Reviewerly/exHarmony huggingface dataset:
W11. GENOME-WIDE ANALYSIS OF THE ANXIETY DISORDER SPECTRUM

The information describing a person is constantly evolving and may become obsolete and contradict other information. A personal database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. This study focuses on proposing a novel approach for dealing with the information obsolescence problem in the elderly-fall prevention context. Our approach aims to continuously monitor elderly information in order to detect the change in the behavior of an elderly person and to prevent him from falls. It consists of detecting contradictions between newly acquired information about a single elderly and what we already know about that person, then identifying among his observations those that have become obsolete and need to be updated. We propose a new approximate concept,£-Contradiction, which represents the confidence level of having a contradiction in a set of observations when a causal Bayesian network is our representation model. We propose a polynomial-time algorithm for detecting obsolete information and show that the resulting obsolete observations are given as an explanation AND-OR tree.
Transformer Ensembles for Sexism Detection

This document presents in detail the work done for the sexism detection task at EXIST2021 workshop. Our methodology is built on ensembles of Transformer-based models which are trained on different background and corpora and fine-tuned on the provided dataset from the EXIST2021 workshop. We report accuracy of 0.767 for the binary classification task (task1), and f1 score 0.766, and for the multi-class task (task2) accuracy 0.623 and f1-score 0.535.
PENGGUNAAN MEDIA GAMBAR UNTUK MENINGKATKAN HASIL BELAJAR SISWA PADA MATA PELAJARAN IPS KELAS IV SD NEGERI 012 TAMBUSAI UTARA KABUPATEN ROKAN HULU TAHUN AJARAN 2016 / 2017

This research is a classroom action research that aims to improve the learning outcomes of fourth grade students at SD Negeri 012 Tambusai Utara T.A 2016/2017. The use of picture media is proven to improve the quality of learning. From the observation of the activity the teacher has shown that the use of media images is very good. Can be seen from the scores obtained in the first cycle of 70% with the perfect category increasing to 86.6% in the second cycle with a very perfect category. The use of picture media is proven to increase student activity. From the results of observations indicate that student activity increases. In the first cycle the activity of students reached 66.6% with a high category and in the second cycle obtained a value of 88.3% with a very high category. By using image media proven to be able to improve the ability of students individually to absorb the subject matter that has been given. This is evident from the number of students who complete. In the first cycle as many as 74% of students who completed and in the second cycle as many as 93% of students who completed
Analysis of the Relevant Characteristics of Online Fraud Cases Happened on College Students

In the Internet era, the college student groups are Internet users who use the Internet more widely and deeply with more frequency than the ordinary Internet users. However, college students have insufficient social experience and they have been living in a relatively simple living environment for a long time, which makes them have low ability to distinguish the true from the false and become the targeted by cyber scammers easily. This paper analyzes the causes and the basic types of online fraud cases happened on college students through studying the current situation of online fraud cases happened on college students so as to guide college students to learn to screen and self-protect, and to cultivate more comprehensive high-quality talents.
Efficient and Portable Distribution Modeling for Large-Scale Scientific Data Processing with Data-Parallel Primitives

The use of distribution-based data representation to handle large-scale scientific datasets is a promising approach. Distribution-based approaches often transform a scientific dataset into many distributions, each of which is calculated from a small number of samples. Most of the proposed parallel algorithms focus on modeling single distributions from many input samples efficiently, but these may not fit the large-scale scientific data processing scenario because they cannot utilize computing resources effectively. Histograms and the Gaussian Mixture Model (GMM) are the most popular distribution representations used to model scientific datasets. Therefore, we propose the use of multi-set histogram and GMM modeling algorithms for the scenario of large-scale scientific data processing. Our algorithms are developed by data-parallel primitives to achieve portability across different hardware architectures. We evaluate the performance of the proposed algorithms in detail and demonstrate use cases for scientific data processing.
Estimating Interpersonal Distance and Crowd Density with a Single Edge Camera

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.
Research Guides: (ECC) Using the Ellsworth Community College Library: ECC Article Databases

Need a refresher on how to use the library? The Electronic Card Catalog? The Article Databases? How to use the Article Databases
Smallbone Library: F1 MYP Induction: September 2021

Resources for use for the F1 MYP Induction Community Project Launch Resources for use for the F1 MYP Induction / Community Project Launch
Sensitive Information Detection Using HMM&amp;SVM

With the use of electronic data, the protection and detection of sensitive information is an important issue for businesses and individuals. However, most sensitive detection methods focus on traditional machine learning methods like CNN and RNN, which are traditional and inefficient compared to HMM and SVM. This paper analyzes text information, using HMM and SVM to detect the sensitive information in the text file. The conclusion is drawn out that besides traditional machine learning methods, SVM and HMM models can also help us do the classification problem to extract the focused information, which cannot be exposed to the public.
MODERN DIGITAL EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY: OPPORTUNITIES AND DEVELOPMENT TRENDS

The article discusses the functioning of the digital educational environment of the SSUGT, and defines its main directions of development and improvement.
Corrigendum: Remark on Algorithm 723: Fresnel Integrals

There are mistakes and typographical errors in Remark on Algorithm 723: Fresnel Integrals , which appeared in ACM Transactions on Mathematical Software 22, 4 (December 1996). This remark corrects those errors. The software provided to Collected Algorithms of the ACM was correct.
Automated generation of geometry questions for high school mathematics

We describe a framework that combines a combinatorial approach, pattern matching and automated deductionto generate and solve geometry problems for high school mathematics. Such a system would help teachersto quickly generate large numbers of questions on a geometry topic. Students can explore and revise specifictopics covered in classes and textbooks based on generated questions. The system can act as a personalizedinstructor - it can generate problems that meet users specific weaknesses. This system may also help standardizetests such as GMAT and SAT. Our novel methodology uses (i) a combinatorial approach for generatinggeometric figures (ii) a pattern matching approach for generating questions and (iii) automated deduction togenerate new questions and solutions. By combining these methods, we are able to generate questions involvingfinding or proving relationships between geometric objects based on a specification of the geometryobjects, concepts and theorems to be covered by the questions. Experimental results show that a large numberof questions can be generated in a short time. We have tested our generated questions on an existing geometryquestion solving software JGEX, verifying the validity of the generated questions.
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams

Attaining prototypical features to represent class distributions is well established in representation learning. However, learning prototypes online from streams of data proves a challenging endeavor as they rapidly become outdated, caused by an ever-changing parameter space in the learning process. Additionally, continual learning assumes a non-stationary nature of the data stream, typically resulting in catastrophic forgetting of previous knowledge. As a first, we introduce a system addressing both problems, where prototypes evolve continually in a shared latent space, enabling learning and prediction at any point in time. In contrast to the major body of work in continual learning, data streams are processed in an online fashion, without additional task-information, and an efficient memory scheme provides robustness to imbalanced data streams. Besides nearest neighbor based prediction, learning is facilitated by a novel objective function, encouraging cluster density about the class prototype and increased inter-class variance. Furthermore, the latent space quality is elevated by pseudo-prototypes in each batch, constituted by replay of exemplars from memory. We generalize the existing paradigms in continual learning to incorporate data incremental learning from data streams by formalizing a two-agent learner-evaluator framework, and obtain state-of-the-art performance by a significant margin on eight benchmarks, including three highly imbalanced data streams.
Collective Information Security in Large-Scale Urban Protests: the Case of Hong Kong

The Anti-Extradition Law Amendment Bill protests in Hong Kong present a rich context for exploring information security practices among protesters due to their large-scale urban setting and highly digitalised nature. We conducted in-depth, semi-structured interviews with 11 participants of these protests. Research findings reveal how protesters favoured Telegram and relied on its security for internal communication and organisation of on-the-ground collective action; were organised in small private groups and large public groups to enable collective action; adopted tactics and technologies that enable pseudonymity; and developed a variety of strategies to detect compromises and to achieve forms of forward secrecy and post-compromise security when group members were (presumed) arrested. We further show how group administrators had assumed the roles of leaders in these 'leaderless' protests and were critical to collective protest efforts.
Alternatives for greedy discrete subsampling: various approaches including cluster subsampling of COVID-19 data with no response variable

An exhaustive selection of all possible combinations of n = 400 from N = 698 observations of the COVID-19 dataset was used as a benchmark.Building a random set of subsamples and choosing the one that minimized an averaged sum of squares of each variable's category frequency returned similar results as a "forward" subselection reducing the dataset one-by-one observation by the same metric's permanent lowering.That works similarly as k-means clustering (with a random clusters' number) over the original dataset's observations and choosing a subsample from each cluster proportionally to its size.However, the approaches differ significantly in asymptotic time complexity.
OadTR: Online Action Detection With Transformers

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism and gradient vanishing, hence it is hard to be optimized. In this paper, we propose a new encoder-decoder framework based on Transformers, named OadTR, to tackle these problems. The encoder attached with a task token aims to capture the relationships and global interactions between historical observations. The decoder extracts auxiliary information by aggregating anticipated future clip representations. Therefore, OadTR can recognize current actions by encoding historical information and predicting future context simultaneously. We extensively evaluate the proposed OadTR on three challenging datasets: HDD, TVSeries, and THUMOS14. The experimental results show that OadTR achieves higher training and inference speeds than current RNN based approaches, and significantly outperforms the state-of-the-art methods in terms of both mAP and mcAP. Code is available at this https URL.
Scale Adaptive Target Tracking Based on Kernel Correlation Filter and Residual Network

In this paper, a scale adaptive target tracking algorithm based on deep residual network and kernel correlation filter is proposed. Although kernel correlation filter is a fast and effective target tracking algorithm, its tracking effect is not ideal in the case of the occlusion, blur and scale change caused by the fast moving target in the real environment. To deal with these problems, corresponding measures are integrated in the proposed new algorithm. Firstly, the structure of the ResNet50 network was adjusted and trained. The deep residual network was used to extract target features and integrate the kernel correlation filter algorithm to carry out adaptive response graph fusion to find the target location. Then, we add the scale estimation module, and use the HOG feature to replace the original deep feature training scale filter. Based on the structure of binary sort tree, binary search is carried out on the scale, and the scale discriminant index is used to judge whether the target has reached the appropriate scale. Experimental results show that our proposed algorithm can achieve scale adaptation of target tracking, and the tracking performance is further improved.
Lithology segmentation using deep neural network

Summary This paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision. First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task. The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation. % Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.
An Unsupervised Discriminative Random Vector Functional Link Network for Efficient Data Clustering

Random Vector Functional Link (RVFL) is a single hidden layer feed forward network which can be trained with non-iterative learning methods. Concretely, the input weights and hidden biases in RVFL can be randomly generated and then the calculation of the output weights can be reduced to solve a regularized least squares regression formula. Though RVFL network obtained promising performance in diverse applications, most of its current variants work mainly for supervised tasks such as classification and regression while less effort was made to utilize the unlabeled data. In this paper, we propose an unsupervised RVFL model by simultaneously considering the local manifold structure and the global discriminative information of data. The newly formulated unsupervised discriminative RVFL (UDRVFL) model can be efficiently solved by handling an eigen-value decomposition operation. We conduct experiments by comparing UDRVFL with several state-of-the-arts on representative benchmark data sets and the results demonstrate that it has very competitive performance in data clustering.
Diálogos sobre la enseñanza de la matemática en tiempos de pandemia: una experiencia de docentes salvadoreños

The aim of this text is to know the application and understanding of the educational policies implemented by the Ministry of Education, Science and Technology of El Salvador through the narratives of the teachers who taught mathematics during the year 2020 in Basic and Secondary Education. For this purpose, a general description of education in El Salvador, structure, system, main characteristics, mathematics curriculum is made. Then, the main actions taken by the Departament for the execution of the school year during the crisis caused by the Covid 19 pandemic are described. At the end of the paper there are teacher narratives, obtained through the use of Oral History, in which the experiences, difficulties and opportunities of teaching mathematics in this context are discussed. It is hoped that this document will contribute to the understanding of educational policies in pandemic contexts, and how these are redefined in the mathematics classroom by teachers.
Literary “higher dimensions” quantified: a stylometric study of nine stories

Abstract The study will focus on the quantitative assessment of nine stories, considered important contributions in the supernatural and in the early and modern science - fiction prose. Besides the two treatments of the topic of imaginary Flatland – penned by E. A. Abbott and C. H. Hinton –, the corpus includes writings by H. G. Wells, A. Blackwood, M. Leinster, G. Waldeyer, R. A. Heinlein, L. Padgett, and A. C. Clarke. Texts are researched on the bases of four analyses (moving - average type - token ratio, average tokens length, Busemann’s coefficient, and collocation associativity), with the results tested for statistical significance; next, the textual comparisons will provide a springboard for sketches of literary criticism interpretations. The analyzed corpus has revealed the distinctive and colorful take writers have in their stories. By the nature of their subject, the texts are expected to share higher dimensions and time warps , a thread implying a meeting point in terms of vocabulary richness, plot development, and possibly of narrative structure. Yet, in most cases, the findings suggest basic and nuanced differences, hinting at clear stylistic physiognomies in the authorship. The outcome affects not only the assessment of the weight individual samples have, but also the interface between a common ( sub ) genre and personal style .
VUT Libraries: Faculty of Applied and Computer Sciences: Mobile Apps - For Research Anytime and Anywhere

This guide contains genral library information for the Faculty of Applied and Computer Sciences.
A Detailed Literature Survey and Analysis of Heterogeneous Cross-Project Defect Prediction

To improve the quality of software and efficiency of the testing process, identification of the defect prone modules at right time is known as software defect prediction. In recent years, the researchers started focusing on automated software defect prediction using cross project previous data instead of same project or with identical measures. This increases the capability of software defect prediction in an optimized manner. But handling the heterogenous cross project defect prediction (HCPDC) is considered as toughest challenge because of the gap between source and target projects. This paper performs a depth analysis of the various existing literatures related to the prediction models of HCPDC and CPDC using machine learning and statistical methods. Finally, the primary challenges in selection of metrics, class imbalance handling and distribution of the datasets are discussed.
Optical computing with spatiotemporal fiber nonlinearities

Optics offers significant advantages for information processing and artificial intelligence in terms of information density, speed, and energy efficiency compared to electronics. Starting with the Hopfield model’s implementation, the initial optical computing studies focused on mimicking trainable artificial neural network (ANN) architectures [1] . Including memory storage methods with holography, various methods were proposed, yet the controllability level of the training optical networks still challenging [2] . In the last decade, brain-inspired ANN designs operating based on projection of information to high order dimension with a fixed parameter ANN structure followed by a trainable decision layer were introduced [3] , [4] . The fixed network structure makes fixed-parameter ANN designs easy to implement with physical systems and various optical implementations were demonstrated [5] . However, optical nonlinear activation implementation was challenging to realize, and generally digital signal processing tools are employed [6] .
Author's Life and Artistic Aesthetics As An Object of Comparative Analysis

This article provides an artistic-aesthetic comparative analysis of the famous American writer J. Salinger and his writing career.
Ideal-theoretic Explanation of Capacity-achieving Decoding.

In this work, we present an abstract framework for some algebraic error-correcting codes with the aim of capturing codes that are list-decodable to capacity, along with their decoding algorithm. In the polynomial ideal framework, a code is specified by some ideals in a polynomial ring, messages are polynomials and their encoding is the residue modulo the ideals. We present an alternate way of viewing this class of codes in terms of linear operators, and show that this alternate view makes their algorithmic list-decodability amenable to analysis. Our framework leads to a new class of codes that we call affine Folded Reed-Solomon codes (which are themselves a special case of the broader class we explore). These codes are common generalizations of the well-studied Folded Reed-Solomon codes and Multiplicity codes, while also capturing the less-studied Additive Folded Reed-Solomon codes as well as a large family of codes that were not previously known/studied. More significantly our framework also captures the algorithmic list-decodability of the constituent codes. Specifically, we present a unified view of the decoding algorithm for ideal theoretic codes and show that the decodability reduces to the analysis of the distance of some related codes. We show that good bounds on this distance lead to capacity-achieving performance of the underlying code, providing a unifying explanation of known capacity-achieving results. In the specific case of affine Folded Reed-Solomon codes, our framework shows that they are list-decodable up to capacity (for appropriate setting of the parameters), thereby unifying the previous results for Folded Reed-Solomon, Multiplicity and Additive Folded Reed-Solomon codes.