Reviewerly/exHarmony huggingface dataset:
Managing the Supply Chain for the Crops Directed from Agricultural Fields using Blockchains

Modern supply chain management relies heavily on crop processing technologies and blockchain technology. However, the majority of crop processing technology fails due to a lack of available supply chain management technologies. A supply chain management system based on crop processing is developed in this research. Digital ledger technology (blockchain) takes care of supply chains and deep learning image processing (crop processing). To process the harvested crops, the study makes use of machine learning techniques. Blockchain supply chain management technology is then used to deliver the processed crops to the shops. Consequently, the research permits the accurate and transparent distribution of crops to users in an optimal and secure manner. The full hybrid model is tested in a simulation to see if it can improve agricultural production and supply chain management. This new strategy improves agricultural processing rates, and when combined with the blockchain distributed ledger technology, this results in optimal crop management for the required users.
Automatic Simplification of Scientific Texts: SimpleText Lab at CLEF-2022

The Web and social media have become the main source of information for citizens, with the risk that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. Non-experts tend to avoid scientific literature due to its complex language or their lack of prior background knowledge. Text simplification promises to remove some of these barriers. The CLEF 2022 SimpleText track addresses the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of NLP and IR researchers working together to resolve one of the greatest challenges of today. The track will use a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.
Introducing macrophages to artificial immune systems for earthquake prediction

Earthquake prediction (EQP) is crucial for taking preemptive measures and accurately predicting damage. Several historical seismic-event-based EQP approaches have been proposed; however, these approaches only identify anomalies without distinguishing noise, reducing the prediction accuracy. Macrophages play an important role in the immune system by recognizing viruses, apoptotic cells, and normal cells, as well as performing immune responses and suppression to ensure homeostasis; that is, macrophages exhibit strong classification capabilities and self-adaptability. Therefore, in this study, a novel artificial macrophage algorithm (AMA) for EQP is proposed. More specifically, we first introduce the biological mechanism of macrophages to establish recognition and learning mechanisms to identify noise and anomalies. Second, we adopt a distance metric to denote the weights of the AMA, instead of using experience-based parameters. Finally, a stochastic gradient descent is introduced to ensure the adaptability of the AMA. The performance of the AMA was assessed through an analysis of historical seismic events in Sichuan and its surroundings. Our experimental results demonstrate that AMA outperforms state-of-the-art EQP algorithms. The parameters and statistical tests of AMA were further analyzed in this study.
Preservice Teachers’ Online Self-Regulated Learning: Does Digital Readiness Matter?

(1) Background: Teaching in today’s schools asks teachers to foster self-regulated learning and digital competences in children and young people. In order to do so, teachers first need to acquire and use these competences themselves. (2) Methods: Based on a mixed-methods approach, the study investigates self-regulated learning in online courses of N = 129 preservice teachers at a German university. (3) Results and conclusions: Perceiving their digital readiness as generally high, preservice teachers appear to not overly self-regulate their learning in the online environment. Finally, preservice teachers’ digital readiness was related only weakly to their online self-regulated learning. A discussion is offered which shows teacher education as a broader phenomenon and implies the need for professional development for teacher educators. Additionally, it is argued to link research on self-regulated learning more closely to research on online learning environments in teacher education.
Development of argument based opinion mining model with sentimental data analysis from twitter content

Abstract In present scenario, social networks have developed massive in practice and society impact. Specifically, micro‐blogging is on trend in various platforms, such as Twitter, Instagram to evaluate public opinions for various issues. In recent times, some methods are developed for evaluating Twitter messages, based on the sentiment and opinions presented in tweets, corresponding to the hash‐tags and keywords. However, these models have some issues in handling the contradictory content and inconsistent data. Considering with this, this article presents an argument based opinion mining model with sentimental data analysis, for extracting specific argument, which is assessed in bottom‐up manner from the content from society emotion's reflects on the messages. Moreover, this model makes the user to pull out the arguments from a document set, which contains content from commercial sites, to extract the mostly argued positive and negative content. This model use natural language processing techniques, extraction of argument words for defining the decisions. The classification Naive Bayes classification is used for categorizing the results widely under agreed or disagreed. The experimental results prove that the proposed model provides feasible and appropriate results in argument analysis from Twitter content.
Face Image Analysis Using Machine Learning: A Survey on Recent Trends and Applications

Human face image analysis using machine learning is an important element in computer vision. The human face image conveys information such as age, gender, identity, emotion, race, and attractiveness to both human and computer systems. Over the last ten years, face analysis methods using machine learning have received immense attention due to their diverse applications in various tasks. Although several methods have been reported in the last ten years, face image analysis still represents a complicated challenge, particularly for images obtained from ’in the wild’ conditions. This survey paper presents a comprehensive review focusing on methods in both controlled and uncontrolled conditions. Our work illustrates both merits and demerits of each method previously proposed, starting from seminal works on face image analysis and ending with the latest ideas exploiting deep learning frameworks. We show a comparison of the performance of the previous methods on standard datasets and also present some promising future directions on the topic.
Session-based recommendation with temporal convolutional network to balance numerical gaps

A session-based recommendation system recommends the next possible item for users by learning the click session sequences of anonymous users. Considering the session data used is anonymous and few background information is available, it is very challenging to solve these problems. Recently, although the session-based recommendation systems based on neural network have achieved gratifying results, there are still two problems in the existing methods: (1) The value of each dimension in the embedded layer result is a non-zero mean distribution and the numerical gaps are very large. Such numerical gaps will increase the variance of the gradient, hindering the parameter optimization, and thus leading to the final prediction results inaccurate; (2) The previous models cannot effectively learn the long-term dependency information and capture the dependencies between non-adjacent items in the session sequence. To solve the above problems, we propose a Session-based Recommendation with Temporal Convolutional Network to Balance Numerical Gaps model. Specifically, we first normalize the embedded layer results, then constrain the embedded results in the unit hypersphere to reduce their impact on gradient calculation, and finally use Temporal Convolution Network (TCN) complements the multi-layer self-attention network to learn the session sequence.The TCN can obtain large enough receptive fields to fully learn session representation and the short-range item dependence that is missing due to the distraction of attention distribution by the self-attention mechanism, and the self-attention method capture the one-to-one interaction of each item, and obtain the long-term dependence of the item. We have conducted a large number of experiments on three real-world datasets. The results show that, in most cases, our proposed method outperforms the state-of-the-arts methods.
How ReadMe files are structured in open source Java projects

Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository. We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories. We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars. While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars. ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity.
Detecting six different types of Thyroid Diseases using Deep Learning approaches

The thyroid gland is extremely one of the vitalglands of our body. As part of the gland system in our body, this very tiny gland in our neck secreting thyroid hormone, which helps for directing all of our body's metabolic functions-starting from digestion to energy. The startingstage of thyroid detection is the first and topmostwork that helpsin saving people's lives. Generally, visual examination of X-Rays and manual techniques are used for these types of thyroid disease diagnosis. Thus, in this project, we apply deep learning algorithms to detect thyroid diseases and their presence without the need for several consultations from different doctors. This leads to the earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences effectively and cheaply avoiding the human error rate. A web application using react JS is developed to ease the prediction of thyroid diagnosis in hospitals. Thus this project is used to predict thyroid diseases effectively.
College Classroom English Teaching Methods and Practice Based on Big Data Technology

The quality of classroom English teaching has become a general concern of the society, because the efficiency of students’ classroom learning can reflect the quality of English teaching. Traditional classroom English teaching has deviated from the development of the times, which is not conducive to the training of diversified, practical, and innovative talents. Big data expands the time and space of education and has a great impact on higher education. The combination of traditional education methods and modern education technology is more conducive to improving teaching level and quality. The application advantages of big data in English teaching under the background of big data are particularly prominent. This paper adopts the big data technology of the MOOC online learning platform to analyze the quality of college students’ classroom English teaching so as to help teachers clearly understand the learning effects of students. Meanwhile, it analyzes the learning behaviors of students and teachers’ teaching behaviors so that teachers can understand each student’s learning status. By comparing with the traditional English teaching data of the first semester, the leap of teachers’ life using MOOC English teaching platform has been improved, the participation degree is higher, and the English teaching quality has been improved. Students who use MOOC English teaching platform have 5% higher overall participation in learning than students in traditional English teaching.
Accurate and efficient privacy-preserving string matching

Abstract The task of calculating similarities between strings held by different organisations without revealing these strings is an increasingly important problem in areas such as health informatics, national censuses, genomics, and fraud detection. Most existing privacy-preserving string matching approaches are either based on comparing sets of encoded characters allowing only exact matching of encoded strings, or they are aimed at long genomics sequences that have a small alphabet. The set-based privacy-preserving similarity functions that are commonly used to compare name and address strings in the context of privacy-preserving record linkage do not take the positions of sub-strings into account. As a result, two very different strings can potentially be considered as a match leading to wrongly linked records. Furthermore, existing set-based techniques cannot identify the length of the longest common sub-string across two strings. In this paper, we propose two new approaches for accurate and efficient privacy-preserving string matching that provide privacy against various attacks. In the first approach we apply hashing-based encoding on sub-strings (q-grams) to compare sensitive strings, while in the second approach we generate one-bit array from the sub-strings of a string to identify the longest common bit sequences. We evaluate our approaches on several data sets with different types of strings, and validate their privacy, accuracy, and complexity compared to three baseline techniques, showing that they outperform all baselines.
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning

There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation.We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method.Manual validation of the RBA confirmed 91-99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems.Our label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.
PaC-trees: supporting parallel and compressed purely-functional collections

Many modern programming languages are shifting toward a functional style for collection interfaces such as sets, maps, and sequences. Functional interfaces offer many advantages, including being safe for parallelism and providing simple and lightweight snapshots. However, existing high-performance functional interfaces such as PAM, which are based on balanced purely-functional trees, incur large space overheads for large-scale data analysis due to storing every element in a separate node in a tree.
Laplacian encoder-decoder network for raindrop removal

Digital raindrop removal is a branch of image restoration that aims at identifying adherent droplets on a glass surface and replacing them with plausible content. When successfully performed, raindrop removal was proven in the past to positively affect both the perceived appearance of the scene, and the performance of computer vision tasks such as semantic segmentation and object detection. In this paper, we design and implement a new encoder-decoder neural network for supervised raindrop removal. Our network, given a rainy input image, produces as output the Laplacian pyramid of a rain-free version of the input, making it possible to handle the variety of appearances of rain droplets by processing different frequency bands independently. To this end, we define and experimentally prove the effectiveness of a custom loss function that combines the errors of the different Laplacian frequency bands. We test our model for raindrop removal on a standard dataset, using multiple objective metrics to provide a detailed analysis of its performance. We confirm the superiority of our proposal in a comparison with other methods from the state of the art.
Comparing Two Safe Distance Maintenance Algorithms for a Gaze-Controlled HRI Involving Users with SSMI

People with severe speech and motor impairment often find it difficult to manipulate physical objects due to spasticity and have familiarity with eye pointing based communication. This article presents a novel eye gaze controlled augmented reality human-robot interface that maintains a safe distance of the robot from the operator. We used a bespoke appearance-based eye gaze tracking algorithm and compared two different safe distance maintenance algorithms. We undertook simulation studies followed by a user trial involving end users. Users with severe speech and motor impairment could bring the robotic arm at any designated point within its working envelope in less than 3 minutes.
Annotation is easy: Learning to generate a shadow mask

Pixel-level annotation for supervised learning tends to be tedious and inaccurate when facing complex scenes, leading models to produce incomplete predictions, especially on self shadows and shadow boundaries. This paper presents a weakly supervised graph convolutional network (namely WSGCN) for generating an accurate pseudo shadow mask from only several annotation scribbles. Given these limited annotations and a priori superpixel segmentation mask, we seek a robust graph construction strategy and a label propagation method. Specifically, our network operates on superpixel-graphs, allowing us to reduce the data dimensions by several magnitudes. Then, under the guidance of scribbles, we formulate the generation of a shadow mask as a weakly supervised learning task and learn a 2-layer graph convolutional network (GCN) separately for each training image. Experimental results on the benchmark datasets SBU and ISTD, show that our network can achieve impressive performance by using a few thousand parameters, and training our re-annotated data can further improve the performance of the state-of-the-art detectors.
Recognition of Japanese Sign Language by Sensor-Based Data Glove Employing Machine Learning

This paper presents a sensor-based data acquisition glove for gesture recognition in Japanese Sign Language (JSL), which uses five flex sensors and an inertial measurement unit (IMU) to detect finger flexion and hand motion information. The detected data is sent from the Arduino Micro to a computer. We collected data from the "A" to "Ta" lines of the Japanese (kana) syllabary and using four different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) to recognize them. RF and KNN have the highest average accuracy, reaching 99.75%. Also, SVM and DT had an average accuracy of 99% and 94.25% respectively. The experimental results show that the proposed system has great potential for gesture recognition in Japanese Sign Language.
An enhanced matrix completion method based on non-negative latent factors for recommendation system

Among the model-based collaborative filtering (CF) recommendation algorithms, matrix factorization (MF) technology is quite efficient. An ever-increasing focus has witnessed that the non-negative latent factor (NLF)-based MF model is superior to other state-of-art models, due to its great ability to guarantee a desirable prediction accuracy while grasping the non-negativity of the LF matrix. However, most existing NLF models have not adequately considered various LFs in different conditions, which might lead to negative impacts on the model performance. In order to address this issue, a novel NLF model, i.e., efficient NLF model (ENLF), is put forward to adequately reflect the various influences of LFs to the target matrix, thereby more freedom is introduced to find the solution of the established minimization problem. Furthermore, to alleviate the computational burden caused by the introduction of more latent factors, a so-called momentum-based additive gradient descent (MAGD) algorithm is employed to learn the model, where the truncating strategy is utilized during the update so as to guarantee the non-negativity of LFs. Finally, the empirical results on six real industrial data sets show that the ENLF model based on MAGD can achieve a desirable performance with relatively low time consumption.