Reviewerly/exHarmony huggingface dataset:
TEACHING COMPUTER SCIENCE STUDENTS WITH SPECIAL NEEDS DURING DISTANCE EDUCATION

Many changes have taken place last two years due to the Covid restrictions. The recent switch to distance education applied to all undergraduate students. Especially for students with disabilities carrying out their duties required extra energy. The change came suddenly, teachers and students had to adapt and find alternative ways for lessons. Personal meetings were replaced by electronic communication, also exams were organized via the internet. The computer science study seems like not having problems during distance education via internet. But some unexpected situations occurred for students with special needs, and we had to find solutions. In this article we deal our experience. Our goal was to find out what problems they encountered during distance learning. Naming these problems will help us facilitate their distance learning in the future. We first describe the way of working with ICT of people with individual types of disabilities and their requirements for accessibility of software and electronic documents. As research participants, we chose students of computer science, math, and physics with different types of disabilities and specific needs: 3 students with autism spectrum disorder, 3 visually impaired students (1 blind and 2 partially sighted), 3 with physical disabilities and 2 students with hearing impairment. The students were in different grades. Three students were in their final year of study.We have used case study research. Our cases were individual students. We analysed data received from interviews with students.We found out that students with autism spectrum disorders had the most serious problems in the social communication. We have noticed these problems:- with adapting to the new study regime, - misunderstanding instructions from email and very weak response from the students,- phobia of electronic communication,- chatting with classmates had a very significant influence on the student's decision-making.Students with visual impairments adapted to changes very quickly. The accessibility of the online platform was the most important for them. For students with hearing impairments, it was necessary to provide either an interpreter in sign language or to add subtitles to the recordings from the online lectures. Distance education was most suitable for students with physical disabilities.
Continuous Parameters in Dreamy Genetic Algorithms

This paper investigates the use of continuous parameters in dreamy Genetic Algorithms, which feature alternating awake and sleep cycles.While awake, the algorithm converges towards a solution in the traditional manner; while dreaming it breaks away to further explore the search space.The dream phase is implemented to overcome a problem inherent to the genetic method: premature convergence.The cycles allow the algorithm to more fully utilize its exploration and exploitation capabilities, increasing diversity in the search space.As originally formulated, the dreamy algorithm used a binary encoding.The success of such a scheme is generally problemdependent, as Genetic Algorithms suffer from encoding brittleness.Continuous parameters are implemented in the dreamy model to demonstrate the effectiveness of the dreamy method on benchmark functions, in addition to showing the importance of robust encodings.
Chatbot

Abstract: Chatbot can be understood as a software that can chat with people using artificial intelligence. This software can also perform tasks such as quickly responding to users, informing them, helping to purchase products and providing the customers better services. A chatbot is a computer software program that can conduct a conversation by an auditory or textual methods. Chatbot has become more popular in business group in the present as it can reduce customers service costs and handles multiple users at a time. But it is yet to accomplish tasks that needs to make chatbots as efficient as possible. In this project, we aim to design a chatbot that provides a genuine and accurate answer for queries using Artificial Intelligence Markup Lanugages (AIML) and with the present of python platform. Keywords: Chatbot, Artificial Intelligence, Computer Software, Services, Users, Artificial Intelligence Markup Languages.
Hemoglobin Status of the Hospital Admitted Patients: A Cross-Sectional Study in A District Hospital of Bangladesh

Introduction: Anemia is a global public health concern, especially in lower and middle income countries like Bangladesh. The present study aimed to find out the hemoglobin level and prevalence of anemia among the patients admitted in a district hospital of Meherpur, Bangladesh. Methods: This cross sectional study was conducted among 500 patients admitted to Medicine and Gynecology department of Meherpur General Hospital. After enrollment, 2-3 ml of blood sample was collected in EDTA tubes from each patients and hemoglobin level was determined using an automated hematology analyzer for determination of anemia as defined by the WHO. All statistical analyses were carried out using SPSS version 24.0 for a significant p-value &lt;0.05. Results: The mean (SD) age of the participants was 48.6 (18.9) years and almost 57% of them were female. Mean (SD) of the Hb level among the participants was 9.8 (1.7) g/dL which was significantly higher among men compared to women (10.1 mg/dL vs 9.5mg/dL). Overall prevalence of anemia was 93.8% (mild 25%, moderate 54% and severe 15%) and the prevalence was comparatively higher among women (96% vs 91%). Female sex and older age were significant risk factors of moderate to severe anemia among the participants though other variables like comorbidities and chronic NSAID use were not revealed as significant. Conclusion: Anemia prevalence was very high among the patients admitted in Meherpur district hospital, especially among female and elderly patients. So, routine assessment and proper management of anemia of the hospital admitted patients is crucial for better treatment outcome. TAJ 2021; 34: No-2: 114-121
The Problem of Subject and Subjectivity in Psychological and Pedagogical Sciences in the Context of Training Future Teachers

The article analyzes the multimodality of approaches to the concept of subjectivity of a primary school teacher in Ukrainian education. The purpose of the article is how to determine the well-established traditional (mostly destructive) and promising directions for the development of the doctrine of subjectivity in the Ukrainian psychological and pedagogical discourse against the background of historical and current experience. We identified a number of contradictions in the qualification of this phenomenon, the stability of traditional approaches and the avoidance of interdisciplinary neurophysiological and pedagogical understanding of the problem in Ukraine. We also found out that the ideas of Ukrainian scientists are of an expressive dual nature: they are divided into sociocentric representations of the subject as an indicator of the social level and nature-centered (subjectivity as an innate permanent resource for self-development). We also made some comments on the use of traditional and innovative ideas of subjectivity in the educational process. The greatest drawback of the Ukrainian scientific discourse is the lack of consideration of neurophysiological patterns in the understanding of subjectivity. Nevertheless, the maximum use of such ideas outlines the prospects for a new stage in the development of psychological and pedagogical science in Ukraine. The international contribution of the article lies in the re-inventory of views on the subjectivity of the post-totalitarian state against the background of world trends. This will allow countries that have little experience in transforming education, psychology and sociology (Ukraine, Russia, Belarus, Kazakhstan, etc.) not to formally adopt the experience of Western democracies, but also to acquire constructive traditions in their own discourse.
Intent detection model based on dual-channel feature fusion

The intelligent customer service system is an important part of power marketing and an important window for the power sector's external services. Spoken language understanding(SLU) is the core module of the dialogue system. Intent detection is a key subtask of the spoken language understanding module, which directly determines whether the spoken language understanding module is correct. Understand the purpose of the user's words. In view of the current use of commonly used deep learning methods such as RNN and CNN to deal with the limited semantic representation and insufficient feature extraction caused by intent detection, this paper proposes an intent detection model based on dual-channel feature fusion (Dual-channel Model), using The capsule network captures rich text features, makes up for the lack of local spatial information in the pooling operation, and further improves the representation limitations of CNN and RNN. At the same time, it uses the high-level features extracted by the pooling operation and the capsule network to form a dual-channel feature extraction mechanism, and The feature fusion of the two channels is used for the final intent detection. Experiments show that on the ATIS and SMP2019-ECDT datasets, the proposed model has an accuracy of 98.1% and 96.25% in intent detection, and an F1 value of 88.5% and 89.76%.
Contributors

The proliferation of video data makes it imperative to develop automatic approaches that semantically analyze and summarize the ever-growing massive visual data. As opposed to existing approaches built on still images, we propose an algorithm that detects recurring primary object and learns cohort object proposals over space-time in video. Our core contribution is a graph transduction process that exploits both appearance cues learned from rudimentary detections of object-like regions, and the intrinsic structures within video data. By exploiting the fact that rudimentary detections of recurring objects in video, despite appearance variation and sporadity of detection, collectively describe the primary object, we are able to learn a holistic model given a small set of object-like regions. This prior knowledge of the recurring primary object can be propagated to the rest of the video to generate a diverse set of object proposals in all frames, incorporating both spatial and temporal cues. This set of rich descriptions underpins a robust object segmentation method against the changes in appearance, shape and occlusion in natural videos. We present extensive experiments on challenging datasets that demonstrate the superior performance of our approach compared with the state-of-the-art methods.
Variational estimation of capacity bounds for quantum channels

We propose a framework to variationally obtain detectable capacity bounds for quantum channels. The proposal of this framework is motivated by the difficulty of estimating the von Neumann entropy of an unknown quantum state. Instead of estimating the von Neumann entropy at the channel output, we propose to estimate the state purity---which can be measured by a single measurement setting---followed by bounding the von Neumann entropy from above and below. This procedure leads to new upper and lower bounds on various communication rates of quantum channels for some fixed input states. Then, by utilizing the variational method to find optimal input states we obtain lower bounds on (i) the quantum capacity of arbitrary channels, (ii) the entanglement-assisted classical capacity of arbitrary channels, and (iii) the classical capacity of covariant channels. Corresponding to these lower bounds, we also obtain upper bounds on (i) $N$-shot coherent information, (ii) the entanglement-assisted classical capacity, and (iii) the $N$-shot Holevo capacity of arbitrary quantum channels. All these bounds can be estimated by a single measurement setting without needing a full process tomography or any further a priori knowledge, e.g., preferred basis of the channel.
Metaphors and Their Meanings in Lady Whistledown's Utterances in the Television Series Bridgerton

&#x0D; &#x0D; &#x0D; Metaphors are used to understand the basic function of a term through another term, where there are some similarities or correlations between the two terms. Figurative language including metaphors is commonly used in dialogue, narrative text, and even in movie and television dialogue, where the dialogue can become more sparkling by the use of metaphors. The goal of this study was to examine Lady Whistledown’s metaphorical expressions in the Bridgerton television series and to explain the meanings of the metaphors. This was a qualitative study and the data were analyzed using Beekman and Callow’s metaphor theory. Full metaphor and abbreviated metaphor were the types of metaphor discovered by the researchers. The results of this study showed that abbreviated metaphor type 3 was the most frequently used. The meaning of each metaphor can be interpreted through the existing context. The findings of this study will be valuable to scholars who examine metaphors and metaphorical expressions.&#x0D; Keywords: metaphor, abbreviated metaphor, meaning, Bridgerton&#x0D; &#x0D; &#x0D;
THE EFFECT OF TECHNOLOGY RESOURCES ON COLLEGE SOPHOMORES' READING HABITS IN ILOCOS SUR POLYTECHNIC STATE COLLEGE PHILIPPINES

Imbibing reading habits is an essential skill necessary in the acquisition of knowledge, which is highly demanding in the life of every student in the academic environment. However, as modern technological resources keep on advancing, the reading habits of students are also changing with reading on screen. This study examined the reading habits of college sophomore students as affected by technological resources. The survey instruments, distributed to 254 college sophomore students at the Ilocos Sur Polytechnic State College, allowed the respondents to assess their reading habits in terms of reading hours, reading purposes, reading content, and reading attitude, with the technological resources as their tool. The data gathered in the study was analyzed statistically using the simple frequency count and percentage, mean, and Pearson correlation. The findings revealed that the college sophomores achieved an average level in their English subjects. In addition, it was found that the technology resources were moderately adequate to be used for reading among the college sophomores. Despite this, the results still showed that college sophomore students spent 2-3 hours a day reading, primarily in preparation for their exams. In addition, they read the available reading content moderately often, and they perceive a positive attitude towards the use of technological resources for reading. Furthermore, it was revealed that there is a significant relationship between reading habits and academic achievements. The study's findings will be used to improve instructional approaches and to launch e-reading initiatives in colleges.
Forms of pedagogical and managerial relationship

&nbsp;&nbsp; The era of computerization and globalization has abandoned traditional management practices, which require more flexibility and constant development in modern conditions. In order to succeed in new conditions it is necessary to learn to think in a new way and possess the appropriate habits. Any person involved in managerial activities is required o be able to analyze the information and use it properly. Modern pedagogy and management have much in common. Both are focused on human knowledge, experience, ability, morality and other shared values. Thus, the ability to choose and use the types of managerial communications is a tactical issue for business management, and their formation is a fundamental problem of upbringing and education, which follows much earlier, in the development of the individual's spiritual world and communication skills.
Study on "Teachers’ Voice" in China during the Past 40 Years

Teachers are an ancient profession, imparting knowledge and preaching morality with words, sounds and actions. Voice is a basic tool for teachers to carry out their professional functions. As an academic object with both instrumentality and purpose, "teacher's voice" is different from ordinary voice application because of the professional characteristics of teacher's work and the individuality of voice expression. Therefore, the knowledge and research of "teacher’s voice" can become a systematic academic object or research topic. This is the basis of the thesis. At present, there are few academic reviews on this topic, which is the origin of this topic.
Extending Decision Tree to Handle Multiple Fairness Criteria

The demand for machine learning systems that can provide both transparency and fairness is constantly growing. Since the concept of fairness depends on the context, studies in the literature have proposed various formalisation and mitigation strategies. In this work, we propose a novel, flexible, discrimination-aware classifier that allows the user to: (i) select and mitigate the desired fairness criterion from a set of available options; (ii) implement more than one fairness criterion; (iii) handle more than one sensitive attribute; and (iv) specify the desired level of fairness to meet specific business needs or regulatory requirements. Our approach is based on an optimised extension to the decision-tree classifier, and aims to provide transparent and fair rules to the final users.
DRSpeech: Degradation-Robust Text-to-Speech Synthesis with Frame-Level and Utterance-Level Acoustic Representation Learning

Most text-to-speech (TTS) methods use high-quality speech corpora recorded in a well-designed environment, incurring a high cost for data collection. To solve this problem, existing noise-robust TTS methods are intended to use noisy speech corpora as training data. However, they only address either time-invariant or time-variant noises. We propose a degradation-robust TTS method, which can be trained on speech corpora that contain both additive noises and environmental distortions. It jointly represents the time-variant additive noises with a frame-level encoder and the time-invariant environmental distortions with an utterance-level encoder. We also propose a regularization method to attain clean environmental embedding that is disentangled from the utterance-dependent information such as linguistic contents and speaker characteristics. Evaluation results show that our method achieved significantly higher-quality synthetic speech than previous methods in the condition including both additive noise and reverberation.
Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction

Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications. One way to ensure high-quality generation is to use multiple sources with complementary information such as different views of the same object. However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques. Therefore, we propose a new general approach which models multiple types of variations among sources, such as view angles, poses, facial expressions, in a unified framework, so that it can be employed on datasets of vastly different nature. We verify our approach on a variety of data including humans bodies, faces, city scenes and 3D objects. Both the qualitative and quantitative results demonstrate the better performance of our method than the state of the art.
I2CNet: An Intra- and Inter-Class Context Information Fusion Network for Blastocyst Segmentation

The quality of a blastocyst directly determines the embryo's implantation potential, thus making it essential to objectively and accurately identify the blastocyst morphology. In this work, we propose an automatic framework named I2CNet to perform the blastocyst segmentation task in human embryo images. The I2CNet contains two components: IntrA-Class Context Module (IACCM) and InteR-Class Context Module (IRCCM). The IACCM aggregates the representations of specific areas sharing the same category for each pixel, where the categorized regions are learned under the supervision of the groundtruth. This aggregation decomposes a K-category recognition task into K recognition tasks of two labels while maintaining the ability of garnering intra-class features. In addition, the IRCCM is designed based on the blastocyst morphology to compensate for inter-class information which is gradually gathered from inside out. Meanwhile, a weighted mapping function is applied to facilitate edges of the inter classes and stimulate some hard samples. Eventually, the learned intra- and inter-class cues are integrated from coarse to fine, rendering sufficient information interaction and fusion between multi-scale features. Quantitative and qualitative experiments demonstrate that the superiority of our model compared with other representative methods. The I2CNet achieves accuracy of 94.14% and Jaccard of 85.25% on blastocyst public dataset.
Including Signed Languages in Natural Language Processing (Extended Abstract)

Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in research.
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance

Probing is popular to analyze whether linguistic information can be captured by a welltrained deep neural model, but it is hard to answer how the change of the encoded linguistic information will affect task performance.To this end, we study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality.Its key idea is to obtain a set of models which are Pareto-optimal in terms of both objectives.From this viewpoint, we propose a method to optimize the Pareto-optimal models by formalizing it as a multi-objective optimization problem.We conduct experiments on two popular NLP tasks, i.e., machine translation and language modeling, and investigate the relationship between several kinds of linguistic information and task performances.Experimental results demonstrate that the proposed method is better than a baseline method.Our empirical findings suggest that some syntactic information is helpful for NLP tasks whereas encoding more syntactic information does not necessarily lead to better performance, because the model architecture is also an important factor.