Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE

We provide an end-to-end Renyi DP based-framework for diﬀerentially private top- k selection. Unlike previous approaches, which require a data-independent choice on k , we propose to privately release a data-dependent choice of k such that the gap between k -th and the ( k + 1) st “quality” is large. This is achieved by a novel application of the Report-Noisy-Max. Not only does this eliminate one hyperparameter, the adaptive choice of k also certiﬁes the stability of the top- k indices in the unordered set so we can release them using a variant of propose-test-release (PTR) without adding noise. We show that our con-struction improves the privacy-utility trade-oﬀs compared to the previous top- k selection algorithms theoretically and empirically. Ad-ditionally, we apply our algorithm to “Private Aggregation of Teacher Ensembles (PATE)” in multi-label classiﬁcation tasks with a large number of labels and show that it leads to signiﬁcant performance gains.
Global Asymptotics of the Sixth Painlevé Equation in Okamoto’s Space

Abstract We study dynamics of solutions in the initial value space of the sixth Painlevé equation as the independent variable approaches zero. Our main results describe the repeller set, show that the number of poles and zeroes of general solutions is unbounded and that the complex limit set of each solution exists and is compact and connected.
Poirot at CMCL 2022 Shared Task: Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different cognitive triggers, however there seems to be some uniform indicatorsacross languages. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features. We train an end-to-end model to extract meaningful information from different languages and test our model on two separate datasets. We compare different transformer models andshow ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 forthe shared task.
A Perspective on Solar Energetic Particles

The author has been fortunate to observe and participate in the rise of the field of solar energetic particles (SEPs), from the early abundance studies, to the contemporary paradigm of shock acceleration in large SEP events, and element abundance enhancements that are power laws in mass-to-charge ratios from H to Pb. Through painful evolution the “birdcage” model and the “solar-flare myth” came and went, leaving us with shock waves and solar jets that can interact as sources of SEPs.
An Exact, Linear Time Barab\'asi-Albert Algorithm

This paper presents the development of a new class of algorithms that accurately implement the preferential attachment mechanism of the Barabási–Albert (BA) model to generate scale-free graphs. Contrary to existing approximate preferential attachment schemes, our methods are exact in terms of the proportionality of the vertex selection probabilities to their degree and run in linear time with respect to the order of the generated graph. Our algorithms utilize a series of precise, diverse, weighted and unweighted random sampling steps to engineer the desired properties of the graph generator. We analytically show that they obey the deﬁnition of the original BA model that generates scale-free graphs and discuss their higher-order properties. The proposed methods additionally include options to manipulate one dimension of control over the joint inclusion of groups of vertices.
Learning Hybrid Locomotion Skills -- Learn to Exploit Residual Dynamics and Modulate Model-based Gait Control

This work aims to combine machine learning and control approaches for legged robots, and developed a hybrid framework to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a fully parametric closed-loop gait generator based on analytical control. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel and to generate compensatory actions for all joints as the residual dynamics, thus significantly augmenting the stability under unexpected perturbations. The performance of the proposed framework was evaluated across a set of challenging simulated scenarios. The results showed considerable improvements compared to the baseline in recovering from large external forces. Moreover, the produced behaviours are more natural, human-like and robust against noisy sensing.
I Controlli Interni Della Pubblica Amministrazione: Criticità E Prospettive Evolutive (Internal Controls in the Public Administration: Current Problems and Future Challenges)

Italian Abstract: La necessita di incrementare il livello di efficienza dell’azione amministrativa ha spinto il legislatore italiano, a partire dagli anni novanta, ad estendere alla Pubblica amministrazione alcuni strumenti gestionali tipici delle realta aziendalistiche, in linea con le esperienze di altri paesi europei; tra questi strumenti un ruolo rilevante e stato assegnato ai controlli interni. Trascorsi oltre vent’anni dalla loro introduzione, il funzionamento dei controlli interni della Pubblica amministrazione presenta ancora molte ombre. Il lavoro, attraverso una valutazione comparata con altri modelli a livello europeo, mette in luce le principali carenze del quadro normativo di riferimento, quali l’assenza di una struttura chiaramente individuata di internal audit, nonche evidenzia l’eccessiva focalizzazione delle attivita di controllo su aspetti formalistici e di produzione documentale. Inoltre, la disamina dei risultati delle attivita di valutazione della performance fa emergere la scarsa diffusione, all’interno della Pubblica amministrazione, di una cultura effettivamente orientata ai principi di “responsabilizzazione” e di “trasparenza” dei risultati gestionali; cio mina alla base l’efficacia e l’utilita potenziale dei controlli interni.English Abstract: At the beginning of the 1990s, in line with trends in other European countries, the need to increase the efficiency of administrative activity resulted in legislative initiatives to extend some of the management tools developed in the private sector to the Public Administration. Among these instruments, an important role was assigned to internal controls. Over twenty years since their introduction, the functioning of public internal controls still displays several critical aspects. Through a comparative assessment with other European models, this paper highlights the problems affecting the legal framework of Italian public internal controls, such as the lack of a clearly identified internal audit structure; the paper also points out that controls focus almost exclusively on formal rather than substantive aspects. Moreover, the results of past performance appraisal activities show that the principles of “accountability” and “transparency” are not widely applied within the Public Administration, thus undermining the effectiveness and usefulness of internal controls.
The Use of Crowdfunding and Social Media Platforms in Strategic Start-up Communication: A Big-data Analysis

ABSTRACT Utilizing big-data analytics for crowdfunding platforms (e.g., AngelList and Crunchbase) and social media sites (e.g., Facebook and Twitter), this study investigates the impact of social media marketing on the start-up fundraising success through the lens of social capital theory. The results show that cognitive, structural, and relational dimensions of social capital sources served as predictors of fundraising for start-ups. Specifically, shared values (e.g., the number of followers, the number of investors of start-up companies) and attention (e.g., product/service descriptions and videos), which account for the cognitive dimension, positively led to the increased amount of investor funding. Trust (e.g., the quality rating, the number of rounds of funding, and the number of investors) as the subconstruct of the relational dimension was a determinant of fundraising. Social interaction ties (e.g., the number of likes, the number of social media followers) were found to increase the amount of funding as an aspect of the structural dimension. The results of a further analysis demonstrate a process of social capital formation by examining the effect of social interaction ties on the amount of funding mediated by shared values and trust. The current study contributes to extend knowledge on the start-up communication aligned with a resource-driven view of strategy.
Generative Adversarial Networks for Face Generation: A Survey

Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style. These GAN-based approaches need to be reviewed, discussed, and categorized in terms of architectures, applications, and metrics. Several reviews that focus on the use and advances of GAN in general have been proposed. However, to the best of our knowledge, the GAN models applied to the face, which we call facial GANs, have never been addressed. In this article, we review facial GANs and their different applications. We mainly focus on architectures, problems, and performance evaluation with respect to each application and used datasets. More precisely, we review the progress of architectures and discuss the contributions and limits of each. Then, we expose the encountered problems of facial GANs and propose solutions to handle them. Additionally, as GAN evaluation has become a notable current defiance, we investigate the state-of-the-art quantitative and qualitative evaluation metrics and their applications. We conclude this work with a discussion on the face generation challenges and propose open research issues.
Modeling and evaluation of Service Announcement in V2X networks

Vehicle-to-Everything (V2X) is a promising technology that aims to resolve many problems of current transportation systems by supporting a wide-scale of Intelligent Transport System (ITS) use cases. V2X communications use channels (distinctive frequency domains from the ITS band) to provide radio resources for an increasing set of cooperative ITS applications. ETSI standardized the Service Announcement (SA) facility to advertise available services on ITS access channels. In the scope of this paper, we try to analyze the effects of the SA messages on the ITS control channel. We investigate different service deployment strategies and examine how the SA, CA (Cooperative Awareness), and the CP (Collective Perception) services could be deployed. We also provide initial results on details about the expected channel occupancy.
Design of BRAVER - a bipedal robot actuated via proprioceptive electric motor

This paper provides a system overview about BRAVER (Bipedal Robot Actuated Via proprioceptive Electric motoRs), a lightweight bipedal robot designed for fast moving. BRAVER stands above 0.5 m tall, weighs approximately 6 kg, and features 8 torque-controlled joints powered by proprioceptive electric motor well-distributed on two legs. This paper describes design and implementation of robot and presents details on hardware system such as microcontroller, actuation and sensor. By organizing the software architecture, we achieve a control frequency of 1khz to meet the control requirement. Several experiments were carried out to prove that the bipedal robot could execute position and torque tracking robustly.
Models of transportation choice with risk: an application to managed lanes

Abstract A rapidly exploding body of recent literature recognizes the limitations of modeling travel behavior with the assumption that the traveler faces no risk. More recent models incorporate risk and can be based on expected utility theory (EUT). However, violations of key assumptions of EUT have led to the development of non-EUT models of behavior. This study focuses on managed lane (ML) choice and examines differences in the value of travel time under cumulative prospect theory (CPT) specification versus the EUT. Travelers in our sample are more risk-averse when faced with possibly being late than they are with respect to potential savings in travel time in general. Travelers exhibit optimism (underweight the probability) when the chances of facing a longer travel time are high. The CPT models performed better than traditional models and thus, may prove to be an important tool when considering the benefits of MLs.
MPPT Techniques Investigation in Photovoltaic System

Pollution, depletion of fossil fuel sources, increasing energy demands. various circumstances encourage the integration of renewable energies, including solar systems, which require a smart and cost-effective solution. In this context, the current investigation conducted a comparative study between the most frequently used MPPT approaches in the industry, notably Enhanced (P&O, INC), and Fuzzy Logic control, in order to extract the Maximum Power Point effectively. A buck-boost converter is applied in this research as an intermediate stage between the load and the power supply. MATLAB and PROTEUS software have been used to implement the enhanced (P&O, INC) and fuzzy logic algorithms, as well as the Buck-Boost converter architecture design, in addition to using the pic18F4550 as a control system.
Motion Acquisition of Vertical Jumping by a Bio-inspired Legged Robot via Deep Reinforcement Learning

Achieving animal-like agility in legged robots is one of the challenging tasks. Motions such as those generated in a simplified or ideal environment to reduce the complexity of the model cannot adapt to changes in the environment specially in the case of dynamic motions. Deep reinforcement learning (DRL) has been attracting attention as an approach to generalize and robustify robot motions. In this paper, we focused on DRL as an approach to achieve dynamic motions for bio-inspired legged robots, and used it to learn a vertical jumping motion, which is one of the dynamic motions. By training the policy while randomizing the values of the robot’s initial posture and environmental parameters, we acquired the general controller. The controller enabled the robot to jump in various situations without having to rerun the optimization routine whenever those values change, as in the optimization approach. The controller also enabled the robot to utilize the dynamical interference of the body to achieve high jumps.
Application of AI and ML Techniques for Revolutionized Health Care System

In current days, Artificial Intelligence (AI) has rapidly evolved in requisites of computerized methodologies, hardware implementations, and approaches in numerous areas. This review summarizes the most recent developments in AI, and the practical approaches of AI in biomedicine such as, disease diagnosis, life support, biomedical information processing, and biomedical research. Once an algorithm learns how to use information, it can perform automatically which is the key advantage of machine learning. Cardiac diseases, neural disorders and cancer are the major illness areas that exploit AI technology. The goal of this study is to keep track of new research findings, explore technological availability, appraise the enormous potential of AI in biomedicine, and motivate biomedical investigators. Like the AI itself, it can be argued that its application to biomedicine is still in its infancy. Further progress and discoveries push the boundaries and in the foreseeable future, the scope of AI is persisted to increase.
Research on the Application of "AR/VR+" Traditional Cultural Education Based on Artificial Intelligence

The increasing progress of artificial intelligence(AI) technology affects the development across the board. As an important base for cultivating talents in the new era and nurturing development forces, colleges and universities will inevitably usher in unprecedented intelligent changes. Based on big data analysis platforms such as statista, this paper analyzes the AI industry and predicts the development of VR market scale. The research found that AR/VR has great potential in education and can profoundly affect teaching reform. Therefore, this paper combines the development of AI technology in AR/VR and discusses the use of "AR/VR+" technology in traditional cultural teaching in colleges and universities before, during and after class. The purpose of is to explore the intelligent, new and technological teaching mode, and provide appropriate suggestions and countermeasures for intelligent teaching, the deep integration of education and technology, and the improvement of teaching quality.
Single MCDCTA Based Current-mode Multiphase Sinusoidal Oscillator

The multiphase sinusoidal oscillator is proposed in this article. A single multifunction current differencing cascaded transconductance amplifier (MCDCTA), three resistors, and three capacitors are used in the proposed circuit with high-output-impedance nodes. The proposed circuit can be operated by electronic adjusting the oscillation condition without affecting the oscillation frequency. The proposed circuit was verified via a computer simulation that is agreed to the theoretical analysis.
Dynamic Service Placement in Multi-access Edge Computing: a Systematic Literature Review

The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.
Attitude Algorithm and Calculation of Limb Length Based on Motion Capture Data

In allusion to the difficulty of measuring the length of human limbs, a length calibration method based on the motion capture data is proposed. Motion capture makers are respectively pasted on each limb to collect the motion data in the calibration experiment and iteratively calculate the relative coordinates of joints. This paper also proposes an attitude algorithm under the motion capture system. The Euler angles of the limb at every moment can be calculated only by the motion data of the markers, which overcomes the singularity problem of Euler angles in the common motion patterns. Through horizontal comparison, the results show that the above two algorithms are effective and feasible.
Improving Human-Robot Interaction Safety through Compliant Motion Constraints in Bilateral Upper Limb Rehabilitation

Robot-assisted bilateral upper limb training helps to activate the secondary motor brain areas and improve arms’ coordinative capabilities for patients with neurological injuries. Due to the shared workspace of the patient and the robot, it is necessary to ensure human users’ safety in a compliant way. While several approaches have been proposed for safe human-robot interaction, few considered evaluation of human motor function to provide subject-specific and compliant motion constraints. This study proposes a safety metrics for bilateral training from two aspects. On one hand, a human-kinematics-based method is used to customize safe interactive workspace. On the other hand, a repulsive potential function strategy is employed to ensure movement safety when towards workspace boundary, and a performance-based fuzzy logic is developed as an adaptive law to deal with mechanical collision. The proposed strategy was preliminarily validated for bilateral upper limb training with an end-effector robotic system. Experimental results validated the effectiveness and potentiality of the proposed safety strategies.
Robust Indoor Visual-Inertial SLAM with Pedestrian Detection

Most Simultaneous Localization and Mapping (SLAM) systems are designed based on the scene rigidity assumption, which limits the applications of SLAM systems in highly dynamic environments. In addition, a single sensor cannot meet the needs of complex environments, and the fusion of visual and inertial sensors that can provide robust and accurate 6DOF pose estimation has become popular in robotics. In this work, a novel visual-inertial SLAM system towards dynamic indoor scenarios is proposed. Our approach is built on ORB-SLAM3, and a semantic segmentation thread is added to detect pedestrians. Feature points on people are avoided extracting to improve the localization accuracy in dynamic environments. The parallel tracking thread and segmentation thread can improve the real-time performance of our algorithm. We test our system both on TUM-VI dataset and in real-world environment, and the results show that our system can achieve better robustness and accuracy than ORB-SLAM3 in populated environments.