Reviewerly/exHarmony huggingface dataset:
Free Speech Rhetoric and Normalizing Violence: Setting Higher Standards for University Guest Speaker Policies

In response to recent protests against controversial guest speakers, many Canadian universities have implemented new policies that conflate academic freedom and freedom of speech. The resultant free speech policies often protect speakers regardless of the content of their speech; this paper discusses the importance of barring speech that normalizes acts of harm.
Arabic NLG Language Functions

The Arabic language has very limited supports from NLG researchers. In this paper, we explain the challenges of the core grammar, provide a lexical resource, and implement the first language functions for the Arabic language. We did a human evaluation to evaluate our functions in generating sentences from the NADA Corpus.
PENERAPAN MODEL PEMBELAJARAN MURDER (MOOD, UNDERSTAND, RECALL, DIGEST, EXPAND REVIEW) DALAM PENGUASAAN KOSAKATA BAHASA MANDARIN SISWA KELAS X SMA ISLAM ATHIRAH MAKASSAR

Abstract. The purpose of this research was to find out the application of MURDER learning model in mastery of Chinese vocabulary for 10th Grade students of Athirah Islamic High School 1 Makassar. This type of research is experimental research which consist experimental classes and control classes. Data from this research was obtained by giving the students pre-test and post-test with the material about “Part of Body”. The result of t-test calculation at a significant level of 0.05 obtained the value of t-count = 0,013 which was less than t table 2,120.
Machine Learning Methods in E-mail Spam Classification

Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section.
Indexing Isodirectional Pointer Sequences.

Many sequential and temporal data have dependency relationships among their elements, which can be represented as a sequence of pointers. In this paper, we introduce a new string matching problem with a particular type of strings, which we call isodirectional pointer sequence, in which each entry has a pointer to another entry. The proposed problem is not only a formalization of real-world dependency matching problems, but also a generalization of variants of the string matching problem such as parameterized pattern matching and Cartesian tree matching. We present a 2nlgσ+2n+o(n)-bit index that preprocesses the text T[1:n] so as to count the number of occurrences of pattern P[1:m] in 𝒪(mlgσ) where σ is the number of distinct lengths of pointers in T. Our index is also easily implementable in practice because it consists of wavelet trees and range maximum query index, which are widely used building blocks in many other compact data structures. By compressing the wavelet trees, the index can also be stored into 2nH^*₀(T)+2n+o(n) bits where H^*₀(T) is the 0-th order empirical entropy of the distribution of pointer lengths of T.
Continuing Education Environment In Developing Students’ Educational Abilities

The identification of educational and other creative and special abilities of students and their proper development are the most important pedagogical tasks that require special attention, special solutions and approaches.To bring productive results, educators and scientists need to pay attention to pedagogical conditions.It is the conditions of pedagogical and educational activity that organize the systematic and planned activities.The article discusses the pedagogical conditions contributing the development of educational abilities of students, as well as the development of students' abilities associated with their preparation for the actual technological activity and design.The conditions of education should be such that they can contribute to the renewal and reform of modern education in Russia: both in preparing students in engineering and technical universities, and in training students in humanitarian universities.The authors analyse the pedagogical conditions to expand the content developed by D.V. Legenchuk concept of continuity of secondary vocational and higher education.In this model, the essential components of general secondary education are combined, including in the preparation of future designers, as well as technology and design teachers.The combination of secondary education in the direction of specialized training of students and the development of their educational and special abilities (artistic technological, etc.) in the structure of multi-level vocational education acts as an important direction of its development.
Handwritten Character Recognition using Convolution Neural Networks in Python with Keras

In the field of Deep Learning for Computer Vision, scientists have made many enhancements that helped a lot in the development of millions of smart devices. On the other hand, scientists brought a revolutionary change in the field of image processing and one of the biggest challenges in it is to identify documents in both printed as well as hand-written formats. One of the most widely used techniques for the validity of these types of documents is ‘Character Recognition’. This project seeks to classify an individual handwritten word so that handwritten text can be translated to a digital form. It demonstrates the use of neural networks for developing  a  system  that  can  recognize  handwritten  English alphabets. In this system, each English alphabet is represented by  binary  values  that are  used  as  input to  a  simple  feature extraction  system, whose  output is  fed to  our neural  network system. The CNN approach is used to accomplish this task: classifying words directly and character segmentation. For the former, Convolutional Neural Network (CNN) is used with various architectures to train a model that can accurately classify words. For the latter, Long Short Term Memory networks  are used with convolution to construct bounding boxes for each character. We then pass the segmented characters to a CNN for classification, and then reconstruct each word according to the results of classification and segmentation.
Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models

In this paper we present a character-level sequence-to-sequence lemmatization model, utilizing several subword features in multiple configurations.In addition to generic n-gram embeddings (using FastText), we experiment with concatenative (stems) and templatic (roots and patterns) morphological subwords.We present several architectures that embed these features directly at the encoder side, or learn them jointly at the decoder side with a multitask learning architecture.The results indicate that using the generic n-gram embeddings (through FastText) outperform the other linguistically-driven subwords.We use Modern Standard Arabic and Egyptian Arabic as test cases, with up to 22% and 13% relative error reduction, respectively, from a strong baseline.An error analysis shows that our best system is even able to handle word/lemma pairs that are both unseen in the training data.
Using Bilingual Patents for Translation Training

While bilingual corpora have been instrumental for machine translation, their utility for training translators has been less explored. We investigate the use of bilingual corpora as pedagogical tools for translation in the technical domain. In a user study, novice translators revised Chinese translations of English patents through bilingual concordancing. Results show that concordancing with an in-domain bilingual corpus can yield greater improvement in translation quality of technical terms than a general-domain bilingual corpus.
Study on the Identification Method of Human Upper Limb Flag Movements Based on Inception-ResNet Double Stream Network

To investigate the recognition effect of flag motions based on 9 axis sensors, starting with deep learning methods, this paper proposes a framework for feature extraction and recognition of flag movements recognition by employing an improved Inception-ResNet dual-stream network. In traditional signal recognition studies, Support Vector Machine (SVM), Random Forest (RF) and One Dimensional Convolutional Neural Network (CNN) are usually used to extract signals' features. In the meanwhile, the time series data sets such as flag movements are usually the standard data set after processing. Therefore, there are usually some limitations in traditional systems. First, in actual environment, there exists a lack of effective segmentation detection method for the samples of long time series, resulting in the deviation of the data set in the recognition process. Second, the One-Dimensional CNN framework used and the machine learning frameworks used in previous studies are difficult to process large quantities of data with too high computational memory. Based on these problems, this study proposes a signal change point detection model based on the diversity factory function in the signal segmentation and detection stage, miniaturizes the convolution kernel in the original CNN by using the Inception-ResNet(I-R) dual-flow network separable convolution method, and proposes a CrossEntropy-Logistic(C-L) joint classification loss function. Through conducting comparative experiments, it is found that the average calculation parameter of the CNN framework based on the Inception-ResNet model is 2.7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> , which is approximately 37% lower than the number of 3.7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> in the original CNN model. Finally, the recognition rate between C-L joint loss function and other models such as Multi-Layer Perceptron and Ensemble Learning in recent years are compared. Compared with Ensemble Learning-CrossEntropy (ELC) model, the C-L joint loss function can improve the recognition rate by nearly 5% according to the results of flag movements identification measured by several classification models.
A Resource for Detecting Misspellings and Denoising Medical Text Data

In this paper we propose a method for collecting a dictionary to deal with noisy medical text documents. The quality of such Italian Emergency Room Reports is so poor that in most cases these can be hardly automatically elaborated; this also holds for other languages (e.g., English), with the notable difference that no Italian dictionary has been proposed to deal with this jargon. In this work we introduce and evaluate a resource designed to fill this gap.1
Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation

Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extraction, and finally classification and performance analysis. The case sample mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. In the preprocessing phase the ROI is cropped and resized by 128 x 128. at the very beginning of the feature extraction process, we have applied Haar Wavelet Transform (HWT) for five levels and, in each level, Discrete Cosine Transform applied with various selection of coefficients. After that, different types of features are fed into the feature similarity measure City Block for the diagnosis of breast cancer. The images are of two classes benign and malignant classes. Finally, K-Nearest Number is employed here as a classifier. In our proposed system, we found competitive results.
Semantic Segmentation Based Unsupervised Domain Adaptation via Pseudo-Label Fusion

In this paper, we propose a pseudo label fusion framework (PLF), a learning framework developed to deal with the domain gap between a source domain and a target domain for performing semantic segmentation based UDA in the unseen target domain. PLF fuses the pseudo labels generated by an ensemble of teacher models. The fused pseudo labels are then used by a student model to distill out the information embedded in these fused pseudo labels to perform semantic segmentation in the target domain. To examine the effectiveness of PLF, we perform a number of experiments on both GTA5 to Cityscapes and SYNTHIA to Cityscapes benchmarks to quantitatively and qualitatively inspect the improvements achieved by employing PLF in performing semantic segmentation in the target domain. Moreover, we provide a number of parameter analyses to validate that the choices made in the design of PLF is both practical and beneficial. Our experimental results on both benchmarks shows that PLF indeed offers adequate performance benefits in performing semantic segmentation in the unseen domain, and is able to achieve competitive performance when compared to the contemporary UDA techniques.
Analisis Buku Ajar Sejarah Kebudayaan Islam Menurut Badan Standar Nasional Pendidikan

This study was conducted to find out how the feasibility of the ministry of religius Affairs of the republik of Indonesia with the criteria of teaching book According to BNSP. This study is considered important which given the feasibility of teaching book should refer to the criteria of BNSP. The research was conducted  With the qualitative approach and used literature research in data mining. The research method is conducted in three phases, they are:1) analyzing students books SKI class XIII MTS, 2) develop instrument of teaching book criteria According to BNSP, 3) relevant data collection, grouping data, provide assessment and explanation in narative. The feasibility rate of the teaching book in value with the predicate is less decent, decent enough, decent, and very decent. This study obtained the result that the conformity of Islamic cultural history book. According to BNSP has a very decent criteria to be used as teaching materials with the value of 93,02.
Validity of items and Identification of aspects to be improved based on the Client Perception in a Surgery Clinic using the Lawshe Method

The goal of this study was to validate items of a questionnaire and identify issues to be improved in nursing care, according to the perception of patients, in a surgery clinic sector of a private clinic, in the municipality of Macaé, Rio de Janeiro State, Brazil.The Lawshe method (1975) was adopted in order to maintain or exclude items on the questionnaire, comprised by 21 items.The set of questions was applied to 81 patients in the immediate pre-operative period, from November 2018 to March 2019, before undergoing elective surgeries (scheduled and non-emergency).In terms of results, the application of this method presented the items that were given prominence in the socioeconomic profile.The item with the greatest importance in terms of average is "Comfort"; in satisfaction, it is the "Number of professionals"; the greatest GAP is "Timeliness in referral to surgery"; in dissatisfaction and weighted dissatisfaction, the item emphasized is "Timeliness in referral to surgery".It was also presented an analysis of the scatter graph demonstrating the coherence of most items to the right upper quadrant in "maintain".
Interdisciplinary Approach to Social-Economic Simulation

Model of social-economic phenomena based on solid-state Ising model is formulated and used for computer simulation. The model is based on the lattice model of ferromagnetic or antiferromagnetic matter but takes deal with social phenomena. Set of agents of social-economic interaction is introduced into consideration. Agents take decision about participation in some sort of activity at discrete moments of time. The decision may lead to profit or to loss. It depends on previous history of the social-economic system. Dynamics of a system is defined by stochastic Markov process. Stochastic nature of the process is influenced by external and individual factors. The model is formulated algorithmically as a sequence of steps and is studied by means of computer simulation. Numerical results are given.
Team Recommendation for the Pokémon GO Game Using Optimization Approaches

Pokemon GO is one of the most popular Pokemon games. This game consists of walking around the world and collecting Pokemon characters using augmented reality. In addition, you can battle with friends, join a gym, or make attacks. These battles must happen between teams with the same size, and this poses a question that is related to the best combination for a team to beat a given opposing team. In order to solve this problem, one can use optimization algorithms. In this paper, we investigate three optimization algorithms to solve this problem: genetic algorithm (GA), memetic algorithm (MA), and iterated local search (ILS). In our experiments, we use time and fitness as evaluation metrics. Our findings indicate that the fastest algorithm is ILS with an execution time of 1.49 ± 0.11 seconds, followed by GA with an execution time of 1.51 ± 0.10 seconds, and MA with an execution time of 13.41 ± 1.00 seconds. However, when we consider the fitness metric, MA achieves the best average fitness of 50, 366.27 ± 12, 055.53, followed by GA, 43,113.00 ± 10, 482.30, and ILS, 31,224.32 ± 7,943.70. All these results are statistically significant to the others according to the post-hoc Friedman test. Analyzing all the obtained results, we recommend the use of the ILS algorithm when the execution time is of utmost importance. However, if fitness is important, then we recommend the use of the memetic algorithm. Finally, if both the execution time and fitness are deemed equally important, then, we recommend the usage of the genetic algorithm because it has a runtime similar to ILS and reasonable fitness.
Latin Squares and their Bruhat Order

In this paper we investigate the Bruhat order on the class of Latin squares. We study its cover relation and minimal elements. We prove that the class of Latin squares of order $n$, with $n\not\in\{1,2,4\}$, has at least two minimal elements, and we present a process to construct some minimal Latin squares for this relation.
A method of inpainting moles and acne on the high‐resolution face photos

With the rapid development of mobile phones, more and more high-resolution photos are taken. The demand for high-resolution image inpainting is becoming increasingly urgent. In order to repair high-resolution face images automatically and quickly, this paper proposes an improved generative adversarial networks method. Firstly, we made a high-resolution dataset for training and testing, and abandoned the traditional 256 ×256 size data. Secondly, since the existing methods can only repair the mask with fixed size and shape on the image, when the global average pooling layer is used in the network, the improved network can repair the moles and acne with arbitrary sizes and shapes on the human face photos. Finally, in order to achieve optimal performance of the network, a mixed loss function is used in training. The experimental results prove that our method has not only achieved good results in qualitative results, but also achieved excellent results in quantitative results.
Prediction of crime location in a brazilian city using regression techniques

There are relevant rates of violence in Brazil that have increased in recent years. Intelligence and efficiency are required to combat this issue, in order to reduce public money spending and time from public officials. Consequently, this operation may improve the safety of the population. There are several solutions that use intelligent systems to predict where and when a crime will occur, which allows police routes to be sent to areas with a higher risk of danger. In this paper, four machine learning methods are used to predict the location of where a crime will occur in a city of Fortaleza, Brazil. The final result shows that simple algorithms can be efficient in the task of crime prediction. In this paper, the Decision Tree and Bagging Regressor methods obtained the best predictions results.
A reference measurement framework of software security product quality (SPQ <sup>NFSR</sup> )

Currently, the customer's demands have expressively amplified their expectations of getting software at a high-quality level. However, the non-functional requirements of the software products attention have been expanded in both the academic and the industrial fields; so, there is no framework for specifying and measuring such kinds of quality constraints for the security requirements of software product quality. This paper presents an integrated framework of the early specification and measurement of the functional and non-functional software security requirements. Such a measurement framework would help software and systems engineers to improve product qualities whether the software has already been delivered or has yet to be built. The main steps that have been followed include: identify, specify and measure the software security requirements based on ISO/IEC SQuaRE series of international standards for software product quality. A standard measurement framework used to measure the functional size of the software product quality to develop a functional size measurement of the functional and non-functional security requirements is described. As a result, a functional size measurement framework of the functional and non-functional security requirements (SPQNFSR) using international standards is proposed. An automatic teller machine case study for the measurement of security requirements based on perspectives of a software functional user requirements is presented. Finally, it is concluded that it is essential to develop such a functional size measurement framework for functional and non-functional security requirements to support developers to face the challenges derived from early dealing with such requirements.
Path Planning and Tracking Strategy under Emergency for Vehicle Obstacle Avoidance System

In this paper, we propose a strategy to solve the problems of obstacle in path planning and tracking for automated vehicles. First, an improved ant colony algorithm considering vehicle parameters and stability is introduced for path planning with obstacle avoidance environment. Second, PID method is improved for vehicle path tracking by introducing motion control containing steering control and speed control. Moreover, some cooperative braking control strategies based on fuzzy PID are added into the motion control to enhance path tracking and handing performance under emergency obstacle avoidance. Simulation results show that the proposed ant colony algorithm can be used to plan the collision-free path and the fuzzy PID motion controller work well in both speed and path following and has a better stability of rollover under emergency.
REVIEW OF ACO ALGORITHM ON NETWORK AND SCHEDULING PROBLEM

The ant colony optimization algorithm is based on the behaviour of real ants. This algorithm was introduced in the 1990s with the aim of finding solutions to problems which simulates the decision-making processes through the use of ants artificial. This paper provides an overview of some of the previous studies and research progress on the traditional and specialized applications of the ACO algorithm towards scheduling and network problems, such as oil pipelines, water distribution system, and natural gas pipelines.