Reviewerly/exHarmony huggingface dataset:
The Development of Transformational Leadership in Public Higher Education

Nowadays, transformational leadership plays an increasingly important role in the construction and management of universities. In terms of the leadership system, most countries in the world adopt the leadership system combining the committee system and individual responsibility. For example, the Board of Directors is used in the United States, the University Council is used in Britain and Germany, and the University Council is used in France. In China, the president assumes responsibility under the leadership of the party committee. In recent years, the application of transformational leadership has been gradually popularized in the government and enterprises, which has played a great role in promoting social development. However, the development of transformational leadership in public universities is always slow. In this paper, I will review a number of literature to critically analyze transformational leadership and its characteristics, as well as the application of leadership in public university management, so as to explore its development in higher education.
Hierarchical Multi-Supervision Multi-Interaction Graph Attention Network for Multi-Camera Pedestrian Trajectory Prediction

Pedestrian trajectory prediction has become an essential underpinning in various human-centric applications including but not limited to autonomous vehicles, intelligent surveillance system and social robotics. Previous research endeavors mainly focus on single camera trajectory prediction (SCTP), while the problem of multi-camera trajectory prediction (MCTP) is often overly simplified into predicting presence in the next camera. This paper addresses MCTP from a more realistic yet challenging perspective, by redefining the task as a joint estimation of both future destination and possible trajectory. As such, two major efforts are devoted to facilitating related research and advancing modeling techniques. Firstly, we establish a comprehensive multi-camera Scenes Pedestrian Trajectory Dataset (mcScenes), which is collected from a real-world multi-camera space combined with thorough human interaction annotations and carefully designed evaluation metrics. Secondly, we propose a novel joint prediction framework, namely HM3GAT, for the MCTP task by building a tailored network architecture. The core idea behind HM3GAT is a fusion of topological and trajectory information that are mutually beneficial to the prediction of each task, achieved by deeply customized networks. The proposed framework is comprehensively evaluated on the mcScenes dataset with multiple ablation experiments. Status-of-the-art SCTP models are adopted as baselines to further validate the advantages of our method in terms of both information fusion and technical improvement. The mcScenes dataset, the HM3GAT, and alternative models are made publicly available for interested readers.
AutoCFR: Learning to Design Counterfactual Regret Minimization Algorithms

Counterfactual regret minimization (CFR) is the most commonly used algorithm to approximately solving two-player zero-sum imperfect-information games (IIGs). In recent years, a series of novel CFR variants such as CFR+, Linear CFR, DCFR have been proposed and have significantly improved the convergence rate of the vanilla CFR. However, most of these new variants are hand-designed by researchers through trial and error based on different motivations, which generally requires a tremendous amount of efforts and insights. This work proposes to meta-learn novel CFR algorithms through evolution to ease the burden of manual algorithm design. We first design a search language that is rich enough to represent many existing hand-designed CFR variants. We then exploit a scalable regularized evolution algorithm with a bag of acceleration techniques to efficiently search over the combinatorial space of algorithms defined by this language. The learned novel CFR algorithm can generalize to new IIGs not seen during training and performs on par with or better than existing state-of-the-art CFR variants. The code is available at https://github.com/rpSebastian/AutoCFR.
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes

We consider the challenge of policy simplification and verification in the context of policies learned through reinforcement learning (RL) in continuous environments. In well-behaved settings, RL algorithms have convergence guarantees in the limit. While these guarantees are valuable, they are insufficient for safety-critical applications. Furthermore, they are lost when applying advanced techniques such as deep-RL. To recover guarantees when applying advanced RL algorithms to more complex environments with (i) reachability, (ii) safety-constrained reachability, or (iii) discounted-reward objectives, we build upon the DeepMDP framework to derive new bisimulation bounds between the unknown environment and a learned discrete latent model of it. Our bisimulation bounds enable the application of formal methods for Markov decision processes. Finally, we show how one can use a policy obtained via state-of-the-art RL to efficiently train a variational autoencoder that yields a discrete latent model with provably approximately correct bisimulation guarantees. Additionally, we obtain a distilled version of the policy for the latent model.
Rendering-Aware HDR Environment Map Prediction from a Single Image

High dynamic range (HDR) illumination estimation from a single low dynamic range (LDR) image is a significant task in computer vision, graphics, and augmented reality. We present a two-stage deep learning-based method to predict an HDR environment map from a single narrow field-of-view LDR image. We first learn a hybrid parametric representation that sufficiently covers high- and low-frequency illumination components in the environment. Taking the estimated illuminations as guidance, we build a generative adversarial network to synthesize an HDR environment map that enables realistic rendering effects. We specifically consider the rendering effect by supervising the networks using rendering losses in both stages, on the predicted environment map as well as the hybrid illumination representation. Quantitative and qualitative experiments demonstrate that our approach achieves lower relighting errors for virtual object insertion and is preferred by users compared to state-of-the-art methods.
LIGHTWEIGHT RESIDUAL LAYERS BASED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC SIGN RECOGNITION

System for Traffic Sign Recognition and Classification is significantly important for especially traffic safety, traffic surveillance, artificial driver services and by all means, for self-driving cars. Traffic sign recognition plays an important role to tackle the traffic related obstacles. And, as traffic sign recognition is particularly applied to portable devices, lightweight models are essential aspect of the agenda. To overcome the mentioned problems, we propose lightweight convolutional neural networks with residual blocks based deep learning model for traffic recognition systems. We not only present the model efficiency but also show the several conducted experiments will well known deep CNN architectures over publicly available German traffic sign recognition benchmark. Our model showed 99.9 % accuracy by F-score, exceeding other models. At last, our model shows generally validity for traffic sign classification problem.
Protea

Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While successful, FL research is currently limited by the difficulties of establishing a realistic large-scale FL system at the early stages of experimentation. Simulation can help accelerate this process. To facilitate efficient scalable FL simulation of heterogeneous clients, we design and implement Protea, a flexible and lightweight client profiling component within federated systems using the FL framework Flower. It allows automatically collecting system-level statistics and estimating the resources needed for each client, thus running the simulation in a resource-aware fashion. The results show that our design successfully increases parallelism for 1.66 X faster wall-clock time and 2.6X better GPU utilisation, which enables large-scale experiments on heterogeneous clients.
Capturing Racial &amp; Gender Inequities on Social Media Platforms using Machine Learning

Online social media platforms provide a continuously evolving database due to the highly increasing popularity and rapid expansion of its user base. Users share their life experiences towards various inequity incidents faced at the workplace on the basis of their race or gender on these platforms while maintaining their anonymity. We aim at utilising famous social media platforms to perform extensive analysis and classification tasks for posts capturing instances of various types of Inequalities prevalent in today’s workplace. We present a framework to mine opinions expressed towards sexual harassment, mental health, racial injustice and gender-based bias in the corporate workplace using NLP techniques on social media data. The documents are represented by semantic similarity to aspect embedding’s captured using an attention-based framework for aspect extraction. In addition, we used scores from Empath categories to add information related to emotional facets.
Government Intervention and Support on Smes Firm Performance

The small and medium enterprise firms are renowned for improving the nation’s economy through the provision of job opportunities for its populace, improving the financial and investment status of the countries and provision of innovative products that meets the need of the people. SMEs are key players in the economy, through sustainable development goals (SDGs) enabling them to thrive in the open business environment and actively applying the digital transformation, promoting inclusive and sustainable industrialization should be the core of any country government. This research is designed to explore the influence of government intervention and support on enterprise performance and growth in five business sectors- Hospitality, Education, Health care, Bottled Water Production and Transport and Logistics organizations. The research paper had a descriptive research design with a randomly selected sample size of one hundred and ninety- seven respondents.The research hypotheses were tested using (Factor Analysis and Ordinal Logistic Regression Analysis) with Statistical Packages for Social Science (SPSS) version 25. The results of the study reveal that government intervention policies and intervention programmes when instituted will promote the growth of business firms in Nigeria. Also identified are relevant recommendations that serve as motivation for policy administrators, entrepreneurs, and business managers to ensure that relevant intervention programme is adopted to boost performance and growth of the SME sector in Nigeria.
Atmospheric turbulence degraded image restoration using a modified dilated convolutional network

Long-distance imaging is generally blurry and warped due to the presence of atmospheric turbulence, which harms the performance of the photoelectric system. Among these, real-time restoration based on a single turbulence-degraded image has always been a challenging topic that everyone is concerned about. The approach performed here optimizes the convolutional neural network using residual learning and smoothed dilated convolutions, which may increase the field of vision under the situation of limited GPU memory. To identify the model's performance, the authors employ training and test data with strong, medium, and weak levels synthesized by the Fried kernel, the real-time data captured by the Ritchey–Chretien telescope, the Open Turbulent Images Set and the real comparative data. Furthermore, the proposed model is compared to previous state-of-the-art approaches. The experimental results demonstrate that the proposed novel model can recover turbulently degraded images more effectively.
Risks associated with the development process of in-house information system projects

To provide a risk management perspective for managers responsible for in-house project development, we investigated how the risks associated with an in-house information system (IS) project evolve during the software development process. We conducted interviews and content analysis to examine the risks that affect project delivery quality. Three companies participated in this research. The results of this study indicate that risks related to organizational structure persist throughout the software development process. Content analysis indicated that in the conducted interviews, sentences regarding task- and actor-related risks characterized the first two phases of this process, and sentences regarding technology-related risks characterized the last two (third and fourth) phases. The results also suggest that different types of risks exert pressure on in-house project teams to reassess the weaknesses and resource allocation in a project and the possible solutions to any potential problems. This research explains risk dynamics throughout the life cycle of in-house IS development. Moreover, the findings of this study can help project managers identify the risks associated with the project development process that directly affect the project results.
CNN-Based Energy Learning for MPP Object Detection in Satellite Images

This article presents a method combining marked point processes and convolutional neural networks in order to detect small objects in optical satellite images. In this setting, objects are scattered densely: the energy based formulation of a point process allows us to factor in priors to account for object interactions. Classical marked point process approaches use contrast measures to account for object location and shape, which prove limited in complex scenes. Instead, we use convolutional neural networks to learn energy terms that are more resilient to object and context visual diversity. Finally we present a procedure to learn the relative weights of prior and likelihood terms. We test our approach on remote sensing images and compare it to contrast based approaches. Code is available at https://github.com/Ayana-Inria/MPP_CNN_RS_object_detection.
Open Innovation: Analisis Gamification terhadap Intention Of Engagement dan Brand Attitude

This study aims to analyze the effect of gamification on the intention of engagement and brand attitude. This study also aims to identify and analyze user involvement in gamification activities as an open innovation process in the field of marketing. Questionnaires were distributed to 170 respondents and analyzed using SEM-PLS. The results of this study are the dimensions of perceived enjoyment have a positive and significant effect on the intention of engagement and brand attitude. The dimensions of perceived usefulness and perceived social influence only affect brand attitude. The perceived ease of use has no significant effect on the intention of engagement and brand attitude. In the context of user participation in open innovation, collaboration between users and Shopee does not seem to have a strong level of involvement. In the protection dimension, all games presented at Shopee Games have met the element of protection of intellectual property, which indicates an element of unique ideation as part of the sustainability of open innovation practices.
CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations

Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions.In this paper, we aim to solve two key challenges in this task: utilizing multilingual instructions for improved instruction-path grounding and navigating through new environments that are unseen during training.To address these challenges, first, our agent learns a shared and visually-aligned cross-lingual language representation for the three languages (English, Hindi and Telugu) in the Room-Across-Room dataset.Our language representation learning is guided by text pairs that are aligned by visual information.Second, our agent learns an environment-agnostic visual representation by maximizing the similarity between semantically-aligned image pairs (with constraints on object-matching) from different environments.Our environment agnostic visual representation can mitigate the environment bias induced by low-level visual information.Empirically, on the Room-Across-Room dataset, we show that our multi-lingual agent gets large improvements in all metrics over the strong baseline model when generalizing to unseen environments with the cross-lingual language representation and the environmentagnostic visual representation.Furthermore, we show that our learned language and visual representations can be successfully transferred to the Room-to-Room and Cooperative Visionand-Dialogue Navigation task, and present detailed qualitative and quantitative generalization and grounding analysis. 1
Determining the Coefficient of Restitution Through the “Bouncing Ball” Experiment using Phyphox

This study aims to determine the restitution coefficient based on the reflected sound from the “bouncing ball” experiment. The experiment used a Phyphox-based smartphone. The produced sound came from a reflection between marble and the floor. Theoretically, the value of the coefficient of restitution is obtained based on the square root of the final height of the object’s reflection divided by its initial height. In this study, the determination of the height of the bounce from the “bouncing ball” was measured using the Phyphox application, which was analyzed based on the sound of the bouncing ball and the time interval of the reflection. The results show that the value of the coefficient of restitution for each marble were 0.93, 0.92, and 0.92, while the average error were 0.65%, 0.85%, and 1.43%, respectively. Furthermore, the average error value of the overall measurement is 0.97%. This error is highly dependent on the shape of the object. The rounder a thing is, the higher the level of accuracy will be. In this study, the determination of the coefficient of restitution was carried out in two ways: by comparing the height of the ball’s bounce and the time intervals for the n and n+1 bounce. The value of the coefficient of restitution generated by these methods was identic. Thus, this study had confirmed that the bounce ball experiment using the Phyphox indicated valid data well so that it could be implemented for determining the coefficient of restitution.
Assessing the Challenges of Information, Communication and Technology Education in the Rural Communities of Ghana: A Case of Sekyere South District

The Ghanaian government developed and implemented an ICT in Education Policy in 2008. ICT was integrated into instructing and education at all levels of the country's educational system as a result of this approach. This study looks at the challenges that rural communities in Ghana confront in securing the benefits of ICT in tutoring at various levels. The study used a sample size of 142 ICT instructors and heads of schools, as well as a small number of students, with a 90% confidence level. The district's respondents were chosen using planned and practical sampling methods. A structured form was used to gather primary data from all participants; however, only 93.67 percent responded. With the use of SPSS, tables were used to examine the primary data. According to the students, the number of hours per week dedicated to teaching ICT in schools is positively connected with their dedication to learning ICT. In terms of the impact of ICT on students' daily lives in the district, the reading discovered that ICT instruction had both beneficial and adverse consequences for kids. Based on the findings, the various stakeholders in education should conduct periodic on-the-job training for all teachers on evolving e-learning high-tech and approaches to provide them with the necessary abilities to effectively teach ICT.&#x0D; &#x0D; Received: 29 April 2022 / Accepted: 29 June 2022 / Published: 5 July 2022
Cognitive regulation of independent knowledge management in the training of the educational psychologist

The article presents the results of the research in which a didactic model for the independent management of knowledge with a professional approach is elaborated as a theoretical contribution. The didactic model contains the representation of the essential characteristics of the process aimed at activating learning towards independent knowledge management, with a dynamic character, based on dialectic relations of coordination and subordination among the subsystems and components that integrate it. It has a systemic nature that integrates the subsystems: Procedural cognitive conditioning, Cognitive logical reconstruction, and Cognitive regulation, from whose relations, dynamized by the research method of reflexive cooperation, and the resulting quality is the professional relevance of this process. In the logic followed for its elaboration, the relationship between the components of the teaching-learning process (objective, content, form, method, evaluation) is systematized in the first place, as an expression of its dynamics; in addition to the unity of the instructive, educational, developmental; the theory-practice link, the unity of the cognitive and affective, as well as the requirements for a developmental teaching-learning process.
Considerations Regarding Theories of Modern Learning and Special Intelligent Technologies in Higher Education

Abstract The emergence and evolution of modern learning theories and artificial intelligence technologies have left their mark on education of all levels, but also on other areas of activity, thus facilitating the work of people able to operate with them. Speaking especially of the field of higher education, we can appreciate that this field has been “invaded” by intelligent technologies, generating an increase in the offer of courses, a higher number of students and “recognizing distance learning as an educational model ˮ. We shall further present some of the theories of modern learning, as well as the role of smart technologies recommended for use in modern education.
Predicting Out-of-Domain Generalization with Neighborhood Invariance

Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments. Although recent work has proposed a variety of methods that can directly predict or theoretically bound the generalization capacity of a model, they rely on strong assumptions such as matching train/test distributions and access to model gradients. In order to characterize generalization when these assumptions are not satisfied, we propose neighborhood invariance, a measure of a classifier's output invariance in a local transformation neighborhood. Specifically, we sample a set of transformations and given an input test point, calculate the invariance as the largest fraction of transformed points classified into the same class. Crucially, our measure is simple to calculate, does not depend on the test point's true label, makes no assumptions about the data distribution or model, and can be applied even in out-of-domain (OOD) settings where existing methods cannot, requiring only selecting a set of appropriate data transformations. In experiments on robustness benchmarks in image classification, sentiment analysis, and natural language inference, we demonstrate a strong and robust correlation between our neighborhood invariance measure and actual OOD generalization on over 4,600 models evaluated on over 100 unique train/test domain pairs.