Reviewerly/exHarmony huggingface dataset:
A Practice Report of VR Video Project by Students for Awareness-Raising Activities of Barrier-Free Mindset

This paper reports a virtual reality (VR) video production project conducted in a digital contents production course. The project is notable in that it is a real client project requested by a local government. The project objective is to product VR video contents for awareness-raising activities of barrier-free mindset. In this paper, we show the project summary and the VR video contents as the result in this project. We also report the evaluation of our VR video contents in the project. By this evaluation, we found that VR video contents can be effective media for awareness-raising activities of barrier-free mindset.
TMchain: A Blockchain-based Collaboration System for Teaching Materials

Using existing resources to create teaching materials can save effort and achieve the desired quality easily. Yet while some resources can be used freely for educational purposes, others such as textbooks or online course materials cannot. This is a particular problem during a pandemic when much teaching has gone online and the risk of teachers violating copyright is even higher. Therefore, a solution that facilitates the usage of copyright-restricted resources for generating teaching materials with royalty sharing is needed. Our work exploits the advantage of blockchain technology and proposes a system to bond participants with a smart contract; it securely registers records of multiple authorships and contribution distribution of a teaching material that reuses in part, existing resources. Such records can be used as authorship evidence to claim economic benefits when a material is used. We implement TMchain on Ethereum-Remix IDE with a core smart contract. To lower the cost of using blockchain, the material files are stored off-chain and tied to the word processing system for the final authorship and contribution share determination when a material is completed. Furthermore, we test TMchain with teaching material creation scenarios to demonstrate its effective and practical potential.
Visually Grounded Interpretation of Noun-Noun Compounds in English

Noun-noun compounds (NNCs) occur frequently in the English language.Accurate NNC interpretation, i.e. determining the implicit relationship between the constituents of a NNC, is crucial for the advancement of many natural language processing tasks.Until now, computational NNC interpretation has been limited to approaches involving linguistic representations only.However, research suggests that grounding linguistic representations in vision or other modalities can increase performance on this and other tasks.Our work is a novel comparison of linguistic and visuolinguistic representations for the task of NNC interpretation.We frame NNC interpretation as a relation classification task, evaluating on a large, relationally-annotated NNC dataset.We combine distributional word vectors with image vectors to investigate how visual information can help improve NNC interpretation systems.We find that adding visual vectors yields modest increases in performance on several configurations of our dataset.We view this as a promising first exploration of the benefits of using visually grounded representations for NNC interpretation.
Algorithmic Differentiation

Differentiation is core to many scientific applications including maximising or minimising functions, solving systems of ODEs, and non-linear optimisation such as KKT optimality conditions. Algorithmic differentiation (AD) is a computerfriendly technique for performing differentiation that is both efficient and accurate. A recent important application of algorithmic differentiation is in machine learning and artificial intelligence. [1] Training a neural network involves two phases, namely forward and back propagation. The latter is essentially the calculation of the derivative of the whole neural network as a large function. [2] In this chapter, we will introduce this topic using an hands-on approach. Starting from the basic definition, we build up an simplified version of AD engine step by step. We then move to the AD engine in Owl to present its usage and more implementation details.
Application of automorphic forms to lattice problems

Abstract In this article, we propose a new approach to the study of lattice problems used in cryptography. We specifically focus on module lattices of a fixed rank over some number field. An essential question is the hardness of certain computational problems on such module lattices, as the additional structure may allow exploitation. The fundamental insight is the fact that the collection of those lattices are quotients of algebraic manifolds by arithmetic subgroups. Functions in these spaces are studied in mathematics as part of the number theory. In particular, those form a module over the Hecke algebra associated with the general linear group. We use results on these function spaces to define a class of distributions on the space of lattices. Using the Hecke algebra, we define Hecke operators associated with collections of prime ideals of the number field and show a criterion on distributions to converge to the uniform distribution, if the Hecke operators are applied to the chosen distribution. Our approach is motivated by the work of de Boer, Ducas, Pellet-Mary, and Wesolowski (CRYPTO’20) on self-reduction of ideal lattices via Arakelov divisors.
AI In The Library Research Environment

What is the current state of artificial intelligence (AI) in the world of scholarly communication? What impact does AI have on the practices and strategies of publishers, libraries, information technology companies, and researchers? What exactly is AI and what are those in the realm of scholarly communication actually thinking about it and doing with it? This Charleston Briefing seeks to provide some answers to these very important questions, offering both general essays on AI and more specific essays on AI in scholarly publishing, academic libraries, and AI in information discovery and knowledge building. The essays will help publishers, librarians, and researchers better understand the actual impact of AI on libraries and publishing so that they can respond to the potentially transformative impact of AI in a measured and knowledgeable manner. Charleston Briefings: Trending Topics for Information Professionals is a thought-provoking series of brief books concerning innovation in the sphere of libraries, publishing, and technology in scholarly communication. The briefings, growing out of the vital conversations characteristic of the Charleston Conference and Against the Grain, will offer valuable insights into the trends shaping our professional lives and the institutions in which we work. With Chapters By: Daniel W. Hook And Simon J. Porter Catherine Nicole Coleman And Michael A. Keller James W. Weis And Amy Brand Ruggero Gramatica Haris Dindo Todd A. Carpenter
Application of Expectation–Maximization Algorithm to Solve Lexical Divergence in Bangla–Odia Machine Translation

This paper shows the word alignment between Odia–Bangla languages using the expectation–maximization (EM) algorithm with high accuracy output. The entire mathematical calculation is worked out and shown here by taking some Bangla–Odia sentences as a set of examples. The EM algorithm helps to find out the maximum likelihood probability value with the collaboration of the ‘argmax function’ that follows the mapping between two or more words of source and target language sentences. The lexical relationship among the words between two parallel sentences is known after calculating some mathematical values, and those values indicate which word of the target language is aligned with which word of the source language. As the EM algorithm is an iterative or looping process, the word relationship between source and target languages is easily found out by calculating some probability values in terms of maximum likelihood estimation (MLE) in an iterative way. To find the MLE or maximum a posterior (MAP) of parameters in the probability model, the model depends on unobserved latent variable(s). For years, it has been one of the toughest challenges because the process of lexical alignment for translation involves several machine learning algorithms and mathematical modeling. Keeping all these issues in mind, we have attempted to describe the nature of lexical problems that arise at the time of analyzing bilingual translated texts between Bangla (as source language) and Odia (as the target language). In word alignment, handling the ‘word divergence’ or ‘lexical divergence’ problem is the main issue and a challenging task, though it is not solved by EM algorithm, it is only possible through a bilingual dictionary or called as a lexical database that is experimentally examined and tested only mathematically. Problems of word divergence are normally addressed at the phrase level using bilingual dictionaries or lexical databases. The basic challenge lies in the identification of the single word units of the source text which are converted into multiword units in the target text.
Subject-Aware Image Outpainting

Image outpainting aims at extending the field of an existing image. One of the current challenges with image outpainting is that background noise may interfere with the expansion of the subject, resulting in visual distortions and artifacts. In this paper, a subject-aware image outpainting method (SAIO) is proposed to solve this issue, which reduces background pixel interference and emphasizes the subject. After training a Matting model as a pre-extractor for the subject, two networks are trained in series: the subject outpainting network (SO-Net) for subject extension and the background completion network (BC-Net) for background extension. Firstly, the Matting model is used to extract the input image subject and separate the background at the same time, and the subject is transferred to SO-Net to generate the predicted subject. Secondly, the predicted subject is fused with the background separated in the previous step as the input of the second network. Finally, BC-Net outputs the complete image. In order to improve the training ability of the network and the quality of the output image, both networks adopt the conditional training strategy. Our method achieved good performance of 28.99 in PSNR on the test dataset. The qualitative and Quantitative experimental results show that our method achieves good performance. Our method can be widely applied to intelligent image processing systems.
Visualization in Deep Neural Network Training

Visualizing the decision making procedure of a deep neural network is one of the main challenges towards transparent and trustworthy artificial intelligence. This paper presents an approach which extracts latent variables from a trained network and, through clustering, constructs a set of anchors that represent the network’s data driven knowledge. This set is then used to inform users about the features that create network’s decision.
Common Reasoning in a Computational Context

In the field of Computation Science, ‘Commonsense Reasoning’ usually expresses the formalization of Logic systems in order to efficiently automate replication of human performances. This is done by employing methodologies from Computational Learning, and deals with the construction of information of both deductive and inductive nature. The process often happens in an ecologic, natural context (i.e., in the real world, not in an artificial laboratory setting), and in presence of incomplete and imprecise information.
A Functional Account of Probabilistic Programming with Possible Worlds

While there has been much cross-fertilization between functional and logic programming—e.g., leading to functional models of many Prolog features—this appears to be much less the case regarding probabilistic programming, even though this is an area of mutual interest. Whereas functional programming often focuses on modeling probabilistic processes, logic programming typically focuses on modeling possible worlds. These worlds are made up of facts that each carry a probability and together give rise to a distribution semantics. The latter approach appears to be little-known in the functional programming community. This paper aims to remedy this situation by presenting a functional account of the distribution semantics of probabilistic logic programming that is based on possible worlds. We present a term monad for the monadic syntax of queries together with a natural interpretation in terms of boolean algebras. Then we explain that, because probabilities do not form a boolean algebra, they—and other interpretations in terms of commutative semirings—can only be computed after query normalisation to deterministic, decomposable negation normal form (d-DNNF). While computing the possible worlds readily gives such a normal form, it suffers from exponential blow-up. Using heuristic algorithms yields much better results in practice.
Transformer-based Hierarchical Topic-to-Essay Generation

With the development of 5G network and Internet of things (IOT), a large amount of information is required. In this work, we focus on Topic-to-Essay Generation (TEG), which aims to generate the text based on the topics. Existing methods utilize the RNN-based models, and it’s not so useful to capture the long dependencies in the text. The information of topics is insufficient to generate a long text and the existing methods also suffer from the problem about the topic relevance of the text. To fill these gaps, we propose a Transformer-based Hierarchical Topic-to-Essay Generation Model (THTEG), and the experimental results on a real dataset show that our model performs better than the baselines in terms of automatic evaluation and human evaluation.
Internal Conditions for Responsible Attitude to Work

The article presents the internal conditions that influence the manifestation of the responsible attitude of an individual to work. The indicators of the responsible attitude which are determined in individual’s behavior are consistently considered and analyzed. The formation of internal conditions for the manifestation of an individual’s responsible attitude to activity is associated with the presence of one’s own individual style of work, a temporary type of personality, the ability of an individual to be aware of functional states, the ability to independently create the necessary and sufficient conditions for the high-quality, timely completion of the task and stress-resistance.
DASP: Dual-autoencoder Architecture for Skin Prediction

This paper introduces a novel human skin detection approach based on the application of a dual autoencoder architecture, composed of models to detect background and skin zones concomitantly. This method, named Dual-Autoencoder Skin Predictor (DASP), associates the outputs of two autoencoders through a composite loss that minimizes the error between predicted skin/background areas and the groundtruth. More importantly, the composite loss penalizes overlapping zones between autoencoders predictions, leading our approach to better capture fine-grained and complementary information between skin and background. To combine semantic information with the skin color distribution, heavily tackled by handcrafted skin detection methods, our architecture relies on a main input that considers multiple colorspaces. Besides, a secondary input provides a standard skin/background patch vector to the model, granting information regarding their color distribution. Our experiments support the accurate performance of the proposed architecture and highlights the contributions of the composite loss and multiple inputs. For instance, DASP achieves the best and the second best results on Pratheepan and Mutual Guidance datasets, respectively.
Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features

Japanese packed-meal or Bento preparation or packaging automated by a robot is a recent challenge. It is one of the latest cementations in human activity recognition systems. It shows contingency on physical gesture recognition techniques using some sensor-based datasets, especially some motion capture data or skeleton data. To get a firm grip on the recognition, we (Team Boson Kona) implement a method dealing with some handcrafted features gained from the filtered data. We employ several pre-processing steps to handle any inconsistency of the dataset: after cleaning the data, we use filtering and windowing techniques to process the raw data. Then we figure out multiple statistical and geometrical features from joints-based handcrafted features. After considering the most significant features for model training, we try out support vector machine classifier, random forest classifier and extra tree classifier. After applying all these methodologies, we obtain maximum accuracy of 64.6% using the extra tree classifier.
Technical Invoice Data Extraction System: State of the Art, Research Challenges and Countermeasures

In the world of E-commerce, recording sales play a significant role. To achieve it, companies use invoices where each seller generates an invoice for each transaction. As tons of invoices are generated by different sellers in different formats, it becomes difficult for the E-commerce industries to track trade and transactions manually. With exponential increment in the invoices, E-commerce industries need a solution to replace the manual intervention and adapt a completely automated data extraction system. Invoice data extraction systems are widely used in the E-commerce industries to remove the need of middleman for manual data extraction and automate the information retrieval process. Conventionally, an optical character recognition is used to salvage data from the invoice document, followed by a regress text analysis, which is an expensive and impractical solution. With the advancement in this field, pattern matching became a popular methodology, but the absence of specified patterns restricted the accuracy. Usage of recurrent neural network and graph neural network tackles most of these issues conveniently. This paper aims to give a glance of the researches in this domain and categorize the literature survey based on the adapted methodology, dataset involved, year of publication, and precision in data extraction. Furthermore, we have discussed the limitations and improvement in the research. We have also stated some applications of the invoice data extraction system and drawn the inference from the available researches.
Roadblocks in Gender Bias Measurement for Diachronic Corpora

The use of word embeddings is an important NLP technique for extracting meaningful conclusions from corpora of human text.One important question that has been raised about word embeddings is the degree of gender bias learned from corpora.Bolukbasi et al. (2016) proposed an important technique for quantifying gender bias in word embeddings that, at its heart, is lexically based and relies on sets of highly gendered word pairs (e.g., mother/father and madam/sir) and a list of professions words (e.g., doctor and nurse).In this paper, we document problems that arise with this method to quantify gender bias in diachronic corpora.Focusing on Arabic and Chinese corpora, in particular, we document clear changes in profession words used over time and, somewhat surprisingly, even changes in the simpler gendered defining set word pairs.We further document complications in languages such as Arabic, where many words are highly polysemous/homonymous, especially female professions words.
Study on English Majors’ Teaching, Learning and Assessment Reform Innovative Path

Cultivation of English talents; Teaching-learning-assessment reform; Innovative pattern
Specific View of Schoolchildren and Students on Distance Learning

The article presents arguments for necessity to determine the attitude of education actors to a new distant form of learning. The results of the study of semantic assessment of distant learning by students and schoolchildren have been presented. The sematic differential has been used to analyze the data obtained. The semantics of distant learning identified by its actors on the basis of the categories of “comfort”, “intellectual efficiency”, “workload”, and “externality”. There are differences in the semantic assessment of distant learning by students and schoolchildren. Students gave much lower estimates to distance learning by intellectual efficiency parameter, while estimating the workload parameter higher. Schoolgirls gave negative assessment to distance learning by all parameters. The findings indicate that there is a need to arrange personalized distance learning.
An Innovative Approach Towards Designing Efficient K-Nearest Neighbour Algorithm

k-Nearest Neighbour (kNN) Algorithm is extensively used in applications of machine learning since it is easy to implement and leads to high performance. Although literature covers machine learning aspects of kNN algorithm, there is a dearth of work in terms of improving its execution efficiency. In order to classify an object X using kNN, algorithm needs to compute the distances between object X and all the N training objects. Computing N distances is costly in terms of running time. In order to reduce this cost, the data structure such as K-D tree leads to minimize the running time, but increases the software complexity and maintenance cost at the same time. Thus, there remains a scope to overcome these challenges. An innovative and efficient approach in kNN is designed viz. easy to implement and maintain. This approach will be highly useful in developing, classifying and implementing real-time systems based on machine leaning.
Didactic Strategy with Mobile Devices: An Approach for the correct use of English Verb Tenses

In the teaching-learning process, didactic strategies have become a valuable methodological tool, which allows learning a difficult subject in a structured way. In English, the correct conjugation of verb tenses: simple and progressive in the elementary level, requires a structured and innovative methodology to motivate their learning. The main problem of this research consists of the increasing difficulty of the fifth-grade students from an institution located in the district 04 of Costa Rica, in correctly assimilating the conjugation of the simple and progressive verb tenses of the English language. For its solution, the design, implementation and validation of a didactic strategy was proposed as a way to support the teaching-learning process of the correct conjugation of the simple and progressive verb tenses, through the use of mobile devices. The methodology followed presented a mixed study (qualitative-quantitative) through the application of surveys, expert judgment and a case of execution, as sources to collect and validate the proposed didactic strategy, within the context of philosophy and methodology of action research. The results obtained from both, teachers and students, have been representative. The teachers have identified a great interest of the students in learning the subject, making use of the proposed strategy, causing this, motivation and openness. In fact, the students who used the strategy as a supportive tool for their evaluations, obtained better academic performance on average: 5C (74.28%) compared to those who did not use it as a supportive tool: 5A (44.76%) and 5B (64%).