Reviewerly/exHarmony huggingface dataset:
Handwriting Classification for the Analysis of Art-Historical Documents

Digitized archives contain and preserve the knowledge of generations of scholars in millions of documents. The size of these archives calls for automatic analysis since a manual analysis by specialists is often too expensive. In this paper, we focus on the analysis of handwriting in scanned documents from the art-historic archive of the Wildenstein Plattner Institute. Since the archive consists of documents written in several languages and lacks annotated training data for the creation of recognition models, we propose the task of handwriting classification as a new step for a handwriting OCR pipeline. We propose a handwriting classification model that labels extracted text fragments, e.g., numbers, dates, or words, based on their visual structure. Such a classification supports historians by highlighting documents that contain a specific class of text without the need to read the entire content. To this end, we develop and compare several deep learning-based models for text classification. In extensive experiments, we show the advantages and disadvantages of our proposed approach and discuss possible usage scenarios on a real-world dataset.
TransBERT

Recent advances, such as GPT, BERT, and RoBERTa, have shown success in incorporating a pre-trained transformer language model and fine-tuning operations to improve downstream NLP systems. However, this framework still has some fundamental problems in effectively incorporating supervised knowledge from other related tasks. In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task. Particularly, we propose utilizing three kinds of transfer tasks, including natural language inference, sentiment classification, and next action prediction, to further train BERT based on a pre-trained model. This enables the model to get a better initialization for the target task. We take story-ending prediction as the target task to conduct experiments. The final results of 96.0% and 95.0% accuracy on two versions of Story Cloze Test datasets dramatically outperform previous state-of-the-art baseline methods. Several comparative experiments give some helpful suggestions on how to select transfer tasks to improve BERT. Furthermore, experiments on six English and three Chinese datasets show that TransBERT generalizes well to other tasks, languages, and pre-trained models.
Voice Assistants as Learning Companions: An Initial Exploration With Computer Science Students

With the hands-free and mobile interaction capabilities, and conversational potential, Voice Assistants (VA) like Apple Siri and Google Assistant can become ubiquitous learning companions that students wear in their pockets. Yet, studies addressing the potential and challenges of using VA in education are scant in Human-Computer Interaction. To begin filling this gap, this paper is an initial, qualitative exploration of how smartphone-based VA like Apple Siri and Google Assistant play the role of learning companions of Computer Science (CS) students. Without having been designed for this purpose, there is room for thinking that neither Apple Siri nor Google Assistant can play this role. Yet, the results of a participant observational study conducted over a semester in two modules, show that Apple Siri played the role of a convenient and motivating collaborator, who also clashed with, and changed, students' perceived use of VA. Open-ended questions prompted by this study are raised.
Integrity Check Method of Relay Protection Data Based on Petri Net

The integrity of relay protection data is a fundamental basis for the relay protection big data platform. Aiming at the characteristics of the integrity check as multiple data types and attributes and large volume of relay protection data, this paper proposed a Petri net-based relay protection data integrity check method by taking the advantage in modeling of professional application process and resources by Petri net. The method focuses on the data requirement of relay protection professional application, searches the indispensable data for the relay protection professional application by the method of tracing based on Petri net. Accordingly, a relay protection data integrity check dataset is established, relying on this, the integrity check of the relay protection data is realized. A practical example verifies the effectiveness of this method.
A Formal Framework for Gate- Level Information Leakage Using Z3

Ahstract- The hardware intellectual property (IP) cores from untrusted vendors are widely used, which has raised security concerns for system designers. Although formal methods provide powerful solutions for detecting malicious behaviors in hardware, the participation of manual work prevents the methods from practical applications. Information Flow Tracking (IFT) is a powerful approach to prevent sensitive information leakage. However, existing IFT solutions are either introducing overhead in hardware or lacking practical automatic working procedures. To alleviate these challenges, we propose a framework that fully automates information leakage detection in the gate level of hardware. This framework introduces Z3, an SMT solver, in checking the violation of the confidentiality automatically. On the other hand, a parser converting the gate-level hardware to the formal model is developed to further remove the manual workload. To validate the effectiveness, the proposed solution is tested on 11 gate-level netlist benchmarks. The Trojans leaking information from circuit outputs can be automatically detected. We also account for time consumption during the whole working procedure to show the efficiency of the proposed approach.
Novel framework for automatic localisation of gun carrying by moving person using various indoor and outdoor mimic and real‐time views/Scenes

Hand held gun detection has an important application in both the field of video forensic and surveillance, because, gun is operative by hand only while committing any crime with it. The significant application encompasses the vulnerable places, such as around airport, marketplace, shopping malls, etc. In view of non-availability of relevant public data set, this study provides a newly created mimicked video data set for detection of gun carried by a person and entitled as Tripura University Video Data set for Crime-Scene-Analysis (TUVD-CSA). Effects of illumination, occlusion, rotation, pan, tilt, scaling of gun are effectively demonstrated in it. Moreover, the authors proposed an Iterative Model Generation Framework (IMGF) for gun detection, which is immune to scaling and rotation. Instead of locating the best matched object (gun) in the whole reference image to a query model via exhaustive search, IMGF searches only where the moving person carrying gun appears, which drastically reduces the computational overhead associated with a general template matching scheme. This has been employed by the background subtraction algorithm. Experimental results demonstrate that the proposed IMGF performs efficiently in gun detection with lesser number of true-negatives compared with the state-of-the-art methods.
Application of Generative Adversarial Networksbased Semi-supervised Learning For Unlabeled Bearing Diagnosis

In many industrial processes, most of monitoring data are significant unlabeled data with limited accidental information so as to ensure long-term stable and safe operations of the monitored machines. Many classification methods based on the supervised learning usually lead to unsatisfied results, for example, the misclassification of data from minority categories as dominant categories. Compared with the unsupervised learning, the semi-supervised learning fully utilizes few labeled data and is one of the most promising learning algorithms to improve the labeling accuracy. After combining with the generative adversarial networks (GANs) and feature matching, the GANsbased semi-supervised learning (SSL) takes better learning performance, and thus is introduced for intelligent diagnosis of bearings in this paper. For the condition monitoring of bearings in rotating machinery, there are few historical data with labels and a large of amount monitoring data without labels. To process such bearing vibration data, the discriminator in the GANs is used as a classifier, and the generator with feature matching output expected features of an intermediate layer in the discriminator. Both of them are trained at the same time by using an adversarial learning procedure. In experiments, the GAN-SSL method is trained and verified on different bearings. Furthermore, the performances on the classification accuracy are also compared between the GAN-SSL and the commonly used convolutional neural network. The experimental results demonstrate that the GAN-SSL can efficiently learn the information from some bearings and predict states of other bearings with higher accuracy, and thus it can be further extended to intelligent knowledge transfer and online diagnosis.
An approach for suggestion mining based on deep learning techniques

Abstract An organization often uses forums and social media channels for getting feedback from costumers or users. The ratings of products on rating platforms are a useful feedback to make a product better. The feedback from a customer is in the form of a suggestion which appears in a rating text or is directly asked from the customer. Suggestion mining is a binary classification problem that labels sentences as Suggestion or Non-suggestion. The suggestion mining is similar to sentiment analysis which is associated with common linguistic properties and challenges irrespective of the domain and application. Most of the previous works of suggestion mining proposed rule based methods and a very few developed statistical classifiers by using manually identified features. Recently, several researchers paid attention on deep learning technique based solutions to suggestion mining where features are automatically learned. In this work, various deep learning techniques like RNN, LSTM, Attention based LSTM and GRU are used in the experimentation of suggestion mining. The experiment carried out on the dataset provided in SemEval 2019 suggestion mining competition. The Attention based LSTM achieved best accuracies for suggestion mining when compared with other deep learning techniques.
Worksheets, discovery learning, and 3D media based on QR-Code: The ability to analyze is formed in physics practicum

The learning process in the 21st century must be supported using technology. One of the competencies that are trained in physics learning is the ability to analyze. High school physics worksheets have not been optimal in training high school students' analytical skills. This article will describe the results of research and development of a physics practicum worksheet equipped with 3D media based on QR-Code technology to train it. The worksheet was developed using discovery learning stages. The research and development method used the Dick and Carey model. The resulting worksheet product was tested for material feasibility, learning stages, and practicum media by experts using a Likert scale questionnaire instrument. The results showed that the worksheet product with discovery learning stage equipped with QR-code based 3D media was feasible to be used to train analytical skills in learning physics in high scool.
Adaptive Testing Based on Knowledge Space Theory

At present, most of the tests are still testing the students’ ability level, lacking the diagnosis of the students’ knowledge structure and knowledge level. This thesis is guided by the knowledge space theory to design and implement a student knowledge structure testing system. The system can test the knowledge structure of the students according to the students’ answering conditions, so that students can understand the weak links that they can be targeted in their learning. On the other hand, teachers can design programs suitable for students’ specific situations, carry out teaching in a targeted manner, and realize “teach students in accordance with their aptitude” in a certain sense. After the system development is completed, a certain amount of testing is carried out to verify the feasibility of the system.
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing

Abstract In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis is beneficial to in-depth evaluation of several representative parsing techniques and leads to new directions for parser development.
Exploiting Text Data to Improve Critical Care Mortality Prediction

There has been a significant increase in the quantity, quality, and availability of unstructured clinical notes, motivating numerous machine learning approaches that leverage such data to improve predictive capabilities in medical settings. However, the question of whether patient group properties under observation influence the effectiveness of including unstructured data sources remains unanswered. The inclusion of unstructured clinical notes adds both an acquisition cost such as recording the notes by a clinician and converting records to an appropriate digital format, and a computational cost such as more complex and computationally expensive machine learning algorithms. Thus, it is important to understand the potential benefits offered by these unstructured data sources before attempting to use them. We empirically evaluate the performance impact of including unstructured clinical notes when performing mortality prediction by reproducing 29 previously published studies in this area. We use two common feature extraction methods, Word2Vec and Bag-Of-Words, with two existing machine learning models, XGBoost and Logistic Regression. Our results show that our approaches have significantly different performances depending on the properties of the patient group under study. Additionally, we identify several key findings that can be used to predict whether the inclusion of data from unstructured clinical notes will be beneficial based on properties of the patient groups.
Estimating Levels of Engagement for Social Human-Robot Interaction using Legendre Memory Units

In this study, we examine whether the data requirements associated with training a system to recognize multiple 'levels' of an internal state can be reduced by training systems on the 'extremes' in a way that allows them to estimate "intermediate" classes as falling in-between the trained extremes. Specifically, this study explores whether a novel recurrent neural network, the Legendre Delay Network, added as a pre-processing step to a Multi-Layer Perception, produces an output which can be used to separate an untrained intermediate class of task engagement from the trained extreme classes. The results showed that identifying untrained classes after training on the extremes is feasible, particularly when using the Legendre Delay Network.
Age prediction based on a small number of facial landmarks and texture features

BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.
Decision support system for novel recommendation using analytical hierarchy process and Vikor

Fake news or often called hoaxes have been circulating which has caused unrest in the community. One way to combat hoaxes is by cultivating literacy, especially novel literary works. Periodically, several websites contain novel recommendations that are sourced from other readers’ judgments that tend to be subjective. The purpose of this research is to build a web-based novel recommendation application using the AHP-VIKOR integration method. The criteria for evaluating novels used in this research are novel rating value, author rating value, author popularity value, and total award value. This research has six stages, namely data collection, data processing, system analysis, system design, system implementation and system evaluation. The results of the system evaluation showed that 81.8% of the respondents agreed that the system was easy to operate, 72.7% agreed with the ease of understanding the language of the system, 45.5% agreed and 54.5% strongly agreed with the results generated according to user expectations.
Domain Translation of Images Using Auto Encoders and Coupled GANs with Unsupervised Learning

Abstract Domain translation of images is nothing but learning from marginal distribution across different to obtain a joint distribution from the listed domains. But there exist a limitless number of joint distributions, but we couldn’t infer that without additional assumptions. It is nothing but changing the entire scenario of the given input image by translating and reconstructing into an entirely new image but with the key features of the input image. Here for the domain translation of images from different domains in video, we are using Coupled-GAN’s for domain translation with making use of shared latent space.
HSCKE: A Hybrid Supervised Method for Chinese Keywords Extraction

Automatic keywords extraction refers to extracting words or phrases from a single text or text collection. Supervised methods outperform unsupervised methods, but it requires a large volume of labeled corpus for training. To address the problem, extra knowledge is obtained through labels generated by other tools. Moreover, the preprocessing of Chinese text is more challenging than that in English because of the fragments caused by word segment. Hence the named entity recognition in the preprocessing is introduced to enhance the accuracy. On the other hand, text contains different separate parts, and each part conveys information to readers on different levels. Thus, we present a text weighting method based on priority that takes into consideration the importance of different texture parts. In this paper, we integrate the three ideas above and propose a novel hybrid method for Chinese keywords extraction (HSCKE). To evaluate the performance of our proposed approach, we compare HSCKE with four most commonly used methods on two typical Chinese keywords extraction datasets. The experimental results show that the proposed approach achieves the optimal performance in terms of precision, recall and F1 score.
Analyzing and Enhancing Communication Platforms available for a Deaf-Blind user

When it comes to living in a modern world with lots of technology around you the physically-disabled people face a lot of problems while communicating with the world and using this available technology. This paper explores the current research effort towards building user friendly application that connects two normal people, deaf and dumb, blind and deaf people together. Nowadays there are many applications available when it comes to hearing and visually impaired but every application has a certain limit till now. The work includes three approaches viz. a voice, text and video based input-output interaction. When it comes to deaf and dumb communication, the model to learn sign language was implemented and there was conversion of Indian Sign Language into the text. When it comes to communication between the deaf-blind users, Morse code the language of dash, spaces and dots has always been an effective communication tool. Also in some of the processes, there is use of image to text and text to speech conversion. All the work focuses on how these techniques were developed and available to implement and their effectiveness at the same time. It also provides different ways for the visually and hearing impaired to communicate by converting the texts as voice signals and morse code signals. This paper also proposes and explores another method that can be implemented for a full-fledged interaction between visually and hearing impaired without any limitations and the work depends on Morse code, translations such as Morse code to text, speech and vice-versa.
Banking technology and cashless economy in selected Sub-Saharan African countries: does education matter?

Purpose This study aims to analyze the effect of banking technology [automated teller machine (ATM) and mobile cellular devices (MOBs)] and other traditional factors on the level of currency in circulation for a sample of 21 selected sub-Saharan African (SSA) countries. It also assessed the mitigating effect of education on the relationship between banking technology and the cashless economy. Design/methodology/approach The study used a panel data approach to design a cashless economy model with banking technology – ATM and MOBs – as well as their interaction with education as regressors. Findings This study finds that MOB is significant for promoting a cashless economy, whereas ATM is insignificant in sample SSA countries. The level of education and the number of bank branches were also found to be significant in promoting a cashless economy. The interaction between education and ATM was insignificant but negatively signed, whereas that between education and MOB was significant but had a positive sign. Research limitations/implications Non-availability of data restricted this work to a panel study of selected SSA countries. Subsequent studies should consider single-country case studies. Practical implications Findings from the study imply that for banking technology to drive a cashless economy effectively, education has to be improved. Originality/value The ratio of cash in circulation to total money supply was used as a measure of the cashless economy. The study also evaluated the moderating effect of education on banking technology.