Reviewerly/exHarmony huggingface dataset:
Software Restructuring at the Function Level Using Entropy Based Fuzzy C-means Clustering for Object Oriented Programming Language

Software restructuring aims at restructuring the functions and is used to improve the cohesiveness of the software system.However existing restructuring algorithms have some limitations such as difficulty to restructure the functions, delays to obtain solution quality which is bug-free, expensive in terms of cost and low cohesion.In entropy-based fuzzy c-means clustering algorithms, member functions are restructured and achieve high cohesion and low coupling.This paper suggests the use of vectors to calculate the similarities between member functions as patterns for the member variables which are further used in clustering.After finding the similarities between the functions, the proposed entropy based FCM algorithm is used in order to restructure the member functions.The proposed entropy-based fuzzy c-means will be tested by using different object-oriented system software to ensure that the model is accurate when compared with Fuzzy c-means.The proposed system will increase the cohesion and reduces cost.
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions

Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors. It has attracted great attention in recent years based on the massive emergence of social communication platforms and an urgent requirement for efficient dialogue information understanding and digestion. Different from news or articles in traditional document summarization, dialogues bring unique characteristics and additional challenges, including different language styles and formats, scattered information, flexible discourse structures and unclear topic boundaries. This survey provides a comprehensive investigation on existing work for abstractive dialogue summarization from scenarios, approaches to evaluations. It categorizes the task into two broad categories according to the type of input dialogues, i.e., open-domain and task-oriented, and presents a taxonomy of existing techniques in three directions, namely, injecting dialogue features, designing auxiliary training tasks and using additional data.A list of datasets under different scenarios and widely-accepted evaluation metrics are summarized for completeness. After that, the trends of scenarios and techniques are summarized, together with deep insights on correlations between extensively exploited features and different scenarios. Based on these analyses, we recommend future directions including more controlled and complicated scenarios, technical innovations and comparisons, publicly available datasets in special domains, etc.
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics

State-of-the-art approaches in computer vision heavily rely on sufficiently large training datasets. For real-world applications, obtaining such a dataset is usually a tedious task. In this paper, we present a fully automated pipeline to generate a synthetic dataset for instance segmentation in four steps. In contrast to existing work, our pipeline covers every step from data acquisition to the final dataset. We first scrape images for the objects of interest from popular image search engines and since we rely only on text-based queries the resulting data comprises a wide variety of images. Hence, image selection is necessary as a second step. This approach of image scraping and selection relaxes the need for a real-world domain-specific dataset that must be either publicly available or created for this purpose. We employ an object-agnostic background removal model and compare three different methods for image selection: Object-agnostic pre-processing, manual image selection and CNN-based image selection. In the third step, we generate random arrangements of the object of interest and distractors on arbitrary backgrounds. Finally, the composition of the images is done by pasting the objects using four different blending methods. We present a case study for our dataset generation approach by considering parcel segmentation. For the evaluation we created a dataset of parcel photos that were annotated automatically. We find that (1) our dataset generation pipeline allows a successful transfer to real test images (Mask AP 86.2), (2) a very accurate image selection process - in contrast to human intuition - is not crucial and a broader category definition can help to bridge the domain gap, (3) the usage of blending methods is beneficial compared to simple copy-and-paste. We made our full code for scraping, image composition and training publicly available at https://a-nau.github.io/parcel2d.
INTERLINKED FEATURE QUERYBASED IMAGE RETRIEVAL MODEL FOR CONTENT-BASED IMAGE RETRIEVAL

Visual data retrieval and management have become significant research areas due to the rapid expansion of image data on the internet and the rise of multimedia technologies.Many professional organizations, such as journalists, engineers, agriculture, urban planning to meteorology and art historians, have a shared requirement to discover a particular image in an extensive collection.Researchers were inspired by their difficulties with text-based picture retrieval to create new ways to encode visual index input.Researchers are focused on Content-Based Image Retrieval (CBIR) to overcome these issues and improve the performance of image retrieval models.Using the optical characteristics of an image, such as colour, body, and texture, CBIR searches massive databases for user-requested photos with a query image.An image's visual features are extracted using CBIR's feature extraction method, which is done automatically, without human intervention.The extraction of crucial points in a picture is done using the feature extraction algorithm in this research.It is possible to think of a CBIR system's design as a group of modules that work together to get database images in response to a specific query.This research proposes an Interlinked Feature Query-based Image Retrieval Model (IFQ-IRM) for accurate image extraction from the image database.Image queries are transformed into feature vectors by the system.The extracted features of the query instance and the photos in the databases are then compared, and an indexing strategy is used to retrieve the images.The proposed model results show that image extraction accuracy is better than traditional models.
A Study of Blockchain-Based Internet of Things

Blockchain and the Internet of Things (IoT), two of the most emerging technologies, are already reconfiguring our digital future, as described by the drastic change in the current network architecture. The incorporation of IoT has brought the objects around us to life, making them “smart” and capable of communicating with one another, thereby amassing massive data by consistently acquiring the physical world for analysis and intelligent action. It has made our dream of seamless integration of the digital and physical worlds a reality, altering the essence of our perception of the physical world. However, the issue with current IoT solutions is the requirement for a centrally controlled party (similar to a cloud server). This research covers the most essential implications of IoT’s most challenging scenarios, as revealed by the current studies, when connecting and communicating via the Internet. The vast amounts of generated sensitive data pose a significant risk to privacy and security; interestingly, the original architecture design calls for a decentralized system, such as the distributed or peer-to-peer (P2P) system. This scenario highlights the relevance of the blockchain, as it can offer a secure and trustworthy method of sharing information via a distributed/P2P model for achieving transparency, security, privacy, auditability, resilience, access authentication, data immutability, and so on. In this study, we explain how blockchain and IoT technologies are combined to overcome their specific shortcomings and ultimately achieve their maximum benefits. This research also discusses the technical aspects of IoT and bitcoin. We present a comprehensive overview of both technologies, including the bloc
PARADIGMSYSTEMIC IN THE EDUCATIONAL SCOPE - APPLICABILITY IN THE KNOWLEDGE SOCIETY

In order to direct efforts towards improving education, some concepts in the fields of research need to be addressed, thus, it is possible to observe the meaning of teaching quality in a process.The school is the universe where knowledge meets the craving for knowledge, which needs to be well understood, at the same time seeking new ways of teaching and systemic thinking has been gaining increasing space in the educational context.In this sense, the theory of systems arises, which has its foundation located in the book by this biologist entitled General Theory of systems, given birth in 1968, principles that are mainly focused on Physics, Sociology and Biological Sciences, in addition to general models for any of the sciences involved.Technological changes and innovations, in the Knowledge Era, happen at such a fast pace that Knowledge Management is essential in organizations, especially in the educational field.
Teacher readiness to implement learning through play in Ukrainian primary schools: a preliminary study

The article examines the problem of teachers' professional readiness to implement play-based, activity-based learning methods and the “learning through play” approach in Ukrainian primary schools in the context of holistic reform of school education - the introduction of the New Ukrainian School Concept.&#x0D; The modern school reform, which was introduced in 2018, brought changes to all aspects of education in Ukraine, however, it was mainly focused on the approach of teaching and learning. A new educational strategy based on the integrative approach of “learning through play”, which combines the principles and methods of active learning, experimental learning and guided discovery learning, inquiry learning, problem-based and project-based learning, and Montessori pedagogy, was introduced in the professional activity of primary school teachers.&#x0D; The results of the conducted research indicate that the vast majority of interviewed Ukrainian primary school teachers showed an insufficient level of professional readiness for the introduction of play-based and activity-based learning methods due to: a negative attitude to play in education; their insufficient awareness of the developmental, didactic and other functions of the play in the comprehensive development of a person at all stages of his/her life; the dominance of an authoritarian style of communication with students, etc. It was found, that there is an apparent contradiction between the predominantly high level of self-esteem of primary school teachers of their own readiness to implement play-based learning methods and a negative attitude towards the need to change their professional activity.&#x0D; The difficulties, revealed by the results of the research, provide the basis to make corrections in the system of higher and postgraduate pedagogical education in order to develop Ukrainian school education based on the principles of priority of the individual needs of the child, the competence approach, partnership and a safe educational environment
Hybrid BYOL-ViT: Efficient approach to deal with small datasets

Supervised learning can learn large representational spaces, which are crucial for handling difficult learning tasks. However, due to the design of the model, classical image classification approaches struggle to generalize to new problems and new situations when dealing with small datasets. In fact, supervised learning can lose the location of image features which leads to supervision collapse in very deep architectures. In this paper, we investigate how self-supervision with strong and sufficient augmentation of unlabeled data can train effectively the first layers of a neural network even better than supervised learning, with no need for millions of labeled data. The main goal is to disconnect pixel data from annotation by getting generic task-agnostic low-level features. Furthermore, we look into Vision Transformers (ViT) and show that the low-level features derived from a self-supervised architecture can improve the robustness and the overall performance of this emergent architecture. We evaluated our method on one of the smallest open-source datasets STL-10 and we obtained a significant boost of performance from 41.66% to 83.25% when inputting low-level features from a self-supervised learning architecture to the ViT instead of the raw images.
Towards One Shot Search Space Poisoning in Neural Architecture Search

We evaluate the robustness of a Neural Architecture Search (NAS) algorithm known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the original search space with carefully designed ineffective operations. We empirically demonstrate how our one shot search space poisoning approach exploits design flaws in the ENAS controller to degrade predictive performance on classification tasks. With just two poisoning operations injected into the search space, we inflate prediction error rates for child networks upto 90% on the CIFAR-10 dataset.
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines

Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms. Many approaches address the issue of interpreting artificial neural networks, but often provide divergent explanations. Moreover, different hyperparameters of an explanatory method can lead to conflicting interpretations. In this paper, we propose a technique for aggregating the feature attributions of different explanatory algorithms using Restricted Boltzmann Machines (RBMs) to achieve a more reliable and robust interpretation of deep neural networks. Several challenging experiments on real-world datasets show that the proposed RBM method outperforms popular feature attribution methods and basic ensemble techniques.
Versatile Inverse Reinforcement Learning via Cumulative Rewards

Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert. Current approaches usually do this with generative and uni-modal models, meaning that they encode a single behavior. In the common setting, where there are various solutions to a problem and the experts show versatile behavior this severely limits the generalization capabilities of these methods. We propose a novel method for Inverse Reinforcement Learning that overcomes these problems by formulating the recovered reward as a sum of iteratively trained discriminators. We show on simulated tasks that our approach is able to recover general, high-quality reward functions and produces policies of the same quality as behavioral cloning approaches designed for versatile behavior.
Evaluating Metrics for Bias in Word Embeddings

Over the last years, word and sentence embeddings have established as text preprocessing for all kinds of NLP tasks and improved the performances significantly. Unfortunately, it has also been shown that these embeddings inherit various kinds of biases from the training data and thereby pass on biases present in society to NLP solutions. Many papers attempted to quantify bias in word or sentence embeddings to evaluate debiasing methods or compare different embedding models, usually with cosine-based metrics. However, lately some works have raised doubts about these metrics showing that even though such metrics report low biases, other tests still show biases. In fact, there is a great variety of bias metrics or tests proposed in the literature without any consensus on the optimal solutions. Yet we lack works that evaluate bias metrics on a theoretical level or elaborate the advantages and disadvantages of different bias metrics. In this work, we will explore different cosine based bias metrics. We formalize a bias definition based on the ideas from previous works and derive conditions for bias metrics. Furthermore, we thoroughly investigate the existing cosine-based metrics and their limitations to show why these metrics can fail to report biases in some cases. Finally, we propose a new metric, SAME, to address the shortcomings of existing metrics and mathematically prove that SAME behaves appropriately.
String Model Building, Reinforcement Learning and Genetic Algorithms

We investigate reinforcement learning and genetic algorithms in the context of heterotic Calabi-Yau models with monad bundles. Both methods are found to be highly efficient in identifying phenomenologically attractive three-family models, in cases where systematic scans are not feasible. For monads on the bi-cubic Calabi-Yau either method facilitates a complete search of the environment and leads to similar sets of previously unknown three-family models.
Informative Storytelling with Provenance Research Sources

Provenance has underpinned archival practice in America across the past century, guiding archivists in the arrangement, description, and accessioning of new and legacy collections. Provenance research is a necessary step taken to secure pending donations and title information, and ultimately tell the story of the object’s journey to the current possessor. Across four domains of key interest in the “Developing Extensible Methods for Provenance Research” project, we examine source-specific methods that are most useful to practitioners, and communicate them in an open educational resource (OER) meant to broaden access and awareness of provenance research among administrators and students engaging with collections. Our poster will demonstrate how archival workers seek and then combine information laboriously gathered from photographs, legal documents, and handwritten materials among other resources in creative, inventive, and engaging ways that invite visitors into stories that the objects – if only they are displayed – can tell about our shared pasts and futures. Such information sources can serve a multitude of stakeholder goals, e.g. employing datapoints about transfers and handoffs to encourage object-based learning, transparency, and accountability.
Preparing Students for Managing Large-Scale Scientific Data

This poster presents preliminary observations and feedback from the pilot year of a student internship program that was created to prepare and train students in managing large-scale scientific data. This student internship program is part of the CI Compass project, which is a National Science Foundation (NSF) funded project to provide support and enhance the data lifecycles of NSF Major Facilities (MFs). MFs are the largest-scale scientific efforts that the NSF supports and can take years to build, cost hundreds of millions of dollars to construct, are highly diverse, have heterogeneous data, and a wide range of cyberinfrastructure for capturing, processing, archiving, and disseminating data. MFs span science domains, including astronomy, climate, ecology, natural hazard, ocean science, physics, and seismology. This poster describes the technical curriculum, research program, project-based learning experiences, and future goals.
International SIG

LIS education is an ever-evolving process as the nature and perception of information shifts, as technology creates new access and barriers to information, and as the society reacts to and address the changing concerns and expectations. Part of LIS education is to consider how these concepts influence the profession both locally and globally.
Inhibiting Factors in Beginning Reading Ability of Class I Elementary School Students

This study aims waas to describe the inhibiting factors for early reading in grade 1 elementary school students and find the right solution to overcome these students' early reading difficulties. The type of research used was escriptive qualitative with case study method. The subjects in this study were IC class students at SDN Srengseng Sawah 15 Pagi totaling 32 students. Data collection techniques in this study were observation, interviews with (teachers, students and parents of students) and documentation. Data analysis technique was one by data reduction, data presentation and conclusion drawing. The results of this study indicate that the inhibiting factors for early reading in grade I students at SDN 15 Pagi South Jakarta include: 1). Factors of interest in reading students who are still very lacking, (2). Family factors become significant influencing students' reading ability 3). Environmental factors where they come from effect the development of children. Solutions that can overcome the difficulties of early reading are: (1) Teachers provide additional study hours for children to learn to read, (2) Parents must give attention, motivation and encouragement to children, (3) The home environment, the need to limit and monitor children in playing and provide learning facilities at home for children.
The Multi-Hot Representation-Based Language Model to Maintain Morpheme Units

Natural language models brought rapid developments to Natural Language Processing (NLP) performance following the emergence of large-scale deep learning models. Language models have previously used token units to represent natural language while reducing the proportion of unknown tokens. However, tokenization in language models raises language-specific issues. One of the key issues is that separating words by morphemes may cause distortion to the original meaning; also, it can prove challenging to apply the information surrounding a word, such as its semantic network. We propose a multi-hot representation language model to maintain Korean morpheme units. This method represents a single morpheme as a group of syllable-based tokens for cases where no matching tokens exist. This model has demonstrated similar performance to existing models in various natural language processing applications. The proposed model retains the minimum unit of meaning by maintaining the morpheme units and can easily accommodate the extension of semantic information.
Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation

Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the estimated high-resolution image. Therefore, multi-frame blind super resolution with unknown blur kernel is more challenging. For the purpose of reducing the impact of inaccurate motion estimation and blur kernel estimation on the super-resolved image, we propose a novel method combining motion estimation, blur kernel estimation and super resolution. The confidence weight of low-resolution images and the parameter value of the motion model obtained in image reconstruction are added to the modified motion estimation and blur kernel estimation. At the same time, Jacobian matrix, which can better describe the motion change, is introduced to further correct the error of motion estimation. Based on the results acquired from the experiments on synthetic data and real data, the superiority of the proposed method over others is obvious. The reconstructed high-resolution image retains the details of the image effectively, and the artifacts are greatly reduced.
A calculus for intuitionistic fuzzy values

We introduce <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" alttext="{}^{\oplus}" display="inline"><m:msup><m:mi /><m:mo>⊕</m:mo></m:msup></m:math> calculus and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" alttext="{}^{\otimes}" display="inline"><m:msup><m:mi /><m:mo>⊗</m:mo></m:msup></m:math> calculus for intuitionistic fuzzy values and prove some basic theorems by using multiplicative calculus which has useful tools to represent the concepts of introduced calculi. This paper reveals also new calculi for fuzzy sets in particular.