Reviewerly/exHarmony huggingface dataset:
Sistem E-Document dan E-Meeting Sebagai Manajemen Pengetahuan untuk Menuju Tata Kelola Yang Baik Pada Universitas Pembangunan Jaya

The Knowledge in a study program or faculty at Higher Education is an asset that must be utilized by universities. Explicit knowledge can be in the form of meeting attendance, meeting schedules, minutes of meetings, teaching materials, documentation of the final session, letters (SK, ST, contract letters, etc.), certificates, reports, guidelines, manuals , memos, strategic plans, curriculum and so on. However, knowledge cannot be easily accessed by cross study programs or cross faculties at the college. Some study programs have done documentation so well that it has an impact on satisfying accreditation assessments. Study programs that have satisfactory accreditation can be an example for other study programs. A knowledge management system is needed to bridge the distance between study programs that have good governance and others. The results of the knowledge management system testing allow for the exchange of knowledge to become a means of improving the quality of governance both at the study program level and at the faculty level.
Goal Directed Design of Rewards and Training Features for Self-learning Agents in a Human-Autonomy-Teaming Environment

For architects of reinforcement learning (RL) agents in real-world applications, the design of a suitable training environment is challenging. Feature engineering and reward function design are mainly guided by human intuition and domain knowledge. We propose the application of a goal directed task analysis (GDTA) to structure knowledge about an application domain in order to guide the design of a RL agent embedded in a complex environment. Results from the task analysis can be leveraged for feature selection and reward function design. We showcase this approach in a human-autonomy-teaming application for military airborne operations.
The Implementation of the RAD model in the Development of Tender Selection Programs Using the AHP Method

The activity of selecting bidders is one of the most important activities carried out by companies to order goods or services for bidders, but at meetings of the company leadership it is difficult to select bidders to make business partners because there are only a few things that are not in accordance with who agree to end the shipment of goods, prices do not match and end the company that receives losses. That is caused by the lack of competence of selected tender partners. From the problems mentioned above, the tender selection process that has been conducted so far is still lacking and inaccurate. The purpose of this study is to create an application program using the Rapid Application Development Model (RAD Model) that can assist leaders in selecting business partners through a tender process by giving weights according to the criteria specified requirements. It is expected that the tender selection process is objective and accurate. Assessment method Decision making (SPK) Analytichal Hierarchy Process (AHP) method is one method in the process of resolution by completing each case by completing it first by using weighting criteria. In the end the selection application program can help lead in making objective, fast and accurate decisions.
Graph Generators: State of the Art and Open Challenges

The abundance of interconnected data has fueled the design and implementation of graph generators reproducing real-world linking properties, or gauging the effectiveness of graph algorithms, techniques and applications manipulating these data. We consider graph generation across multiple subfields, such as Semantic Web, graph databases, social networks, and community detection, along with general graphs. Despite the disparate requirements of modern graph generators throughout these communities, we analyze them under a common umbrella, reaching out the functionalities, the practical usage, and their supported operations. We argue that this classification is serving the need of providing scientists, researchers and practitioners with the right data generator at hand for their work. This survey provides a comprehensive overview of the state-of-the-art graph generators by focusing on those that are pertinent and suitable for several data-intensive tasks. Finally, we discuss open challenges and missing requirements of current graph generators along with their future extensions to new emerging fields.
TOPICS OF DISSERTATION RESEARCHES OF LIMITED DISTRIBUTION IN THE EARLY XXI CENTURY

The article is devoted to the results of the analysis of the situation on defense of dissertations marked «for official use» in the USSR and Russia. There were rules and regulations governing this process. Due to the numerous abuses associated with the reluctance of open publication of scientific works, the defense of dissertations without secret information, not posted on the Internet, has become impossible. Thus, the receipt of dissertations «DSP» and the authors’ papers into the library stocks has ceased.
2. Methodological approaches to modeling criminal trajectories

Criminal offenders compose a heterogeneous population. Criminal trajectory research aims to capture this heterogeneity in terms of the frequency or severity of offending. This chapter describes the concept a criminal trajectory and the statistical technique used to derive trajectories from longitudinal data. Both the semiparametric group-based trajectory modeling (SGBTM) and latent growth mixture modeling (GMM) approaches are described in nontechnical terms, and the differences between them are noted. Despite some similarities, these approaches are also distinguished from conventional growth curve modeling. Guidelines and factors to consider in building and testing trajectory models are discussed. Last, extensions of SGBTM and GMM are presented.
Conceptual systems align to aid concept learning

Our understanding of concepts can differ depending on the modality — such as vision, text or speech — through which we learn this concept. A recent study uses computational modelling to demonstrate how conceptual understanding aligns across modalities.
The Effect of Intelligence Numeric, Learning Discipline and Learning Motivation on Formative Test Results

The purpose of this study was to determine the direct effect and indirect influence of a variable. The research data was processed and analyzed using path analysis method. Conclusions are obtained (1) There is a significant direct effect of intelligence numeric on the results of formative tests; (2) There is a significant direct effect of learning discipline on the results of formative tests; (3) There is a significant direct effect of learning motivation on the results of formative tests; (4) There is a significant direct effect of intelligence numeric on learning motivation; (5) There is a significant direct influence of intelligence numericalon learning discipline; (6) There is a significant direct effect of learning discipline on learning motivation; (7) There is no significant indirect effect of intelligence numeric on motivation to learn through learning discipline; (8) There is no significant indirect effect of intelligence numeric on the results of formative tests through learning discipline; (9) There is no significant indirect effect of intelligence numeric on the results of formative tests through learning motivation; and (10) There is no significant indirect effect of intelligence numeric on the results of formative tests through learning discipline and learning motivation.
Can camp get you into a better secondary school? A field experiment of targeted instruction in Kenya

Access to quality secondary schooling can be life-changing for students in developing contexts. In Kenya, entrance to such schools was historically determined by performance on a high-stakes exam. Understandably then, preparation for this exam is a high priority for Kenyan families and educators. To increase the share of students entering these schools, some educational providers offer targeted instruction for students they believe have a chance of securing a spot. We use a randomized control trial to evaluate the impact of these “symposia” programs—week-long, sleep-away camps where eighth grade students receive a burst of academic instruction from teachers selected based on merit. While similar models have been tested in the U.S., less is known about this type of intervention in developing settings. We find these programs were not particularly effective for the average nominated student relative to a typical week of school. However, we find large, positive effects among students attending schools from which few students are nominated for symposia. We provide suggestive evidence that this was because students from lowrepresentation schools had less pre-camp practice test resources outside of school. The results have implications for program design and the growing literature on the effectiveness of appropriately targeted individualized instruction.
Continual Local Replacement for Few-shot Image Recognition

The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few of labeled data could not accurately represent the true data distribution and thus it's hard to learn a good decision function for classification. In this work, we use a sophisticated network architecture to learn better feature representation and focus on the second issue. A novel continual local replacement strategy is proposed to address the data deficiency problem. It takes advantage of the content in unlabeled images to continually enhance labeled ones. Specifically, a pseudo labeling method is adopted to constantly select semantically similar images on the fly. Original labeled images will be locally replaced by the selected images for the next epoch training. In this way, the model can directly learn new semantic information from unlabeled images and the capacity of supervised signals in the embedding space can be significantly enlarged. This allows the model to improve generalization and learn a better decision boundary for classification. Our method is conceptually simple and easy to implement. Extensive experiments demonstrate that it can achieve state-of-the-art results on various few-shot image recognition benchmarks.
Crowd counting by the dual‐branch scale‐aware network with ranking loss constraints

Image crowd counting is a challenging problem. This study proposes a new deep learning method that estimates crowd counting for the congested scene. The proposed network is composed of two major components: the first ten layers of VGG16 are used as the backbone network, and a dual‐branch (named as Branch_S and Branch_D) network is proposed to be the second part of the network. Branch_S extracts low‐level information (head blob) through a shallow fully convolutional network and Branch_D uses a deep fully convolutional network to extract high‐level context features (faces and body). Features learnt from the two different branches can handle the problem of scale variation due to perspective effects and image size differences. Features of different scales extracted from the two branches are fused to generate predicted density map. On the basis of the fact that an original graph must contain more or equal number of persons than any of its sub‐images, a ranking loss function utilising the constraint relationship inside an image is proposed. Moreover, the ranking loss is combined with Euclidean loss as the final loss function. Our approach is evaluated on three benchmark datasets, and better results are achieved compared with the state‐of‐the‐art works.
The LIG system for the English-Czech Text Translation Task of IWSLT 2019

In this paper, we present our submission for the English to Czech Text Translation Task of IWSLT 2019. Our system aims to study how pre-trained language models, used as input embeddings, can improve a specialized machine translation system trained on few data. Therefore, we implemented a Transformer-based encoder-decoder neural system which is able to use the output of a pre-trained language model as input embeddings, and we compared its performance under three configurations: 1) without any pre-trained language model (constrained), 2) using a language model trained on the monolingual parts of the allowed English-Czech data (constrained), and 3) using a language model trained on a large quantity of external monolingual data (unconstrained). We used BERT as external pre-trained language model (configuration 3), and BERT architecture for training our own language model (configuration 2). Regarding the training data, we trained our MT system on a small quantity of parallel text: one set only consists of the provided MuST-C corpus, and the other set consists of the MuST-C corpus and the News Commentary corpus from WMT. We observed that using the external pre-trained BERT improves the scores of our system by +0.8 to +1.5 of BLEU on our development set, and +0.97 to +1.94 of BLEU on the test set. However, using our own language model trained only on the allowed parallel data seems to improve the machine translation performances only when the system is trained on the smallest dataset.
Incorporating Social Media in Blended Learning Courses at Higher Learning Institutions

Social media in teaching and learning has become the latest trend at higher institutions. The paper shares the reasons of incorporating social media in the blended learning courses at Universiti Teknologi MARA, Sarawak Branch. Five social media platforms were incorporated in teaching and learning. The platforms were Facebook, Twitter, Instagram, Pinterest and WhatsApp. The social media platforms were fully used during the Week Without Wall which was implemented by university for one to two weeks in every semester. A survey method was used to collect the data. Open ended questions were used to scrutinize the reasons lecturers incorporated social media in the blended learning courses. Google form was used to collect the survey data. The survey form was disseminated via social media platforms. 74 lecturers responded to the online survey. The study found that incorporating social media in blended learning courses were to reduce the generation gap, allowed flexibility, extended face-toface teaching, ease of use, convenient, easy to apply different teaching approaches, allowed linkage, allowed self-pace learning, enhanced teaching and learning process, monitoring and evaluation, sharing, fast response and learning community.
Small Memory Robust Simulation of Client-Server Interactive Protocols over Oblivious Noisy Channels

We revisit the problem of low-memory robust simulation of interactive protocols over noisy channels. Haeupler [FOCS 2014] considered robust simulation of two-party interactive protocols over oblivious, as well as adaptive, noisy channels. Since the simulation does not need to have fixed communication pattern, the achieved communication rates can circumvent the lower bound proved by Kol and Raz [STOC 2013]. However, a drawback of this approach is that each party needs to remember the whole history of the simulated transcript. In a subsequent manuscript, Haeupler and Resch considered low-memory simulation. The idea was to view the original protocol as a computational DAG and only the identities of the nodes are saved (as opposed to the whole transcript history) for backtracking to reduce memory usage. In this paper, we consider low-memory robust simulation of more general client-server interactive protocols, in which a leader communicates with other members/servers, who do not communicate among themselves; this setting can be applied to information-theoretic multi-server Private Information Retrieval (PIR) schemes. We propose an information-theoretic technique that converts any correct PIR protocol that assumes reliable channels, into a protocol which is both correct and private in the presence of a noisy channel while keeping the space complexity to a minimum. Despite the huge attention that PIR protocols have received in the literature, the existing works assume that the parties communicate using noiseless channels.
Research and Analysis on the Construction of College Teaching Material Management Information System in the Age of “Internet +”

As one of the basic components of college teaching management, the management level of school teaching materials is closely related to the overall management efficiency and quality. However, the management of school teaching materials is different from other management tasks. The management of teaching materials is characterized by high complexity, low information content, and low level of intelligence. It is not suitable for the modern educational environment and development speed. Therefore, it is particularly important to establish a teaching material management system that is compatible with the actual situation of colleges and universities.The article mainly focuses on the in-depth analysis of the status quo of information management of college teaching materials, and attempts to construct teaching material management information system from the selection of teaching materials, the ordering of teaching materials, the distribution of teaching materials, the storage of teaching materials, and the delivery of teaching materials. In order to achieve the complete release of traditional manual management, fundamentally promote the improvement of management efficiency.
Ring-LWE on 8-Bit AVR Embedded Processor

Fast implementation of Ring-LWE is a challenge for the low-end embedded processors. One of the most expensive operation for Ring-LWE is Number Theoretic Transform (NTT). Many works have investigated the optimized implementation for the NTT operation. In this paper, we further optimized the NTT operation on the low-end 8-bit AVR microcontrollers. We focused on the optimized and secure polynomial multiplication to ensure countermeasures against timing attacks and high performance. In particular, we propose the combined Look-Up Table (LUT) based fast reduction techniques in regular fashion. With the optimization techniques, the proposed NTT implementation enhances the performance by 14.6% than previous best results. Finally, proposed NTT implementations are applied to the Ring-LWE key scheduling and encryption operations, which require the only 1,325,171 and 1,430,601 clock cycles for 256-bit security levels.
A Parallel Approach to Advantage Actor Critic in Deep Reinforcement Learning

Deep Reinforcement learning (DRL) algorithms recently still take a long time to train models in many applications. Parallelization has the potential to improve the efficiency of DRL algorithms. In this paper, we propose an parallel approach (ParaA2C) for the popular Actor-Critic (AC) algorithms in DRL, to accelerate the training process. Our work considers the parallelization of the basic advantage actor critic (Serial-A2C) in AC algorithms. Specifically, we use multiple actor-learners to mitigate the strong correlation of data and the instability of updating, and finally reduce the training time. Note that we assign each actor-learner MPI process to a CPU core, in order to prevent resource contention between MPI processes, and make our ParaA2C approach more scalable. We demonstrate the effectiveness of ParaA2C by performing on Arcade Learning Environment (ALE) platform. Notably, our ParaA2C approach takes less than 10 min to train in some commonly used Atari games when using 512 CPU cores.
Pengaruh Lingkungan Belajar Terhadap Hasil Belajar Mahasiswa Dalam Matakuliah Akuntansi Pemerintah

One of the functions of face-to-face lectures in class is as a transfer of knowledge from lecturer to students. The student learning environment is an external factor that influences student learning outcomes. Government Accounting Course I is a continuation of the Public Sector Accounting course. The purpose of this course is to provide knowledge to students of the Diploma 4 Public Financial Accounting Study Program that focuses on the process of financial recording and reporting to the government. This study was conducted to determine whether there are differences in learning outcomes in Government Accounting courses I between classes A and B. The test instrument used is the non-parametric Mann-Whitney test. The population in this study was 52 students in the fifth semester of Diploma 4 Public Financial Accounting Study Program. The results of this study showed that the learning outcomes or students' abilities in Government Accounting courses I between classes A and class B did not differ. Differences in the learning environment in each class do not affect student learning outcomes.
Waters to buy lab automation firm

The scientific instrument maker Waters will acquire Andrew Alliance, a developer of lab automation technology, including software and robotics. Andrew Alliance has about 40 employees in Switzerland, France, and the US. One of its first products was a pipetting robot called Andrew. Waters CEO Chris O’Connell says the acquisition will benefit customers in pharmaceutical, life sciences, and materials science markets.
Reasoning Continuously: A Formal Construction of Continuous Proofs

Abstract We begin with the idea that lines of reasoning are continuous mental processes and develop a notion of continuity in proof. This requires abstracting the notion of a proof as a set of sentences ordered by provability. We can then distinguish between discrete steps of a proof and possibly continuous stages , defining indexing functions to pick these out. Proof stages can be associated with the application of continuously variable rules, connecting continuity in lines of reasoning with continuously variable reasons. Some examples of continuous proofs are provided. We conclude by presenting some fundamental facts about continuous proofs, analogous to continuous structural rules and composition. We take this to be a development on its own, as well as lending support to non-finitistic constructionism.
The Impact of More, Better, Faster, Cheaper: How the PCC Influenced Everything That Followed

The beginnings and the early years of the Program for Cooperative Cataloging (PCC) had a profound impact on the approach taken to cataloging in North America and around the world. The commitment to standards, cooperation, and expanded access also had an impact that went far beyond cataloging operations. One library dean traces the evolution of her vision and contemplates how the PCC’s principles have shaped her entire career and have affected the libraries in which she has worked.
Exploring Music and Technology through Code and Chords (resource exchange)

Abstract Code and Chords believes that showing students ways in which technology, music, and visual art can interact will increase their motivation to explore whichever of those fields they are less familiar with. This is a large part of why the Code and Chords team from the University of St. Thomas has created software that uses real time audio inputs to create a visual representation of a song’s affect. These visuals can be customized through coding to present different displays. The team, excited to bring Code and Chords software to K-12 education, created a workshop designed to motivate students ages 7-17 to explore technology, music, and visual art. The two-hour workshop was broken down into 5 larger sections: 1.) Introduction to Computer Programming, 2.) Coding Shapes, 3.) Coding Colors, 4.) Understanding the Emotion of Music, and 5.) Combining Coding and Music through Code and Chords. This workshop was tested at various informal educational settings. By the end of the workshop, students were able to code shapes and colors, describe technical and emotional aspects of music, and alter the Code and Chords software to better display a song’s affect. The resources provided in the resource exchange will equip educators with activities from the workshop that help students engage with computer engineering, as well as how those activities follow the engineering design process.