Reviewerly/exHarmony huggingface dataset:
A survey of multimodal machine learning

“Big data” is always collected from different resources that have different data structures. With the rapid development of information technologies, current precious data resources are characteristic of multimodes. As a result, based on classical machine learning strategies, multi-modal learning has become a valuable research topic, enabling computers to process and understand “big data”. The cognitive processes of humans involve perception through different sense organs. Signals from eyes, ears, the nose, and hands (tactile sense) constitute a person’s understanding of a special scene or the world as a whole. It reasonable to believe that multi-modal methods involving a higher ability to process complex heterogeneous data can further promote the progress of information technologies. The concepts of multimodality stemmed from psychology and pedagogy from hundreds of years ago and have been popular in computer science during the past decade. In contrast to the concept of “media”, a “mode” is a more fine-grained concept that is associated with a typical data source or data form. The effective utilization of multi-modal data can aid a computer understand a specific environment in a more holistic way. In this context, we first introduced the definition and main tasks of multi-modal learning. Based on this information, the mechanism and origin of multi-modal machine learning were then briefly introduced. Subsequently, statistical learning methods and deep learning methods for multi-modal tasks were comprehensively summarized. We also introduced the main styles of data fusion in multi-modal perception tasks, including feature representation, shared mapping, and co-training. Additionally, novel adversarial learning strategies for cross-modal matching or generation were reviewed. The main methods for multi-modal learning were outlined in this paper with a focus on future research issues in this field.
Short term prediction of COVID-19 cases by using various types of neural network model

Coronavirus disease 2019 (Covid-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The increasing number of positive cases caused by this virus in an area or country is suspected to form a certain pattern. The pattern of growth is thought to follow certain statistical distributions or model. In this research, the three types of neural network model are used to predict the number of Covid-19 cases in Indonesia. The types are Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), General Regression Neural Network (GRNN) and Recurrent Neural Network (RNN). The pattern of adding cases which always increases continuously makes the data pattern not easy to predict in the long term. In this study, repeated short-term predictions were carried out. In-sample predictions are repeated after new data are obtained, and so are out-sample predictions. The results show that the out-sample predictions of the three types of neural network are always under the actual value for each repetition. This condition is of course very worrying because the cases have a high possibility to increase more sharply than expected. However, the CFNN as the only type which giving a positive and negative variations of Mean Percentage Error (MPE), is the best model with the smallest error.
A Study on Core Technologies and Application Trends for Smart Logistics Implementation

본 연구는 기업들이 경쟁력을 향상시키기 위해 주목하고 있는 4차 산업혁명 기술에 있어 물류적용에의 핵심기술들을 살펴봄과 함께 이들이 조달, 생산, 판매, 배송으로 이어지는 여러 물류흐름에 있어서 적용되고 있는 동향을 살펴봄으로써 기업들이 참고할 수 있는 스마트 물류의 동향자료를 제공하고 시사점을 제시하고자 하였다. 스마트 기술의 물류분야에 적용 분야를 살펴보면 조달에서부터 생산, 판매, 배송으로 이어지는 물류 흐름의 전 분야에 적용이 가능하다고 하겠으며 현재 다양한 기업들이 각 흐름에서 신기술을 접목하여 물류개선을 위한 많은 활동들을 전개하고 있음을 파악할 수 있었다. 스마트 팩토리, 스마트 항만, 스마트 창고, 판매, 배송 등의 여러 영역에서 신기술이 적용되어 물류효율화는 지속적으로 진행되고 있으며 성공적인 물류 스마트화는 기업의 중요한 경쟁력의 원천이 되고 있다. 그리고 효과적인 스마트 물류의 구축을 위해서는 물류의 각 흐름에서 우선 실현 가능한 부문을 도출하고 단계적으로 실현하고 통합하는 것이 필요하며, 전사적인 차원에서의 물류 스마트화 로드맵 수립과 지속적인 추진 등 여러 노력이 필요함을 확인할 수 있었다.Purpose : This study seeks to examine the core technologies of the Fourth Industrial Revolution that can be applied to logistics. Considering the trends applied by new technologies in various logistics flows, this study intends to provide trends in smart logistics and suggest implications that can help businesses. Research design, data, methodology : A literature review was conducted along with case studies on trends in logistics flow applications, from the procurement of new technologies to production and sales delivery. Results : New technologies are applied in various areas, such as smart factory, smart port, smart warehouse, sales, and delivery, which reveal the marked progress in logistics efficiency, and successful logistics smartness is becoming an important aspect of competitiveness among companies. Conclusions : The study findings confirmed that effective smart logistics need to first derive feasible sectors from each flow of logistics, realize them step by step, integrate them, and make various efforts, including the establishment and continuous implementation of a roadmap, for smart logistics at the enterprise level.
Il “giudice” algoritmo di fronte al fenomeno religioso

The paper aims to highlight possible issues related to judicial applications of Artificial Intelligence and religion, starting from the specific case of content moderation in social media.Keywords: Religion; AI; Social media; Internet; Hate speech.
Assessment of Science Education Teachers’ Quality Work

&lt;p style="text-align:justify"&gt;The quality of science education teachers’ work determines the effectiveness of education and science education programs in many respects. Given that the results of students are not the same for teachers with the same formal characteristics, we formulate the research problem: when assessing the effectiveness of teachers, we can distinguish a system of indicators that affect the effectiveness of education and educational programs. The purpose of this article is to analyze the quality of work of science education teachers in the Kirov region and their teaching practice. The leading research methods in this case are the concept of the third international study of teaching and learning “Teaching and Learning International Survey”, collecting data obtained through a questionnaire of science education teachers, analyzing the quality of work and conditions of pedagogical practices, statistical processing of the research results, modeling and conversations with heads of secondary schools and representatives of executive authorities. As a result of a study conducted in 2017–2020, in which 1146 teachers of secondary schools of the European part of Russia took part, including 310 science education teachers, the author of the article found: the workload of a school teacher of science education is 0.65; subjects teachers spend on average 42.2 hours every week to perform their official duties, urban teachers have more work than rural teachers; with age, teachers of science education have a partial redistribution of labor activity from teaching to administrative work; actual teaching takes 53% of working time in the structure of workload for teachers of science education; teachers evaluate the completeness of their knowledge upon completion of training at the level of 38% of the required level for performing labor activities; there is a predominant share of teachers with a moderate level of need for knowledge in most areas of professional development. The results of the study allow us to develop a set of group measures for training and methodological support of science education teachers. These measures should take into account the specifics of workload and the characteristics of professional deficits.&lt;/p&gt;
PETR MOISEEV: SCIENTIFIC ACTIVITY AND PUBLICATIONS

This article shortly describes scientific researches of Petr Moiseev, talented Professor of Perm State Institute of Culture who died in October of 2020. He had a broad specialization, was fond of different humanitarians studies including philosophy of Dionysius the Areopagite, English classical literature (Dickens studies) and theory of detective.
Educational Activities of a Teacher from the Point of View of Humanitarian Methodology

The article examines the issues of educational activity of a modern teacher from the point of view of humanitarian methodology, which is based on the subject-subject relationship of the teacher and the pupil; states that the humanistic position of the educator presupposes the presence of deep knowledge about a person, about the laws of his development, his place in the world around him. The principles of humanitarian pedagogy in the educational activities of higher educational institutions direct the understanding of the teacher both to the students as a whole, and to each individual separately, forms a "soft" apparatus for the differential perception of each student by the teacher, develops pedagogical activity in overcoming situations of misunderstanding, provides the depth of comprehension of each student, brings the teacher closer to solving urgent problems of humanizing the life of young people, forms their life creative competence; it is proved that existential philosophy enriches the educational activity of a teacher with scientific information about a person and the mechanisms of its personal dynamics; synergetics gives an idea of creating a flexible, open dialogue space, in which zones of self-organization of the individual are provided; acmeology helps the teacher to understand the path of a person, as an exit to the heights of creativity, but because of difficulties and overcoming; hermeneutics helps a teacher understand a young person through comprehending the meanings of his behavior.
Implementasi Pembelajaran Berbasis E-Learning Saat Pandemi Covid-19 Di MI Muhammadiyah Srebegan Klaten

This study aims to describe: (1) planning for e-learning during the Covid-19 epidemic, (2) implementing e-learning during the COVID-19 epidemic, (3) evaluating e-learning learning during the Covid-19 epidemic at MI Muhammadiyah Srebegan Klaten. This type of research is qualitative research with a phenomenological research design, with data collection techniques, interviews, documentation, and observations. Data analysis used Miles and Huberman's techniques, namely data reduction, data presentation, and conclusion drawing. The results showed that: (1) e-learning planning includes school accessibility, teacher planning, the lesson plans used, the technology used (2) the implementation of e-learning during the Covid-19 epidemic includes learning time, material, media, process learning 3) evaluation of e-learning includes evaluation planning, what aspects are assessed, types of assessment, value processing.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH SQ3R TECHNIQUE OF THE FIRST GRADE STUDENTS AT MAN TOLITOLI

The purpose of this study was to find out the empirical evidence concerning whether SQ3R technique was effective or not to teach students reading comprehension. This study was held in March 2015 at MAN Tolitoli. This study used a quantitative method in the design of pre-experimental study.  The sampling technique used in this study was purposive sampling. The researcher took one class as the subject of this study. The sample was X IIS 3. The improvement of students’ score can be seen from the differences between pretest and posttest. The mean score of pretest was 65.88 and it improved to 81.48 in posttest. Moreover, the minimum mastery criterion of English lesson was 76 (seventy-six). Then the data of both test were analyzed by using T-test  and the result showed that the T-test value 8.39 was greater than T-table value 2.064. according to the criteria of the test the result showed that there was a significant improvement between students’ achievement in reading comprehension by using SQ3R technique before and after pretest and posttest were conducted. It meant that SQ3R technique was effective and applicable at the first grade of MAN Tolitoli to teach students’ reading comprehension. Keywords : Improving, Reading, SQ3R technique, Comprehension.
Road ahead for indian telemedicine providers and online mobile health apps – A proposed theoretical framework

Like the demonetisation of 2016 which paved way for widespread adoption of digital payments methods in India, COVID -19 has turned into a similar black swan moment for the online mobile health apps On one side, COVID-19 challenged the health infrastructure of India, and one the other side it opened opportunities for digital healthcare Many healthcare start-ups rose to the challenge and brought innovations in the digital healthcare space Taking leverage of being the second largest smartphone market in the world, these technology players brought online medical consultations within the reach of 1 5 billion people of India The next challenge for these service providers is to spread awareness of these online medical health apps among smartphone users of India and encourage them to adopt and try these online medical consultation services In this paper, an endeavour has been made to propose a research model towards the adoption of these OMHAs
Simest

Generally speaking, the computer is used as an afterthought, as a quick calculator with a large memory. Actually, the computer could change, fundamentally. the modeling and problem-solving process. This chapter notes that SIMEST requires minimal communication between the processors, allowing maximal benefit from computation on the simplest and cheapest of the current generations of parallel machines, namely, the transputer-based architectures. The SIMEST algorithm gives us a means of approaching a host of problems from the axiomatic level without the necessity of trying to come up with a closed-form expression for the likelihood. The very thrust of SIMEST is to approach the actual aggregation of random phenomena that make up reality in most real-world situations. The randomness itself would appear to be sufficient to protect the user against the butterfly effect of chaos. The chapter deals with a number of models that might be expected to be unstable, according to the ethos of chaos theory.
English language teachers’ literacy competence in literature

The study was carried out to describe English teachers’ literacy competence in English literature which had a very important role in the teaching of English language through literature to strengthen character educational values. The study applied descriptive statistics in the forms of frequencies “to describe and summarize the data.” In reporting the data, percentages were used (Leavy, 2017: 111). The respondents of the study consisted of fifteen English language teachers working for five senior high schools organizing special interest classes in languages. The techniques for collecting data were questionnaires and FGD (focus group discussion). The data were analyzed by using quantitative descriptive techniques consisting of central tendency and percentage analysis. The results of the study showed that all the teachers got between two and fourteen credits in literature from their undergraduate colleges. Their reading hours in literature after graduation did not show any regular base. This was particularly due to the fact that the portion of literature materials to be taught to the students was very limited. Thus, they thought that it was not necessitated to develop literary competence to support their teaching activities. However, all of them agreed that the teaching of literature to high school students was important and useful to improve their language skills as well as their character educational values.
2020 Transatlantic Security Jam: Resilience Going Forward

The 2020 Security Jam, which took place in May, focused on non-traditional challenges. Interestingly, the solutions to these non-traditional challenges are also non-traditional. Many different innovative solutions were put forward.[1]&nbsp;One of the
Single versus Multiple Annotation for Named Entity Recognition of Mutations

The focus of this paper is to address the knowledge acquisition bottleneck for Named Entity Recognition (NER) of mutations, by analysing different approaches to build manually-annotated data. We address first the impact of using a single annotator vs two annotators, in order to measure whether multiple annotators are required. Once we evaluate the performance loss when using a single annotator, we apply different methods to sample the training data for second annotation, aiming at improving the quality of the dataset without requiring a full pass. We use held-out double-annotated data to build two scenarios with different types of rankings: similarity-based and confidence based. We evaluate both approaches on: (i) their ability to identify training instances that are erroneous (cases where single-annotator labels differ from double-annotation after discussion), and (ii) on Mutation NER performance for state-of-the-art classifiers after integrating the fixes at different thresholds.
Towards an Ai-Based Advisor for Capturing Interpretive Trails and Supporting Geological Exploration Activities

Summary Many activities in the Oil &amp; Gas (O&amp;G) industry rely on expert interpretation over unstructured data and interpretation of elaborate geological concepts. Keeping track of the consumption and production of conceptual knowledge and data is crucial to structure such investigative processes. Nevertheless, capturing and structuring activities of this nature is a complex requirement if an advisor system is to be designed and implemented to support decision making in such domain. We propose a novel representation to keep track and model experts’ interaction with different systems, along with multimodal data and conceptual knowledge they consume and produce during interpretive activities. The proposed representational approach aims at supporting the design and implementation of intelligent advisor systems for knowledge-intensive processes, such as the ones observed in the multidisciplinary domain of O&amp;G.
ANTI RIGGING VOTING MACHINE

Now a days elections are notconducted by the usual fair processes through which people choose their Democratic Leader instead it becomes a war ground in most of the places in India. We can see that most parts which are affected with these problems are where the ruling political parties are unfairlycast fake votes and create violence using muscle power and influence. So we came up with a smart voting machine which helps us to conduct a proper and fair voting process and passively decreases the scope of violation taking place due to unfair Election process. Now a days every organisation has the biometric security or identification system. So we implement the biometric identification system along with the traditional EVM to make an advanced and highly secured voting system. It basically helps us to stop the fake voting and conduct a peaceful voting process.
Vulnerability Remediation in ICS Infrastructure Based on Source Code Analysis

The Industrial Control Systems (ICS) and its sub-processes, hardware and software, make possible the management and operation of industrial critical infrastructure and services such as: energy, water, defense, transportation. Nowadays, the biggest vendors on the market started developing for the ICS marketplace new systems with more power, control, stability, but these complex systems are susceptible to different threats such as: insider attack, third-party, technical or physical failure, external attacks. Therefore, is critical to protect ICS assets. Paying attention to the ISA/IEC 62443 standard, this paper is proposing methods for source code analysis using open source tools that can be used in development or testing phase by ICS professionals in order to detect new vulnerabilities and bugs (e.g. weak encryption, code disclosure, clear text passwords) using a vulnerability remediation management tool in order to have a complete view of new and existing security breaches. The purpose of this research paper is providing valuable information to ICS developers to increase security level in the production area with very little effort for the Internet exposed Programmable Logic Controllers (PLC).
The impact of COVID-19 on teaching in statistics and operations research in higher education

This work explores the adaptation of the learning process during the COVID-19 pandemic, while students and teachers kept confined at home and classes were online. For this task, a survey among teachers in Statistics and Operations Research departments at Spanish universities was conducted. 131 teachers from 25 universities answered a 28-item questionnaire, structured according to five thematic blocks. This survey finds a preference for synchronous learning, sometimes combined with asynchronous support. Their answers also show that in most cases the participants were satisfied with the fast technological adaptation to learning, although they point out the assessment as the main challenge, due to suspicion of unethical behaviors. © 2020 SEIO
TOUR VIRTUAL SEBAGAI SARANA PENGGANTI LIBURAN DI MASA PANDEMIC COVID 19

Due to the pandemic COVID-19, the government made a regulation called lockdown. This aims to decrease the spreading of COVID-19. Since lockdown had started, people felt so bored where stayed at home for a quiet long period. As a consequence, we realized that people need to go on holiday. We tried to solve the problem by making a virtual tour video. In this case, a virtual tour video is a video recording in which the content is about the tourist attraction that could be watched by viewers. The methods which relevant we used are interview and observation. After doing the survey, we found that the interviewers had pretty satisfied with the video. The implication of the next survey is to increase the virtual tour video’s quality.
Automatic Image Captioning Using Neural Networks

Automatically generating a natural language description of an image is a major challenging task in the field of artificial intelligence. Generating description of an image bring together the fields: Natural Language Processing and Computer Vision. There are two types of approaches i.e. top-down and bottom-up. For this paper, we approached top-down that starts from the image and converts it into the word. Image is passed to Convolutional Neural Network (CNN) encoder and the output from it is fed further to Recurrent Neural Network (RNN) decoder that generates meaningful captions. We generated the image description by passing the real time images from the camera of a smartphone as well as tested with the test images from the dataset. To evaluate the model performance, we used BLEU (Bilingual Evaluation Understudy) score and match predicted words to the original caption.
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model

A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their accuracy is limited. Recently, deep learning methods were proposed to predict stock return in terms of the multi-factor model. Although these methods perform quite well, they have significant disadvantages such as a lack of transparency and limitations in the interpretability of the prediction. It is thus difficult for institutional investors to use black-box-type machine learning techniques in actual investment practice because they should show accountability to their customers. Consequently, the solution we propose is based on LSTM with LRP. Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM. Then, we approximate and linearize the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent factor model. Finally, we perform an empirical analysis of the Japanese stock market and show that our recurrent model has better predictive capability than the traditional linear model and fully-connected deep learning methods.