Reviewerly/exHarmony huggingface dataset:
Using AI to Optimize O2O Integrated Talent Plan

At present, the market scale of Internet recruitment industry continues to expand, and the market demand base for talents is growing. There is no comprehensive and effective connection between talents and market demand. However, the social talents optimization service products for college students and job seekers have not yet formed a systematic and professional system. Through the undergraduate career planning, college students can continuously improve themselves, become all-round talents, and lay the foundation for their future employment or entrepreneurship. In other words, college students plan well through a series of links such as full cognition, goal setting, clear path and implementation adjustment, so as to promote personal growth in a planned and step-by-step way. Therefore, we use AI model to put forward O2O integration talent optimization, through the visual personal growth path map, with the campus as the network point, set up the social campus network of talents, meanwhile, build the talent pool, realize the four in one talent blue ocean of personal growth, talent value-added, talent supply and demand docking, and resource integration and sharing.
Generating Explorable Narrative Spaces with Answer Set Programming

Previous approaches to narrative generation have required a new planner implementation for each set of constraints deemed relevant to the narrative domain, each consisting of thousands of lines of code and supporting one primary mode of interaction: fully specifying a domain and problem, and receiving a plan as output. We present a lightweight, flexible narrative planner written with Answer Set Programming, designed specifically to support constraint-based narrative generation, show how it generalizes previous approaches, and show how it can be easily extended with notions of thematic plot schema such as “betrayal.” Finally, we demonstrate how the ASP model can be explored through interactive question answering, where answers take the form of generated narratives. In the long term, we intend this work to support understanding of complex rule systems through interactive exploration.
Epistemological Proposal on the Concept of Hybridity for Libraries

The Information Science field has discussed different shared specifications of a conceptualization which comprises the diversity of support, processes, resources and people by which the library needs to adapt. The question here is about the conceptual identification of the terminology ‘hybrid libraries; The aim is to contribute to the literature in the Information Science field by proposing a concept of hybrid libraries which comprises the social sphere, in terms of the developments existing in the society, in libraries.
A new construction of odd-variable rotation symmetric boolean functions with good cryptographic properties

&lt;p style='text-indent:20px;'&gt;Rotation symmetric Boolean functions constitute a class of cryptographically significant Boolean functions. In this paper, based on the theory of ordered integer partitions, we present a new class of odd-variable rotation symmetric Boolean functions with optimal algebraic immunity by modifying the support of the majority function. Compared with the existing rotation symmetric Boolean functions on odd variables, the newly constructed functions have the highest nonlinearity.&lt;/p&gt;
Second-Harmonic Generation Image Based Histomics for the Prediction of Breast Cancer Recurrence

Ductal carcinoma in situ (DCIS) of the breast is a prevalent disease among screened populations. However, it remains a challenge to predict individual risk for recurrence to improve personalize approaches to care. While DCIS is contained within the duct, prior studies have pointed to the importance of stromal collagen in cancer progression. Using Second-Harmonic Generation (SHG) based images, we studied the collagen structure surrounding DCIS in a case-control cohort of women who recurred and those who did not to determine whether stromal collagen could provide additional risk information. We present a quantitative histology image analysis pipeline (termed “histomics”) that consists of collagen segmentation, quantitative feature extraction and statistical analysis to predict recurrence. A cohort of 59 patients with DCIS (30 cases who had recurrence matched with 31 controls) were retrospectively analyzed. SHG images were obtained from pathologic slides and regions-of-interest (ROI) were selected and the collagen in the stroma adjacent to DCIS was automatically segmented using an in-house trained deep learning algorithm. A total of 123 histomics features (intensity, statistical and textural) were then extracted from the segmented ROI of each patient. Kruskal-Wallis test and receiver-operating characteristic methodology was used to assess performance of the features to differentiate patients with and without recurrence. Six histomics features were significantly associated with DCIS patient recurrence. Among them 5 were local scale texture features (AUC: 0.65-0.68, p-value: 0.02-0.05): inverse difference moment, contrast, difference average, dissimilarity, difference variance, inverse difference norm and 1 intensity histogram based global scale feature interquartile range (AUC: 0.65, p-value: 0.048). SHG image based histomics features reflect the stromal features of progression in DCIS. SHG features can be extracted and is a promising tool to develop predictive signatures of recurrence in patients with DCIS. We are processing additional 350 patients through our quantitative image analysis pipeline to determine the significance of histomics features compared to conventional clinical features for the prediction of DCIS recurrence.
BIOMETRIC HUMAN AUTHENTICATION SYSTEM THROUGH SPEECH USING DEEP NEURAL NETWORKS (DNN)

Biometrics offers more security and convenience than traditional methods of identification. Recently, DNN has become a means of a more reliable and efficient authentication scheme. In this work, we compare two modern teaching methods: these two methods are methods based on the Gaussian mixture model (GMM) (denoted by the GMM i-vector) and methods based on deep neural networks (DNN) (denoted as the i-vector DNN). The results show that the DNN system with an i-vector is superior to the GMM system with an i-vector for various durations (from full length to 5s). DNNs have proven to be the most effective features for text-independent speaker verification in recent studies. In this paper, a new scheme is proposed that allows using DNN when checking text using hints in a simple and effective way. Experiments show that the proposed scheme reduces EER by 24.32% compared with the modern method and is evaluated for its reliability using noisy data, as well as data collected in real conditions. In addition, it is shown that the use of DNN instead of GMM for universal background modeling leads to a decrease in EER by 15.7%.
Software For Sense Compatibility Analysis Of Educational Texts

One of the areas of innovative development in pedagogy is the search for ways to increase the availability of educational text material due to a decrease in the reading competence of students. To overcome the fragmented perception of educational texts it is necessary to draw on the results of modern research in various sciences. A preliminary analysis showed that the effectiveness of the perception and assimilation of information can be increased by involving the unconscious component of the psyche by fixing attention on the most repeated terms and concepts. One of the solutions is the development of a mathematical model and software product for didactic analysis of the compatibility of educational information by defining an expanded thesaurus, which in relation to educational information implies terminology as the main fixer of the meanings of educational information. The software was developed based on the Django framework, the mathematical model is implemented in python 3.7 using the scikit-learn library. The developed tool is relevant in the context of lifelong education for the analysis and processing of educational texts without taking into account their semantic compatibility and patterns of perception of educational information. The gradual increment of the thesaurus within the framework of the necessary level of perception will allow us to level negative trends in changing the quality of perception of information by students.
Contributions to improve the combined selection of concurrent software testing techniques

[Context] There are a variety of testing techniques available that present different and often complementary characteristics (e.g., cost of application, effectiveness in revealing defects, types of defects). Considering these complementary characteristics make the selection process even more complex. Testers must make decisions on which techniques they will use in a specific situation. Combining different testing techniques outperforms the use of any single technique alone. [Objective] This paper proposes an approach to support the combined selection of testing techniques for concurrent software projects. The approach is implemented in the SeleCTT-v2 tool, supporting testers to find complementary testing techniques in the body of knowledge proposed in SeleCTT-v1. [Method] We conducted a case study to evaluate the combined selection approach (SeleCTT-v2) with the previous version of the SeleCTT-v1 and to investigate how testers perform a combined selection task for concurrent software projects. [Results and Conclusions] The results indicated a rise in the effectiveness of the combined selection of concurrent testing techniques suggested, which demonstrates SeleCTT-v2 is less likely to make an incorrect recommendation of combined techniques for a concurrent software project, thus avoiding costs associated with the incorrect application of a testing technique. If the testers could access a tool that supports the selection process, the effectiveness of the results would have reached a higher value, as evidenced by our approach. Performing the combination of testing techniques is essential to ensure that software under test has all their features tested to prevent possible errors.
Performance Evaluation of Explainable Machine Learning on Non-Communicable Diseases

The advancements in machine learning and artificial intelligence can significantly benefit the diagnosis of Non-Communicable Diseases (NCDs). However, the inherent complexity of black-box models hinders the interpretability of the model. Potential regulatory issues arise, and the lack of trust within the medical community is apparent due to the lack of understanding of how and why a model made a prediction. In this study, we demonstrate how model-agnostic methods of eXplainable AI (XAI) can help provide explanations to understand black-box models on NCDs datasets better.
Towards logical negation for compositional distributional semantics

The categorical compositional distributional model of meaning gives the composition of words into phrases and sentences pride of place. However, it has so far lacked a model of logical negation. This paper gives some steps towards providing this operator, modelling it as a version of projection onto the subspace orthogonal to a word. We give a small demonstration of the operators performance in a sentence entailment task.
Automated Superpixel-borders-guided Deformation Image Registration for Adaptive Radiotherapy

Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn’t require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.
Design and Implementation of a Student Archive Retrieval Method Based on Image Processing

Compared with traditional manual archive organization and review, the student archive management system can manage massive student archives in a refined, regular, and scientific manner. The effectiveness and efficiency of the retrieval method directly bears on the utilization effect of student archives. Based on image processing, this paper puts forward a novel method for student archive retrieval, which greatly improves the classification, recognition, and information management of images in student archives during the retrieval. Firstly, a framework of student archive retrieval was introduced based on image processing. Next, a deep convolutional neural network (DCNN) was constructed for hash learning, and the functions of the three network modules were detailed, including image feature extraction, hash function learning, and similarity measurement. Finally, several indices were selected to evaluate the retrieval effect of student archives. The proposed method was proved effective and feasible through contrastive experiments. The research results provide a theoretical reference for the application of our method in other fields of image retrieval.
A new approach to clustering time series data using the ARIMA model uncertainty

The piccolo distance is a simplified approach in clustering time-series data, and it needs the data analysts to determine the ARIMA model for each series. Some problems may arise in the modeling step because different criteria may lead to different orders of the best models. This current paper is discussing how to handle this model uncertainty problem by borrowing the concept of an ensemble in the area of data science. Instead of using a single criterion to identify the best model, we proposed to generate the best models using several different criteria. Each series was characterized by the average of the estimates of model parameters obtained. In the clustering process, we employed a hierarchical approached where the optimal number of clusters identified using the Silhouette coefficient. An extensive simulation was completed within this research, and we revealed that our proposed methodology could increase the correct cluster membership by more than 10%. We also implemented our methodology to identify clusters of areas in Indonesia (i.e., Province of Banten) based on the pattern of rainfall level and found an impressive result. Keywords: model-based clustering; uncertainty model; time series clustering; rainfall data.
Sentiment Parser based on X-Bar Theory to Brazilian Portuguese

Sentiment Analysis is a field of Natural Language Processing (NLP) that deals with the identification and extraction of opinions within texts. One approach involves analyzing the linguistic structure of the sentence using syntactic analysis, lexicons and sentiment composition. Parsing checks whether the sentence is generated by a particular grammar using computer programs known as parsers. The grammar defines the rules for combining items in the formation of sentences. The rules are based on a linguistic formalism that describes the internal structure of the sentence, such as X-Bar Theory, which defines a model of rules based on structural similarities in all types of phrases and in all human languages. In this work, we propose a parser model for Sentiment Analysis through a grammar, based on the X-Bar Theory to Brazilian Portuguese, which allows validating whether the sentence has a positive, negative or neutral polarity structure. The model was applied to a twitters dataset and performance evaluation was compared with Machine Learning (ML) methods. The results show the importance of the proposed model as it presents a performance similar to ML, with the advantage of not having the cost with the training process.
A Unified Approach to The Orbital Tracking Problem

Consider an object in orbit about the earth for which a sequence of angles-only measurements is made. This paper looks in detail at a one-step update for the filtering problem. Although the problem appears very nonlinear at first sight, it can be almost reduced to the standard linear Kalman filter by a careful formulation. The key features of this formulation are (1) the use of a local or adapted basis rather than a fixed basis for three-dimensional Euclidean space and the use of structural rather than ambient coordinates to represent the state, (2) the development of a novel "normal:conditional- normal" distribution to described the propagated position of the state, and (3) the development of a novel "Observation- Centered" Kalman filter to update the state distribution.A major advantage of this unified approach is that it gives a closed form filter which is highly accurate under a wide range of conditions, including high initial uncertainty, high eccentricity and long propagation times.
Stock Pattern Classification from Charts using Deep Learning Algorithms

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.
On Slicing Software Product Line Signatures

A Software Product Line (SPL) is a family of similar programs (called variants) generated from a common artifact base. Variability in an SPL can be documented in terms of abstract description of functionalities (called features): a feature model (FM) identifies each variant by a set of features (called a product). Delta-orientation is a flexible approach to implement SPLs. An SPL Signature (SPLS) is a variability-aware Application Programming Interface (API), i.e., an SPL where each variant is the API of a program. In this paper we introduce and formalize, by abstracting from SPL implementation approaches, the notion of slice of an SPLS K for a set of features F (i.e., an SPLS obtained from by K by hiding the features that are not in F). Moreover, we formulate the challenge of defining an efficient algorithm that, given a delta-oriented SPLS K and a set of features F, sreturns a delta-oriented SPLS that is an slice of K for F. Thus paving the way for further research on devising such an algorithm. The proposed notions are formalized for SPLs of programs written in an imperative version of Featherweight Java.
‟Deep lexicography” – Fad or Opportunity?

In recent years, we are witnessing staggering improvements in various semantic data processing tasks due to the developments in the area of deep learning, ranging from image and video processing to speech processing, and natural language understanding. In this paper, we discuss the opportunities and challenges that these developments pose for the area of electronic lexicography. We primarily focus on the concept of representation learning of the basic elements of language, namely words, and the applicability of these word representations to lexicography. We first discuss well-known approaches to learning static representations of words, the so-called word embeddings, and their usage in lexicography-related tasks such as semantic shift detection, and cross-lingual prediction of lexical features such as concreteness and imageability. We wrap up the paper with the most recent developments in the area of word representation learning in form of learning dynamic, context-aware representations of words, showcasing some dynamic word embedding examples, and discussing improvements on lexicography-relevant tasks of word sense disambiguation and word sense induction.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS WEBSITE UNTUK MENINGKATKAN HASIL BELAJAR SISWA PADA MATERI KESETIMBANGAN KIMIA

This study aims to develop and see the effect of website-based learning media on improving student learning outcomes. The population of this research is all students of grade XI of SMA Panca Budi Medan. Samples were taken by simple random sampling of two classes consisting of experimental class 1 which was taught using website-based media with a problem based learning model and experimental class 2 which was taught using power point media with direct instruction model. Based on the results of data analysis, website-based learning media that have been made have met the eligibility standard with a score of 3.71 out of 4,00 with valid criteria and do not need to be revised. Based on the results of the t test, the tcount data (2.44) is greater than ttable, so the hypothesis can be accepted.. So it can be concluded that the website-based learning media on the chemical equilibrium material made has met the website's eligibility standards; Student learning outcomes using website-based learning media with problem based learning models are higher than student learning outcomes using power point learning media with direct instruction models.
Automated Resonance Assignment Via Boolean Satisfiability and Quadratic Programming

Author(s): Sherman, Benjamin Canfield | Advisor(s): Comandur, Seshadhri | Abstract: The community of researchers using nuclear magnetic resonance (NMR) spectroscopy to study the structure and dynamics of proteins are interested in solving the following problem. Let G and H be undirected graphs such that H is isomorphic to at least one subgraph of G. For each vertex v ∈ V (H), compute the set of vertices to which v is mapped by a subgraph isomorphism from H to a subgraph of G. This thesis introduces variations of this problem, reviews prior work, and proposes algorithmic and heuristic methods that outperform the incumbent state of the art on published benchmarks.