Reviewerly/exHarmony huggingface dataset:
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports.

Safe deployment of deep learning systems in critical real world applications requires models to make few mistakes, and only under predictable circumstances. Development of such a model is not yet possible, in general. In this work, we address this problem with an abstaining classifier tuned to have $>$95% accuracy, and identify the determinants of abstention with LIME (the Local Interpretable Model-agnostic Explanations method). Essentially, we are training our model to learn the attributes of pathology reports that are likely to lead to incorrect classifications, albeit at the cost of reduced sensitivity. We demonstrate our method in a multitask setting to classify cancer pathology reports from the NCI SEER cancer registries on six tasks of greatest importance. For these tasks, we reduce the classification error rate by factors of 2-5 by abstaining on 25-45% of the reports. For the specific case of cancer site, we are able to identify metastasis and reports involving lymph nodes as responsible for many of the classification mistakes, and that the extent and types of mistakes vary systematically with cancer site (eg. breast, lung, and prostate). When combining across three of the tasks, our model classifies 50% of the reports with an accuracy greater than 95% for three of the six tasks and greater than 85% for all six tasks on the retained samples. By using this information, we expect to define work flows that incorporate machine learning only in the areas where it is sufficiently robust and accurate, saving human attention to areas where it is required.
Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation

Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-the-art on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to-CityScapes.
A Directed Reading-Thinking Activity (Dr-Ta) Towards Students ‘Critical Reading

This current study aimed to find out whether DR-TA (Directed Reading- Thinking Activity) had a significant effect on the critical reading skills of the English department students of FPBS IKIP Mataram or not.The population of this research was 238 students and the samples were taken by using cluster random sampling technique the sample was 80 students which was consisted of 41 students in the control group and 39 students in the experimental. This research is quantitative with a quasi-experimental design: the non-equivalent control group design. To obtain the data needed, the instrument was administered namely tests which was consisted of pre-test and post-test. Pre-test was applied to obtaining the primary data meanwhile the post-test was applied after the treatment accomplished. The data on the reading test was analyzed by using descriptive statistic and inferential statistics. The result of data analysis showed that the experimental group mean score on pre-test was 62.5 and posttest was 73.07, meanwhile the result of control group means score on pre-test was 57.92 and post-test 64.25. There was an improvement of the students in each group from pre-test to post test. And the result hypothesis or inferential analysis proved that t-test score was higher than the result t-table (3.843&gt;1.990) with degree of freedom (df) was 75 in significant level 95% or α = 0.05. It can be concluded that DR-TA Strategy has significant effect on critical reading skills of the English department students of FPBS IKIP Mataram. The effect was significant because DR-TA Strategy lead the students to have prediction before reading, create background knowledge, analyzing the fact to support the prediction, confirming the prediction and evaluating the text and context in order to have comprehensive comprehension. The results of this research hopefully could be an alternative strategy in teaching critical thinking in reading and could be reference for further researchers
Self learning mode of flash animation course under open studio mode

In order to further develop the self-learning mode of flash animation course under the open studio mode, it is necessary to reform the courses. Under the open studio mode, the reform of self-learning mode of flash animation course needs to be carried out under the premise of a comprehensive understanding of the purpose and goal of education. Flash animation course, as a course with strong application and operation, focuses on training students’ practical design and operation ability. It is a course suitable for teachers to guide students’ learning process. Under the open studio mode, the reform of self-learning mode of flash animation course must put forward higher requirements for teachers and learners in order to promote the development of education.
[Exploration on the relationship between acupuncture for mind-regulation and flow theory].

Based on the story of Chinese idiom, paodingjieniu (a magical and skilled form of craftsmanship) as the breakthrough point, this paper discusses the both paoding (cook) and the experienced acupuncture practitioner have the same high skills and explores the potential relationship between mind-regulation in treatment with acupuncture and flow theory. It is believed that the skills of ancient acupuncture practitioner in mind-regulation with acupuncture is not only a kind of Tao mode, but also a state of flow. By the discussion on mind-regulation and flow theory, modern people may have more clear recognition on the mind regulation in treatment with acupuncture so as to better determine the therapeutic methods of acupuncture for mind-regulation.
Measure Contribution of Participants in Federated Learning

Federated Machine Learning (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculate the contributions of multiple parties in FML, in the context of both horizontal FML and vertical FML. For Horizontal FML we use deletion method to calculate the grouped instance influence. For Vertical FML we use Shapley Values to calculate the grouped feature importance. Our methods open the door for research in model contribution and credit allocation in the context of federated machine learning.
Context-Based Question-Answering System for the Ukrainian Language

This work presents a context-based question answering model for the Ukrainian language based on Wikipedia articles using Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) model, which takes a context (Wikipedia article) and a question to the context. The result of the model is an answer to the question. The model consists of two parts. The ﬁrst one is a pre-trained multilingual BERT model, which is trained on the top-100, the most popular languages on Wikipedia articles. The second part is the ﬁne-tuned model, which is trained on the dataset of questions and answers to the Wikipedia articles. The training and validation data is Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016). There are no question answering datasets for the Ukrainian language. The plan is to build an appropriate dataset with machine translation and use it for the ﬁne-tuning training stage and compare the result with models which were ﬁne-tunedon the other languages. The next experiment is to train a model on the Slavic language datasets before ﬁne-tuning on the Ukrainian language and compare the results.
Depth-Adapted CNN for RGB-D cameras

Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt spatial sampling locations. The new model presented is invariant to scale and rotation around the X and the Y axis of the camera coordinate system. Moreover, when depth data is constant, our model is equivalent to a regular CNN. Experiments of benchmarks validate the effectiveness of our model.
A Study on the Teaching of Professional-Oriented English Writing in Applied-Type University Based on I Write 2.0

As one of the indispensable public compulsory courses, College English is required to be practical application-oriented and cultivation of students' application ability-focused.English Writing, which is an essential component of English practical abilities, needs more attentions and explorations by college English teachers and researchers, particularly in applied-type universities.By experimental studies, this paper aimed to contrast the differences of English comprehensive ability and English writing ability under the teaching mode combining iWrite assistance and major orientation and the teaching mode with traditional approaches.It was found through research that by means of iWrite 2.0 students could achieve measurable progress both in professional-oriented English writing skills and English comprehensive abilities.Meanwhile, students' autonomous learning ability was also significantly promoted.Then, with data collection and analysis, the writer deeply discussed and explored the causes of what had been found in the research.The limitations of the research and the conclusion were also expounded in the final part of this paper.
Compact Learning for Multi-Label Classification

Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC encourages a popular framework named label compression (LC) for capturing label dependency with dimension reduction. Nevertheless, most existing LC methods failed to consider the influence of the feature space or misguided by original problematic features, so that may result in performance degeneration. In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance. The proposal is a versatile concept, hence the embedding way is arbitrary and independent of the subsequent learning process. Following its spirit, a simple yet effective implementation called compact multi-label learning (CMLL) is proposed to learn a compact low-dimensional representation for both spaces. CMLL maximizes the dependence between the embedded spaces of the labels and features, and minimizes the loss of label space recovery concurrently. Theoretically, we provide a general analysis for different embedding methods. Practically, we conduct extensive experiments to validate the effectiveness of the proposed method.
Public Libraries: context, trends and models

Public libraries are not unaware of the constant change in their environment, they follow technological trends and apply models appropriate to the needs of their users Based on this concept, this work aims to briefly contextualize the current era in the field of science, technology, society and economy, including the health crisis of COVID-19, as well as the trends that are applied in the library management and what models are being adopted in the library field For this, a documentary investigation of various institutions and specialized authors on the subject in recent years was carried out It is indicated that several libraries worldwide are diversifying their services and products with the support of technological tools and have proven to be flexible in difficult situations It is concluded that the context influences the development of library programs Likewise, trends and models are strongly related to technology, even more so when the health crisis has caused an acceleration of the virtualization of library management In addition, the idea of implementing spaces for learning, creation, participation, inspiration, social encounters, reliable information and citizen empowerment is pursued, both physically and virtual
Multivariate binary probability distribution in the Grassmann formalism

We propose a probability distribution for multivariate binary random variables. The probability distribution is expressed as principal minors of the parameter matrix, which is a matrix analogous to the inverse covariance matrix in the multivariate Gaussian distribution. In our model, the partition function, central moments, and the marginal and conditional distributions are expressed analytically. That is, summation over all possible states is not necessary for obtaining the partition function and various expected values, which is a problem with the conventional multivariate Bernoulli distribution. The proposed model has many similarities to the multivariate Gaussian distribution. For example, the marginal and conditional distributions are expressed in terms of the parameter matrix and its inverse matrix, respectively. That is, the inverse matrix represents a sort of partial correlation. The proposed distribution can be derived using Grassmann numbers, anticommuting numbers. Analytical expressions for the marginal and conditional distributions are also useful in generating random numbers for multivariate binary variables. Hence, we investigated sampling distributions of parameter estimates using synthetic datasets. The computational complexity of maximum likelihood estimation from observed data is proportional to the number of unique observed states, not to the number of all possible states as is required in the case of the conventional multivariate Bernoulli distribution. We empirically observed that the sampling distributions of the maximum likelihood estimates appear to be consistent and asymptotically normal.
3D Pseudo Stereo Visualization with Gpgpu Support

This article discusses the study of a computer system for creating 3D pseudo-stereo images and videos using hardware and software support for accelerating a synthesis process based on General Purpose Graphics Processing Unit (GPGPU) technology. Based on the general strategy of 3D pseudo-stereo synthesis previously proposed by the authors, Compute Unified Device Architect (CUDA) method considers the main implementation stages of 3D pseudo-stereo synthesis: (i) the practical implementation study; (ii) the synthesis characteristics for obtaining images; (iii) the video in Ultra-High Definition (UHD) 4K resolution using the Graphics Processing Unit (GPU). Respectively with these results of 4K content test on evaluation systems with a GPU the acceleration average of 60.6 and 6.9 times is obtained for images and videos. The research results show consistency with previously identified forecasts for processing 4K image frames. They are confirming the possibility of synthesizing 3D pseudo-stereo algorithms in real time using powerful support for modern Graphics Processing Unit/Graphics Processing Clusters (GPU/GPC).
TEACHING PASSIVE VOICE THROUGH TRANSFORMATIONAL DRILL TECHNIQUE TO THE TENTH GRADE STUDENTS OF SMK MUHAMMADIYAH 1 PALU

This research was conducted to find out that the use of transformational drill technique was effective to improve the students’ ability in learning passive voice. The research was pre-experimental research. The sample of the research was the tenth grade students of SMK Muhammadiyah 1 Palu that consisted of 29 students. The researcher employed total sampling technique. The researcher used two instruments, namely test and questionnaire. The data were analysed by statistical analysis. The result of data analysis show that the t-counted (13.9) was greater than the t-table (1.70). In short, transformational drill technique is effective to teach passive voice at the tenth grade students of SMK Muhammadiyah 1 Palu.Key words: Passive Voice, transformational drill technique.
State Transition Modeling of the Smoking Behavior using LSTM Recurrent Neural Networks

The use of sensors has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch sensors to recognize smoking activity. More specifically, we have reformulated the previous work in detection of smoking to include in-context recognition of smoking. Our presented reformulation of the smoking gesture as a state-transition model that consists of the mini-gestures hand-to-lip, hand-on-lip, and hand-off-lip, has demonstrated improvement in detection rates nearing 100% using conventional neural networks. In addition, we have begun the utilization of Long-Short-Term Memory (LSTM) neural networks to allow for in-context detection of gestures with accuracy nearing 97%.
Memories of war and problems of forming defence consciousness by means of book business

Actuality and significance of problems of forming social consciousness with its necessary defence component are conditioned by unremitting tension of inter-state relations, transiting into a certain degree of war confrontation. Experience of all wars testifies to the fact that all victories in war hostility are achieved not only by rivalry of military potentials and generals’ skills but also by firmness of spiritual bases and people moral strengths. War history many times has proved patriotic incite and the society unity to be important factors influencing the course and the result of wars. Since the moment of writing invention, oral traditions began to be supplemented by document evidences of war history and invention of book printing not only has widened possibilities of memorable history events’ data circulation but also has transformed the printed word into effective means of information hostility of struggling sides. This article aim is to actualize problems, connected with transformations of war memories, and using book for to preserve it and form a defence component of public consciousness. Historical and book-study analysis of military publishing practice of the XVII–XX centuries proves the tradition of describing war history not only for to study battle experience by military specialists but also for to consolidate the tragedy and lessons of war in public consciousness. Memories of war cannot be similar for winners and losers, and that produces different evaluations of war events and their relevant reflection on pages of fundamental militaryhistorical works and memoirs. Comparison of different memorialists’ viewpoints confirms memory variability, but permits the researchers to approach evaluations that are more objective. To observe the 75-th anniversary of the Victory in the Great Patriotic War of 1941–1945 by carrying out the Year of memory and glory in Russia has become a new impulse for turning to historical memory and research of poorly studied problems. This war experience contains actual lessons of book usage as an important means of forming defence consciousness in the pre-war period and its reconstruction in the years of war.
Towards Behavior-Level Explanation for Deep Reinforcement Learning.

While Deep Neural Networks (DNNs) are becoming the state-of-the-art for many tasks including reinforcement learning (RL), they are especially resistant to human scrutiny and understanding. Input attributions have been a foundational building block for DNN expalainabilty but face new challenges when applied to deep RL. We address the challenges with two novel techniques. We define a class of \emph{behaviour-level attributions} for explaining agent behaviour beyond input importance and interpret existing attribution methods on the behaviour level. We then introduce \emph{$\lambda$-alignment}, a metric for evaluating the performance of behaviour-level attributions methods in terms of whether they are indicative of the agent actions they are meant to explain. Our experiments on Atari games suggest that perturbation-based attribution methods are significantly more suitable to deep RL than alternatives from the perspective of this metric. We argue that our methods demonstrate the minimal set of considerations for adopting general DNN explanation technology to the unique aspects of reinforcement learning and hope the outlined direction can serve as a basis for future research on understanding Deep RL using attribution.
식자재 유통기한이 고려된 혼류 생산 계획 수립 방법론 - 수제 베이커리 공장 사례 -

As the industry paradigm shifts from mass production to customization, it is inevitable that a mixed production of various products in the production line is becoming a tool to enhance the competitiveness of enterprises. In the food production industry, business find it challenging to adopt a custom...
AN ERROR ANALYSIS OF WRITING RECOUNT TEXT BASED ON GENDER

in the recount text. Students errors were analyzed using errors analysis. This research used qualitative descriptive method as the research methodology. The data would be presented descriptively and the result was in explanation of words form which would be following by data presented in form of tables. The errors made by female there are 12 errors of omission, 13 errors in addition, 21 errors in misformation and 1 errors in misordering. while the errors are made by male there are 5 errors of omission, 5 errors in addition, 27 errors in misformation and 1 errors in misordering. The instrument in this research was a test item created by students. From this research it can be deduced that the most mistakes made by female and male are errors in the way of misformation because students lack understanding of how to make stories in the writing recount text. Keywords: Writing, Error Analysis, Recount text.