Reviewerly/exHarmony huggingface dataset:
Structure information learning for neutral links in signed network embedding

Nowadays, signed network has become an important research topic because it can reflect more complex relationships in reality than traditional network, especially in social networks. However, most signed network methods that achieve excellent performance through structure information learning always neglect neutral links, which have unique information in social networks. At the same time, previous approach for neutral links cannot utilize the graph structure information, which has been proved to be useful in node embedding field. Thus, in this paper, we proposed the Signed Graph Convolutional Network with Neutral Links (NL-SGCN) to address the structure information learning problem of neutral links in signed network, which shed new insight on signed network embedding. In NL-SGCN, we learn two representations for each node in each layer from both inner character and outward attitude aspects and propagate their information by balance theory. Among these three types of links, information of neutral links will be limited propagated by the learned coefficient matrix. To verify the performance of the proposed model, we choose several classical datasets in this field to perform empirical experiment. The experimental result shows that NL-SGCN significantly outperforms the existing state-of-the-art baseline methods for link prediction in signed network with neutral links, which supports the efficacy of structure information learning in neutral links.
A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant

Almond is an extendible open-source virtual assistant designed to help people access Internet services and IoT (Internet of Things) devices. Both are referred to as skills here. Service providers can easily enable their devices for Almond by defining proper APIs (Application Programming Interfaces) for ThingTalk in Thingpedia. ThingTalk is a virtual assistant programming language, and Thingpedia is an application encyclopedia. Almond uses a large neural network to translate user commands in natural language into ThingTalk programs. To obtain enough data for the training of the neural network, Genie was developed to synthesize pairs of user commands and corresponding ThingTalk programs based on a natural language template approach. In this work, we extended Genie to support Chinese. For 107 devices and 261 functions registered in Thingpedia, 649 Chinese primitive templates and 292 Chinese construct templates were analyzed and developed. Two models, seq2seq (sequence-to-sequence) and MQAN (multiple question answer network), were trained to translate user commands in Chinese into ThingTalk programs. Both models were evaluated, and the experiment results showed that MQAN outperformed seq2seq. The exact match, BLEU, and F1 token accuracy of MQAN were 0.7, 0.82, and 0.88, respectively. As a result, users could use Chinese in Almond to access Internet services and IoT devices registered in Thingpedia.
The Use of Tangible User Interfaces in K12 Education Settings: A Systematic Mapping Study

Tangible User Interfaces have enriched and expanded the user experience when interacting with computers and smart devices. The monopoly of graphical user interfaces has been broken thanks to the emergence of new complementary technologies that allow for new ways of interacting with computer systems, such as tangible interaction, among others. Due to the scope and number of research articles addressing the Tangible User Interface that have been published, it can now be considered an interaction mechanism that is relatively mature and integrated within society. However, while the application of tangible interfaces in different areas is described as a success, there are only a limited number of research articles about their impact on education and learning systems. As a result, it is difficult to show the actual impact of Tangible User Interface technology in K12 education settings. This study tries to fill this gap by performing a systematic mapping study that shows the current state of research on the impact of this technology in these settings, analyzing the findings and identifying the main advances and limitations of this novel technology.
Balancing between holistic and cumulative sentiment classification

Sentiment analysis is a fast-accelerating discipline that develops algorithms for knowledge discovery from opinionated content. The challenges however, when it comes to analyzing user reviews are plenty. Bad-quality, informal use of language and lack of labels, are only a few obstacles. Most importantly, users, consciously or subconsciously, use different approaches for expressing their opinion about a product or a service. Some of them go sentence by sentence mentioning some positive and negative aspects whereas others provide a mixed piece of text where the reader is supposed to see the big picture to understand the message. In this work, we propose a novel neural network that deals with both situations. Our method, by combining convolutional, recurrent and attention neural networks can extract rich linguistic patterns that reveal the user’s sentiment towards the entity under review. We evaluate our method in nine datasets that represent both binary and multi-class classification tasks. Experimental evaluation indicates that our method outperforms well-established deep learning approaches. Our approach outperformed the competitive methods in 8 out of 9 cases.
Automatic Essay Grading System for Japanese Language Exam using CNN-LSTM

This paper discusses the design for the development of an automatic essay grading system (SIMPLEO) using variations of the Convolutional Neural Network (CNN) and hybrid Convolutional Neural Network (CNN)-Long Short-term Memory (LSTM) for the assessment of the Japanese essay exam which is being developed by the Department of Electrical Engineering, University of Indonesia. Of the several variations tested, the most stable model is a model that has CNN-LSTM with kernel sizes of 5, the number of filters 64, pool size of 4, LSTM hidden units of 25, batch size of 50, repeated training of 50 epochs, and the SGD optimizer with a learning rate of 0.01 produces the highest prediction accuracy, which is 70.07%.
Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression

Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification are required to make these data broadly available. Disassociation is a popular method for transactional data anonymization against re-identification attacks in privacy-preserving data publishing. The anonymization algorithm of disassociation is performed in parallel, suitable for the asymmetric paralleled data process in IoT where the nodes have limited computation power and storage space. However, the anonymization algorithm of disassociation is based on the global recoding mode to achieve transactional data km -anonymization, which leads to a loss of combinations of items in transactional datasets, thus decreasing the data quality of the published transactions. To address the issue, we propose a novel vertical partition strategy in this paper. By employing local suppression and global partition, we first eliminate the itemsets which violate km-anonymity to construct the first km-anonymous record chunk. Then, by the processes of itemset creating and reducing, we recombine the globally partitioned items from the first record chunk to construct remaining km-anonymous record chunks. The experiments illustrate that our scheme can retain more association between items in the dataset, which improves the utility of published data.
COVID-19 Face Mask Detection Using CNN and Transfer Learning

The trauma produced by the COVID-19 sickness, which was proclaimed by the World Health Organization (WHO) in 2020, has impacted the entire world. WHO has recommended several recommendations and precautions to effectively prevent the spread of the deadly disease, including social distance, hand sanitizer, and the use of a face mask or face shield. Most particularly in crowded settings, which is what inspired this investigation into one of the WHO recommended preventive measures, namely the use of a face mask. This research used a Convolutional Neural Network and a Transfer Learning Model to determine whether or not a citizen wears a mask. This suggested model is trained and tested on the Face Masked Dataset, then image augmentation on limited available data for improved training and testing, with a 98 percent accuracy rate during training and testing.
Examination of jet growth and jet-drive in the recorder by means of linearized numerical and lumped models

Physical modeling of the sound generation in recorder-like instruments is a challenging task due to the complex interaction of the air jet and the acoustical field occurring in the mouth region of the instrument. On one hand, simplified, lumped models were shown to capture some of the key properties of the process, while on the other hand, well-resolved direct computational fluid dynamic (CFD) simulation of the sound production is becoming more and more feasible thanks to the increasing computational capacity being available at hand. In this paper, the results of two-dimensional CFD simulations are combined with a one-dimensional acoustical system and a lumped jet-drive model as an attempt to bridge the gap between fully lumped models and direct CFD computations. A linearized model of the propagation of perturbation waves along the jet is proposed which takes the real velocity profile of the jet into account. Then, the Orr–Sommerfeld equation is solved for attaining the growth rate and phase speed of the perturbations in the frequency domain. The results are inserted into state-of-the-art lumped jet-drive models and are coupled to an acoustical waveguide. One novel model also accounts for the transient growth. Finally, the coupled system is utilized for predicting the playing frequencies of a tenor recorder as a function of the blowing pressure in the case of both steady-state and transient operation. Comparisons with experimental results show the validity of both the flow and acoustical models and also demonstrate the capabilities of the proposed fully linear model with respect to the prediction of the sounding frequencies and register change thresholds.
CatVRNN: Generating category texts via multi-task learning

Controlling the model to generate texts of different categories is a challenging task that is receiving increasing attention. Recently, generative adversarial networks (GANs) have shown promising results for category text generation. However, the texts generated by GANs usually suffer from problems of mode collapse and training instability. To avoid the above problems, in this study, inspired by multi-task learning, a novel model called category-aware variational recurrent neural network (CatVRNN) is proposed. In this model, generation and classification tasks are trained simultaneously to generate texts of different categories. The use of multi-task learning can improve the quality of the generated texts, when the classification task is appropriate. In addition, a function is proposed to initialize the hidden state of the CatVRNN to force the model to generate texts of a specific category. Experimental results on three datasets demonstrate that the model can outperform state-of-the-art text generation methods based on GAN in terms of diversity of generated texts.
Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis Detection

As the third-place winning method for the MIDOG mitosis detection challenge, we created a cascade algorithm consisting of a Mask-RCNN detector, followed by a classification ensemble consisting of ResNet50 and DenseNet201 to refine detected mitotic candidates. The MIDOG training data consists of 200 frames originating from four scanners, three of which are annotated for mitotic instances with centroid annotations. Our main algorithmic choices are as follows: first, to enhance the generalizability of our detector and classification networks, we use a state-of-the-art Residual Cycle-GAN to transform each scanner domain to every other scanner domain. During training, we then randomly load, for each image, one of the four domains. In this way, our networks can learn from the fourth non-annotated scanner domain even if we don’t have annotations for it. Second, for training the detector network, rather than using centroid-based fixed-size bounding boxes, we create mitosis-specific bounding boxes. We do this by manually annotating a small selection of mitoses, training a Mask-RCNN on this small dataset, and applying it to the rest of the data to obtain full annotations. We trained the follow-up classification ensemble using only the challenge-provided positive and hard-negative examples. On the preliminary and final test set, the algorithm scores an F1 score of 0.7578 and 0.7361, respectively, putting us as the preliminary second-place and final third-place team on the leaderboard.
Qunomon: A FAIR testbed of quality evaluation for machine learning models

Rapid development of artificial intelligence (AI) technologies brings quality and reliability issues to real-world applications and business products, as well as their advanced performance. However, traditional testing methods of the quality of engineering systems have difficulties supporting AI systems with machine learning (ML) based on large-scale data due to their uncertainty, non-deterministic, and vulnerability. Academic fields have studied new techniques to manage and guarantee high-quality ML components in AI systems with the importance of realizing trustworthy AI. Moreover, regulatory authorities have developed new guidelines and rules for safe and broad market adoption to control quality. Although there is a lot of effort from both sides, ML quality control and assessment pose challenges that arise from gaps between their different points of view. This paper proposes a new testbed called “Qunomon (QUality + gNOMON)” that harmonizes gaps of two sides and supports the combination and comparison of various testing methods in ML component quality. The testbed is designed to improve the findability, accessibility, interoperability, and reusability of testing methods. Furthermore, we show the efficiency of quality testing and reporting with case studies where our testbed is applied.
Position-Encoding Convolutional Network to Solving Connected Text Captcha

Abstract Text-based CAPTCHA is a convenient and effective safety mechanism that has been widely deployed across websites. The efficient end-to-end models of scene text recognition consisting of CNN and attention-based RNN show limited performance in solving text-based CAPTCHAs. In contrast with the street view image and document, the character sequence in CAPTCHA is non-semantic. The RNN loses its ability to learn the semantic context and only implicitly encodes the relative position of extracted features. Meanwhile, the security features, which prevent characters from segmentation and recognition, extensively increase the complexity of CAPTCHAs. The performance of this model is sensitive to different CAPTCHA schemes. In this paper, we analyze the properties of the text-based CAPTCHA and accordingly consider solving it as a highly position-relative character sequence recognition task. We propose a network named PosConv to leverage the position information in the character sequence without RNN. PosConv uses a novel padding strategy and modified convolution, explicitly encoding the relative position into the local features of characters. This mechanism of PosConv makes the extracted features from CAPTCHAs more informative and robust. We validate PosConv on six text-based CAPTCHA schemes, and it achieves state-of-the-art or competitive recognition accuracy with significantly fewer parameters and faster convergence speed.
Usage of Blockchain Technology in e-Voting System Using Private Blockchain

In India, the conventional voting system consists of paper polling, electronic ballot system, and associated mechanical devices. However, for the improvement of conventional voting system, its scalability and accessibility from anywhere, a digital voting system can be built using the electronic polling devices like e-voting, mobile voting, and IoT-related centralized voting system. But the conventional system faces some limitations—fake voters, costing, quicker outcomes, constant Internet connectivity, secure user interface, snooping, and DoS attack. To remove those barriers, blockchain is introduced in e-voting applications that provide stability and anonymity of voters due to the use of Merkle tree and hashed confidential data and any changes can be detected if the hash value is changed and the message is conveyed immediately. In this scheme, we have proposed an e-voting application using private blockchain, practical Byzantine fault-tolerant (PBFT) non-competitive consensus algorithm, and ECC-based session generated by server.
Survey on IoT Enabled Motion Detector Camera in Smart Industry

Internet of Things (IoT) holds a crucial part in the most recent advancements in various areas. It works in numerous areas consistently which incorporates structures, frameworks, urban communities, houses, guards, resources, e-wellbeing, and transport the executives. Despite these, devices and sensors generate large amount of data which is one of the major problems. Storage of data has become a critical consideration because devices and sensors perform their tasks on a 24/7 basis, emitting huge amount of data every second. Since nothing much can be done for the storage of data in a developing country with large population, such as India, people can be encouraged to use these latest technologies. Moreover, to make the industry more reliable, motion sensors are added to keep track of real-time data and store the data only when motion is detected. As a result, storage and energy can be saved by reducing the wastage of data storage and energy.
Evaluation of Deep Learning Approaches for Aspect Based Sentiment Analysis on Movie Dataset

Aspect-Based Sentiment Analysis (ABSA) permits users to compute the sentiment for an aspect in a specific context. The domain-specific knowledge plays a major role in analyzing the sentiments of aspects. This res earch work dis covers the probability of enhancing the knowledge-driven ABSA concerning effectiveness and efficiency. These problems can be tackled here by building the comparative analysis on different machine learning algorithms on movie data. The main intent of this work is, to analyze the sentiment analysis on movie datasets to conduct comparative analysis through classifiers like SVM, DT, NB, KNN, and NN, where the classification accuracy can be improved by extracting the s ignificant features and aspects from the contexts. In the input movie data, s top words removal, removing punctuation, lower case conversion, and stemming are performed as the pre-processing step. The opinion words are collected in the as pect extraction phas e, and further, the features are extracted via determining the polarity s core and word2vector. The extracted features are considered as the input to the different machine learning algorithms like K-Neares t Neighbor (KNN), Decis ion Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN). Experiment res ults and in-depth analys ess how that the five approaches yield better attention towards enhancing the performance.
Improved TDV algorithm for three-dimensional space path planning in a complex radioactive environment with obstacles

Decommissioning of nuclear power plants is a complex process that exposes workers to radiation risks. Recently, many shortest path algorithms have been proposed to find a minimum dose path and the applications are generally based on three concepts: environmental modeling, radiation dose computation, and shortest path algorithm. However, the conventional minimum dose path search methods are sub-optimal because they suffer from semi-empirical approximation, complex geometries restrictions, and lack integrated environmental modeling capability. This study presents an improved three-degree of vertex (iTDV) algorithm, an extension of the novel three-degree of vertex (TDV) shortest path algorithm previously developed. In this work, we enhance its search mechanism to find the shortest path with the least amount of radiation dose in a complex three-dimensional (3D) space. The work also outlines the effective coupling of TDV with FLUKA Monte Carlo environmental modeling, which prevents interpolation uncertainty associated with the conventional path planning methods when dealing with large geometries. To demonstrate its efficiency, the iTDV is used to find the minimum dose walking path in environments with and without obstacles. The performance of the iTDV algorithm is also compared with the original TDV, and the Dijkstra's algorithm. The evaluation results and the improvements of the proposed method are discussed in this paper.
Problems of managing the university human capital at the educational environment digitalization context

The article discusses modern approaches to the «human capital» category definition, trends in developing human capital and its management system in higher education institutions of the Russian Federation. The paper determines that the problem of the educational environment is a current direction of modern pedagogical research. The authors study issues of qualitative changes at the university educational environment under conditions of dominating a new technological order in socio-economic processes, the digital economy development. The educational environment of Tomsk Agricultural Institute – Novosibirsk State Agrarian University branch is structured based on the analysis of psychological, pedagogical and economic literature, regulatory and legal support of the educational process, as well as the university general management system, the human capital of its teaching staff, and the analysis of the capital itself in particular. The article outlines the problematic field of managing the teaching staff human capital under conditions of the educational environment digitalization of the educational organization under study determining its actual tasks based on observation results of the human capital management process at the university, questionnaires and interviews of its teaching staff, calculation and analysis of estimated indicators of human capital. The most problematic areas of management activity of Tomsk Agricultural Institute under conditions of growing the informatization and digitalization processes are the following: human capital management in the field of educational, methodological and research work of university teachers. The authors conclude on the need to improve the human capital management system of the educational organization under study. They propose to implement the mid-term project «The teaching staff motivation of Tomsk Agricultural Institute – Novosibirsk State Agrarian University branch to work effectively under conditions of digitalization of the educational environment». It will allow raising the mechanism effectiveness of interacting the university administration and employees belonging to the teaching staff at the Institute e-learning field through forming a system of employee tangible and intangible incentives.