Reviewerly/exHarmony huggingface dataset:
CORRELATION OF LEARNING MOTIVATION AND READING INTEREST TO STUDENTS’ ENGLISH ACHIEVEMENT OF THE SECOND YEAR STUDENTS OF SMPN 1 SANANKULON BLITAR

Abstrak: Penelitian ini bertujuan untuk mengetahui apakah ada hubungan yang signifikan antara motivasi belajar dan minat baca terhadap prestasi belajar bahasa Inggris pada siswa kelas 2 SMPN 1 Sanankulon Blitar. Peneliti menghitung data dengan menggunakan rumus dari Pearson Product Moment. Hasil perhitungan menunjukkan bahwa koefisien korelasi dari hubungan antara motivasi belajar, munat baca dan prestasi Bahasa Inggris siswa (R) adalah 0,54 dan dari nilai F (9) lebih besar dari nilai F (3,22) 0,01 di tingkat signifikansi. Ini dapat disimpulkan bahwa hipotesis dari penelitian ini diterima. Dengan demikian, hal ini menunjukkan bahwa ada hubungan yang signifikan motivasi belajar, dan minat baca belajar terhadap prestasi Bahasa Inggris siswa kelas 2 SMPN 1 Sanankulon Blitar.Kata Kunci: Motivasi, Minat Baca, Hasil Pembelajaran Bahasa InggrisAbstract: This study aimed to find out whether there is a significant of learning motivation and reading interest to English achievement of the second year’s student of SMPN 1 Sanankulon Blitar. The researcher calculated the data by using Pearson Product Moment Formula. The result the computation shows that the coefficient correlation from correlation among learning motivation, reading interest and students’ English achievement (R) is 0.54 and the computing of the value of F (9) is higher than critical value of F (3.22) at 0.01 in the level significance. It means that the alternative hypothesis is accepted. It can be concluded that there is a significant correlation of learning motivation, and reading interest to students’ English achievement of the second year students of SMPN 1 Sanankulon Blitar.Keywords: Correlation, Learning Motivation, Reading Interest, English achievement
On Component Interactions in Two-Stage Recommender Systems

Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest. These systems produce recommendations in two steps: (i) multiple nominators -- tuned for low prediction latency -- preselect a small subset of candidates from the whole item pool; (ii)~a slower but more accurate ranker further narrows down the nominated items, and serves to the user. Despite their popularity, the literature on two-stage recommenders is relatively scarce, and the algorithms are often treated as the sum of their parts. Such treatment presupposes that the two-stage performance is explained by the behavior of individual components if they were deployed independently. This is not the case: using synthetic and real-world data, we demonstrate that interactions between the ranker and the nominators substantially affect the overall performance. Motivated by these findings, we derive a generalization lower bound which shows that careful choice of each nominator's training set is sometimes the only difference between a poor and an optimal two-stage recommender. Since searching for a good choice manually is difficult, we learn one instead. In particular, using a Mixture-of-Experts approach, we train the nominators (experts) to specialize on different subsets of the item pool. This significantly improves performance.
LDTC-guzg01-001-01

Swadesh list audio recording (Items 1-29) recording. There are two recordings filed of this list of items because of background noise, and this recording not having much pause between lexical items
Organizational and methodological support of the innovative program of digital transformation of the personalized form of STL

The article presents the stages, criteria, levels of organizational and methodological support for an innovative program of digital transformation of a personalized form of student-centered learning (SCE). Based on the conducted experiment with 1st – 4th year students, the results of studying the attractiveness of an innovative program for the transformation of a personalized form of SSO in a digital environment are substantiated. This article was carried out within the framework of the scientific project of the Ministry of Education and Science of the Republic of Kazakhstan No. AR08857119 "Transformation of student-centered training of a future teacher in a digital environment" (Contract No. 294 of November 16, 2020).
Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation

Universal adversarial perturbations are image-agnostic and model-independent noise that, when added to any image, can mislead the trained deep convolutional neural networks into the wrong prediction. Since these universal adversarial perturbations can seriously jeopardize the security and integrity of practical deep learning applications, the existing techniques use additional neural networks to detect the existence of these noises at the input image source. In this paper, we demonstrate an attack strategy that, when activated by rogue means (e.g., malware, trojan), can bypass these existing countermeasures by augmenting the adversarial noise at the AI hardware accelerator stage. We demonstrate the accelerator-level universal adversarial noise attack on several deep learning models using co-simulation of the software kernel of the Conv2D function and the Verilog RTL model of the hardware under the FuseSoC environment.
Data Efficient Masked Language Modeling for Vision and Language

Masked language modeling (MLM) is one of the key sub-tasks in vision-language pretraining. In the cross-modal setting, tokens in the sentence are masked at random, and the model predicts the masked tokens given the image and the text. In this paper, we observe several key disadvantages of MLM in this setting. First, as captions tend to be short, in a third of the sentences no token is sampled. Second, the majority of masked tokens are stop-words and punctuation, leading to under-utilization of the image. We investigate a range of alternative masking strategies specific to the cross-modal setting that address these shortcomings, aiming for better fusion of text and image in the learned representation. When pre-training the LXMERT model, our alternative masking strategies consistently improve over the original masking strategy on three downstream tasks, especially in low resource settings. Further, our pre-training approach substantially outperforms the baseline model on a prompt-based probing task designed to elicit image objects. These results and our analysis indicate that our method allows for better utilization of the training data.
Blockchain or Not? Optimal Decisions for Food Retailers Considering Consumer Expertise

This paper studies the benefit of the blockchain food traceability system (BFTS). Based on game theory and the willingness-to-pay model, pricing models are formulated considering important factors like the proportion of consumers with high expertise in traceability, risk attitude to doubtful traceability information and perceived convenience of traceability information checking. By compared the optimal total welfare under the BFTS and that under the traditional food traceability system in valuation analysis, conditions where applying the BFTS is more valuable than applying the TFTS are figured out. Finally, insightful management implications are given.
Skills of distance teaching of physiology to students of Bukovina State Medical University

The use of information technology in teaching process has increased, especially in the last decade. Modern computer programs allow for the transfer of knowledge and access to a variety of educational information, and new ones technologies such as interactive e-learning manuals, multimedia content, Internet, contribute to more active involvement of students in the process teaching. Distance learning has not gone unnoticed and medicine. The purpose of the article is to reveal achievements and problems distance teaching of theoretical disciplines in Bukovynian State Medical University, already has been working with a distance learning server for more than 10 years Moodle, which over the years of its existence constantly is improving. The novelty of the article is to find out the reasons popularity of the resource among students, because it is known that students-physicians are primarily trying to obtain information in Moodle. On the server there are: methodical materials for training, lecture material, textbooks, calendar plans, guides for students, information about working off, results of working off of the missed employment. The methodology of the article includes comparative and analyticalmethods of assessing the achievements and problems of distance learning subject to students of BSMU. Our task the study began to draw intermediate conclusions about the shortcomings and advantages of distance learning in the study of physiology 2nd year students of BSMU. Conclusions. Department of Physiology BSMU has gained some experience during teaching distance learning. The staff of the department in progress teaching, improved computer skills, have been updated and enhanced Moodle servers. Distance learning reduces learning time due to communication speed teacher and student. Access to medical information is significant expanded and it is possible to use all forms of learning, c including independent work in the electronic library. IN in rural areas an electronic medical library can become one of the most effective uses information progress in clinical medicine. Timely and prompt implementation of distance learning in medical university allowed to interest students and in as a result, to develop a distance learning format into a single one information and educational environment of the university, to expand electronic educational and methodical base of the university andpreserve the former educational potential. The disadvantage distance learning is the impossibility of acquiring and practice manual skills and perform various medical manipulations under the control of the teacher that is the basis of quality training in medical universities.
Characteristics of Structural Components, Criteria and Indicators, Levels of Formation of Value-Semantic Experience of Future Specialists in Foreign Philology

Changing axiological guidelines is a fundamental topic for higher education. In particular, the modern professional training of future foreign philologists cannot ignore the need to turn to axiological discourse. The need for special attention to axiological issues in the context of professional training of specialists of the relevant profile led to the definition of pedagogical conditions for the formation of value-semantic experience of these specialists. The implementation of such pedagogical conditions is ensured, including by the evaluation of the achieved results. The latter determines the research interest in the elements of diagnosis - components, criteria, indicators and levels of their implementation.The purpose of the research is to practically define and substantiate the components of the formation of value-semantic experience, the criteria, corresponding to them, the indicators characteristic of the criteria and the levels of their manifestation are determined. Achieving this goal involves (1) determining these elements of the diagnosis of the formation of a certain competence (or desired quality) and (2) their practical justification.The generalized research experience shows that the diagnosis of the formation of certain competencies (individual qualities) is usually accompanied by the selection of components of cognitive, motivational (value-motivational), operational-activity (functional-activity) and organizational. Taking into account the practice of applying the competence approach to determine the criteria in the work, the criteria of formation of value-semantic experience of future specialists in foreign philology are applied: cognitive, value-motivational, operational-activity, organizational with corresponding indicators, as well as certain levels of their manifestation.The researched and substantiated elements of diagnosis open the way to practical modeling of the structural and semantic model of the process of formation of value-semantic experience of future specialists in foreign philology.
A scheme for quantum logic gate based on coherent-state qubits in cavity QED system

A two-qubit quantum swap gate scheme based on coherent state qubit is proposed in cavity quantum electrodynamics system. Our scheme adopt the interaction between the &Lambda;-type atomic ensemble and the two-mode optical cavity, and the swap gate can be realized directly only one step, so as to save a lot of quantum resources and reduce the number of single qubit rotation operations in the actual quantum information processing. It is found that under the condition of large detuning limit, the excited states of atoms are adiabatically eliminated, and the decoherence caused by the spontaneous emission of atoms is effectively suppressed. All atoms in the optical cavity are universally addressed, which reduces the experimental operation difficulty of the scheme. The fidelity of the quantum logic gate is maintained at a very high level when the photon loss of the cavity field is considered. Compared with the single-atom scheme, it is found that the atom-cavity interaction time required by the atomic ensemble scheme is inversely proportional to the number of atoms, so that the speed of the quantum logic gate can be increased by N times. Therefore, in principle, as long as enough atoms interacting with the optical field, an effective coupling of any atom-cavity field can be generated, which is conducive to selecting the appropriate quantum gate operation time in practical operation and effectively avoiding decoherence.
Can contrastive learning avoid shortcut solutions

The generalization of representations learned via contrastive learning depends crucially on what features of the data are extracted. However, we observe that the contrastive loss does not always sufficiently guide which features are extracted, a behavior that can negatively impact the performance on downstream tasks via shortcuts, i.e., by inadvertently suppressing important predictive features. We find that feature extraction is influenced by the difficulty of the so-called instance discrimination task (i.e., the task of discriminating pairs of similar points from pairs of dissimilar ones). Although harder pairs improve the representation of some features, the improvement comes at the cost of suppressing previously well represented features. In response, we propose implicit feature modification (IFM), a method for altering positive and negative samples in order to guide contrastive models towards capturing a wider variety of predictive features. Empirically, we observe that IFM reduces feature suppression, and as a result improves performance on vision and medical imaging tasks. The code is available at: \url{this https URL}.
Using HDFS Data-Aware to Handle Big Data and the Evolutionary Clustering Technique

The growing usage of cyber-enabled technologies and IoT leads to a huge volume with diverse structures of data. A lot ofBig data solutions are based on the environment. In these systems, investigations had revealed when dealing with current data. Some study has overcome the following difficulties for particular parts of graphs; however, The data of today is in several forms of data. These efficiency concerns lead to major problems like greater space in data centers and waste of resources (such as electricity usage), resulting in environmental issues (such as increased carbon emissions) [1], in line with researchers. We offer the Hadoop eco-system data-aware module. We also offer a distributed genetic algorithm encoding technology. Our approach enables Hadoop to control the distribution and placement of data depending on cluster analyses. We can handle a wide range of data formats and improve query speed and resource use. We conducted our tests on many LUBM data sets.
ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR MORPHOLOGY: OPPORTUNITIES AND PROSPECTS

In this work, a program was developed based on U-Net and VGG neural network architectures for segmentation and analysis of scan images of kidney and colon tissues. The program maps up to 12 classes of anatomical structures in tissues, and also allows the calculation of predictive histological biomarkers to support physician decision-making.
Structure-Augmented Keyphrase Generation

This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (, tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then injecting these information in the encoding, using existing keyphrases of other documents, complementing missing/incomplete titles. We propose novel structure-augmented document encoding approaches that consist of the following two phases: The first phase, generating structure, extends the given document with related but absent keyphrases, augmenting missing context. The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text. Our empirical results validate that our proposed structure augmentation and augmentation-aware encoding/decoding can improve KG for both scenarios, outperforming the state-of-the-art.
Secure Image Transmission Using Style Transfer

In this paper we introduce a novel method for secure transmission of images using style transfer and image cryptography. Style transfer is a process of modifying the style of an image while still preserving its content. In our system the secret image is encoded with a key image and the encoded image is sent over an untrusted network. The key image is converted into a styled image by Cycle GAN and this styled image is transmitted to the receiver. Cycle GAN is used on the receiver end to convert the style image to the key image. The receiver decodes the encoded image and gets the secret image. The proposed novel architecture provides opportunity for a new scope of research and development in the field of image transmission using style transfer.
The Logic of Inference and Decision for Scientific Evidence

Uncertainty is an inevitable complication encountered by members of the judiciary who face inference and decision-making as core aspects of their daily activities. Inference, in this context, is mainly inductive and relates to the use of incomplete information, to reason about propositions of interest. Applied to scientific evidence, this means, for example, to reason about whether or not a person of interest is the source of a recovered evidential material and factfinders are required to make decisions about ultimate issues, for example, regarding a defendant’s guilt. The role of forensic scientists, whose duty is to <italic>help</italic> assess the probative value of scientific findings, is to offer to mandate authorities’ conclusions that are scientifically sound and logically defensible. This chapter lays out the fundamentals of inference and decision-making under uncertainty with regard to forensic evidence. The authors explicate explain the subjectivist version of Bayesianism and analyze the usefulness of the likelihood ratio in for measuring the degree to which the evidence discriminates between competing propositions in a trial. They also underscore emphasize the importance of decision analysis as a framework that forces helps decision-makers to formalize preference structures.
Implementation of Implantation-Stagger Measuring Unit using Image Processing

The electrical traction system of railways is a combination of physical upright structures and OCL(Overhead Contact Lines). The horizontal distance from the center of the track to the OHE mast called implantation, horizontal displacement of overhead contact wire with respect to the center of the railway track called stagger, and the perpendicular height of overhead contact wire from the ground are periodically checked by a lineman in order to ensure a safe distance from the railway track. In this paper, we have put forth an idea of building a distance measuring device to measure the implantation and stagger without touching the objects using Open CV on raspberry pi with a camera module which will be placed at the center of the track. The system will be having two features. To measure the distance of the nearest poles, the camera has to be placed facing the mast perpendicular to a circle of diameter appropriate which is placed on the pole for measurement purposes. And to measure the stagger, the camera has to be placed facing the overhead wire from the center of    the track.
Hierarchical Skills for Efficient Exploration

In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. However, prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design. In previous work on continuous control, the sensitivity of methods to this trade-off has not been addressed explicitly, as locomotion provides a suitable prior for navigation tasks, which have been of foremost interest. In this work, we analyze this trade-off for low-level policy pre-training with a new benchmark suite of diverse, sparse-reward tasks for bipedal robots. We alleviate the need for prior knowledge by proposing a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner. For utilization on downstream tasks, we present a three-layered hierarchical learning algorithm to automatically trade off between general and specific skills as required by the respective task. In our experiments, we show that our approach performs this trade-off effectively and achieves better results than current state-of-the-art methods for end-to-end hierarchical reinforcement learning and unsupervised skill discovery.
Best Computer For Dragon Naturally Speaking

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LibGuides Home: Lyman Beecher Brooks Library Virtual Assistance (ENG 102.45A): Articles and Databases

This guide will show you how to search for various library resources to support your assignment..  Click on one of the tabs below to get more information
Website Interactivity as Representations of Social Actions?

This chapter presents reflections on how to develop a framework for analysis of website interactivity as discourse. Interactivity, i.e. the actions that a person can do (up) on a webpage, is the constitutive element of websites, social media and other digital devices. Without hyperlinks, buttons, search fields and other interactive features, any digital text would simply be a static electronic display of a text. It is also by way of interactive elements that files connect to each other in an ever-expanding web and one can create a hypertext (Landow 1992). It follows that interactivity presents itself as an object of interest in digital text studies. But broadly speaking, existing studies of interactivity across disciplines tend to focus on either the context of actions supported by interactivity or on the interactive elements themselves. As an example of the former, human–computer interaction (hereafter HCI) provides only a generic description of how, e.g., links function as a semiotic resource. Examples of the latter can be found in semiotic studies that focus primarily on multimodal meaning-making aspects and to a lesser degree on the context of usage and how the actions on a webpage relate to social practices, i.e. sequences of activities that are socially regulated.