Reviewerly/exHarmony huggingface dataset:
The Students’ Error in Pronouncing Ed-ending Words Made by the Second Year Students in English Department of Bung Hatta University. Thesis. English Department, Bung Hatta University

Ed-ending refers to the word that is used in past form. The past tense and the past participle of a regular verb adding –ed and pronounced in three ways; while the formation of the past tense and past participle of an irregular verb is determined by irregular verb itself (HRL et. al, 1995). Ed-ending is one of the pronunciation materials taught in the class, but most of the students still had error in pronouncing that sounds. Relating to this, the researcher conducted a study of finding the students’ error in pronounce ed-ending word. The population of this research was the second year students in the  English Department of Bung Hatta University. The total sample members were 23 students, and they were chosen by using total sampling technique. The instrument used to collect the data was oral pronunciation test. The coefficient correlation of the test was 0.90. It means that the test was reliable.The result of this research showed that the students at the English Department of Bung Hatta University in general had errors in pronouncing ed-ending words. It was proved that 17 (73.91%) students had errors in pronouncing ed-ending words. The students had error in pronouncing ed-ending sounds /id/. It was proved that 14 (60.86%) students had error in pronouncing that sound. The students had error in pronouncing ed-ending words sounds /d/. It was proved that 16 (69.56%) students had error in pronouncing that sound. The students had error in pronouncing ed-ending words sounds /t/. It was proved that 22 (95.65)% students had error in pronouncing that sound.            Based on those findings, it could be concluded that the second year students at the English Department of Bung Hatta University had errors in pronouncing ed-ending words sounds /id/, /d/, and /t/. In line of the conclusion of this research, students should more learn how to pronounce ed-ending words sounds /id/, /d/, and /t/, also more practices to pronounces such sounds, especially ed-ending words sounds /t/. Besides, the lecturers are suggested to give more explain about producing ed-ending words and provided more exercise and practice for students to pronounce sounds. Key Words : Ed-Ending words, Pronunciation, Error.
Prospects for the Development of Practice-Oriented Training in Professional Educational Institutions

This article considers one model of a practice-oriented approach to the organization of the educational process aimed at the commercialization of learning outcomes in professional educational institutions. It is proposed to create a marketing and innovation department in a professional educational institution.
Rectifying the Shortcut Learning of Background for Few-Shot Learning

The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time empirically identify image background, common in realistic images, as a shortcut knowledge helpful for in-class classification but ungeneralizable beyond training categories in FSL. A novel framework, COSOC, is designed to tackle this problem by extracting foreground objects in images at both training and evaluation without any extra supervision. Extensive experiments carried on inductive FSL tasks demonstrate the effectiveness of our approaches.
Application of Random Region Augmentation Algorithm in Deep Learning

Abstract In the field of computer vision, the collection and sorting of image data is the core driving force. However, the current data collection work cannot perfectly collect the image data of each actual landing scene. The purpose of the data augmentation algorithm is to increase the diversity of the data set and improve the robustness of the model. Traditional data augmentation methods include geometric augmentation and color augmentation, mainly including flipping, rotating, cropping, translation, stretching, zooming, adding noise, blurring, Dropout, Cutout, color jittering. Traditional data augmentation methods have certain limitations, and the effect is not obvious. Based on the idea of Cutout algorithm, this paper proposes the RRA augmentation algorithm, which divides four quadrant regions in the image, and randomly selects the ROI region in each region, and is different from the Cutout algorithm directly discarding the region, but randomizing the region Enhance the color, and finally do geometric augmentation processing on the overall image. Compared with the original single data augmentation operation, the algorithm improves precision by 7%, and recall improves by 7%.
Outlier detection over data streams: Survey

Outlier detection is regarded as one of the most important applications of data mining widely applied in various application fields, such as healthcare, telecommunication, etc. It contributes to th...
Develop an algorithm to delete near-duplicate images in Hadoop

The concept of near-duplicate images refers to images that are subjected to noise, that have been compressed, or whose resolution is reduced as a result of their transmission, and other images to which digital image operations are applied. The ideal storage system aims to optimize the storage space, by managing, structuring and organizing data in an efficient manner, so that the storage space is preserved including valuable and useful information, and we get rid of useless data. The space occupied by insignificant data is called wasted space, and this space increases with the increase of these files, resulting in a waste of storage space, which makes it difficult to manage storage space and organize data, which affects the overall system performance. Hadoop is used to store and process big data, and depends on branching in storing data, as the data is divided into parts (blocks), and these parts are distributed in computer devices, called these devices (Data Nodes). Researchers have developed techniques to get rid of fragments of duplicate data, in order to save storage space in the Hadoop system, but each node may contain unimportant files occupying part of this space, so we will present in this research a technique to delete the near-duplicate images stored within data nodes, using a Discrete Cosine Transform (DCT).
Commentary:<i>the intentions of washing machines</i>

Hancock makes a range of claims but the most important is this: if a machine ‘learns,’ then, eventually, it will become ‘self-aware.’ It is self-awareness, he argues, that will distinguish machines...
The Impact of Corporate Governance on Corporate Social Responsibility: A Field Study at Private Hospitals in Southern Amman Governorate

This research aimed to investigate the impact of corporate governance on corporate social responsibility in private hospitals in the southern of Amman Governorate. The study sample consisted of (135) employees from all the administrative levels, a rate of (80.7%) from the study sample were analyzed under (109) questionnaires. To achieve the study’s objectives, the quantitative approach was used and the (SPSS, Vir. 25) was used to analyze the research data. Results showed that there is a significant impact for the variable of corporate governance dimensions on corporate social responsibility in private hospitals in southern Amman Governorate. Mean that, there is a statistically significant effect for corporate governance on corporate social responsibility in the private Jordanian hospitals.It also revealed that there is a positive significant impact for disclosure and transparency, responsibilities of the Board of Directors on the economy dimension for corporate social responsibilities, while having a solid and effective foundation for corporate governance, has no effect towards legal dimension and ethics. In light of the results, the study recommended several recommendations and focused on the main one that organizations and hospitals must focusing on formulate a solid rules and effective basses in order to set a set of clear policies and procedures that would create an environment full of challenges in order to enhance the economic dimension for social responsibility.
Finite‐Size Analysis of Thermal States Quantum Cryptography with the Optimal Noise

Abstract A modified thermal states quantum cryptography is proposed by adding the noise at Bob's side and the impact of finite‐size effects focused on by performing the channel estimation. An optimal noise which can maximize the key rate is demonstrated to exist and, by using numeric simulations, is shown to vary with the transmission distance. Adding the optimal noise can enhance the effectiveness of the thermal states quantum cryptography protocol under noisy and lossy channel in terms of secret key rate, the maximum transmission distance and the tolerable channel noise. Although finite‐size effects deteriorate the performance of thermal quantum cryptography, this approach can compensate the impact of this degradation, and even make the performance of the modified protocol in the finite‐size regime better than that of the original protocol in the ideal asymptotic regime.
SOME ASPECTS OF USING INFORMATION TECHNOLOGIES IN TEACHING PHYSICS

The article argues that the constant development of the use of ICT in physics lessons is a historical necessity. A brief analysis of scientific and methodological literature on teaching physics with the help of ICT is given. Different directions of using computer technologies in the educational process are noted. The essence of the most frequently used modeling method in teaching physics is explained. It was pointed out that, unlike other exact sciences and the simulation model, it is partly difficult to describe the model of physical events and processes. An example from our own experience is used to describe the Stern model. It is noted here that such models are described using a 3D package. Listed are the principles that a teacher should follow when modeling. The conditions necessary for the development of the use of ICT in physics lessons are noted. Among them, special attention was paid to the content of problems in physics. Thus, it was noted that solving problems with game, contextual and research content is the most important tool for the development of students' creative thinking. The specificity of ICT tools used in teaching physics is noted.
Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis

Purpose: This study proposes a methodology for detecting tumor severity using data mining of databases relating to breast imaging modalities. In doing so, it proposes creating a software application that can serve as an efficient decision-making support system for medical practitioners, especially those in areas where there is a shortage of modern medical diagnostic devices or specialized practitioners, such as in developing countries.Method: we investigated the data of approximately 3754 screened women by using “BI-RADS” categories as a quality assessment tool to screening, measure, and identify the size and location of lesions, determine the number of lymph nodes, collect biopsy samples, determine final diagnoses, prognoses, and age which were all available from the screening registry. Result: The application of each algorithm on BI-RADS values 4 and 5 for Invasive Ductal Carcinoma lesions was assessed, and the following accuracy was acquired: CART: 84.71%. In order to get the best result, four optimum clusters based on tumor size were applied to constructing simple rules with significant confidence. Conclusion: This study presents a hybrid approach - a combination of k-means with GRI and CART decision tree - to better assess breast cancer data sets.
Learning Opinion Summarizers by Selecting Informative Reviews.

Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. In this work, we collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training. However, the number of reviews per product is large (320 on average), making summarization – and especially training a summarizer – impractical. Moreover, the content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates. In order to deal with both of these challenges, we formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets. The choice of the review subset is treated as a latent variable, predicted by a small and simple selector. The subset is then fed into a more powerful summarizer. For joint training, we use amortized variational inference and policy gradient methods. Our experiments demonstrate the importance of selecting informative reviews resulting in improved quality of summaries and reduced hallucinations.
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach

In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages.We propose a knowledge-transfer approach that heuristically induces chunk labels from stateof-the-art unsupervised parsing models; a hierarchical recurrent neural network (HRNN) learns from such induced chunk labels to smooth out the noise of the heuristics.Experiments show that our approach largely bridges the gap between supervised and unsupervised chunking.
La competenza lessicale nei metodi d’insegnamento linguistico

The article analyzes the importance of the development of lexical competence within the various glottodidactic methods. In particular, it is highlighted how, at present, teaching vocabulary has definitively freed itself from a subordinate, almost accessory, position with respect to grammar teaching. The article then focuses on the role of the dictionary in learning vocabulary and on the necessary skills for its use. It is highlighted the lack, in Italian L2, of pedagogical dictionaries, i.e. simplified monolingual dictionaries specifically designed for a non-Italian speaking public. The article ends with the description of the most important corpora of Italian language, which could became useful operational tools in teaching vocabulary.
Serials Exchanges: and Elimination

A full reexamination of serials exchange programs at the project level may reveal fertile ground for academic libraries striving to achieve both efficiency and economy of scale in collection management. Both institutional factors such as reduced staffing levels as well as global political shifts clearly justify such a review. The results of one such study are detailed with emphasis upon statistical correlation and procedural requirements. Methods by which interdepartmental cooperation can be achieved are also outlined. [Article copies available for a fee fivm The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Adoption of Technology Enhanced Teaching and Learning Innovations during Covid-19 Lockdown in Rural Uganda

Teaching and learning in the 21st century has witnessed unprecedented changes, which call for the introduction of technology in the process to connect people and create interaction among them regardless of time, place and person. This study examined the level of acceptance of technology-enhanced innovations among learners and teachers in rural Tororo District during the Covid-19 lockdown. It also examined the challenges faced and garnered strategies to mitigate the challenges in future. Concurrent mixed methods design was employed. Participants in the study were 370 Senior 4 students chosen by simple random sampling to fill a questionnaire, and 4 teachers selected purposively for an interview. The results revealed a moderate level of acceptance among the learners (min = 25, Max = 125, M = 85.44, SD =17.65). Results further revealed that the majority of learners (77.3%) had access to technology tools and the commonly used media were radios, televisions and WhatsApp platforms. Learners were faced with challenges that were teacher related, psychosocial, socio economic, management and technical in nature. The strategies included training of both teachers and learners on the use of technology, provision of technology tools, Internet connectivity and ensuring a stable power supply.
A Block Permutational Steganographic Algorithm for Scanned Documents and other Images

Steganography studies the embedding of messages into cover mediums, while obscuring the fact that any message exists.A supplement to encryption, steganographic methods help to avoid attention from adversaries, who may take additional measures if made aware of such messages.Common forms of image steganography, such as Least Significant Bit steganography, alter the first-order statistics of a cover image, allowing for easier detection by methods such as the Wavelet Motion Analyzer.We study steganographic methods based on permutation of pixels in grayscale images, which do not share this disadvantage.A generalization of pixel-swapping methods, our algorithm identifies invariant sets of pixels and intensities, called Permissible Sets, within an image block, and allow their full permutation in the encoding or decoding of messages.This increase in the number of permissible permutations serves to reduce the detectability of our method, while increasing the bit-per-pixel embedding rate.Through direct implementation and comparison, we find our method to be an improvement over previous swap-based steganography for the Microsoft Research Cambridge dataset of general images, and a large improvement for the higher-resolution NoisyOffice dataset of scanned images.
Identifying Factors Impact Cloud Computing Adoption in UAE Large Business Organization

Big Data Engineering (BDE) requirements within cloud computing to be used by large employers in the UAE have been extensively studied in the literature review. Research that is available currently is in constant support of the theoretical power that Technology Organizational Environmental (TOE) framework owns in investigating factors in the firm-level that support the technology acceptance theory with mixed theories from TAM, TOE and BDE variables. There has been a new interest in Cloud Computing Adoption (CCA) founded within large organizations in UAE, and that’s because of all the new technologies that are surfacing which have very strong relations to CCA and BDE.  BDE is responsible for the implementation of CCA into large business organizations. It’s a necessity for CCA to develop and transform from basic knowledge to a higher level of corporate results. The motives to construct a forecast model for CCA which involves BDE variables and variables from the other two most involved technology adoption theories which are, TAM, and TOE, the act of inserting BDE related variables helped evolve cloud computing’s mixed theory of the approach they should take when implementation takes place. Six independent variables were involved. They were seen as useful, easy to use, cost-effective, security capability, had the intention of using BDE, and needed it. Information obtained from UAE. were studied using binary logistic regression. The outcome described that a model which includes all six independent variables was more statistically dependable for speculating CCA with an accuracy of 90.6%. Separately, however, only usefulness was the variable that can be used to elevate the use of CCA. These results illustrated that CCA could rise if it's accompanied by BDE movements in order to make it more helpful for its purchaser.
Not all parameters are born equal: Attention is mostly what you need

Transformers are widely used in state-of-the-art machine translation, but the key to their success is still unknown. To gain insight into this, we consider three groups of parameters: embeddings, attention, and Feed-Forward Neural network (FFN) layers. We examine the relative importance of each by performing an ablation study where we initialise them at random and freeze them, so that their weights do not change over the course of the training. Through this, we show that the attention and FFN are equally important and fulfil the same functionality in a model. We show that the decision about whether a component is frozen or allowed to train is at least as important for the final model performance as its number of parameters. At the same time, the number of parameters alone is not indicative of a component’s importance. Finally, while the embedding layer is the least essential for machine translation tasks, it is the most important component for language modelling tasks.
Analyzing IKEA’s International Investment in China Based on the Theory of International Production Compromise

This study analyzes IKEA’s localized operation in China through the eclectic theory of international production. Firstly, the development history of IKEA is discussed along with its development in China. Secondly, IKEA’s direct investment in China is analyzed from the perspective of IKEA’s eclectic theory of direct investment in international production, the corporate ownership, internalization, and location advantages of the company, in addition to the challenges of IKEA’s investment and operation in China, hoping to enlighten the process of formulating overseas expansion strategies for foreign direct investment. This study aims to guide students to strengthen their skills in formulating and implementing strategies in regard to the international investment process of multinational companies. On the one hand, they can analyze the strategies used and challenges faced by IKEA in its international investment in China to stimulate their thinking on the international investment of Chinese enterprises; on the other hand, they can also strengthen their understanding of the international investment theory by analyzing IKEA’s international investment in China. This study hopes to enhance students’ understanding and application skills in regard to companies’ transnational operations.
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

We present COCO-LM, a new self-supervised learning framework that pretrains Language Models by COrrecting challenging errors and COntrasting text sequences. COCO-LM employs an auxiliary language model to mask-and-predict tokens in original text sequences. It creates more challenging pretraining inputs, where noises are sampled based on their likelihood in the auxiliary language model. COCO-LM then pretrains with two tasks: The first task, corrective language modeling, learns to correct the auxiliary model's corruptions by recovering the original tokens. The second task, sequence contrastive learning, ensures that the language model generates sequence representations that are invariant to noises and transformations. In our experiments on the GLUE and SQuAD benchmarks, COCO-LM outperforms recent pretraining approaches in various pretraining settings and few-shot evaluations, with higher pretraining efficiency. Our analyses reveal that COCO-LM's advantages come from its challenging training signals, more contextualized token representations, and regularized sequence representations.
A Parallel Algorithm for Neural Computing

This paper presents a high-performance technique for implementing artificial neural networks (ANNs) on hypercube-based general-purpose massively parallel machines. The paper synthesizes a tree-based parallel structure which is embedded into the hypercube topology. This structure is referred to as mesh-of-appendixed-sheered-trees (MAST). Both the recall and the learning phases of the multilayer with backpropagation ANN model are mapped on the MAST architecture. Unlike other techniques presented in the literature which require 0(N) time, where N is the size of the largest layer, our implementation requires only 0(log N) time. Moreover, it allows pipelining of more than one input pattern and thus further improves the performance.