Reviewerly/exHarmony huggingface dataset:
The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation.

Recent text generation research has increasingly focused on open-ended domains such as story and poetry generation. Because models built for such tasks are difficult to evaluate automatically, most researchers in the space justify their modeling choices by collecting crowdsourced human judgments of text quality (e.g., Likert scores of coherence or grammaticality) from Amazon Mechanical Turk (AMT). In this paper, we first conduct a survey of 45 open-ended text generation papers and find that the vast majority of them fail to report crucial details about their AMT tasks, hindering reproducibility. We then run a series of story evaluation experiments with both AMT workers and English teachers and discover that even with strict qualification filters, AMT workers (unlike teachers) fail to distinguish between model-generated text and human-generated references. We show that AMT worker judgments improve when they are shown model-generated output alongside human-generated references, which enables the workers to better calibrate their ratings. Finally, interviews with the English teachers provide deeper insights into the challenges of the evaluation process, particularly when rating model-generated text.
End-to-End Russian Speech Recognition Models with Multi-head Attention

The aim of the current research was to improve the Russian end-to-end speech recognition system developed in SPC RAS by application of multi-head attention. The system was created by joining Connectional Temporal Classification model and attention-based encoder-decoder. The models with following attention types were created and researched: dot-product attention, additive attention, location-based attention, and multi-resolution location-based attention. The experiments of using different number of attention vectors were performed. The models were trained on a small Russian speech corpus of 60 h by application of transfer learning with English as non-target language. The usage of multi-head attention reduced word error rate for dot-product and additive attention comparing to the results obtained with one attention vector.
Syntactic Requirements for Well-defined Hybrid Probabilistic Logic Programs

Hybrid probabilistic logic programs can represent several scenarios thanks to the expressivity of Logic Programming extended with facts representing discrete and continuous distributions.The semantics for this type of programs is crucial since it ensures that a probability can be assigned to every query.Here, following one recent semantics proposal, we illustrate a concrete syntax, and we analyse the syntactic requirements needed to preserve the well-definedness.
Mining Fuzzy Temporal Gradual Emerging Patterns

Gradual emerging patterns (GEPs) are gradual item sets that occur less frequently in one data set and more frequently in another. For instance, let ‘fan speed’ and ‘temperature’ be attributes of two numerical data sets. A gradual item set “the higher the speed, the lower the temperature” (which correlates a data set’s attributes) becomes a GEP if it is less frequent (in terms of support as in frequent pattern mining) in one data set and more frequent in another. However, such patterns do not indicate how time gap impacts the emergence. Many correlations appear over time, for instance when phenomena appear after some meteorological situation due to latency. Previous works have not taken this temporal aspect into account. In this paper, we introduce temporal gradual emerging patterns (TGEPs) which are temporal gradual patterns (TGPs) whose frequency supports increase significantly between transformed data sets. For instance, a TGP “the higher the speed, the lower the temperature, almost 3 minutes later” becomes a TGEP if it occurs more frequently in one transformed data set than in another. Furthermore, we extend border manipulation to the case of mining TGEPs. In addition, we propose a more efficient ant colony optimization technique that exploits a heuristic approach to construct TGEPs.
Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence

Can reproduction alone in the context of survival produce intelligence in our machines? In this work, self-replication is explored as a mechanism for the emergence of intelligent behavior in modern learning environments. By focusing purely on survival, while undergoing natural selection, evolved organisms are shown to produce meaningful, complex, and intelligent behavior, demonstrating creative solutions to challenging problems without any notion of reward or objectives. Atari and robotic learning environments are re-defined in terms of natural selection, and the behavior which emerged in self-replicating organisms during these experiments is described in detail.
Use of e-resources in the University Libraries of northern India: A comparative study

The purpose of the study was to investigate the awareness and examine the use of electronic resources by the research scholars and faculty members of selected five university libraries in Northern India. The study aimed to determine frequency of use, purpose of access of resources and their satisfaction from the existing e-resources subscribed by their respective libraries. The survey was conducted to find out the use of e-resources in the selected university libraries of Northern India. The respondents included research scholars and faculty members (n=465) from all disciplines (i.e., Sciences, Social Sciences and Humanities) in five academic libraries. The representative samples were selected based on sample size given by Krejcie and Morgan (1970). The representative sample was formed by taking 15% of respondents from each category within each university. The 'Proportionate Stratified Random Sampling Technique ’was used and the hypothesis was tested using Chi-Square test. The findings revealed that the majority of the respondents (i.e., research scholars and faculty members) across the libraries were aware about the e-resources being subscribed by their respective libraries. Faculty members used maximum e-resources in comparison to the research scholars across the libraries. 58% respondents found contents of e-resources qualitative, 53% respondents found them easy to access, 54% respondents believed that required information is always available. A non-significant difference (X2= 3.190, df=4, p-value=0.527) was found in satisfaction level from the electronic collection between research scholars and faculty members across the libraries. The findings of this study will help the library authorities understand the usage of e-resources subscribed by their respective libraries between the research scholars and faculty members to promote their usage by giving training in the use of such resources.
Use of Augmented and 3D Visualization as a Tool to Support the Teaching of Spatial Geometry

Geometry is a branch of Mathematics that helps us to work with distances, volumes, and areas in the real world. However, learning content related to Spatial Geometry, whose visual appeal should be an invitation to study, presents an unusual obstacle for the student: how to understand the elements of a three-dimensional object if they are normally presented in a two-dimensional way (e.g., drawn on the whiteboard or book)? There is several software that can be used in the teaching of Space Geometry. However, in most times, the teacher has no contact with these tools, either due to unavailability of hardware to interact; or due to difficulty in acquiring the software, which in turn may be due to the cost or lack of knowledge of the means to make the acquisition. The focus of this paper is to present the experience report on the use of Augmented Reality using tutorials developed by NIEP group, aiming to generate personalized material for use in the classroom by high school students in Mathematics, in the teaching/learning of Space Geometry. The results found demonstrate improved learning, better understanding of the content and reduced learning time.
Application of Complex Choquet Fuzzy Integral Classification Model in Scientific Decision Making

The method to determine the measure of fuzzy complex Choquet integral is based on experience evaluation, which has great subjectivity. Therefore, based on complex fuzzy measure and complex fuzzy valued fuzzy measure in complex field, the complex interval valued function and complex field are provided. Then, according to the classical classification model and fuzzy integral classification model, a classifier classification method based on complex Choquet fuzzy integral is proposed and its properties are discussed. A formula for calculating the correlation coefficient of Choquet integral from each alternative to a positive ideal one is derived, and a multi-attribute decision making strategy based on the formula is also put forward. Finally, the feasibility of the proposed method is illustrated by the comparison of an example and the relevant decision-making methods.
Argument Strength in Probabilistic Argumentation Using Confirmation Theory

It is common for people to remark that a particular argument is a strong (or weak) argument. Having a handle on the relative strengths of arguments can help in deciding on which arguments to consider, and on which to present to others in a discussion. In computational models of argument, there is a need for a deeper understanding of argument strength. Our approach in this paper is to draw on confirmation theory for quantifying argument strength, and harness this in a framework based on probabilistic argumentation. We show how we can calculate strength based on the structure of the argument involving defeasible rules. The insights appear transferable to a variety of other structured argumentation systems.
Ontology‐Based Search Engine For Simulation Models From Their Related System Function

Abstract In a highly competitive industrial environment, reducing development time while maintaining a low level of risk makes simulation‐based design an effective way to cope with the complexity of multidisciplinary systems. To accelerate the design cycle, capitalization and reuse processes can support the search for similar models in an existing database. Therefore, we propose to prototype a search engine for simulation models from system modeling. This paper focuses on the presentation of our initial works around an ontology‐based search engine of simulation models representing the behavior of a given system function. Algorithms and similarity metrics developed are based on both thesaurus reference functions associated with semantic relations, and on various port attributes. Finally, an implementation of the corresponding inference engine has been demonstrated in an autonomous vehicle case study.
The Gifted Programming Standards and Their Relationship to Content Standards

The chapter discusses the content standards-Next Generation Science Standards, CCSS-M, and Common Core State Standards for English Language Arts (CCSS-ELA)-as originally designed and then through the gifted lens. Providing critical benchmarks and a guide for consistency among gifted programs and services, the Gifted Programming Standards are vital to ensure that gifted and talented learners have access to appropriate opportunities for growth commensurate with their abilities. The CCSSELA are K-12 content standards that illustrate the curriculum emphases needed for students to develop the skills and concepts required for the 21st century. The learning progressions or sequences in the content standards are the starting points for differentiating for gifted and advanced learners across the same content standards because concepts or task demands increase in depth and complexity moving from one grade level or band. The Standards are constructed to emphasize a shared responsibility across content areas in reading, writing, speaking, listening, and language in grades K—5.
Identifying Marine Sources of Beached Plastics Through a Bayesian Framework: Application to Southwest Netherlands

Earth and Space Science Open Archive This work has been accepted for publication in Geophysical Research Letters. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Identifying Marine Sources of Beached Plastics Through a Bayesian Framework: Application to Southwest NetherlandsAuthorsBramvan DuineniDMikaelKaandorpiDErikvan SebilleiDSee all authors Bram van DuineniDInstitute for Marine and Atmospheric Research Utrecht, Utrecht UniversityiDhttps://orcid.org/0000-0003-3683-6304view email addressThe email was not providedcopy email addressMikael KaandorpiDCorresponding Author• Submitting AuthorInstitute for Marine and Atmospheric Research Utrecht, Utrecht UniversityiDhttps://orcid.org/0000-0003-3744-6789view email addressThe email was not providedcopy email addressErik van SebilleiDInstitute for Marine and Atmospheric Research Utrecht, Utrecht UniversityiDhttps://orcid.org/0000-0003-2041-0704view email addressThe email was not providedcopy email address
Early Prediction of Human Action by Deep Reinforcement Learning

Early action prediction in video is a challenging task where the action of a human performer is expected to be predicted using only the initial few frames. We propose a novel technique for action prediction based on Deep Reinforcement learning, employing a Deep Q-Network (DQN) and the ResNext as the basic CNN architecture. The proposed DQN can predict the actions in videos from features extracted from the first few frames of the video, and the basic CNN model is adjusted by tuning the hyperparameters of the CNN network. The ResNext model is adjusted based on the reward provided by the DQN, and the hyperparameters are updated to predict actions. The agent's stopping criteria is higher or equal to the validation accuracy value. The DQN is rewarded based on the sequential input frames and the transition of action states (i.e., prediction of action class for an incremental 10 percent of the video). The visual features extracted from the first 10 percent of the video is forwarded to the next 10 percent of the video for each action state. The proposed method is tested on the UCF101 dataset and has outperformed the state-of-the-art in action prediction.
Towards Multi-agent Reinforcement Learning using Quantum Boltzmann Machines

Reinforcement learning has driven impressive advances in machine learning. Simultaneously, quantum-enhanced machine learning algorithms using quantum annealing underlie heavy developments. Recently, a multi-agent reinforcement learning (MARL) architecture combining both paradigms has been proposed. This novel algorithm, which utilizes Quantum Boltzmann Machines (QBMs) for Q-value approximation has outperformed regular deep reinforcement learning in terms of time-steps needed to converge. However, this algorithm was restricted to single-agent and small 2x2 multi-agent grid domains. In this work, we propose an extension to the original concept in order to solve more challenging problems. Similar to classic DQNs, we add an experience replay buffer and use different networks for approximating the target and policy values. The experimental results show that learning becomes more stable and enables agents to find optimal policies in grid-domains with higher complexity. Additionally, we assess how parameter sharing influences the agents behavior in multi-agent domains. Quantum sampling proves to be a promising method for reinforcement learning tasks, but is currently limited by the QPU size and therefore by the size of the input and Boltzmann machine.
Implementation of an Integrated Online Class Model using Open-Source Technology and SNS

Before Covid-19, the class model was divided into online, offline, and blended learning. Due to Covid-19, we have the only online class environment. We need a new online class model under the new circumstances. In the new model, all technology and educational methods need to be well-adapted, organized and harmonized to compensate for the absence of offline learning sessions. In this paper, we propose a new online class model. Because of the absence of offline sessions, the model emphasizes the integration of synchronous and asynchronous activities seamlessly and effectively. The model also emphasizes the instructor's role as a content prosumer because the instructor in the new model is either reusing other's and the one's own contents or supplying those contents for others. This model uses open-source solutions or free services like Moodle, OBS, Tubestory, and Snap Camera for both budget-saving and stability purposes. It actively uses Moodle's monitoring capability and adopts various learning technologies. It consists of three activity sessions: a pre-Zoom session, a Zoom session, and a post-Zoom session. Each session is composed of modules that describe the process and action for the class. The process, methods, and techniques for each module are explained in this paper. The official students survey for class evaluation held by Kongju national university showed that the new class model's application obtained a higher score than the same class of the previous year that is performed by conventional teaching.
A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.
Automatic Morpheme-based Distractors Generation for Fill-in-the-Blank Questions using Listwise Learning-To-Rank Method for Agglutinative Language

Automatic question generation facilitates the smart assessment for the evaluator to assess the student skills. Several methods were proposed to generate distractors for non-factoid cloze question using different similarity measures. This study presents a method for automatic generation of affix based distractor for Tamil fill-in-the-blank questions which are mainly used for learning Tamil grammar morphological details and vocabulary. In this study, affix based distractor generation is proposed as two step pipelined process: 1) Distractor candidate collection: This generation mainly relies on certain regularities manifest in high dimensional spaces which implicitly hybrids the orthographic and semantic features. 2) Distractor filtering: Filtering is trained as Learning-to-Rank models to persist the reliability in distractor generation. Feature based Listwise approaches (ListNet and ListMLE) were used which uses caserole relationship, subject-verb agreement, POS tag in addition to similarity measures. Experiments were done with annotated dataset (TamilMCQs) taken from 5th to 12th grade Tamil text books. Experimental results show that hybridization of spelling and semantic features highly improves the plausibility in distractor generation and then, ListMLE method improves the reliability in distractor generation compared to ListNet method. As a overall, our proposed pipelined process increases plausibility and reliability in distractor generation.
AirCode: A Robust Object Encoding Method.

Object encoding and identification is crucial for many robotic tasks such as autonomous exploration and semantic relocalization. Existing works heavily rely on the tracking of detected objects but have difficulty to recall revisited objects precisely. In this paper, we propose a novel object encoding method, which is named as AirCode, based on a graph of key-points. To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it to be robust to viewpoint changes, scaling, occlusion, and even object deformation. In the experiments, we show that it achieves superior performance for object identification than the state-of-the art algorithms and is able to provide reliable semantic relocalization. It is a plug-and-play module and we expect that it will play an important role in various applications.
MORS 2021: 1st Workshop on Multi-Objective Recommender Systems

Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often need to take into account multiple objectives simultaneously. These objectives can be either from the users’ perspective or they could come from other stakeholders such as item providers or any party that could be impacted by the recommendations. Such multi-objective and multi-stakeholder recommenders present unique challenges and these challenges were the focus of the MORS workshop.
A FrontPage Online News Analysis: An Opinion Mining Approach

The advent of the internet with its attendant democratization of data and deluge of information had given rise to the avalanche of news media agencies. These agencies publish news articles with varying emotional reports especially stories conveying bad sentiments to the public. As major news agencies operate micro-blogging websites and establish their presence on social media channels, the distribution of bad news increases. It has been shown that constant exposure to bad news presented in a body of texts, graphics, and videos/audios contribute to increase in high blood pressure, anxiety attacks, bowel disorders, stroke and/or heart failure. In this work, we presented a sentiment analysis framework to extract news articles from FrontPage of online newspapers and generate contextual wordlists to support positive news broadcasting. Using a set of 12 Nigerian online news channels, we employed a hybrid method of dictionary and corpus-based lexicon approaches to achieve the wordlist derivation. The result advocates for an alternative way of reporting negative news to reduce the adverse impact it has on the masses.