Reviewerly/exHarmony huggingface dataset:
Development of Predicted Power Generation Nomogram of Photovoltaic System by Installation Conditions Using Typical Meteorological Year

Recently, with the increase in the use of photovoltaic systems, the importance of evaluating data for the follow-up management of installed systems has also gradually increased. In particular, the power generation prediction data of photovoltaic systems play an important role in evaluating the performance of installed systems. Therefore, in this study, the power-generation performance index of the photovoltaic system was derived by simulating the power-generation performance according to the installation angle using a typical meteorological year of the seven regions. Consequently, a monogram of the annual yield and performance factors was derived according to the installation angle in the seven regions. As a result of examining annual yield in the range of azimuth angle 180°±30° and inclination angle 30°±15°, the annual average yield for the seven regions was 1,238 kWh/kWp. The performance factors based on the installation inclination angle exhibited similar characteristics in most regions, except for Jeju. Jeju showed a high performance factor under a relatively low inclination-angle, and accordingly, it showed different characteristics from the other regions.
Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification

Online product reviews have become a valuable resource for consumers seeking detailed information and making informed choices. The process of automatically extracting sentiment or opinions from these reviews heavily relies on sentiment analysis, a branch of Natural Language Processing (NLP). This research article focuses on sentiment categorization in online product evaluations, utilizing innovative techniques for mining consumer opinions. The project aims to establish a robust framework for sentiment analysis that accurately classifies emotions expressed in these reviews. The proposed system incorporates advanced deep learning and machine learning methods to enhance data classification and extract fine-grained sentiment information. The study addresses the unique challenges of sentiment analysis in the context of online product evaluations, including polarity changes, sarcasm, and domain-specific sentiment expressions, which often pose significant obstacles to precise sentiment classification. The approach combines feature engineering and deep learning techniques, extracting lexical, syntactic, and semantic features such as part-of-speech tags, n-grams, sentiment lexicons, and word embeddings from the review texts. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed as sophisticated neural network architectures to leverage these features, creating robust representations and capturing contextual information. The suggested architecture is extensively evaluated on a large dataset of online product reviews, demonstrating superior performance in sentiment categorization compared to existing approaches. The evaluation encompasses various sentiment classes, measuring metrics like accuracy, recall, and F1-score, and assessing the framework's adaptability to different product domains. The study showcases the effectiveness of advanced machine learning and deep learning algorithms in sentiment categorization, advancing the field of sentiment analysis for online product evaluations. Businesses can gain valuable insights into customer sentiment and make well-informed decisions regarding product enhancements and marketing strategies by leveraging the proposed framework
Optimization Design for Federated Learning in Heterogeneous 6G Networks

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research.
Design and Implementation of the Chinese Character Font Recognition System Based on Binary Convolutional Encoding and Decoding Network

With the in-depth research of computer vision and natural language processing, Chinese character font recognition technology has been widely used. However, due to the large number, complex shape, and changeable style of Chinese characters, the traditional methods based on image feature extraction and classifier design are difficult to deal with the differences among different fonts effectively. Based on the above situation, this paper mainly studies designing and implementing the Chinese character font recognition system based on a binary convolutional encoding and decoding network (BCEDN). First, this paper introduces the principle and structure of Convolutional Encoding and Decoding Networks (CEDN) and how they can be applied to image generation tasks. Second, this paper proposes a binarization strategy to convert real-valued weights and activation functions in CEDN into binary weights and activation functions, thereby reducing the model's storage space and computational complexity while maintaining high generation quality. Then, the BCEDN model is trained and tested on this dataset. Finally, this paper analyses the performance of the BCEDN model on the Chinese character font recognition task through the results of quantitative and qualitative experiments and compares them with other related methods. The proposed BCEDN method has the advantages of high efficiency, stability, and scalability, having vital research significance in the field of Chinese character font recognition.
EQConvMixer: A Deep Learning Approach for Earthquake Location From Single-Station Waveforms

We present a novel deep-learning method using the ConvMixer network for automatic earthquake location. The proposed ConvMixer network utilizes three-component waveform recordings of single stations for estimating the hypocenter location. The ConvMixer network is a patch-based architecture that combines depthwise and pointwise convolutions to extract the global and local information of the earthquake waveforms. We train and test the proposed method using the Italian seismic dataset (INSTANCE). The ConvMixer network estimates the earthquake hypocenter locations with high accuracy, reaching a mean absolute error (MAE) of 2.71 km for the epicenter distance, and 1.15 km for the depth. In addition, we use the global STanford EArthquake Dataset (STEAD) to further evaluate the performance of the ConvMixer. As a result, the ConvMixer network achieves MAEs of 2.27 km and 1.19 km for the distance and the depth, respectively. The proposed ConvMixer network is compared to the benchmark methods, i.e., ResNet, AlexNet, MobileNet, and Xception, and outperforms all of them.
The Use of Mind Map in Collaborative Learning Activities of a Literary Reading Class

The use of mind map as collaborative learning activities in literary reading class will be examined. The students will choose any kind of technology tools to make a mind map in reading class. The aims of this study are to identify the implementation of using mind map in collaborative learning activities of a literary reading class and students’ perceptions on it. This is a mixed method research. The data are taken from observation and questionnaires. The observation results are used to analyze the implementation of using mind map in collaborative learning activities of a literary reading class. The questionnaires are used to identify students’ perception on it. This study results that the students had positive perception toward the use of mind map and believed that it helps them learn the materials faster, save more time in doing collaborative work, understand the relationship between ideas, and more motivated to read literary texts.
Integrating Multiple Intelligence Learning Approach to Upgrade Students’ English Writing Skills

The educational curriculum’s current challenge is to use appropriate approaches during the Covid-19 days. This study aimed to investigate the multiple intelligence learning approaches (MILA) which were used to provide students’ competence in English writing skills in an institute of IAIN Sorong, West Papua Province, Indonesia. In terms of it the multiple intelligence learning approaches introduced interactive, analytic, and introspective domains which cover elements of linguistics, mathematics, visual, kinesthetic, music, intra-personal, interpersonal, naturalistic, and existential or spiritual. Those elements would be appropriately implemented to upgrade students’ English writing skills. This study employed a survey method using lists of questionnaires to obtain qualified data which were collected through eight English lecturers, four staff curriculum developers, and eighty students. The findings conclude that most respondents felt contented and interested in developing and upgrading English writing skills using the Multiple Intelligence Learning Approaches. The results of this research claimed that the lecturers require the MILA to be more widely used to upgrade students’ writing skills. Also, the practitioners on campus should make together the strategies to be included in MILA for teaching English writing skills as recommended by the national education curriculum.
Enhancing Secure Communication in the Cloud Through Blockchain Assisted-CP-DABE

The use of encryption is essential to protect sensitive data, but it often poses challenges when it comes to locating and retrieving information without decryption. Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. In this paper a new approach to address the fine-grained search and to protect sensitive data, Blockchain Assisted ciphertext policy decentralized attribute-based encryption (BA-CP-DABE) in cloud has been developed. The CP-DABE is employed to manage data access, secure key generation, while the immutability of blockchain ensures the confidentiality of ciphertext. By leveraging searchable encryption, it becomes possible to securely search encrypted data stored on the blockchain. Keywords are encrypted using attribute-based encryption and stored on a remote server, along with the corresponding ciphertext in the blockchain. One of the significant challenges in this approach is the assumptions-based technique i.e., bilinear mapping, which involves keyword ciphertext and trapdoor security. However, through extensive numerical experiments, the system has demonstrated its ability to generate key and trapdoor structures, as well as effectively find keywords within the encrypted data.
Implementation of Data Classification Using K-Means Algorithm in Clustering Stunting Cases

Stunting is still a serious public health problem in Indonesia, where the prevalence of this condition is 37.2%, up from 35.6% in 2019 and 36.8% in 2020. The length or height of a child who is short (dwarf) is below average for his age. Stunting has a negative impact on IQ deficiencies, infectious diseases, mental health problems, and child development. Toddlers with stunting cases are detected when their growth and development does not match their age, but currently there is no data grouping based on these criteria that is of concern to parents and posyandu cadres. Data can be grouped using the K-Means data mining technique. The K-Means algorithm is often used by researchers as a grouping procedure to ascertain whether children are stunted or not. 395 datasets are used in this research data. The Knowledge Discovery In Databases (KDD) approach, a comprehensive nontrivial procedure for detecting and recognizing patterns in data, underlies this research. Based on the variables of age, weight and height, this study aims to identify groups or clusters of stunting status in children under five. The best number of clusters with K = 2 was determined by the findings of this investigation. There are 392 children in cluster 0-Shanum, Rizka, Nurjanah, and others-and three toddlers in cluster 1-Ezra, M. Abidza, and Abd Mahmud. With a total of 287 stunted toddlers and 108 toddlers with normal development, the most ideal DBI value is 0.007 which is close to 0, this shows that the clusters under review provide quality clusters.
MHG-ERC: Multi-hypergraph Feature Aggregation Network for Emotion Recognition in Conversations

The modeling of conversational context is an essential step in Emotion Recognition in Conversations (ERC). To maintain high performance and a low GPU memory consumption, this article proposes a new idea of using multiple hypergraphs to model the conversational context and designs a multi-hypergraph feature aggregation network for ERC. We use context window, speaker information, position information between utterances, and specific step size to construct different hyperedges. Then, various hypergraphs generated by different hyperedges are used to aggregate local and remote context information in turn. Experiments on two dialogue emotion datasets, IEMOCAP and MELD, demonstrate the effectiveness and superiority of this new model. In addition, our model requires only relatively low GPU memory consumption.
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: (1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, (2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and (3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.
Quantum Computing with Dartboards

We present a physically appealing and elegant picture for quantum computing, using rules constructed for a game of darts. A dartboard is used to represent the state space in quantum mechanics, and the act of throwing the dart is shown to have close similarities to the concept of measurement or collapse of the wave function in quantum mechanics. The analogy is constructed in arbitrary dimensional spaces, that is, using arbitrary dimensional dartboards, and for such arbitrary spaces this also provides us a “visual” description of uncertainty. Finally, connections between qubits and quantum computing algorithms are also made, opening the possibility to construct analogies between quantum algorithms and coupled dart throws.
Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications

The Industrial Internet of Things (IIoT) paradigm's fast expansion in the amount of information created from linked devices creates new opportunities for improving the service quality and applications through sharing of data. Data security is a significant problem since training and Support Vector Machine (SVM) classifier often involves compiling tagged IoT data from several organizations. Beyond causing the suppliers to lose money, disclosing sensitive information might cause significant problems. To solve these issues, introduced a secure SVM, which is a Privacy-Preserving SVM (PP-SVM) training method using block chain-based encoded IoT data, to fill the void between ideal assumptions and practical restrictions. IoT messages are encrypted before being stored on a decentralized system because Block chain offers safe and dependable data exchange platforms across several data sources. Using a homomorphic cryptographic algorithm, Paillier, reliable modules have been developed, such as protected algebraic multiplying and protected comparison. Furthermore, a protected SVM learning algorithm that only needs two conversations during a single step has been created. According to stringent security assessment, proposed approach guarantees SVM model parameters for data professionals and secrecy of sensitive information for each data source. Extensive testing backs up effectiveness of the suggested plan.
REVISITING THE FINANCE-GROWTH NEXUS IN GHANA: EVIDENCE FROM THRESHOLD MODELLING

This article examines the non-linearities in Ghana’s finance-growth nexus from 1960 to 2019. A non-linear relationship between finance and growth was established using Hansen’s sample-splitting technique. This suggests that finance contributes to growth only up to a certain level. Beyond that, any further financial development is likely to be detrimental to economic growth. Additionally, the study discovered that the threshold value is conditional on the proxy of finance employed. As a result, it is critical for Ghana to establish its own distinct financial development threshold and work within the optimal level for economic growth promotion and sustainability. This study adds to the body of knowledge by establishing a financial development threshold, which provides policymakers in Ghana with a clear direction for pursuing economic growth and financial development.
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning

Machine translation (MT) involving Indigenous languages, including endangered ones, is challenging primarily due to lack of sufficient parallel data. We describe an approach exploiting bilingual and multilingual pretrained MT models in a transfer learning setting to translate from Spanish into ten South American Indigenous languages. Our models set new SOTA on five out of the ten language pairs we consider, even doubling performance on one of these five pairs. Unlike previous SOTA that perform data augmentation to enlarge the train sets, we retain the low-resource setting to test the effectiveness of our models under such a constraint. In spite of the rarity of linguistic information available about the Indigenous languages, we offer a number of quantitative and qualitative analyses (e.g., as to morphology, tokenization, and orthography) to contextualize our results.
Romanian Multiword Expression Detection Using Multilingual Adversarial Training and Lateral Inhibition

Multiword expressions are a key ingredient for developing large-scale and linguistically sound natural language processing technology. This paper describes our improvements in automatically identifying Romanian multiword expressions on the corpus released for the PARSEME v1.2 shared task. Our approach assumes a multilingual perspective based on the recently introduced lateral inhibition layer and adversarial training to boost the performance of the employed multilingual language models. With the help of these two methods, we improve the F1-score of XLM-RoBERTa by approximately 2.7% on unseen multiword expressions, the main task of the PARSEME 1.2 edition. In addition, our results can be considered SOTA performance, as they outperform the previous results on Romanian obtained by the participants in this competition.
DiatopIt: A Corpus of Social Media Posts for the Study of Diatopic Language Variation in Italy

We introduce DiatopIt, the first corpus specifically focused on diatopic language variation in Italy for language varieties other than Standard Italian. DiatopIt comprises over 15K geolocated social media posts from Twitter over a period of two years, including regional Italian usage and content fully written in local language varieties or exhibiting code-switching with Standard Italian. We detail how we tackled key challenges in creating such a resource, including the absence of orthography standards for most local language varieties and the lack of reliable language identification tools. We assess the representativeness of DiatopIt across time and space, and show that the density of non-Standard Italian content across areas correlates with actual language use. We finally conduct computational experiments and find that modeling diatopic variation on highly multilingual areas such as Italy is a complex task even for recent language models.
Evaluating Sentence Alignment Methods in a Low-Resource Setting: An English-YorùBá Study Case

Parallel corpora are still crucial to train effective Machine Translation systems. This is even more true for low-resource language pairs, for which Neural Machine Translation has been shown to be less robust to domain mismatch and noise. Due to time and resource constraints, parallel corpora are mostly created with sentence alignment methods which automatically infer alignments. Recent work focused on state-of-the-art pre-trained sentence embeddings-based methods which are available only for a tiny fraction of the world’s languages. In this paper, we evaluate the performance of four widely used algorithms on the low-resource English-Yorùbá language pair against a multidomain benchmark parallel corpus on two experiments involving 1-to-1 alignments with and without reordering. We find that, at least for this language pair, earlier and simpler methods are more suited to the task, all the while not requiring additional data or resources. We also report that the methods we evaluated perform differently across distinct domains, thus indicating that some approach may be better for a specific domain or textual structure.
Towards End-to-End Open Conversational Machine Reading

In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Existing works generally utilize two independent modules to approach this problem’s two successive sub-tasks: first with a hard-label decision making and second with a question generation aided by various entailment reasoning methods. Such usual cascaded modeling is vulnerable to error propagation and prevents the two sub-tasks from being consistently optimized. In this work, we instead model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework on both sub-tasks by a large margin, achieving new state-of-the-art results. Further ablation studies support that our framework can generalize to different backbone models.