Reviewerly/exHarmony huggingface dataset:
Speedup of the <i>k</i>-Means Algorithm for Partitioning Large Datasets of Flat Points by a Preliminary Partition and Selecting Initial Centroids

Abstract A problem of partitioning large datasets of flat points is considered. Known as the centroid-based clustering problem, it is mainly addressed by the k -means algorithm and its modifications. As the k -means performance becomes poorer on large datasets, including the dataset shape stretching, the goal is to study a possibility of improving the centroid-based clustering for such cases. It is quite noticeable on non-sparse datasets that the resulting clusters produced by k -means resemble beehive honeycomb. It is natural for rectangular-shaped datasets because the hexagonal cells make efficient use of space owing to which the sum of the within-cluster squared Euclidean distances to the centroids is approximated to its minimum. Therefore, the lattices of rectangular and hexagonal clusters, consisting of stretched rectangles and regular hexagons, are suggested to be successively applied. Then the initial centroids are calculated by averaging within respective hexagons. These centroids are used as initial seeds to start the k -means algorithm. This ensures faster and more accurate convergence, where at least the expected speedup is 1.7 to 2.1 times by a 0.7 to 0.9 % accuracy gain. The lattice of rectangular clusters applied first makes rather rough but effective partition allowing to optionally run further clustering on parallel processor cores. The lattice of hexagonal clusters applied to every rectangle allows obtaining initial centroids very quickly. Such centroids are far closer to the solution than the initial centroids in the k -means++ algorithm. Another approach to the k -means update, where initial centroids are selected separately within every rectangle hexagons, can be used as well. It is faster than selecting initial centroids across all hexagons but is less accurate. The speedup is 9 to 11 times by a possible accuracy loss of 0.3 %. However, this approach may outperform the k -means algorithm. The speedup increases as both the lattices become denser and the dataset becomes larger reaching 30 to 50 times.
Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks (CNNs) or recurrent neural networks (RNNs). However, with the advancement of graph neural networks (GNNs), a new approach has been introduced involving transforming time series data into graph structures. In this study, we propose a CNN-transformer graph neural network (CTGNet) for modulation classification, to uncover complex representations in signal data. First, we apply sliding window processing to the original signals, obtaining signal subsequences and reorganizing them into a signal subsequence matrix. Subsequently, we employ CTGNet, which adaptively maps the preprocessed signal matrices into graph structures, and utilize a graph neural network based on GraphSAGE and DMoNPool for classification. Extensive experiments demonstrated that our method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. This underscores CTGNet’s significant advantage in capturing key features in signal data and providing an effective solution for modulation classification tasks.
A cell image segmentation method based on edge feature residual fusion

In recent years, cancer has seriously damaged human health, and the morphological structure of cells serves as an important basis for cancer diagnosis and grading. Automatic cell segmentation based on deep learning has become an important means of computer-aided pathological diagnosis. Aiming at the existing problems of rough segmentation boundaries and inaccurate segmentation in cell image segmentation, this paper designs a cell image segmentation network model (ERF-TransUNet) based on edge feature residual fusion from the perspective of mutual complementarity and constraint between edge features and object features. The model uses a hybrid architecture of CNN and Transformer to extract multi-scale features from cell images, and adds independent edge feature extraction modules and residual fusion modules to enhance the extraction of edge features and their constraints when fusing with cell object features, improving the accuracy of cell contour positioning. Through experiments on two gland cell datasets, CRAG and Glas, and comparing the segmentation effects with current popular deep learning models, the network model proposed in this paper has achieved good performance in both Dice coefficient and Hausdorff distance, which can effectively improve the segmentation effect of cell images.
Optimizing the combination of data-driven and model-based elements in hybrid reservoir computing

Hybrid reservoir computing combines purely data-driven machine learning predictions with a physical model to improve the forecasting of complex systems. In this study, we investigate in detail the predictive capabilities of three different architectures for hybrid reservoir computing: the input hybrid (IH), output hybrid (OH), and full hybrid (FH), which combines IH and OH. By using nine different three-dimensional chaotic model systems and the high-dimensional spatiotemporal chaotic Kuramoto-Sivashinsky system, we demonstrate that all hybrid reservoir computing approaches significantly improve the prediction results, provided that the model is sufficiently accurate. For accurate models, we find that the OH and FH results are equivalent and significantly outperform the IH results, especially for smaller reservoir sizes. For totally inaccurate models, the predictive capabilities of IH and FH may decrease drastically, while the OH architecture remains as accurate as the purely data-driven results. Furthermore, OH allows for the separation of the reservoir and the model contributions to the output predictions. This enables an interpretation of the roles played by the data-driven and model-based elements in output hybrid reservoir computing, resulting in higher explainability of the prediction results. Overall, our findings suggest that the OH approach is the most favorable architecture for hybrid reservoir computing, when taking accuracy, interpretability, robustness to model error, and simplicity into account.
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems

Open-domain dialog systems aim to generate relevant, informative and engaging responses. In this paper, we propose using a dialog policy to plan the content and style of target, open domain responses in the form of an action plan, which includes knowledge sentences related to the dialog context, targeted dialog acts, topic information, etc. For training, the attributes within the action plan are obtained by automatically annotating the publicly released Topical-Chat dataset. We condition neural response generators on the action plan which is then realized as target utterances at the turn and sentence levels. We also investigate different dialog policy models to predict an action plan given the dialog context. Through automated and human evaluation, we measure the appropriateness of the generated responses and check if the generation models indeed learn to realize the given action plans. We demonstrate that a basic dialog policy that operates at the sentence level generates better responses in comparison to turn level generation as well as baseline models with no action plan. Additionally the basic dialog policy has the added benefit of controllability.
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning

This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation, i.e., point clouds, involves discrete samples of the underlying continuous 3D surface. This discretization process introduces sampling variations on the 3D shape, making it challenging to develop transferable knowledge of the true 3D geometry. In the standard autoencoding paradigm, the encoder is compelled to encode not only the 3D geometry but also information on the specific discrete sampling of the 3D shape into the latent code. This is because the point cloud reconstructed by the decoder is considered unacceptable unless there is a perfect mapping between the original and the reconstructed point clouds. This paper introduces the Implicit AutoEncoder (IAE), a simple yet effective method that addresses the sampling variation issue by replacing the commonly-used point-cloud decoder with an implicit decoder. The implicit decoder reconstructs a continuous representation of the 3D shape, independent of the imperfections in the discrete samples. Extensive experiments demonstrate that the proposed IAE achieves state-of-the-art performance across various self-supervised learning benchmarks. Our code is available at https://github.com/SimingYan/IAE.
Evaluation Of Ieee 802.15.4 For Use In Smart Home Medical Care

The IEEE 802.15.4 wireless standard has been identified as a potential candidate to be used in smart home medical care.This undergraduate research project evaluates the performance of IEEE 802.15.4 under interference from other wireless devices that operate in the same frequency band.Specifically, we focus on two very common wireless interfering systems in typical home environments: IEEE 802.11WLANs and microwave ovens.The measurement results give a rough indication about the mutual interference of different systems and showcase the challenges of utilizing IEEE 802.15.4 for smart home medical applications.
A Connectionist model of Planning via Back-chaining Search

A connectionist model for emergent planning behavior is proposed.The model demonstrates that a simple planning schema, acting in concert with two general purpose cognitive functionalities, namely, episodic memory and perception, can solve a restricted class of planning problems by backchaining from the goal to the current state.In spite of its simple structure, the schema can search for and execute plans involving multiple steps.We discuss how this simple model can be extended into a more powerful and expressive planning system by incorporating additional control and memory structures.
Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web

Accurate results for queries of keyword-based search engines are hard to come by as the queries may return irrelevant URLs even though the given keyword is present in them and some relevant URLs may be lost as they may have the synonym of the keyword and not the original one. The proposed algorithm provides solutions for these problems by making use of the Search Engine Result Pages (SERPs) to generate a ranked list of candidate synonyms for individual keywords, where the relevance of the URLs depicted by these synonyms is proved by comparing it with the URL depicted by the original keyword. This scalable technique can be applied to online data on the dynamic, domain-independent and unstructured World Wide Web. The candidate synonyms are ranked using Co-occurrence Frequencies and various page count based measures. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard.
Interferencias de otras lenguas observadas en estudiantes polacos de filología española

Other Languages Interference in Polish Speaking Students of Spanish Philology&#x0D; Although a lot has been written about the interference of other languages different from the mother tongue in learning a new foreign language, the observation of the signs of this phenomenon in a specific group of students may still lead to interesting conclusions. A study on other languages interference performed in 2011 regarding students attending the 1st course of Spanish Philology at the Jagiellonian University, has revealed an important change in the characteristics of this group of learners in comparison to the one analysed in 2003. The evolution may be attributed to the change in the political situation of the country in the international arena and also to the politics of education in Poland. Besides, it has been proved that for the correct interpretation of the data, it is essential to take into consideration the whole context of the study, i.e. not only the languages learned before, but also those studied simultaneously. Another key factor is the number of languages known by the investigator himself. The samples of interference gathered during the study can be discussed in order to find the possible ways to prevent this phenomenon before it occurs.
A Collaborative, Multinational Cyberinfrastructure for Big Data Analytics

Abstract A collaborative, multinational curriculum and cyberinfrastructure for big data analyticsThe emergence of Big Data and Data Intensive Systems as specialized fields in computing isdriving the need for the development of new courses and curricula. Additionally, the skills andknowledge from these specializations are needed to support analytics for many other disciplines,such as life sciences or the financial industry. This paper details the curriculum and supportingcyberinfrastructure for the graduate-level course developed and delivered between X Universityand Y University. This course was delivered in a synchronous manner between the twouniversities, with faculty from both universities delivering portions of the curriculum.We detail the pedagogy of each major curriculum topics and the mapping of those topics tocourse outcomes which potentially support the specialized computing areas. We frame thesecurricular topics with respect to the “four V’s of big data: volume, variety, velocity, andveracity.” By highlighting the complexities and challenges in each of these “V’s”, we are able topresent theory and praxis of the impact(s) different computing/network architectures andsolutions have for analytics. Additionally, we provide details and discussions on the hands-onassignments and laboratory projects that directly support the lecture topics.In order to detail the assignments and projects, we will also discuss the physical cyber-infrastructure environment used to provide students with direct hands-on learning of big dataanalytics. This environment is also used to provide demonstrations of the topics presented duringlectures. The platform includes Fedora 19, Hadoop, Java, Perl, and VMWare. In addition, thearchitecture of network access is discussed as students from both universities require secureaccess to the computing resources, as well as the need for securing the computing resources fromunauthorized access.Finally, we discuss our next steps and potential improvements, modifications, and directions forfuture course offerings.
Academic Women’s Studies Serials on the Web: A Pilot Study

This study focuses on academic women's studies serials that are found on the Web: what the characteristics of these serials are and whether they can be helpful in the academic's constant struggle to keep up with new ideas and literature.
Symmetric assembly puzzles are hard, beyond a few pieces

We study the complexity of symmetric assembly puzzles: given a collection of simple polygons, can we translate, rotate, and possibly flip them so that their interior-disjoint union is line symmetric? On the negative side, we show that the problem is strongly NP-complete even if the pieces are all polyominos. On the positive side, we show that the problem can be solved in polynomial time if the number of pieces is a fixed constant.
Literature, Geolocation and Wikidata

Littar was the second-prize winning entry in an app competition. It implemented a system for visualizing places mentioned in individual literary works. Wikidata acted as the backend for the system. Here I describe the Littar system and also some of the issues I encountered while developing the system: How locations and literature can be related, what types of location-literature relations are possible within Wikidata, what limitations there are and what questions we may ask once we have enough data in Wikidata.
Sociology In Software Engineering

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Sociology in Software Engineering Craig Caulfield, Gurpreet Kohli , S. Paul Maj Edith Cowan University, Perth, Western Australia Introduction The sociology of software project management is an often under-represented component in the education and professional development of software engineers even though factors such as team formation, role assignment, motivation, training, hiring, and many other peopleware18 practices have been identified many times as at least equally important to the success of software projects as the technical14,16,18,42,44,45,46. The reasons for this may be two-fold: the seeming arbitrariness of the sociological factors in software development is at odds with the formal and familiar technical aspects; and the lack of suitable tools with which to model and understand human dynamics. However, these impediments may be overcome. For example, system dynamics is a modelling approach to dynamic socio-technical problems, stemming from the work of Forrester20,21,22 at MIT and since developed36,39,43, that allows a modeller to mix soft variables (morale, perceptions, motivations) with familiar hard variables (time, cost, resources). A system dynamics model is not so much a tool for time-point prediction, but more of an experimental device to see how certain variables might change over time under the influence of unappreciated causal relationships, dynamic complexity, and structural delays. The end result is hopefully a more informed mind set with which to manage the situation at hand13. By way of illustration, this paper presents some initial results of a system dynamics model based on Frederick Brooks’11 well-known informal law which warns against adding more software developers to a late project for risk of making matters worse. Brooks’ law, the crystallisation of many years of practical software project experience, has been critiqued many times in the literature and generally enjoys wide support, making it a solid basis for any model of the socio-technical aspects of software project management. However, it operates at a high level of aggregation and is most often associated with large-scale software development projects. In contrast, the system dynamics model presented here creates a small- team, small-project environment more likely to be encountered by software engineers in the current market. Brooks Law Frederick Brooks was an IBM programmer and hardware architect who in 1964 became the manager of IBM’s OS/360 development. Then and now, OS/360 was one the largest and most complex operating systems ever attempted6,27, and was a significant business risk for IBM given that it would not be backward-compatible with IBM’s older machines19,38. Brooks’ experiences on the OS/360 project and his observations of the industry in general are Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright © 2004, American Society for Engineering Education
Educational Applications Of The Portland, Oregon Regional Archived Data User Service (Adus)

The Portland, Oregon regional intelligent transportation systems (ITS) data archive (known as PORTAL) was inaugurated in July 2004 via a direct fiber optic connection between the Oregon Department of Transportation (ODOT) and Portland State University (PSU).The data archive receives 20-second data from the 436 inductive loop detectors comprising the Portland area's Advanced Traffic Management System (ATMS).PSU is designated as the region's official data archiving entity, consistent with the nationally specified ITS Architecture being developed.This paper discusses the unique educational opportunities now available to PSU engineering students due to access to these data.Several transportation engineering topics are detailed.First is the understanding of various measures of congestion using real measured data.Second is the development of real transportation performance measures.Students are now able to move beyond the use of simplistic Level of Service (LOS) measures for assessing performance of the transportation system.The data archive can be used to estimate average traffic speed, duration of congestion, fuel consumption, vehicular delay, travel time, and travel time variance, to name a few.The extent of data available to the student and researcher now allows for flexibility and creativity that was not always available in a standard classroom environment.
Encontrabilidade da informação em ambientes informacionais: diálogo teórico entre os conceitos Intencionalidade e affordance

Informational environments needs to adopt mechanisms that make it possible to find information. Affordances are object/environments elements that shows the subjects action possibilities. The Intentionality consists of the subject’s experiences, competences, skills and knowledge related to environments and objects interaction. The research questioned how the studies of the Intentionality and affordance could contribute to the information findability on informational environments. It aims to understand the theoretically relationship between Intentionality and affordance concepts, and propose informational environments design and assessment recommendations from bibliographical and exploratory research using the quadripolar method. The results show the concepts effective dialogue and development of digital, analog or hybrid informational environments project and evaluation recommendations. It’s concluded that both theories can be reflected in the informational environment design as they contribute to information findability as well as that subject’s Intentionality influences the affordances detection process.
Descriptor extraction based on a multilayer dictionary architecture for classification of natural images

This paper presents a descriptor extraction method in the context of image classification, based on a multilayer structure of dictionaries. We propose to learn an architecture of discriminative dictionaries for classification in a supervised framework using a patch-level approach. This method combines many layers of sparse coding and pooling in order to reduce the dimension of the problem. The supervised learning of dictionary atoms allows them to be specialized for a classification task. The method has been tested on known datasets of natural images such as MNIST, CIFAR-10 and STL, in various conditions, especially when the size of the training set is limited, and in a transfer learning application. The results are also compared with those obtained with Convolutional Neural Network (CNN) of similar complexity in terms of number of layers and processing pipeline.
Attacking AES-Masking Encryption Device with Correlation Power Analysis

Modern communication system use cryptography algorithm to ensure data still confidentiality, integrity, and authentic. There is a new vulnerability in a cryptographic algorithm when implemented on a hardware device. This vulnerability is considered capable of uncovering a secret key used in a cryptographic algorithm. This technique is known as a power analysis attack. Previous and other research introduces countermeasure to countering this new vulnerability. Some researchers suggest using logic level with encoding the AES. The countermeasure using logic is meager cost and efficient. The contribution of this paper is to analyze CPA on encryption device that has been given logic level countermeasure. Our finding of this paper is the use of encoding with one-hot masking technique does not provide the maximum countermeasure effect against CPA-based attacks. In this research, CPA attack can be successfully revealing the AES secret-key