Reviewerly/exHarmony huggingface dataset:
A computational psycholinguistic evaluation of the syntactic abilities of Galician BERT models at the interface of dependency resolution and training time

This paper explores the ability of Transformer models to capture subject-verb and noun-adjective agreement dependencies in Galician. We conduct a series of word prediction experiments in which we manipulate dependency length together with the presence of an attractor noun that acts as a lure. First, we evaluate the overall performance of the existing monolingual and multilingual models for Galician. Secondly, to observe the effects of the training process, we compare the different degrees of achievement of two monolingual BERT models at different training points. We also release their checkpoints and propose an alternative evaluation metric. Our results confirm previous findings by similar works that use the agreement prediction task and provide interesting insights into the number of training steps required by a Transformer model to solve long-distance dependencies.
KLASIFIKASI CITRA IKAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Fish Image Classification Using Convolutional Neural Network is an application that helping classification process. The application is used by user for knowing an information about what is the name of fish species from image that visitor captured. The application is designed by Python programming language. Method of designing the application using System Development Life Cycle. The method used in training model is Convolutional Neural Network, Data used in training process are the species data set is more than 10 species and each species is more than 1000 images, The data collected has been divided into training data and test data. In training process, Fish Image Classifiation Program produce a train loss value of 0.189203, validation loss value of 0.033459 and accuracy value of 0.991029. The evaluation process is carried out using a Confusion Matrix where the diagonal data is the correct prediction data, while the other data is the wrong prediction. By evaluating the Confusion Matrix, predicted accuracy reaches 99.1%precision and recall is 0.98. The resulting accuracy is very good accuracy so that it can predict the image inputted by the user accurately.
Deep Dense Local Feature Matching and Vehicle Removal for Indoor Visual Localization

Visual localization is an essential component of intelligent transportation systems, enabling broad applications that require understanding one's self location when other sensors are not available. It is mostly tackled by image retrieval such that the location of a query image is determined by its closest match in the previously collected images. Existing approaches focus on large scale localization where landmarks are helpful in finding the location. However, visual localization becomes challenging in small scale environments where objects are hardly recognizable. In this paper, we propose a visual localization framework that robustly finds the match for a query among the images collected from indoor parking lots. It is a challenging problem when the vehicles in the images share similar appearances and are frequently replaced such as parking lots. We propose to employ a deep dense local feature matching that resembles human perception to find correspondences and eliminating matches from vehicles automatically with a vehicle detector. The proposed solution is robust to the scenes with low textures and invariant to false matches caused by vehicles. We compare our framework with alternatives to validate our superiority on a benchmark dataset containing 267 pre-collected images and 99 query images taken from 34 sections of a parking lot. Our method achieves 86.9 percent accuracy, outperforming the alternatives.
Robustness of artificial intelligence in the face of novelty

A critical factor in utilizing agents with Artificial Intelligence (AI) is their robustness to novelty. AI agents include models that are either engineered or trained. Engineered models include knowledge of those aspects of the environment that are known and considered important by the engineers. Learned models form embeddings of aspects of the environment based on connections made through the training data. In operation, however, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. Worse still, adversarial environments are subject to change by opponents. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop the science necessary to develop and evaluate agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments.
CA-XTree: Age Estimation of Grouped Gradient Regression Tree with Local Channel Attention

Face age estimation has been widely used in video surveillance, human-computer interaction, market analysis, image processing analysis, and many fields. There are several problems that need to be solved in image-based face age estimation: (1) redundant information of age characteristics; (2) limitations of age estimation methods in solving age estimation problems; (3) the performance of age estimation models being also affected by gender factors. This paper proposes CA-XTree network. Firstly, features are extracted through the convolution layer and then combined with the local channel attention module to strengthen the ability of age feature information interaction between different channels. Secondly, extracted features are inputted into the recommendation score function to obtain the recommendation score, by combining the recommendation score with the gradient ascending regression tree. The lifting tree processed loss function is the mean square loss function, and the final age value is obtained by the leaf node. This paper improves state of the art for image classification on MORPH and CACD datasets. The advantage of our model is that it is easy to implement and has no excess memory overhead. In the age dataset CACD, the mean absolute error (MAE) has reached 4.535 and cumulative score (CS) has reached 63.53%, respectively.
Pembuatan Aplikasi MOSTRANS Transporter Berbasis Mobile Menggunakan React-Native JavaScript

MOSTRANS Mobile Transporter, the mobile application in this paper aims to advance technology in the health supply chain ecosystem so that every part of the operation can be more efficient and modern. In dealing with this problem as well, MOSTRANS Mobile Transporter aims to meet the needs of MOSTRANS Software as a Service (SaaS) clients by assisting them in carrying out their daily operational activities by answering problems such as what features are needed. This application is built using JavaScript and React-Native for the front-end. With GraphQL as a complementary component for the back-end. To test this application, the Black Box Testing method is used as well as testing by the user with the User Acceptance Test to ensure the application can be used for everyday use. From the test results, it was concluded that the application created had fulfilled the purpose of assisting the operational activities of the MOSTRANS SaaS client by facilitating the features required by the MOSTRANS SaaS client. By making this application, the daily operational activities of the parties involved will be much more efficient and mobile..
Assessing the Level of Cybersecurity Knowledge of Computer Science and Applied Automation Undergraduates

The importance and role of cybersecurity awareness is more pressing than ever in today’s modern world and therefore, this concern gave voice to a number of studies evaluating the level of preparedness of individuals and professionals regarding cybersecurity aspects. Furthermore, the number of open positions on the job market in the field of cybersecurity highlight the topicality of the subject. In this paper, the author analyzed the level of knowledge that last year Computer Science and Applied Automation undergraduate students possess and also examined current practices around the world regarding cybersecurity education with the role of understanding the implications of cybersecurity education and proposing a set of measures and activities that can be applied to facilitate the educational process in the field of cybersecurity.
A Hybrid Quantum-Classical CFD Methodology with Benchmark HHL Solutions

There has been significant progress in the development of quantum algorithms for solving linear systems of equations with a growing body of applications to Computational Fluid Dynamics (CFD) and CFD-like problems. This work extends previous work by developing a non-linear hybrid quantum-classical CFD solver and using it to generate fully converged solutions. The hybrid solver uses the SIMPLE CFD algorithm, which is common in many industrial CFD codes, and applies it to the 2-dimensional lid driven cavity test case. A theme of this work is the classical processing time needed to prepare the quantum circuit with a focus on the decomposition of the CFD matrix into a linear combination of unitaries (LCU). CFD meshes with up to 65x65 nodes are considered with the largest producing a LCU containing 32,767 Pauli strings. A new method for rapidly re-computing the coefficients in a LCU is proposed, although this reduces, rather than eliminates, the classical scaling issues. The quantum linear equation solver uses the Harrow, Hassidim, Lloyd (HHL) algorithm via a state-vector emulator. Test matrices are sampled from the classical CFD solver to investigate the solution accuracy that can be achieved with HHL. For the smallest 5x5 and 9x9 CFD meshes, full non-linear hybrid CFD calculations are performed. The impacts of approximating the LCU and the varying the number of ancilla rotations in the eigenvalue inversion circuit are studied. Preliminary timing results indicate that the classical computer preparation time needed for a hybrid solver is just as important to the achievement of quantum advantage in CFD as the time on the quantum computer. The reported HHL solutions and LCU decompositions provide a benchmark for future research. The CFD test matrices used in this study are available upon request.
Effectiveness of Schoology Learning Media During the Covid-19 Pandemic

This study aims to find out how the application and effectiveness of schoology learning media during the Covid-19 pandemic on biology learning outcomes at MTS Al-Wasliyah Tembung. This research is a qualitative research using case study method. The subjects of this study were the vice principal, teachers of science studies, and 85 students of class IX science at MTS Al-Wasliyah Tembung. The instrument used to collect data was by interviewing the vice principal and biology teachers. While the students used a questionnaire for class IX science students as many as 15 items and the results of learning biology on genetic material. Based on the results of this study, it was found from interview data, questionnaires and grades obtained by students in science subjects, especially in genetic material, which was above the KKM with a value of 70, where class IX-3 consisted of 40 students with an average value of 75.75 and class IX-3 IX-5, which consists of 45 students with an average score of 77.22 and the effectiveness of the use of schoology conducted by MTS Al-Wasliyah Tembung during the covid-19 pandemic is 72% and has been running effectively. While the inhibiting factor for learning schoology during the covid-19 pandemic was because students did not have android phones which had an impact on students' delays in joining classes. In addition, the internet network and data packages are also a problem for students at MTS Al-Wasliyah Tembung.
CNN Based Ensemble Approach for Malfunction Detection from Machine Sounds

Together with the meaning and essence of data for the company nowadays; The variety of data also differs. One of these differentiating data types is sound. Borusan Makina ve Güç Sistemleri A.Ş. the data obtained from the Caterpillar construction machines of. The machine sound gives clues about many malfunctions. Artificial intelligence systems of the heard sound will be integrated into business processes. Every tone can be converted. With this, the properties and estimates of the sound grids are used. In this direction; While the incident is getting in the way of his business, an unfortunate project occurs with a similar visitor. The traditional will use a meaningful method by listening to the producer's sound and technology and innovation to develop easy blueprints of decisions that cannot be diverted to sound data. Thanks to the real-time model with short-term audio recording, it is instantly predicted whether there is a problem in the machine. Free from personal and technical comments; By examining the patterns of sound waves, it is aimed to be made without cancellation.
SoftVN

Trusted execution environments (TEEs) in processors protect off-chip memory (DRAM), and ensure its confidentiality and integrity using memory encryption and integrity verification. However, such memory protection can incur significant performance overhead as it requires additional memory accesses for protection metadata such as version numbers (VNs) and MACs. This paper proposes SoftVN, an extension to the current memory protection schemes, which significantly reduces the overhead of today's state-of-the-art by allowing software to provide VNs for memory accesses. For memory-intensive applications with simple memory access patterns for large data structures, the VNs only need to be maintained for data structures instead of individual cache blocks and can be tracked in software with low efforts. Off-chip VN accesses for memory reads can be removed if they are tracked and provided by software. We evaluate SoftVN by simulating a diverse set of memory-intensive applications, including deep learning, graph processing, and bioinformatics algorithms. The experimental results show that SoftVN reduces the memory protection overhead by 82% compared to the baseline similar to Intel SGX, and improves the performance by 33% on average. The maximum performance improvement can be as high as 65%.
HOW EFFECTIVE IS AR IN EDUCATION?// International Journal of Educational and Scientific Research 1(14), 2022/ Chief Editor D. Sidorkin /OEAPS OU - Tallinn, Estonia (Open European Academy of Public Sciences). Marseille, France 30.04.2022: OEAPS OU., 2022 - P. 69-74.

It’s cool how much we can add to our experience of the world with modern tech! For example, augmented reality is one such innovative technology, allowing us to visually represent all kinds of 3D objects with no physical form. To learn more about it, you can read how augmented reality works. Modern AR apps are used for various purposes, from entertainment to enterprise applications, and their usefulness has prompted thousands of organizations to utilize augmented reality in education and training, while educational establishments implement AR in teaching classes.
Hubungan Antara Ketersediaan Bahan Bacaan Berbasis Kebutuhan Anak Dengan Minat Bacanya

This research was motivated by the low interest in reading in TBM Kaba Cambodia Batipuh Panjang, Padang City. This is thought to be caused by the lack of availability of reading materials based on the needs of children at TBM Kaba Cambodia Batipuh Panjang, Padang City. The objectives of this study are 1) to see the description of the availability of reading materials based on the needs of children in TBM Kaba Cambodia Batipuh Panjang, Padang City. 2) to see a picture of the reading interest of the residents studying at TBM Kaba Cambodia Batipuh Panjang, Padang City. 3) to see the relationship between the availability of reading materials based on children's needs and their reading interest in TBM Kaba Cambodia Batipuh Panjang, Padang City.This research is a quantitative research with correlational type which aims to examine the correlation between the availability of reading materials and reading interest. The population in this study were 28 people, the sampling technique was cluster random sampling and taken as many as 60%, namely 17 people. Techniques in data collection using questionnaires and data collection tools in the form of a list of statements. The data analysis technique used the percentage formula and the rho sperm formula.The results of this study indicate that: 1) the description of the availability of reading materials based on the needs of children in TBM Kaba Cambodia Batipuh Panjang Padang City is categorized as incomplete. 2) the description of children's reading interest in TBM Kaba Cambodia Batipuh Panjang Padang City is categorized as low. 3) there is a significant relationship between the availability of reading materials based on children's needs and their reading interest at TBM Kaba Cambodia Batipuh Panjang, Padang City. It is hoped that the TBM manager will increase the number of reading materials according to the children's needs
TRAINING IN ECONOMICS FOR ACQUIRING AND DEVELOPING BUSINESS INTELLIGENCE COMPETENCES AT HIGHER SCHOOLS

The research focuses on some relevant aspects of training at higher schools for building digital competences which facilitate the effective application of modern digital tools for solving planned tasks and achieving objectives.The emphasis falls on the necessary competences for working with integrated resource planning systems (ERP).Special attention is paid to the acquisition and development of the so-called Business Intelligence (BI) competences at higher schools.At the beginning of the research, we have synthesized the necessary activities for quality training as the final objective is that the learners acquire a totality of knowledge, skills and experience for working and managing some heterogeneous in their nature processes, as well as some means and technologies required for the transformation of data into managerial information, whereas the data itselfinto managerial decisions and action plans.The next stage consists of differentiation among the various types of skillsbusiness skills, analytic skills and IT skills, as well as the specific aspects of their acquisition and development through the training at higher schools.The conclusion makes an assessment of the trends, respectively, the topics of training on specific tools for the solution of managerial tasks and the successful realization of strategic aims.We take into account the training in performing business analyses through dashboards and the work with key productivity indicators (KPI).Parallel to this, we have researched the degree of coverage of this matter in existing curricula and courses at higher schools in Central and Eastern Europe using the method of random sampling without replacement.The logic of carrying out the research corresponds to the principle that the training shall comply with the needs and requirements of the practice.The basis for determining the research sample is based on the subject matter of the research.We have encompassed enterprises of the private sector in countries from Central and Eastern Europe.The sample is limited to small, medium and large enterprises which actively use digital tools in making managerial decisions (respectively 16% / 65% / 19%) from the Czech Republic, Romania, Greece and Bulgaria using the method of random sampling without replacement.The methodology of the research secures its reliability by addressing the survey to a specific category of individuals, including the opinions of managers and employees on the digital competences of future specialists with a degree in Economics.The results are used to make a narrow selection of those areas in the content of specific courses which shall be updated and to point out those curricula which shall be updated in the medium term.
Demonstration of LogicLib: An Expressive Multi-Language Interface over Scalable Datalog System

With the ever-increasing volume of data, there is an urgent need to provide expressive and efficient tools to support Big Data analytics. The declarative logical language Datalog has proven very effective at expressing concisely graph, machine learning, and knowledge discovery applications via recursive queries. In this demonstration, we develop Logic Library (LLib), a library of recursive algorithms written in Datalog that can be executed in BigDatalog, a Datalog engine on top of Apache Spark developed by us. LLib encapsulates complex logic-based algorithms into high-level APIs, which simplify the development and provide a unified interface akin to the one of Spark MLlib. As LLib is fully compatible with DataFrame, it enables the integrated utilization of its built-in applications and new Datalog queries with existing Spark functions, such as those provided by MLlib and Spark SQL. With a variety of examples, we will (i) show how to write programs with LLib to express a variety of applications; (ii) illustrate its user experience in Apache Spark ecosystem; and (iii) present a user-friendly interface to interact with the LLib framework and monitor the query results.
Phishing email detection using deep learning algorithms

E-mail is one of the most widely recognised channels of communication, whether for personal or corporate purposes. The worst part about spam emails is that they intrude on user’s privacy without their consent, and the constant bombardment of spam mails fills up the user's entire email space. Additionally, the issue of wasting network capacity and time checking and deleting spam mails makes it an even more concerning issue. Although confrontation tactics are constantly being upgraded, the results of those methods are now unsatisfactory. Furthermore, phishing emails have been on the rise in recent years. To combat the issue of phishing emails, more effective phishing detection technology is required. We intend to create a phishing email detection tool that analyses the email structure first, using an upgraded Convolutional Neural Networks model with multilayer vectors and Long Short-Term Neural Network (LSTM) to model emails at the email header, character level, email content, and word level all at the same time. To assess the efficacy, we'll use an unbalanced dataset with actual phishing and genuine email ratios. The total accuracy of the experiment achieves a high percentage.
FLRA and Its Influence on EFL Students’ Reading Achievement Amid the Pandemic Covid-19

Foreign Language Reading Anxiety (FLRA) is a symptom of anxiety in reading which becomes increasingly challenging for EFL learners. FLRA is an influential interruption that might prevent EFL learners to derive a reading grasp. The pandemic situation borders teachers’ and students’ activities in the learning process. In this condition, numbers of students in Indonesian schools perform different reading comprehension achievements, including low, middle, and high levels. The researcher noted that several Indonesian students feel anxious in the second language (L2) reading with certain reading troubles. The purpose of this study was to examine FLRA and its influence on the students’ reading achievement amid the pandemic COVID-19 schooling including identifying potential factors which affect their anxieties in reading and investigating their influence on their reading achievement. This study used a quantitative research method. The research was carried out in the Eagle Class students of LinkAge education, in Lombok, Indonesia. The sample of this study was 20 students selected using the purposive sampling method. The data were collected using a questionnaire namely the Foreign Language Reading Anxiety Scale (FLRAS) which was developed by Saito et al (1999) with a five-point Likert scale. The researcher analyzed the data using descriptive statistics. The results demonstrate that the students’ anxiety level is 58.25 which indicates that they experience a high level of anxiety although the value is a little bit lower than the limit of moderate level. There are some symptoms of reading anxieties commonly recognized by the students.