Reviewerly/exHarmony huggingface dataset:
Motor-Efficiency Estimation and Control of Multirotors Comprising a Cooperative Transportation System

A cooperative transportation system (CTS) composed of two multirotors has been developed to facilitate the cooperative transportation of a payload, even in the presence of defective motors. The reduced thrust generated by the faulty motors may cause greater tracking errors, making it challenging to control and estimate the system. Moreover, the reduced thrust can also act as a disturbance to the other UAV, further exacerbating the problem. To address these issues, the motor thrust is reconfigured using an efficiency estimator based on an unscented Kalman filter (UKF). This estimator increases the thrust of the defective motors, thus improving the system’s performance. The CTS achieves trajectory tracking despite the defective motors by using a geometric controller that incorporates the motor-efficiency estimator. A stability analysis has been conducted to prove the stability of the tracking errors. The estimation and control performances have been verified through simulations.
A suggested training program for learning handbags design using computer programs

The current research addresses a proposed e-training program to learn handbag design using computer software to give trainees the skill of designing handbags using non-specialized computer programs (Photoshop program) and to take advantage of its advantages and use it to serve women's handbag design in a manner that mimics rapid developments in global fashion and to try to overcome the difficulty of obtaining specialized handbag design systems and programs to raise the level of trainees in design and develop their creative capacities.
Komparativna analiza sustava obrazovanja odraslih u Hrvatskoj i Njemačkoj

In the current conditions of rapid and comprehensive changes in market and economic needs for knowledge and competencies, adult education is the foundation of modern society. The potential of adult education in modern conditions is noticed by international and European organizations and groups of experts who jointly develop strategies, goals and standards and create quality and effective adult education systems. Assuming that adult education systems are at least partially developed according to the same standards, this paper is based on a comparison of Croatian and German adult education systems according to criteria - legislation, adult education providers and programs, adult education funding, population involvement in programs of adult education and barriers to inclusion. The data were collected and analyzed using a qualitative methodological approach, specifically the method of content analysis, and the aim was to identify fundamental similarities and differences and to make justified conclusions and proposals for improving the state of adult education in selected countries.
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each agent within the swarm is defined as its current position and function value within a design space and the agents learn to take favorable actions that maximize reward, which is based on the final value of the objective function. The proposed approach is tested on various benchmark optimization functions and compared to the performance of other global optimization strategies. Furthermore, the effect of changing the number of agents, as well as the generalization capabilities of the trained agents are investigated. The results show superior performance compared to the other optimizers, desired scaling when the number of agents is varied, and acceptable performance even when applied to unseen functions. On a broader scale, the results show promise for the rapid development of domain-specific optimizers.
RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with strict Quality of Service (QoS) requirements. Although recent approaches have demonstrated promising results, those works remain largely impractical in public cloud environments since workloads are not known in advance and may only run for a brief period, thus prohibiting offline learning and significantly hindering online learning. In this paper, we propose RAPID, a novel framework for fast, fully-online resource allocation policy learning in highly dynamic operating environments. RAPID leverages lightweight QoS predictions, enabled by domain-knowledge-inspired techniques for sample efficiency and bias reduction, to decouple control from conventional feedback sources and guide policy learning at a rate orders of magnitude faster than prior work. Evaluation on a real-world server platform with representative cloud workloads confirms that RAPID can learn stable resource allocation policies in minutes, as compared with hours in prior state-of-the-art, while improving QoS by 9.0x and increasing best-effort workload performance by 19-43%.
Generative Knowledge Selection for Knowledge-Grounded Dialogues

Knowledge selection is the key in knowledge-grounded dialogues (KGD), which aims to select an appropriate knowledge snippet to be used in the utterance based on dialogue history. Previous studies mainly employ the classification approach to classify each candidate snippet as "relevant" or "irrelevant" independently. However, such approaches neglect the interactions between snippets, leading to difficulties in inferring the meaning of snippets. Moreover, they lack modeling of the discourse structure of dialogue-knowledge interactions. We propose a simple yet effective generative approach for knowledge selection, called GenKS. GenKS learns to select snippets by generating their identifiers with a sequence-to-sequence model. GenKS therefore captures intra-knowledge interaction inherently through attention mechanisms. Meanwhile, we devise a hyperlink mechanism to model the dialogue-knowledge interactions explicitly. We conduct experiments on three benchmark datasets, and verify GenKS achieves the best results on both knowledge selection and response generation.
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UA-Backprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods.
ImageCaptioner$^2$: Image Captioner for Image Captioning Bias Amplification Assessment

Most pre-trained learning systems are known to suffer from bias, which typically emerges from the data, the model, or both. Measuring and quantifying bias and its sources is a challenging task and has been extensively studied in image captioning. Despite the significant effort in this direction, we observed that existing metrics lack consistency in the inclusion of the visual signal. In this paper, we introduce a new bias assessment metric, dubbed $ImageCaptioner^2$, for image captioning. Instead of measuring the absolute bias in the model or the data, $ImageCaptioner^2$ pay more attention to the bias introduced by the model w.r.t the data bias, termed bias amplification. Unlike the existing methods, which only evaluate the image captioning algorithms based on the generated captions only, $ImageCaptioner^2$ incorporates the image while measuring the bias. In addition, we design a formulation for measuring the bias of generated captions as prompt-based image captioning instead of using language classifiers. Finally, we apply our $ImageCaptioner^2$ metric across 11 different image captioning architectures on three different datasets, i.e., MS-COCO caption dataset, Artemis V1, and Artemis V2, and on three different protected attributes, i.e., gender, race, and emotions. Consequently, we verify the effectiveness of our $ImageCaptioner^2$ metric by proposing AnonymousBench, which is a novel human evaluation paradigm for bias metrics. Our metric shows significant superiority over the recent bias metric; LIC, in terms of human alignment, where the correlation scores are 80% and 54% for our metric and LIC, respectively. The code is available at https://eslambakr.github.io/imagecaptioner2.github.io/.
Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling

We present a method that enables synthesizing novel views and novel poses of arbitrary human performers from sparse multi-view images. A key ingredient of our method is a hybrid appearance blending module that combines the advantages of the implicit body NeRF representation and image-based rendering. Existing generalizable human NeRF methods that are conditioned on the body model have shown robustness against the geometric variation of arbitrary human performers. Yet they often exhibit blurry results when generalized onto unseen identities. Meanwhile, image-based rendering shows high-quality results when sufficient observations are available, whereas it suffers artifacts in sparse-view settings. We propose Neural Image-based Avatars (NIA) that exploits the best of those two methods: to maintain robustness under new articulations and self-occlusions while directly leveraging the available (sparse) source view colors to preserve appearance details of new subject identities. Our hybrid design outperforms recent methods on both in-domain identity generalization as well as challenging cross-dataset generalization settings. Also, in terms of the pose generalization, our method outperforms even the per-subject optimized animatable NeRF methods. The video results are available at https://youngjoongunc.github.io/nia
Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising

Auxiliary features such as geometric buffers (G-buffers) and path descriptors (P-buffers) have been shown to significantly improve Monte Carlo (MC) denoising. However, recent approaches implicitly learn to exploit auxiliary features for denoising, which could lead to insufficient utilization of each type of auxiliary features. To overcome such an issue, we propose a denoising framework that relies on an explicit pixel-wise guidance for utilizing auxiliary features. First, we train two denoisers, each trained by a different auxiliary feature (i.e., G-buffers or P-buffers). Then we design our ensembling network to obtain per-pixel ensembling weight maps, which represent pixel-wise guidance for which auxiliary feature should be dominant at reconstructing each individual pixel and use them to ensemble the two denoised results of our denosiers. We also propagate our pixel-wise guidance to the denoisers by jointly training the denoisers and the ensembling network, further guiding the denoisers to focus on regions where G-buffers or P-buffers are relatively important for denoising. Our result and show considerable improvement in denoising performance compared to the baseline denoising model using both G-buffers and P-buffers.
Diffusion Recommender Model

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs. Such limitations hinder the accurate modeling of the complex user interaction generation procedure, such as noisy interactions caused by various interference factors. In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner. To retain personalized information in user interactions, DiffRec reduces the added noises and avoids corrupting users' interactions into pure noises like in image synthesis. In addition, we extend traditional DMs to tackle the unique challenges in practical recommender systems: high resource costs for large-scale item prediction and temporal shifts of user preference. To this end, we propose two extensions of DiffRec: L-DiffRec clusters items for dimension compression and conducts the diffusion processes in the latent space; and T-DiffRec reweights user interactions based on the interaction timestamps to encode temporal information. We conduct extensive experiments on three datasets under multiple settings (e.g. clean training, noisy training, and temporal training). The empirical results and in-depth analysis validate the superiority of DiffRec with two extensions over competitive baselines.
Federated Learning with Classifier Shift for Class Imbalance

Federated learning aims to learn a global model collaboratively while the training data belongs to different clients and is not allowed to be exchanged. However, the statistical heterogeneity challenge on non-IID data, such as class imbalance in classification, will cause client drift and significantly reduce the performance of the global model. This paper proposes a simple and effective approach named FedShift which adds the shift on the classifier output during the local training phase to alleviate the negative impact of class imbalance. We theoretically prove that the classifier shift in FedShift can make the local optimum consistent with the global optimum and ensure the convergence of the algorithm. Moreover, our experiments indicate that FedShift significantly outperforms the other state-of-the-art federated learning approaches on various datasets regarding accuracy and communication efficiency.
Chapter 8 Italian as heritage language in third generations on social networks: Morphosyntactic code-switching features

The chapter illustrates the first results deriving from an ongoing qualitative study on the parlato-digitato [spoken-typed] (Campagnone 2014) of Italian migrants’ online communities. In the context of intergenerational transmission, the dominant language often prevails over the heritage language. Nevertheless, some forms of heritage language survive in younger generations that are native speakers in their family’s immigration country’s language. These new generations seldom share the same values, symbols, and linguistic system of their birthplace. However, as significant studies have shown, “abilities in the heritage language persist, but the bilingualism is typically heavily imbalanced in favor of the dominant language” (Polinsky and Scontras 2019: 2). This imbalance causes what at first sight seems a remnant of the grandparents’ language, in particular starting from the third generation (Bettoni 2008; Rubino 2014). These latter manifestations have been elicited in face-to-face communication, but not via digital communication. Otherwise, in a context of formal writing, the same subjects rarely use Italian as they perceive it clearly as an extremely weak competence (Prifti 2013; Turchetta and Vedovelli 2018). Digital communication on social networks exhibits the same looseness of the spoken language. Spoken-typed language is overcoming the traditional distinction between written and spoken languages. It is a written communication with a strong interactive component that configures a variety of languages on its own (Berruto 2004). This research represents the first application of this ontological category to Italian as a heritage language. The qualitative research presented is based on the linguistic analysis of three Facebook groups where we can observe the linguistic behaviour of third generation heritage language speakers in the USA. The analysis examines 2,150 cases of linguistic contact. Finally, the analysis focuses on code-switching in an interactional perspective and provides a detailed scrutiny of morphosyntactic integration. In assessing data, we detected contact phenomena and relevant features at a morphological and syntactical level: we can observe cases of morphological integrations consisting of dialectal or Italian lexical units combined with English inflectional morphology and vice-versa.
The 3d structure of hadrons and origin of the proton's spin

The 3d structure of hadrons and origin of the proton's spin The Fundamental Pieces of Visible Matter: Offering an Unprecedented Insight into the 3D Structure of Hadrons and the Proton Spin Puzzle. All the visible matter in the universe is composed of atoms, which consist of electrons orbiting a small but extremely dense nucleus. The nucleus contains protons and neutrons, which also have a substructure of particles called quarks. Quarks come in different types, called flavors: up, down, strange, charm, bottom, and top. The quarks are held together by the strong nuclear force, which is mediated by other particles called gluons. Quarks and gluons, collectively called partons, are some of the most elementary pieces of visible matter in the universe. A “zoo” of particles, generically called hadrons, is made up of quarks and gluons.
S-Values as Equi-Surprisal Classes and Surprisal Intervals as Extensions of Confidence Intervals

Despite decades of warnings, misuse and misinterpretation of statistical signiﬁcance con- tinue to be prevalent in science. The absence of simple and intuitive explanations of this concept often leads researchers to incorrect or misleading conclusions that can have severe consequences, especially in ﬁelds like medicine and public health. For this reason, some statisticians suggest adopting S-values (surprisals) instead of P-values, as they re- late the statistical relevance of an event to the number of consecutive heads when ﬂipping an unbiased coin. Such a comparison with a phenomenon that we encounter in our daily lives makes S-values simpler to understand than P-values and reduces the likelihood of making overstatements. However, to the best of the author’s knowledge, there is cur- rently no natural extension of conﬁdence/compatibility intervals for statistical surprise. This asymmetry forces researchers to remain bound to the signiﬁcance threshold, which is necessary to deﬁne said intervals. To address this issue, this paper introduces the concepts of surprisal intervals (or S-intervals) and S-values as uncertainty classes, ex- plaining their use through straightforward examples. To combined use of S-intervals and S-uncertainties inherently prevent the dichotomous approach in statistical testing. The proposed methodologies impose the assessment of the outcomes in terms of more and less surprising than some ﬁxed values instead of signiﬁcant and non-signiﬁcant, thus making the notion of degree of surprise manifest and ineliminable. Furthermore, despite their simplicity, the recommended formats for presenting the results allow for the extraction of higher statistical information compared to P-values and conﬁdence intervals. Based on the above considerations, the use of surprisal intervals is highly recommended in future scientiﬁc investigations.
Research Grant Decision Support System Based on The PROMETHEE II Method

Community Service (P2M) is a form of lecturer activity in contributing directly to the community. To support lecturers in the implementation is the Internal Service Grant Program. The Internal Grant Service Program is intended as a service activity in the context of fostering and directing lecturers to improve their ability in community service. The purpose of this study is to apply the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) in the case of Lecturers Receiving Internal Service Grants. Assessment of P2M Grant proposals is still manual without a system to assist reviewers. To overcome these problems a Decision Support System (SPK) is needed which is expected to resolve the problem in granting an internal P2M Grant. Comparative analysis is only done to maintain the quality of community service at Graha Nusantara University, Padangsidimpuan. The application of the Promethee II method is expected to help reviewers in determining the prospective lecturers receiving the P2M Internal Grant
Sistem Informasi Helpdesk It Perum Peruri

The purpose of this research is to study and analyze the effectiveness of the IT helpdesk system used to support IT services at Perum Peruri. Responses include services from the IT support department in solving problems related to computer hardware or the internet or company intranets which are very important to help work efficiency. To serve network requests and repair computers, laptops and printers is still done manually and this process takes a long time because it requires approval from superiors. In this case the monitoring of access requests, computer repairs and device network problems that are often complained about within 1 month is not properly recorded so that the process of evaluating these obstacles is solved. Methods In addition to interviews and observation systems, the authors conducted a literature review through information on research topics, scientific journals, books and references. For this reason, a helpdesk system for the IT Support section is needed which is computerized and can be accessed anywhere with an internet connection, so that existing problems can be resolved properly. And the process of tracking and reporting requests sent by users can be properly monitored and evaluated to find the right solution. Results, Describe the workflow of dispatchers in the helpdesk system. The dispatcher is in charge of sorting tickets or requests sent by users to the IT division, here the researcher divides into 3 sections, namely the infrastructure section, the helpdesk services section, and system maintenance. In conclusion, the information system makes it easier to organize data, speeds up the process of manual systems, can see tracking problems and find solutions, processing data can be more efficient and easy to access.