Reviewerly/exHarmony huggingface dataset:
Iraq’s SSR in State-building Process

미군 철수 이후, 이라크는 이라크 전쟁 직후 활동하였던 이라크 알카에다를 전신으로 하는 IS의 영향력으로 국가기능을 제대로 수행할 수 없었고, 2021년 다섯 번째 서방식 민주주의 선거를 완료하였으나 총리 관저가 공격당하는 등의 사회 불안정이 지속되고 있다. 이라크 전쟁 종결 후 약 20년이 되어가는 이 시점에서 이라크의 국가건설은 완료되었다고 단언하기 어렵다. 본 연구는 국가건설 과정에서 안보의 달성이 우선순위가 되어야 하며 그 세부내용으로 안보부문개혁(Security Sector Reform)의 제대로 된 시행이 중요함을 강조하는데 목적이 있다. 국가건설을 위해 다양한 요소가 필요하지만, 안보가 달성되지 않은 상태에서의 재건은 재정과 노력의 낭비를 초래한다. 이를 방지하기 위해 사회적 안정과 재건에 대한 국민적 신뢰를 확보하는 것이 중요하며 SSR은 이를 가능하게 한다. 이에 본고는 2003년 이후 이라크의 SSR 시행 과정을 DDR과 안보기관 개혁으로 구분하여 살핀다. 세부적으로는, 정규군 및 민병대로 나누어 DDR 시행 과정을 확인하고 국방부 및 군, 내무부 및 경찰, 사법 및 교정분야의 사례를 통해 안보기관 개혁과정을 고찰한다. 이를 통해 SSR의 미비가 국가건설에 큰 저해를 불러올 수 있음을 강조한다. After the withdrawal of U.S. troops, Iraq was unable to function properly due to the influence of ISIS, which was based on al-Qaeda in Iraq, and social instability continued, in spite of holding relatively successful the fifth Western democratic election in 2021. At this point, about 20 years after the end of the Iraq War, it is difficult to assert that state-building process in Iraq has been completed. The purpose of this study is to emphasize the importance of reestablishing security first and particularly implementing ‘Security Sector Reform’ in a proper way in state-building process. Several factors could be urgent in the process of state-building. However, without establishing security as higher priority, all efforts and funds are bound to be wasted. To prevent this, it is very important to give public trust of social stability and s tate-building tasks. And SSR makes this possible. Therefore, this study examines the process of implementing SSR in Iraq since 2003 by dividing it into DDR and security institution reform. In detail, DDR implementation is confirmed through cases as regular forces and militias, and security institution reform is confirmed through cases as the Ministry of Defense and the military, the Ministry of Interior and police, justice and correction. A number of deficiencies are identified between each implementation, which confirms how each factor correlates within the large framework of state-building process and how it has a ffected I raqi society.
Hypergraph Transformers for EHR-based Clinical Predictions.

Electronic health records (EHR) data contain rich information about patients' health conditions including diagnosis, procedures, medications and etc., which have been widely used to facilitate digital medicine. Despite its importance, it is often non-trivial to learn useful representations for patients' visits that support downstream clinical predictions, as each visit contains massive and diverse medical codes. As a result, the complex interactions among medical codes are often not captured, which leads to substandard predictions. To better model these complex relations, we leverage hypergraphs, which go beyond pairwise relations to jointly learn the representations for visits and medical codes. We also propose to use the self-attention mechanism to automatically identify the most relevant medical codes for each visit based on the downstream clinical predictions with better generalization power. Experiments on two EHR datasets show that our proposed method not only yields superior performance, but also provides reasonable insights towards the target tasks.
Assistive Control System of Appliances through Gaze Point Eye Tracker with Integrated Graphical User Interface for Bedridden Person

Persons with Disabilities (PWD), specifically, the bedridden person incapable of controlling certain appliances or devices to continue with their daily life which they struggle with. The need for assistive technology arises nowadays, for which artificial intelligence has become valuable and prominent. The implementation of different pattern recognition algorithms is applied to various applications. This paper discusses using the Tobii Eye tracker algorithm to control the appliances with an Arduino with an infrared emitter and relays. In this paper, the measurement of the system response time as a variable leads the parameter of such different calculations. The sample of data was collected at varying head angles of inclination with also varying distances relative to the monitor. The P-values gathered were 0.11947, 0.287368, and 0.120036 at distances of 30 cm, 40cm, and 60 cm, respectively. From the data, it is found that at a level of significance of 0.05, which indicates the system is accurate, leading to not rejecting the null hypothesis that there is a variance in the mean of samples.
MetaBERT: Collaborative Meta-Learning for Accelerating BERT Inference

Early exit methods are used to accelerate inference in pre-trained language models and maintain competitive performance on resource-constrained devices. However, existing methods for training early exit classifiers suffer from the problem of poor classifier representations in different layers, leading to difficulties in adapting to diverse natural language processing tasks. To address this issue, we propose MetaBERT: collaborative Meta-learning for accelerating BERT inference. The main goal of MetaBERT is to train early exit classifiers through collaborative meta-learning, in which case, few gradient updates can be quickly adapted to new tasks. Moreover, this novel meta-training approach produces good generalization performance, thus achieving an effective balance between the inference result and efficiency. Extensive experimental results show that our approach outperforms previous training methods by a large margin, and achieves state-of-the-art results compared to other competitive models.
Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings

The present study aimed to address the issue of imbalanced data in classification tasks and evaluated the suitability of SMOTE, ADASYN, and GAN techniques in generating synthetic data to address the class imbalance and improve the performance of classification models in low-resource settings. The study employed the Generalised Linear Model (GLM) algorithm for class balancing experiments and the Random Forest (RF) algorithm for low-resource setting experiments to assess model performance under varying training data. The recall metric was the primary evaluation metric for all classification models. The results of the class balancing experiments showed that the GLM model trained on GAN-balanced data achieved the highest recall value. Similarly, in low-resource experiments, models trained on data enhanced with GAN-synthesized data exhibited better recall values than original data. These findings demonstrate the potential of GAN-generated synthetic data for addressing the challenge of imbalanced data in classification tasks and improving model performance in low-resource settings.
A Complete Overview of Cloud Computing

The Coronavirus pandemic has had a variety of effects on the legal system, its businesses, and activities, including the closing of law firms and courts, a decline in services, a lack of clients, the withdrawal of client schedules and settlements, a decline in the workforce's accessibility because of the virus or social exclusion, and difficulties holding clear appointments and services. As a result, the law has undergone a comprehensive transformation to include technology and keep up with the rapidly evolving digital world. This essay emphasizes the advantages and risks of cloud computing as it relates to the digitization of the legal profession. Additionally, Case Lines, a cloud-based application that is increasingly used for conducting court proceedings, will be covered.[1],[2]
A multilingual translator to SQL with database schema pruning to improve self-attention

Long sequences of text are challenging in the context of transformers, due to quadratic memory increase in the self-attention mechanism. As this issue directly affects the translation from natural language to SQL queries (as techniques usually take as input a concatenated text with the question and the database schema), we present techniques that allow long text sequences to be handled by transformers with up to 512 input tokens. We propose a training process with database schema pruning (removal of tables and columns names that are useless for the query of interest). In addition, we used a multilingual approach with the mT5-large model fine-tuned with a data-augmented Spider dataset in four languages simultaneously: English, Portuguese, Spanish, and French. Our proposed technique used the Spider dataset and increased the exact set match accuracy results from 0.718 to 0.736 in a validation dataset (Dev). Source code, evaluations, and checkpoints are available at: \underline{https://github.com/C4AI/gap-text2sql}.
Coordination and Awareness Mechanisms to Support Collaboration in Project-based Software Engineering Courses

In project-based software engineering courses, the collaboration among participants is usually the cornerstone that makes these experiences successful and allows reaching the courses’ instructional goals. Prescribing a collaboration process for these courses is not appropriate, since their workflows are ad hoc framed; i.e., most of their activities are defined on-the-fly by the participants, according to their current context and needs. Therefore, only coordination and awareness mechanisms can be defined at the course design time, in order to support and promote collaboration among participants during these courses. This article presents a qualitative study that identifies and relates these coordination and awareness mechanisms, and embeds them in a set of software ceremonies. Course instructors and instructional designers can take advantage of these results, not only to identify design decision points, but also coordination and awareness mechanisms to promote collaboration in this type of courses.
A Normalizing Flow-based Unsupervised Anomaly Detection Approach

Currently, unsupervised industrial anomaly detection and anomaly localization based on deep learning have achieved great success. The most commonly used dataset for industrial anomaly detection and anomaly localization is MV-TAD, and the most commonly used evaluation metric is AUROC. Most research methods are based on the above datasets and experiment evaluation metrics. Although the most advanced method has nearly 100% AUROC index values in the above dataset, the results are still not ideal, as seen by observing the anomaly segmentation maps after the experiments. Therefore, in this paper, we use a new metric, F1-measure, to evaluate the experimental performance of industrial anomaly detection and anomaly localization models. Compared with the AUROC metric, the F1-measure ensures complete and accurate detection by reconciling Precision and Recall. We use ResNet34 and WideRes-Net101 pre-trained encoders based on the current state-of-the-art normalized flow-based generative model anomaly detection method to train and test on the dataset, and achieve good experimental performance. In addition to MVTAD, we extended the dataset to DAGM 2007, BTAD, trained and tested them. Furthermore, we use the new evaluation metric, F1-measure, to evaluate the experimental results.
A multi-microcontroller-based hardware for deploying Tiny machine learning model

&lt;span lang="EN-US"&gt;The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform.&lt;/span&gt;
Life Road and Way of Life

The analysis of empirical and theoretical works has shown that when specifying particular personality traits determining the choice of way of life personality determinants are not considered as a system of interrelations of motivation and axiological spheres of personality, quality of actual life, assessment and relations of life spent. The author views way of life as perception of one’s own future having its goal, as well as the way to achieve it both in global and local aspects of a material, social, and spiritual life of personality. The empirical experience of using “Doroga zhizni” (Life Road) and “Tri tseli” (Three Goals) guided imagery techniques to visualize local goals and strategies, and “Gora lichnostnogo rosta” (Personal Growth Path) guided imagery technique and “LOCK” inventory to visualize global goals of achievement and success has shown that there are differences in the degree of personality traits evidence in people with different choice of way of life depending on dominant material, social, or spiritual identity.
A Fair Share of Ice Cream – A Cooperative Game

In this article, we consider a solution to the problem of fair distribution of ice cream. This task is a canonical problem discussed in the theory of cooperative games. Although the above-described problem is well known, we propose the simplest approach to its solution. We also compare the proposed solution with the solutions based on the nucleolus concept and the constrained minimization of the Euclidean metric.
"Advanced Lecture of Digital Fabrication" in the graduate school of Mechanical Engineering

Digital machine tools such as 3D printers and laser cutters are spreading to manufacturing sites and educational sites. "Advanced Lecture of Digital Fabrication" was opened in the graduated school of mechanical engineering. After students reviewed 3D CAD, they learned how to use a 3D printer and a laser cutter. As a personal assignment work, they made a drink holder and a smartphone stand. As a group assignment work, they made a toolbox and machine tools. They ware able to tackle challenges using mechanical principles such as screws and linkage mechanism. The created digital data was released in Fabble (collections of manufacturing recipes). Such practices are also examples of STEAM education.Digital machine tools such as 3D printers and laser cutters are spreading to manufacturing sites and educational sites. "Advanced Lecture of Digital Fabrication" was opened in the graduated school of mechanical engineering. After students reviewed 3D CAD, they learned how to use a 3D printer and a laser cutter. As a personal assignment work, they made a drink holder and a smartphone stand. As a group assignment work, they made a toolbox and machine tools. They ware able to tackle challenges using mechanical principles such as screws and linkage mechanism. The created digital data was released in Fabble (collections of manufacturing recipes). Such practices are also examples of STEAM education.
A practical case report of teamwork education aimed at developing people who can take advantage of deep-level diversity in collaborative designed teams

Diversity is emphasized in terms of responding to change and creating innovation. However, if we only expect the positive aspects of diversity and do not deal with the negative aspects, we would be unable to utilize diversity fully. Therefore, we focused on cognitive style as a fundamental element that creates deep-level diversity. Since 2012, the author and other faculty members have developed an educational program that uses Jungian psychological type theory to understand the differences in cognitive styles of oneself and others and to use it in collaboration. In 2018, it was introduced as a compulsory "Teamwork Exercise" subject for first-year students in the Faculty of Creative Engineering of Kagawa University. The characteristics of the subject are as follows: "Development of metacognition to self-understanding, understanding of others, and tolerance," "Low-end using way of Jungian type theory," and "Thorough observance of ethics in the use of type theory." Similar teamwork education has not been seen in previous studies. This report reported the overview (e.g., development background, characteristics, curriculum, implementation results) as of 2021, which has been improved since its introduction, and the verification results of educational effects (e.g., promotion of self-understanding, promotion of understanding of others, improvement of tolerance).
A Practical-Education Trial Combining Quality Control Techniques and Examination Questions for a Small and Medium Enterprise Management Consultant

The purpose of this research is to develop an educational method to enable students in higher education to acquire practical problem-solving skills. In an attempt to do so, "Concept development theory", which is offered as a compulsory subject of the course, has been designed and its effectiveness is being discussed in this report. First, students learn seven quality control (QC) tools and new seven tools for QC mainly used in the manufacturing industry for the application to actual social or enterprise issues. Subsequently, the students are tasked to work on the past business cases described in the national examination of the qualification for a small and medium enterprise (SME) management consultant. The cases are prepared by the Japanese government to assess the consulting capability of the applicants for the qualification and are assumed as quasi-real business situations, which they would face in the real world. The students read these cases and answer questions that are prepared especially for this class. With these case studies, they are expected to find and resolve issues, which leads to their practical skill acquisition. Although this approach is still in its infancy, the reaction of students has been very positive and is being presented as a case study.
児童・生徒を対象にしたものづくり教育

An open lecture was held at the Tokyo Metropolitan College of Industrial Technology for 4th grade elementary school students and above and junior high school students to learn English while making things. In this lecture, students first learned about the mechanism of flying objects and further deepened their knowledge by actually experiencing flying objects. Next, the students made rubber-powered airplanes using teaching materials written in both Japanese and English. They also learned the English names of airplane parts, English expressions used in the manufacturing process, and English grammar. After the public class, a questionnaire was conducted among the students and children who took part in the course. As a result, from the efforts of this open lecture, it was confirmed that the teaching materials created in this open lecture were easy to understand for students and children.
シングルボードコンピュータを用いた機械工学プロジェクト授業への実践

In the Department of Mechanical Engineering of Kanagawa Institute of Technology, first-year students take a class called "Mechanical Engineering Project I" which aims to develop an interest in, awareness of, and understanding of the importance of mechanical engineering. By the way, many of the products around us are composed of mechatronics, which is a fusion of mechanical engineering and electronic engineering. We believe that it is important to be exposed to and understand the fundamentals of mechatronics technology. Therefore, we launched and practiced a new theme of "moving things by programming" and "learning the basics of dynamics of machinery" using a single board computer in the class.
製造現場で使用される「からくり」装置の体験展示

Japan's unique "Clockwork" developed in the Edo period. The mechanism, which uses manual power, mainspring, gravity, etc., and does not use electric power or fossil energy, is very important in building a production line with SDGs. In addition, devising and practicing "Clockwork" is an important means of human resource development. At the Toyota Commemorative Museum of Industry and Technology, there is a hands-on exhibition that uses the inner race and cage assembly equipment that is actually used by Toyota Motor Corporation, manual work without using a "Clockwork" device, and easy assembly with a "Clockwork" device. As a result, the exhibition aimed at a wide range of age groups to gain new awareness of not only the importance of "Clockwork", but also that "Clockwork" devices can replace work based on intuition and tricks by combining simple movements. Not only that, we succeeded in providing an opportunity to feel more familiar with machinery and manufacturing by using simple equipment.
Green-Inclusive-Finance: The Case of Selected MENA and SSA Countries

Eradicating extreme poverty remains one of the most significant and challenging Sustainable Development Goals (SDGs) in the Middle East and North African (MENA) and Sub-Saharan African (SSA) region. MENA ranks third among developing regions for extreme poverty and fell short of halving extreme poverty by 2015 – the target established by the United Nations’ Millennium Development Goals, the precursor to the SDGs. Green-Inclusive-Finance (GIF) can offer a wide range of financial tools that can help low-income people reduce their vulnerabilities to climate change. Additionally, SSA continues to be plagued by extreme poverty and is estimated to persist through 2030 if no major actions are taken to address the issue. GIF proposes a potentially long-term and sustainable tools for eradicating extreme poverty in these two regions. Using System General Method of Moments approach estimation methodology on annual data 73 developed and developing countries including 12 MENA and 45 SSA countries, the study estimates the impact of green finance on the eradication of extreme poverty by 2030, the first goal of the Sustainable Development Goals (SDGs). Using green bond issuance as a measure of green finance, the results of the study is expected to indicate that the penetration of green finance measures have a positive, statistically significant impact on reducing extreme poverty for the full sample as well as the MENA region. Policy considerations can be directed towards developing and promoting the infrastructure needed for the widespread delivery and usage of green inclusive financial tools, especially for the MENA and SSA countries lagging the extreme poverty reduction target.
Composing Music by Machine Using Particle Swarm Optimization

The paper proposes a methodology based on Particle Swarm Optimization (PSO) to generate musical compositions. It uses the Indian percussion instrument tabla for the purpose. The strokes, known as bōls, are used to create random positions of particles, which are taken as compositions. The velocity of the particle is determined using a custom-made formula. The evolution of the movement of particles is guided through the PSO process driven by a fitness function. Extensive experiments were performed with many particles. A human test involving five experts was undertaken to validate the quality of compositions generated.