Reviewerly/exHarmony huggingface dataset:
PENGARUH PELATIHAN, MOTIVASI DAN LINGKUNGAN KERJA TERHADAP PRESTASI KERJA PEGAWAI DINAS KEARSIPAN DAN PERPUSTAKAAN KABUPATEN PASAMAN

Abstract: This study aims to examine the effect of training, motivation and work environment on employee performance in the Archives and Pasaman District Library. The first hypothesis tested the effect of training on employee performance in the Archives and Pasaman District Library Office. The second hypothesis examines the effect of motivation on the achievements of the Pasaman District Archives and Library employees. The third hypothesis examines the influence of the work environment on employee performance in the Archives and Library of Pasaman Regency. The fourth hypothesis examines the effect of training, motivation and work environment on employee achievement of the Office of Archives and Library of Pasaman Regency together. The population in this study were employees of the Archives and Pasaman District Library Office. Samples This study uses a saturated sampling technique. Research data were collected by the method and analyzed by multiple linear regression. The results of the first hypothesis research show that a significant value of 0.154&gt; alpha 0.05 so that training does not have an influence on employee performance in the Archives and Library of Pasaman Regency. The results of the second hypothesis show that a significant value of 0.002
PREPARATION OF UNDERGRADUATES FOR THE FORMATION OF STUDENT TEAMS FOR PROJECT ACTIVITIES

Introduction. The problem of creating pedagogical conditions for the formation of a team building system is one of the relevant areas in modern education. This issue is especially relevant in modern socio-economic conditions that require teamwork to create new technologies. Materials and methods. As the main methods, we used the analysis of sources on the studied problem, including normative documents, scientific articles on the research topic, as well as generalization and systematization of the data obtained. An expert assessment of the ability of graduate students to organize and manage teams was carried out. Results . Graduate students in the learning process when performing practice-oriented tasks performed various team roles. In the process of practical training in an educational organization, students created teams of students for project activities. As a result of mathematical and statistical processing of the obtained results of the expert assessment according to the criteria of team building using the Chi-square at p &lt;0.01 and p &lt;0.05, the following differences were revealed. Statistically significant differences were identified by the criteria of the ability to distribute social roles, powers between team members and the ability to ensure team cohesion. Only satisfaction from group membership and participation in the achievement of a common goal was not defined as significantly different. The latter result means that a more significant sample of students is needed in a further study, or an adjustment of the process of teaching graduate students to the principles of team building in matters of joint research is required. Discussion and Conclusions . The reliability of the effectiveness of the implementation of vocational training in the system of practice-oriented tasks for teaching students the principles of team building has been established. As a result, students of pedagogical magistracy formed the skills of team building and mastery of the technology of forming teams of students for project activities.
Formation of aesthetic culture of a future music teacher as a psychological and pedagogical matter

The paper considers the process of formation of aesthetic culture of a future music teacher in the course of professional training. The author studies the subject matter of such concepts as culture, aesthetic culture and personal culture, and defines the structure, components and essence of the aesthetic culture formation process. The paper substantiates the role of having aesthetic and artistic values as they are one of the components of an ideal model of a personality with mature aesthetic culture. Defining the concept of culture, the author states that there are interrelated factors and results of human activities: material and spiritual ones. Material culture comprises the whole realm of material and practical existence and development of people, the whole sphere of their lives, while spiritual culture comprises values of collective consciousness realm. Aesthetic culture is one of the components of spiritual culture, and spiritual realm is related to material spheres of life, since aesthetic attainments and concepts are generalized and related to different areas of human activities. The paper highlights that culture marks the modern historical level of development of society, creative energies and abilities of human, and is represented by types and forms of organization of lives and activities of people, and this gives rise to a search for modern society needs in formation of a well-rounded educated teacher, capable of developing successfully and nurturing aesthetically the culture of youth. Personal aesthetic culture is defined as a result of a person’s aesthetic education aimed at development of aesthetic orientations and a system of values in the realm of feelings, needs, interests, ideals and preferences, standards for judgments and principles of aesthetic attitude towards reality. This is a process of consistent and deliberate impact on an individual with a purpose of achieving harmonious, well-rounded development, formation of aesthetic attitude towards life, ability to enjoy the beauty of the surrounding reality, works of art, and actively create this beauty. Indicators of mature aesthetic culture of a future music teacher include the ability to see and identify aesthetically expressive elements of the whole and the whole itself in the perception of musical material, ability to express own aesthetic impressions, ability to systematize acquired aesthetic impressions, ability to use aesthetic knowledge and skills in creative activities and in everyday life. It is noted that artistic experience has significant value for a future music teacher, which is gained through the amount of apprehended literary, theatrical, artistic and musical material, scope of common cultural and artistic competence, practical experience in music-making, consideration of personal qualities of students and their psychological traits.
Development of Arabic Language Lifelong Learning (A3L) Tutorial Platform Using WhatsApp Mobile Application

This study examined the use of WhatsApp mobile application as a tutorial platform for Arabic language lifelong learning (A3L) at the Universiti Sains Islam Malaysia (USIM).An exploratory research design was adopted and the purposive sampling technique was used in selecting participants for the study.Specifically, the study comprises 40 USIM A3L learners.The WhatsApp mobile application was used as a tutorial delivery tool for the learners.Two research questions were raised and analyzed qualitatively.The result of the analysis indicated that WhatsApp mobile application could be used as tutorial delivery tool for A3L following the prescribed steps and procedures.Twelve Arabic lessons were learned which include contents for learners, quizzes, feedbacks and learner supports.Based on the findings, it was recommended that researchers need to extensively respond to the growing need of a body of research in this area, especially building models and strategies that work for the use of social media for the improvement of A3L delivery.Additionally, the A3L facilitators should be equipped with the skills of using mobile technologies, particularly the social media for A3L enhancement.
G2MF-WA: Geometric multi-model fitting with weakly annotated data

Abstract In this paper we address the problem of geometric multi-model fitting using a few weakly annotated data points, which has been little studied so far. In weak annotating (WA), most manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. SuchWA data can naturally arise through interaction in various tasks. For example, in the case of homography estimation, one can easily annotate points on the same plane or object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of WA data to boost multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that WA data annotated with the same weak label has a high probability of belonging to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lying on or near the latent model are likely to be associated and further form a subset or cluster for effective proposal generation. Having generated proposals, a-expansion is used for labeling, and our method in return updates the proposals. This procedure works in an iterative way. Extensive experiments validate our method and show that it produces noticeably better results than state-of-the-art techniques in most cases.
ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection

Scene text detection has witnessed rapid development in recent years. However, there still exists two main challenges: 1) many methods suffer from false positives in their text representations; 2) the large scale variance of scene texts makes it hard for network to learn samples. In this paper, we propose the ContourNet, which effectively handles these two problems taking a further step toward accurate arbitrary-shaped text detection. At first, a scale-insensitive Adaptive Region Proposal Network (Adaptive-RPN) is proposed to generate text proposals by only focusing on the Intersection over Union (IoU) values between predicted and ground-truth bounding boxes. Then a novel Local Orthogonal Texture-aware Module (LOTM) models the local texture information of proposal features in two orthogonal directions and represents text region with a set of contour points. Considering that the strong unidirectional or weakly orthogonal activation is usually caused by the monotonous texture characteristic of false-positive patterns (e.g. streaks.), our method effectively suppresses these false positives by only outputting predictions with high response value in both orthogonal directions. This gives more accurate description of text regions. Extensive experiments on three challenging datasets (Total-Text, CTW1500 and ICDAR2015) verify that our method achieves the state-of-the-art performance. Code is available at https://github.com/wangyuxin87/ContourNet.
Zero-Shot Compositional Policy Learning via Language Grounding

Despite recent breakthroughs in reinforcement learning (RL) and imitation learning (IL), existing algorithms fail to generalize beyond the training environments. In reality, humans can adapt to new tasks quickly by leveraging prior knowledge about the world such as language descriptions. To facilitate the research on language-guided agents with domain adaption, we propose a novel zero-shot compositional policy learning task, where the environments are characterized as a composition of different attributes. Since there are no public environments supporting this study, we introduce a new research platform BabyAI++ in which the dynamics of environments are disentangled from visual appearance. At each episode, BabyAI++ provides varied vision-dynamics combinations along with corresponding descriptive texts. To evaluate the adaption capability of learned agents, a set of vision-dynamics pairings are held-out for testing on BabyAI++. Unsurprisingly, we find that current language-guided RL/IL techniques overfit to the training environments and suffer from a huge performance drop when facing unseen combinations. In response, we propose a multi-modal fusion method with an attention mechanism to perform visual language-grounding. Extensive experiments show strong evidence that language grounding is able to improve the generalization of agents across environments with varied dynamics.
Verifying Quantum Communication Protocols with Ground Bisimulation

Abstract One important application of quantum process algebras is to formally verify quantum communication protocols. With a suitable notion of behavioural equivalence and a decision method, one can determine if an implementation of a protocol is consistent with its specification. Ground bisimulation is a convenient behavioural equivalence for quantum processes because of its associated coinduction proof technique. We exploit this technique to design and implement two on-the-fly algorithms for the strong and weak versions of ground bisimulation to check if two given processes in quantum CCS are equivalent. We then develop a tool that can verify interesting quantum protocols such as the BB84 quantum key distribution scheme.
TWO NEW SERIES OF PRINCIPLES IN THE INTERPRETABILITY LOGIC OF ALL REASONABLE ARITHMETICAL THEORIES

Abstract The provability logic of a theory T captures the structural behavior of formalized provability in T as provable in T itself. Like provability, one can formalize the notion of relative interpretability giving rise to interpretability logics. Where provability logics are the same for all moderately sound theories of some minimal strength, interpretability logics do show variations. The logic IL (All) is defined as the collection of modal principles that are provable in any moderately sound theory of some minimal strength. In this article we raise the previously known lower bound of IL (All) by exhibiting two series of principles which are shown to be provable in any such theory. Moreover, we compute the collection of frame conditions for both series.
Today’s Leadership Lesson: Mind the Wildlife and Prepare for Tomorrow’s Disruption

As the world reacts with unprecedented efforts to contain the COVID-19 pandemic, the role of organizational leaders is to embark on a parallel track to keep mission-critical initiatives moving forward. One track includes preparing their organizations for the next “novel” virus. After all, organizations do not hire leaders to maintain the status quo; they are hired to drive the future. As much as death and taxes are inevitable, it is equally predictable that all organizations will sooner or later confront a black swan event. History teaches us that while the order of magnitude may vary, management crises are not entirely novel. This article explores a series of early risk mitigation strategies to prevent the next COVID-19 and prepare leadership to face this inevitable challenge.
Formalized Proofs of the Infinity and Normal Form Predicates in the First-Order Theory of Rewriting

Abstract We present a formalized proof of the regularity of the infinity predicate on ground terms. This predicate plays an important role in the first-order theory of rewriting because it allows to express the termination property. The paper also contains a formalized proof of a direct tree automaton construction of the normal form predicate, due to Comon.
A Context Model for Intelligible Explanations in Adaptive Personalized Learning Environments

Supporting user friendly intelligible and comprehensive explanations in context-based, adaptive systems is a big challenge.They are important for a personalized system to support user acceptance and user trust.In cases, where privacy laws like the General Data Protection Regulation (GDPR) are affected, it's even more challenging.GDPR e. g. demands explanations of data usages, i. e. explanations where and for what purpose personal data is being processed.Currently, users cannot retrace the usage and the storage of their personal data in context-based adaptive collaboration environments.We address the aforementioned problem by developing a context-based adaptive platform linked to an adaptive personalized learning environment (APLE) to support learners with intelligible, comprehensible explanations of system processes.
Learning 1-Dimensional Submanifolds for Subsequent Inference on Random Dot Product Graphs

A random dot product graph (RDPG) is a generative model for networks in which vertices correspond to positions in a latent Euclidean space and edge probabilities are determined by the dot products of the latent positions. We consider RDPGs for which the latent positions are randomly sampled from an unknown $1$-dimensional submanifold of the latent space. In principle, restricted inference, i.e., procedures that exploit the structure of the submanifold, should be more effective than unrestricted inference; however, it is not clear how to conduct restricted inference when the submanifold is unknown. We submit that techniques for manifold learning can be used to learn the unknown submanifold well enough to realize benefit from restricted inference. To illustrate, we test a hypothesis about the Frechet mean of a small community of vertices, using the complete set of vertices to infer latent structure. We propose test statistics that deploy the Isomap procedure for manifold learning, using shortest path distances on neighborhood graphs constructed from estimated latent positions to estimate arc lengths on the unknown $1$-dimensional submanifold. Unlike conventional applications of Isomap, the estimated latent positions do not lie on the submanifold of interest. We extend existing convergence results for Isomap to this setting and use them to demonstrate that, as the number of auxiliary vertices increases, the power of our test converges to the power of the corresponding test when the submanifold is known.
Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction

Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications. While previous studies exist, the state-of-the-art performance has significant room for improvement. Methods: We studied several variants of BERT (Bidirectional Encoder Representations using Transformers) some involving clinical domain customization and the others involving improved architecture and/or training strategies. We evaluated these methods using a direct temporal relations dataset which is a semantically focused subset of the 2012 i2b2 temporal relations challenge dataset. Results: Our results show that RoBERTa, which employs better pre-training strategies including using 10x larger corpus, has improved overall F measure by 0.0864 absolute score (on the 1.00 scale) and thus reducing the error rate by 24% relative to the previous state-of-the-art performance achieved with an SVM (support vector machine) model. Conclusion: Modern contextual language modeling neural networks, pre-trained on a large corpus, achieve impressive performance even on highly-nuanced clinical temporal relation tasks.
Board games for quantum computers

Scalable board games, including Five in a Row (or Gomoku) and Weiqi (or Go), are generalized so that they can be played on or by quantum computers. We adopt three principles for the generalization: the first two are to ensure that the games are compatible with quantum computer and the third is to ensure that the standard classical games are the special cases. We demonstrate how to construct basic quantum moves and use them to set up quantum games. There are three different schemes to play the quantized games: one quantum computer with another quantum computer (QwQ), two classical computer playing with each other on one quantum computer (CQC), and one classical computer with another classical computer (CwC). We illustrate these results with the games of Five in a Row and Weiqi.
New flexible directional filter bank by tuning Hermite transform parameters for content based medical image retrieval

This study presents a flexible directional filter bank (DFB) by tuning Hermite transform parameters for content-based medical image retrieval (CBMIR). Two imperative Hermite transform parameters such as the scale of Gaussian kernel and the order of Hermite polynomial are tuned to adapt the flexible Hermite orthogonal basis to improve the retrieval performance of the CBMIR system. First, the tuned 1D Hermite filter transformed into 2D diamond shape filter by McClellan transformation and made it adequate for the directional decomposition of images using DFB structure. The dominant features of these directionally decomposed images are extracted by the rotation-invariant local neighbourhood frequency pattern. The mean retrieval precision is used as a high-level criterion to measure retrieval performance. The proposed method is assessed on three medical image databases: two for computed tomography and one for magnetic resonance imaging and achieved 99.41, 89.07, and 88.71% mean precision, respectively, when ten images are returned by the system.
Robust Partial Matching for Person Search in the Wild

Various factors like occlusions, backgrounds, etc., would lead to misaligned detected bounding boxes , e.g., ones covering only portions of human body. This issue is common but overlooked by previous person search works. To alleviate this issue, this paper proposes an Align-to-Part Network (APNet) for person detection and re-Identification (reID). APNet refines detected bounding boxes to cover the estimated holistic body regions, from which discriminative part features can be extracted and aligned. Aligned part features naturally formulate reID as a partial feature matching procedure, where valid part features are selected for similarity computation, while part features on occluded or noisy regions are discarded. This design enhances the robustness of person search to real-world challenges with marginal computation overhead. This paper also contributes a Large-Scale dataset for Person Search in the wild (LSPS), which is by far the largest and the most challenging dataset for person search. Experiments show that APNet brings considerable performance improvement on LSPS. Meanwhile, it achieves competitive performance on existing person search benchmarks like CUHK-SYSU and PRW.
Cooperative Multi-Agent Reinforcement Learning with Conversation Knowledge for Dialogue Management

Dialogue management plays a vital role in task-oriented dialogue systems, which has become an active area of research in recent years. Despite the promising results brought from deep reinforcement learning, most of the studies need to develop a manual user simulator additionally. To address the time-consuming development of simulator policy, we propose a multi-agent dialogue model where an end-to-end dialogue manager and a user simulator are optimized simultaneously. Different from prior work, we optimize the two-agents from scratch and apply the reward shaping technology based on adjacency pairs constraints in conversational analysis to speed up learning and to avoid the derivation from normal human-human conversation. In addition, we generalize the one-to-one learning strategy to one-to-many learning strategy, where a dialogue manager can be concurrently optimized with various user simulators, to improve the performance of trained dialogue manager. The experimental results show that one-to-one agents trained with adjacency pairs constraints can converge faster and avoid derivation. In cross-model evaluation with human users involved, the dialogue manager trained in one-to-many strategy achieves the best performance.