Reviewerly/exHarmony huggingface dataset:
Farm Track Application Development Using Web Mining and Web Scraping

As an agricultural country, India's economy is heavily reliant on agricultural yield growth and agroindustry goods. Food demand is rising as the world's population grows by the day. Climatic conditions are the foundation for growing the best produce. The internet technology is advancing on a regular basis, and companies are becoming digitized. Every company has a website or a mobile application that they use to give services to its customers.
Emotion recognition of faces and emoji in individuals with moderate-severe traumatic brain injury

Background Facial emotion recognition deficits are common after moderate-severe traumatic brain injury (TBI) and linked to poor social outcomes. We examine whether emotion recognition deficits extend to facial expressions depicted by emoji.Methods Fifty-one individuals with moderate-severe TBI (25 female) and fifty-one neurotypical peers (26 female) viewed photos of human faces and emoji. Participants selected the best-fitting label from a set of basic emotions (anger, disgust, fear, sadness, neutral, surprise, happy) or social emotions (embarrassed, remorseful, anxious, neutral, flirting, confident, proud).Results We analyzed the likelihood of correctly labeling an emotion by group (neurotypical, TBI), stimulus condition (basic faces, basic emoji, social emoji), sex (female, male), and their interactions. Participants with TBI did not significantly differ from neurotypical peers in overall emotion labeling accuracy. Both groups had poorer labeling accuracy for emoji compared to faces. Participants with TBI (but not neurotypical peers) had poorer accuracy for labeling social emotions depicted by emoji compared to basic emotions depicted by emoji. There were no effects of participant sex.Discussion Because emotion representation is more ambiguous in emoji than human faces, studying emoji use and perception in TBI is an important consideration for understanding functional communication and social participation after brain injury.
Augmentation Pathways Network for Visual Recognition

Data augmentation is practically helpful for visual recognition, especially at the time of data scarcity. However, such success is only limited to quite a few light augmentations (e.g., random crop, flip). Heavy augmentations are either unstable or show adverse effects during training, owing to the big gap between the original and augmented images. This paper introduces a novel network design, noted as Augmentation Pathways (AP), to systematically stabilize training on a much wider range of augmentation policies. Notably, AP tames various heavy data augmentations and stably boosts performance without a careful selection among augmentation policies. Unlike traditional single pathway, augmented images are processed in different neural paths. The main pathway handles the light augmentations, while other pathways focus on the heavier augmentations. By interacting with multiple paths in a dependent manner, the backbone network robustly learns from shared visual patterns among augmentations, and suppresses the side effect of heavy augmentations at the same time. Furthermore, we extend AP to high-order versions for high-order scenarios, demonstrating its robustness and flexibility in practical usage. Experimental results on ImageNet demonstrate the compatibility and effectiveness on a much wider range of augmentations, while consuming fewer parameters and lower computational costs at inference time.
Around the GLOBE: Numerical Aggregation Question-answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks

One of the key AI tools for textual corpora exploration is natural language question-answering (QA). Unlike keyword-based search engines, QA algorithms receive and process natural language questions and produce precise answers to these questions, rather than long lists of documents that need to be manually scanned by the users. State-of-the-art QA algorithms based on DNNs were successfully employed in various domains. However, QA in the genealogical domain is still underexplored, and researchers in this field (and other fields in humanities and social sciences) can highly benefit from the ability to ask questions in natural language, receive concrete answers, and gain insights hidden within large corpora. While some research has been recently conducted for factual QA in the genealogical domain, to the best of our knowledge, there is no previous research on the more challenging task of numerical aggregation QA (i.e., answering questions combining aggregation functions, e.g., count, average, max). Numerical aggregation QA is critical for distant reading and analysis for researchers (and the general public) interested in investigating cultural heritage domains. Therefore, in this study, we present a new end-to-end methodology for numerical aggregation QA for genealogical trees that includes (1) an automatic method for training dataset generation, (2) a transformer-based table selection method, and (3) an optimized transformer-based numerical aggregation QA model. The findings indicate that the proposed architecture, GLOBE, outperforms the state-of-the-art models and pipelines by achieving 87% accuracy for this task compared to only 21% by current state-of-the-art models. This study may have practical implications for genealogical information centers and museums, making genealogical data research easy and scalable for experts as well as the general public.
A Survey on Attention-Based Models for Image Captioning

Image captioning task is highly used in many realworld applications.The captioning task is concerned with understanding the image using computer vision methods.Then, natural language processing methods are used to produce a description for the image.Different approaches were proposed to solve this task, and deep learning attention-based models have been proven to be the state-of-the-art.A survey on attentionbased models for image captioning is presented in this paper including new categories that were not included in other survey papers.The attention-based approaches are classified into four main categories, further classified into subcategories.All categories and subcategories of the attention-based approaches are discussed in detail.Furthermore, the state-of-the-art approaches are compared and the accuracy improvements are stated especially in the transformer-based models, and a summary of the benchmark datasets and the main performance metrics is presented.
Evaluation method of power loss for distribution network fault handling based on distribution transformer outage and restoration information matrix

Abstract To solve the problem of power loss evaluation in distribution network fault handling, an evaluation method of power loss for distribution network fault handling based on distribution transformer outage and restoration information matrix is proposed. Firstly, the distribution transformer outage and restoration information matrix model is established to describe the distribution transformer, its outage time, its restoration time, its outage duration, and its lost power affected by the distribution network failure outage. Then the outage duration in the process of fault handling is discussed in four cases, and the detailed calculation formula is given. At last, the power loss of a single distribution transformer, the total power loss of distribution network failure, and the average power loss of a distribution transformer are described in detail by outage time and predicted power. The example analysis shows that this method can quickly estimate the power loss of a distribution transformer in the process of fault disposal, and the method is simple and reliable, which can meet the needs of field applications and has good promotion value.
Few-Shot Speaker Identification Using Lightweight Prototypical Network With Feature Grouping and Interaction

Existing methods for few-shot speaker identification (FSSI) obtain high accuracy, but their computational complexities and model sizes need to be reduced for lightweight applications. In this work, we propose a FSSI method using a lightweight prototypical network with the final goal to implement the FSSI on intelligent terminals with limited resources, such as smart watches and smart speakers. In the proposed prototypical network, an embedding module is designed to perform feature grouping for reducing the memory requirement and computational complexity, and feature interaction for enhancing the representational ability of the learned speaker embedding. In the proposed embedding module, audio feature of each speech sample is split into several low-dimensional feature subsets that are transformed by a recurrent convolutional block in parallel. Then, the operations of averaging, addition, concatenation, element-wise summation and statistics pooling are sequentially executed to learn a speaker embedding for each speech sample. The recurrent convolutional block consists of a block of bidirectional long short-term memory, and a block of de-redundancy convolution in which feature grouping and interaction are conducted too. Our method is compared to baseline methods on three datasets that are selected from three public speech corpora (VoxCeleb1, VoxCeleb2, and LibriSpeech). The results show that our method obtains higher accuracy under several conditions, and has advantages over all baseline methods in computational complexity and model size.
SARGAN: Spatial Attention-Based Residuals for Facial Expression Manipulation

Encoder-decoder based architecture has been widely used in the generator of generative adversarial networks for facial manipulation. However, we observe that the current architecture fails to recover the input image color, rich facial details such as skin color or texture and introduces artifacts as well. In this paper, we present a novel method named SARGAN that addresses the above-mentioned limitations from three perspectives. First, we employed spatial attention-based residual block instead of vanilla residual blocks to properly capture the expression-related features to be changed while keeping the other features unchanged. Second, we exploited a symmetric encoder-decoder network to attend facial features at multiple scales. Third, we proposed to train the complete network with a residual connection which relieves the generator of pressure to generate the input face image thereby producing the desired expression by directly feeding the input image towards the end of the generator. Both qualitative and quantitative experimental results show that our proposed model performs significantly better than state-of-the-art methods. In addition, existing models require much larger datasets for training but their performance degrades on out-of-distribution images. In contrast, SARGAN can be trained on smaller facial expressions datasets, which generalizes well on out-of-distribution images including human photographs, portraits, avatars and statues.
The Prominence of IoT in Enhancing Business Success in the Context of Industry 4.0

The IoT is emerging as an increasingly pivotal component of contemporary business and commerce, especially within the context of Industry 4.0, which embodies the trend of automation conjoined with data exchange in various industries, including manufacturing. The primary aim of this research paper was to investigate the significance of IoT in enhancing business success within the framework of Industry 4.0. Qualitative methodology was employed to gather data from literature reviews and case studies of companies successfully integrating IoT into their operations. The study revealed that cloud computing, edge computing, actuators, artificial intelligence, machine learning, and blockchain are crucial IoT technologies that enable businesses to automate processes, improve device communication, and securely store and manage data. Furthermore, the study found that IoT technologies such as connected devices, sensors, and big data analytics are essential for enhancing business operations, lowering costs, and enhancing productivity. By enabling real-time monitoring and control of processes, IoT facilitates informed decision-making and swift response to market changes, ultimately leading to improved customer experiences through personalised and customised services and valuable insights into customer behaviour. In light of the constantly evolving market, businesses that adopt IoT technologies are more likely to remain competitive and ensure long-term success. The study underscores businesses' need to invest in IoT technologies to maintain competitiveness and achieve sustained success.
Improving Neural Topic Models with Wasserstein Knowledge Distillation

Topic modeling is a dominant method for exploring document collections on the web and in digital libraries. Recent approaches to topic modeling use pretrained contextualized language models and variational autoencoders. However, large neural topic models have a considerable memory footprint. In this paper, we propose a knowledge distillation framework to compress a contextualized topic model without loss in topic quality. In particular, the proposed distillation objective is to minimize the cross-entropy of the soft labels produced by the teacher and the student models, as well as to minimize the squared 2-Wasserstein distance between the latent distributions learned by the two models. Experiments on two publicly available datasets show that the student trained with knowledge distillation achieves topic coherence much higher than that of the original student model, and even surpasses the teacher while containing far fewer parameters than the teacher's. The distilled model also outperforms several other competitive topic models on topic coherence.
Multi-domain Active Learning for Semi-supervised Anomaly Detection

Active learning aims to ease the burden of collecting large amounts of annotated data by intelligently acquiring labels during the learning process that will be most helpful to learner. Current active learning approaches focus on learning from a single dataset. However, a common setting in practice requires simultaneously learning models from multiple datasets, where each dataset requires a separate learned model. This paper tackles the less-explored multi-domain active learning setting. We approach this from the perspective of multi-armed bandits and propose the active learning bandits (Alba) method, which uses bandit methods to both explore and exploit the usefulness of querying a label from different datasets in subsequent query rounds. We evaluate our approach on a benchmark of 7 datasets collected from a retail environment, in the context of a real-world use case of detecting anomalous resource usage. Alba outperforms existing active learning strategies, providing evidence that the standard active learning approaches are less suitable for the multi-domain setting.
Bi-ESRGAN: A New Approach of Document Image Super-Resolution Based on Dual Deep Transfer Learning

This paper proposes a new super-resolution approach for low-resolution document images based on dual deep transfer learning and GAN Architecture. It is an improvement of an already existing method but constrained by its poor caliber by its low quality on document images. In these images of complex types, it is necessary to preserve the most details and outlines of text and graphic areas. These constraints were the target of our contribution, which aims to improve the ESRGAN method. The proposed approach is called “Bi-ESRGAN”. It is based on the combination of two ESRGAN networks. The networks act in double focal on two different image maps (full image and details on the contour map) with a collaborative decision. Our approach has been tested and compared on our document image dataset that we built from document images presenting different challenges, categories, complexity levels and degradation kinds. The experimental results carried out are encouraging and confirmed the superiority of our approach compared to more than sixteen existing approaches with and without learning.
Unusual Activity Detection Using Machine Learning

In this paper, we discuss a technique developed to detect unusual activities. In detection, the primary aim is to capture and locate different block’s movement. Machine learning techniques have been used, with the help of which we detect real-time individual actions and compute the unusual frames. Also in this study, we determine if the object in motion is an individual or not with the help of its characteristics. Utilizing the movement impact map, we constructed a system which can detect unusual activities in a frame. The given method consists of motion influence map generation for the frames to see interactions that have been inside the frame. The main aim in using the movement impact map is that it correctly analyzes the motion in the block of the individual moving in that frame and gives a clear picture about its characteristics, which includes movement direction, and the objects interacting with other objects in that frame.
Shape‐Guided Mixed Metro Map Layout

Abstract Metro or transit maps, are schematic representations of transit networks to facilitate effective route‐finding. These maps are often advertised on a web page or pamphlet highlighting routes from source to destination stations. To visually support such route‐finding, designers often distort the layout by embedding symbolic shapes (e.g., circular routes) in order to guide readers' attention (e.g., Moscow map and Japan railway map). However, manually producing such maps is labor‐intensive and the effect of shapes remains unclear. In this paper, we propose an approach to generalize such mixed metro maps that take user‐defined shapes as an input. In this mixed design, lines that are used to approximate the shapes are arranged symbolically, while the remaining lines follow classical layout convention. A three‐step algorithm, including (1) detecting and selecting routes for shape approximation, (2) shape and layout deformation, and (3) aligning lines on a grid, is integrated to guarantee good visual quality. Our contribution lies in the definition of the mixed metro map problem and the formulation of design criteria so that the problem can be resolved systematically using the optimization paradigm. Finally, we evaluate the performance of our approach and perform a user study to test if the embedded shapes are recognizable or reduce the map quality.
Challenges of Distance Learning at Universities during the COVID-19 Pandemic in Georgia

Adaptation to distance learning, which is one of the most effective ways of fighting against the COVID-19 pandemic, presented numerous challenges to society and the economy. The study aims to assess the consequences of distance learning as a result of the pandemic from the perspective of students and teachers. Quantitative research was conducted. The students (n = 417) and teachers (n = 47) of all disciplines from Tbilisi universities (Republic of Georgia) participated in the research. Non-probability convenience sampling technique was used for the study. Respondents evaluated the process of distance learning positively since they had the opportunity to attend lectures from any location, thus, saving costs, learning new skills, gaining valuable experience, and having more free time left than before. Using a Likert scale, the distance learning process was positively assessed by students (3.2 points out of 5) and teachers (3 points out of 5). The majority of students (n = 288, 69%) preferred the synthesis of distance and auditory learning methods as they consider the student’s own choices in a superior way. Respondents pointed out some deficiencies, such as limited communication, technical access difficulties, low quality and malfunction of internet access, an inconvenient environment, students’ involvement process and complicated social relationships. The crisis caused by the COVID-19 epidemic has identified the need to advance the methods of high-quality acquisition of knowledge. It is preferable to equip university auditoriums with the necessary technical capabilities and to develop curricula that allow students to decide whether to attend lectures in the classroom or to participate online.
Exploring the quality of the higher educational institution website using data mining techniques

The website of higher educational institutes is considered a vital communication channel to provide main resources to their stakeholders. It plays an important role in disseminating information about an institute to a variety of visitors at a time. Thus, the quality of an academic website requires special attention to respond to the users’ demands. This study aims to explore the quality of the PSAU website based on data mining techniques. The first step: was collecting opinions about the PSAU website using a survey. After that, data mining processes were used as descriptive and predictive models. The descriptive model was applied to describe and extract the major indicators of website quality. Besides, the predictive model was applied to create models for predicting the website quality level. More than one classification algorithm was used. Naive Bayes and Support Vector Machine have given the best results in all performance indicators, and the achieved accuracy rate for both algorithms was 86% and 84% respectively. The results revealed that the overall quality level of the PSAU website is very good. The usability quality and content quality were very good. The service quality needs more attention. which indicated that the service level is inadequate and needs to be further enhanced. The results of the study should be useful to the deanship of Information Technology at PSAU, and website developers, in redesigning with high quality in terms of its usability, content, and service.
An evaluation of LLIN ownership, access, and use during the Magude project in southern Mozambique

The Magude Project assessed the feasibly of eliminating malaria in a low transmission setting in southern Mozambique using a package of interventions. This study measured the ownership, access and use of long-lasting insecticide treated nets (LLINs) and inequalities in these indicators across household wealth, size and population subgroups, to understand the protection that LLINs provided during the project. Data were obtained from various household surveys. At least 31% of the nets distributed during the 2014 and 2017 campaigns were lost during the first year post-distribution. Most nets (77.1%) present in the district were Olyset Nets. LLIN access never exceeded 76.3% and use varied seasonally between 40% and 76.4%. LLIN access limited LLIN use during the project, especially during the high transmission season. LLIN ownership, access and use were lower in harder-to-reach localities, in poorer and larger households. Children and women below 30 had poorer access to LLINs than the overall population. Net use was lowest among school-aged children and young adults, especially among young males, and highest in children under 5, pregnant women, in older adults and in households that received indoor residual spraying (IRS). This study revealed that LLIN mass-distribution campaigns alone are not sufficient to achieve the high level of net protection needed during elimination programs and that reviewing the LLIN allocation scheme, top-up distributions and/or community engagement campaigns is needed, also to reduce inequalities in populations' access to LLINs.