recognition Using Multi-deep Convolutional Neural Network Encoders with Support Vector Machines

Although there have been many breakthroughs in the use of convolutional neural networks (CNN) for image classification, facial expression recognition (FER) in real-life is still a challenge in this research area.This paper proposes a method to leverage state-of-the-art multi-deep CNN encoders with support vector machines (SVM) to classify facial expression.We conducted experiments to show that combining features from multi-deep CNN is better than using a single deep CNN model.As well as combining multiple CNN models, we show that using rules to remove noise images from the training dataset improves the performance of the FER system.The FER2013 dataset was used to evaluate the proposed approach, which achieved 73.78% accuracy.
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

Existing approaches for audio-language taskspecific prediction focus on building complicated late-fusion mechanisms.However, these models face challenges of overfitting with limited labels and poor generalization.In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-and inter-modalities connections between audio and language through two proxy tasks from a large number of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling.After fine-tuning our CTAL model on multiple downstream audioand-language tasks, we observe significant improvements on different tasks, including emotion classification, sentiment analysis, and speaker verification.Furthermore, we design a fusion mechanism in the fine-tuning phase, which allows CTAL to achieve better performance.Lastly, we conduct detailed ablation studies to demonstrate that both our novel cross-modality fusion component and audiolanguage pre-training methods contribute to the promising results.The code and pretrained models are available at https:// github.
Robust 3D Face Reconstruction Using One/Two Facial Images

Being able to robustly reconstruct 3D faces from 2D images is a topic of pivotal importance for a variety of computer vision branches, such as face analysis and face recognition, whose applications are steadily growing. Unlike 2D facial images, 3D facial data are less affected by lighting conditions and pose. Recent advances in the computer vision field have enabled the use of convolutional neural networks (CNNs) for the production of 3D facial reconstructions from 2D facial images. This paper proposes a novel CNN-based method which targets 3D facial reconstruction from two facial images, one in front and one from the side, as are often available to law enforcement agencies (LEAs). The proposed CNN was trained on both synthetic and real facial data. We show that the proposed network was able to predict 3D faces in the MICC Florence dataset with greater accuracy than the current state-of-the-art. Moreover, a scheme for using the proposed network in cases where only one facial image is available is also presented. This is achieved by introducing an additional network whose task is to generate a rotated version of the original image, which in conjunction with the original facial image, make up the image pair used for reconstruction via the previous method.
Incentive Cooperation with Computation Delay Concerns for Socially-Aware Parked Vehicle Edge Computing

Massive computing requirements of mobile applications greatly challenge the limited resources in the cloud. It is observed that parked vehicles (PVs) have redundant computing resources for task execution. Hence, we focus on how to efficiently and economically apply PVs for assisting the cloud. An proposed incentive mechanism, initiated by the cloud, motivates PVs to participate in the offloading of cloud’s computing tasks. We build the utility to characterize a PV’s profits, generated from task execution and socially-aware effects from other PVs. The benefits of the cloud consider many factors, such as computing tasks of mobile users, energy cost, economic compensation for PVs and PVs’ computation delay. The interactions among PVs and the cloud formulate a two-stage Stackelberg game to maximize their profits. The performance of the proposed model is investigated by simulations. Numerical results prove its efficiency for enhancing PVs’ utilities and cloud’s quality of service.
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?

Exposure bias has been regarded as a central problem for auto-regressive language models (LM).It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy.Although a lot of algorithms have been proposed to avoid teacher forcing and therefore alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is.In this work, we focus on the task of open-ended language generation, propose metrics to quantify the impact of exposure bias in the aspects of quality, diversity, and consistency.Our key intuition is that if we feed ground-truth data prefixes (instead of prefixes generated by the model itself) into the model and ask it to continue the generation, the performance should become much better because the training-generation discrepancy in the prefix is removed.Both automatic and human evaluations are conducted in our experiments.On the contrary to the popular belief in exposure bias, we find that the the distortion induced by the prefix discrepancy is limited, and does not seem to be incremental during the generation.Moreover, our analysis reveals an interesting self-recovery ability of the LM, which we hypothesize to be countering the harmful effects from exposure bias.
A Survey on Feature Selection Techniques using Evolutionary Algorithms

Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the preferred feature matters in complex and massive data. Here, furthermore, I discuss algorithms like the genetic algorithm (GA), the Non-Dominated Sorting Genetic Algorithm (NSGA-II), Particle Swarm Optimization (PSO), and some other meta-heuristic strategies for considering the provisional separation of issues. A comparison of these algorithms has been performed; the results show that the feature selection technique benefits machine learning algorithms by improving the performance of the algorithm. This paper also presents various real-world applications of using feature selection.
On the Transferability of Pre-trained Language Models: A Study from Artificial Datasets

Pre-training language models (LMs) on large-scale unlabeled text data makes the model much easier to achieve exceptional downstream performance than their counterparts directly trained on the downstream tasks. In this work, we study what specific traits in the pre-training data, other than the semantics, make a pre-trained LM superior to their counterparts trained from scratch on downstream tasks. We propose to use artificially constructed datasets as the pre-training data to exclude the effect of semantics, and further control what characteristics the pre-training corpora have. By fine-tuning the pre-trained models on GLUE benchmark, we can learn how beneficial it is to transfer the knowledge from the model trained on the dataset possessing that specific trait. We define and discuss three different characteristics in the artificial dataset: 1) matching the token's uni-gram or bi-gram distribution between pre-training and downstream fine-tuning, 2) the presence of the explicit dependencies among the tokens in a sequence, 3) the length of the implicit dependencies among the tokens in a sequence. Our experiments show that the explicit dependencies in the sequences of the pre-training data are critical to the downstream performance. Our results also reveal that models achieve better downstream performance when pre-trained on a dataset with a longer range of implicit dependencies. Based on our analysis, we find that models pre-trained with artificial datasets are prone to learn spurious correlation in downstream tasks. Our work reveals that even if the LMs are not pre-trained on natural language, they still gain transferability on certain human language downstream tasks once the LMs learn to model the token dependencies in the sequences. This result helps us understand the exceptional transferability of pre-trained LMs.
QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS

Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain’s pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services.
Impact of human capital on economic growth: A case study of Pakistan

A country is prosperous if it has efficient development programs. Human capital contains resources like education, health, training, skills etc. For economic progress these qualities are very vital. Basic objective of this research is to explain the impact of human capital on growth and development of economics sector of the Pakistan. Because today in the developing countries, human development and growth has becomes the burning issues. To analyse the association between human capital and economic growth, used GDP as a dependent variable. This study further use Human development index as independent variable. Proxy of human development index consist of education index, health, fertility, infant mortality, life expectancy and sanitation. Our focus will be more on the education. Time series data for the years 1990-2019 were used. ARDL model was used by incorporating the human capital formation with other explanatory variables. The findings shows that the human capital has positive and significant impact on growth and the negative influence on the population and infant mortality rate.
Engaging students to fill surveys using chatbots: University case study

The use of chatbot or conversational agents is becoming common these days by the companies in many fields to make smart conversations with users. Backed by artificial intelligence and natural language processing they provide a strong platform to engage users. These positive aspects of chatbots can be beneficial in the educational sector, especially in conducting online survey. This study aims to explore the feasibility of a new chatbot approach survey as a new survey method in Moroccan university to overcome the web survey’s common response quality problems. Indeed, having student feedback before and after graduation is essential for university assessment. This new approach keeps students engaged, supportive, and even excited to offer feedback without getting bored and dropping the conversation, especially in Moroccan universities known by an overcrowding of students where it is difficult to get their feedback. This feedback feeds into our university' databases for further reporting and decision making to improve the quality of educational content and student-oriented services. Finally, we have shown the effectiveness of our approach by a comparative data study between the traditional online survey and the use of this chatbot.