Miracle-dz/UniFAR-FTUs huggingface dataset:
Putting trust into antitrust? Competition policy and data-driven platforms

Anti-competitive notions, it seems, are increasingly informing the critical debate on a data-driven economy organised into scalable digital platforms. Issues of market definitions, how to value personal data on multisided platforms, and how to detect and regulate misuses of dominant positions have become key nomenclature on the battlefield of addressing fairness in our contemporary digital societies. This article looks at the central themes for this special issue on governing trust in European platform societies through the lens of contemporary developments in the field of competition law. Three main questions are addressed: (1) To what extent are the platforms’ own abilities to govern their infrastructures, that is, to be de facto regulators over both human behaviour and market circumstances, a challenge for contemporary competition regulation? (2) In what way is the collection, aggregation, or handling of consumers’ data of relevance for competition? (3) How can the particular European challenges of governing US-based digital platforms more broadly be understood in terms of the relationship between transparency and public trust? Of particular relevance – and challenge – here are the platforms’ abilities to govern their infrastructures, albeit through automated moderation, pricing or scalable data handling. It is argued that this aspect of coded, and possibly autonomously adapting, intra-platform governance, poses significant anti-competitive challenges for supervisory authorities, with possible negative implications for consumer autonomy and wellbeing as well as platform-dependent other companies.
Resource-Based Theory and the Value Creation Framework

This article explains how viewing resource-based theory within Brandenburger and Stuart’s value creation framework adds clarity to the theory as a whole and to its essential elements including the definition of its dependent variables, its approach to value creation, and its approach to the appropriation of economic value. Building on this foundation, the article addresses several questions about resource-based theory: Is it a theory or a view? Is resource-based theory tautological? Is resource-based theory static? How important are stakeholders within resource-based theory? Does resource-based theory constitute a theory of the firm? Does resource-based theory acknowledge industry structure’s role in explaining firm performance? Does resource-based theory incorporate uncertainty? Does resource-based theory have strong managerial implications? In accomplishing these tasks, the article sets the stage for the further evolution and application of resource-based theory.
Water-Soluble Humic Materials Modulating Metabolism and Triggering Stress Defense in Sinorhizobium fredii

Sinorhizobium fredii CCBAU45436 is a highly effective, fast-growing rhizobium that can establish symbiosis with multiple soybean cultivars. However, it is difficult to maintain the high-density effective viable cells in the rhizobial inoculant for the stressful conditions during production, storage, transport, and application. ABSTRACT Bacteria have evolved a series of mechanisms to maintain their survival and reproduction in changeable and stressful environments. In-depth understanding of these mechanisms can allow for better developing and utilizing of bacteria with various biological functions. In this study, we found that water-soluble humic materials (WSHM), a well-known environment-friendly plant growth biostimulant, significantly promoted the free-living growth and survival of Sinorhizobium fredii CCBAU45436 in a bell-shaped, dose-dependent manner, along with more-efficient carbon source consumption and relief of medium acidification. By using RNA-Seq analysis, a total of 1,136 genes significantly up-/downregulated by external addition of WSHM were identified under test conditions. These differentially expressed genes (DEGs) were enriched in functional categories related to carbon/nitrogen metabolism, cellular stress response, and genetic information processing. Further protein-protein interaction (PPI) network analysis and reverse genetic engineering indicated that WSHM might reprogram the transcriptome through inhibiting the expression of key hub gene rsh, which encodes a bifunctional enzyme catalyzing synthesis and hydrolysis of the “magic spot” (p)ppGpp. In addition, the root colonization and viability in soil of S. fredii CCBAU45436 were increased by WSHM. These findings provide us with new insights into how WSHM benefit bacterial adaptations and demonstrate great application value to be a unique inoculant additive. IMPORTANCE Sinorhizobium fredii CCBAU45436 is a highly effective, fast-growing rhizobium that can establish symbiosis with multiple soybean cultivars. However, it is difficult to maintain the high-density effective viable cells in the rhizobial inoculant for the stressful conditions during production, storage, transport, and application. Here, we showed that WSHM greatly increased the viable cells of S. fredii CCBAU45436 in culture, modulating metabolism and triggering stress defense. The root colonization and viability in soil of S. fredii CCBAU45436 were also increased by WSHM. Our results shed new insights into the effects of WSHM on bacteria and the importance of metabolism and stress defense during the bacteria’s whole life. In addition, the functional mechanism of WSHM may provide candidate genes for improving environmental adaptability and application potential of bacteria through genetic engineering.
Incorporating the Measurement of Moral Foundations Theory into Analyzing Stances on Controversial Topics

This paper investigates the correlation between moral foundations and the expression of opinions in the form of stance on different issues of public interest. This work is based on the assumption that the formation of values (personal and societal) and language are interrelated, and that we can observe differences in points of view in user-generated text data. We leverage the Moral Foundations Theory to expand the scope of stance analysis by examining the narratives in favor or against several topics. Applying an expanded version of the Moral Foundations Dictionary to a benchmark dataset for stance analysis, we capture and analyze the relationships between moral values and polarized online discussions. Using this enhanced methodology, we find that each social issue has different "moral and lexical profiles. " While some social issues project more authority related words (Donald Trump), others consists of words related to care and purity (abortion and feminism). Our correlation analysis of stance and morality revealed notable associations between stances on social issues and various types of morality, such as care, fairness, and loyalty, hence demonstrating that there are certain morality types that are more attributed to stance classification than others. Overall, our analysis highlights the usefulness of considering morality when studying stance. The differences observed in various viewpoints and stances highlights linguistic variation in discourse, which may assist in analyzing cultural values and biases in society.
This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias

During online information search, users tend to select search results that confirm previous beliefs and ignore competing possibilities. This systematic pattern in human behavior is known as confirmation bias. In this paper, we study the effect of obfuscation (i.e., hiding the result unless the user clicks on it) with warning labels and the effect of task on interaction with attitude-confirming search results. We conducted a preregistered, between-subjects crowdsourced user study (N=328) comparing six groups: three levels of obfuscation (targeted, random, none) and two levels of task (joint, two separate) for four debated topics. We found that both types of obfuscation influence user interactions, and in particular that targeted obfuscation helps decrease interaction with attitude-confirming search results. Future work is needed to understand how much of the observed effect is due to the strong influence of obfuscation, versus the warning label or the task design. We discuss design guidelines concerning system goals such as decreasing consumption of attitude-confirming search results, versus nudging users toward a more analytical mode of information processing. We also discuss implications for future work, such as the effects of interventions for confirmation bias mitigation over repeated exposure. We conclude with a strong word of caution: measures such as obfuscations should only be used for the benefit of the user, e.g., when they explicitly consent to mitigating their own biases.
Transfer Learning Strategies for Credit Card Fraud Detection

Credit card fraud jeopardizes the trust of customers in e-commerce transactions. This led in recent years to major advances in the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with short reaction time and high precision. Nevertheless, the heterogeneous nature of the fraud behavior makes it difficult to tailor existing systems to different contexts (e.g. new payment systems, different countries and/or population segments). Given the high cost (research, prototype development, and implementation in production) of designing data-driven FDSs, it is crucial for transactional companies to define procedures able to adapt existing pipelines to new challenges. From an AI/machine learning perspective, this is known as the problem of transfer learning. This paper discusses the design and implementation of transfer learning approaches for e-commerce credit card fraud detection and their assessment in a real setting. The case study, based on a six-month dataset (more than 200 million e-commerce transactions) provided by the industrial partner, relates to the transfer of detection models developed for a European country to another country. In particular, we present and discuss 15 transfer learning techniques (ranging from naive baselines to state-of-the-art and new approaches), making a critical and quantitative comparison in terms of precision for different transfer scenarios. Our contributions are twofold: (i) we show that the accuracy of many transfer methods is strongly dependent on the number of labeled samples in the target domain and (ii) we propose an ensemble solution to this problem based on self-supervised and semi-supervised domain adaptation classifiers. The thorough experimental assessment shows that this solution is both highly accurate and hardly sensitive to the number of labeled samples.
Analytical Solution of Pepper’s Inverse Kinematics for a Pose Matching Imitation System

In this paper, a human-humanoid imitation system is proposed, with a focus on the kinematic model used for translating end effector positions to joint angles. The overall system comprises the humanoid robot Pepper and a Kinect v2 camera for capturing human 3D joint positions. The presented kinematic model is based on analytical solutions of Pepper’s inverse kinematics and also uses the forward kinematics. The aim of the paper is to provide insights into deriving the kinematics of robotic chains for the purpose of pose matching imitation, as well as accuracy evaluation of the derived forward and inverse kinematic solutions. The solutions of the inverse kinematics provide results with a mean error of approximately 0.2° for the angle solutions of the head joints, 0.7° for the arm joints, and 4° for the torso (leg) joints. The evaluated speed lies within a range of 0.002 to 0.08 ms. These results indicate that the presented kinematic model is an effective method for translating end effector positions to joint angles for our pose imitation application in real-time or close to it. Finally, we show preliminary results of the proposed imitation system and discuss future work.
Measurement of Horizontal Local Skin Strain on Grooved Texture

It is important to clarify the relationship between skin deformation and tactile sensation while tracing a real object. This facilitates the presentation of high-quality tactile sensation in tactile displays. For this purpose, we aimed to record the spatial skin strain while passively tracing a one-dimensional groove surface, with 0.6, 1.0, 2.0, 4.0mm wavelength (λ). We found that the skin strain was spatially generated at a similar wavelength as the texture.
Gesture-Based Teleoperated Grasping for Educational Robotics

We present an interactive robotic platform for teleoperated grasping as an educational tool. With this open-source robot application, we engage children and young adults with robotics and make computer science education more vivid. Our teleoperation method uses the Leap Motion optical gesture tracker to simultaneously control each of the four degrees-of-freedom (DOF) of a robotic hand and the six-DOF tool pose of a serial manipulator. A control algorithm is developed to relate the operator’s palm pose to the manipulator’s tool pose. The operator commands the robotic hand with relative finger movements of the thumb, index, and middle finger. We present preliminary results from a pick-and-place demonstration show-cased at a public science fair held at Imperial College London.
Data-driven Haptic Modeling of Plastic Flow via Inverse Reinforcement Learning

Most of the natural and man-made objects that we daily encounter undergo permanent or plastic deformation when the applied force exceeds a certain limit. Furthermore, we frequently use this material property to shape objects to desired forms. Current haptic-enabled VR simulators, however, are limited to interaction with elastic objects. In this paper, we aim to provide a real like haptic experience of manipulation with virtual plastic objects. An end-to-end framework is developed enabling the user to collect the haptic feedback data from real material, building the model, and render it in real-time FEM simulation. We model the plastic flow as a closed-loop block-box controller, which is optimized through inverse reinforcement learning trying to mimic the real deformation. The neural network-based controller in our model allows modeling plasticity with arbitrary complexity. To evaluate the proposed system, data from three real materials with various properties were captured and tested. The experimental results revealed the force feedback to be at the reasonable level of realism having relative error below than the human just noticeable difference of force perception.
Multi-modal Proactive Approaching of Humans for Human-Robot Cooperative Tasks

In this paper, we present a method for proactive approaching of humans for human-robot cooperative tasks such as a robot serving beverages to people. The proposed method can deal robustly with the uncertainties in the robot’s perception while also ensuring socially acceptable behavior. We use multiple modalities in the form of the robot’s motion, body orientation, speech and gaze to proactively approach humans. Further, we present a behavior tree based control architecture to efficiently integrate these different modalities. The proposed method was successfully integrated and tested on a beverage serving robot. We present the findings of our experiments and discuss possible extensions to address limitations.
Enabling Green Mobile-Edge Computing for 5G-Based Healthcare Applications

With the unprecedented growth of wireless body area network (WBAN) users and computation-intensive 5G-based healthcare applications, mobile edge computing (MEC)-enabled healthcare systems that enable computation offloading to edge servers in proximity, are gaining much interest. However, due to the ever-increasing requirement of WBAN users’ quality of experience (QoE), the computational load on the MEC server increases, resulting in high energy costs and heavy carbon emissions. Therefore, in this paper, we focus on joint cost and energy-efficient task offloading in the MEC-enabled healthcare system by designing incentives for WBAN users to curtail their amount of task offloading. In particular, we model the interaction among the MEC server and WBAN users using the Stackelberg game and derive the optimal task offloading decision for WBAN users and corresponding reimbursement amount. As the number of WBAN users is large, we propose an alternating direction method of multipliers (ADMM)-based algorithm to achieve the optimal solution in a distributed manner. Further, simulation results show that the proposed algorithm maximizes the payoffs of both the MEC server and the WBAN users, while also reducing the MEC server energy cost by 52.38% compared to benchmark schemes.
Fault-Tolerant Control for Multi-Robotics System using Variable Gain Super Twisting Sliding Mode Control in Cyber-Physical Framework

This work is concerned with the development of an active fault tolerant scheme using second order sliding mode control for multi-robotics systems in the presence of actuator faults. The fault-tolerant controller based on conventional sliding mode control gives asymptotic convergence, less robustness, slower transient response and large amplitude of chattering that restrict its uses in real-time application. To overcome these issues, a nonlinear terminal sliding variable and a variable gain super twisting reaching law are employed to design nonlinear controller which ensures finite-time convergence and chattering attenuation. The closed-loop finite-time stability is analyzed using Lyapunov stability theory. The actuator faults information provided by the fault detection and diagnosis unit that is constituted by higher order sliding mode observer which will be used for reconfigure the controller parameters to retain the nominal tracking performance with high precision despite the actuator fault. Finally, the proposed methodology efficacy and effectiveness is tested using extensive simulation under both normal and faulty condition.
Towards Out-of-Sight Predictive Tracking for Long-Term Indoor Navigation of Non-Holonomic Person Following Robot*

The ability to predict the movements of the target person allows a person following robot (PFR) to coexist with the person while still complying with the social norms. In human-robot collaboration, this is an essential requisite for long-term time-dependent navigation and not losing sight of the person during momentary occlusions that may arise from a crowd due to static or dynamic obstacles, other human beings, or intersections in the local surrounding. The PFR must not only traverse to the previously unknown goal position but also relocate the target person after the miss, and resume following. In this paper, we try to solve this as a coupled motion-planning and control problem by formulating a model predictive control (MPC) controller with non-linear constraints for a wheeled differential-drive robot. And, using a human motion prediction strategy based on the recorded pose and trajectory information of both the moving target person and the PFR, add additional constraints to the same MPC, to recompute the optimal controls to the wheels. We make comparisons with RNNs like LSTM and Early Relocation for learning the best-predicted reference path.MPC is best suited for complex constrained problems because it allows the PFR to periodically update the tracking information, as well as to adapt to the moving person’s stride. We show the results using a simulated indoor environment and lay the foundation for its implementation on a real robot. Our proposed method offers a robust person following behaviour without the explicit need for policy learning or offline computation, allowing us to design a generalized framework.
Machine Learning and Deep Learning for Malware and Ransomware Attacks in 6G Network

Present technology 5G deals with a variety of fields such as healthcare, industry, transportation making every domain smart. But security and privacy are important factors from previous to upcoming network 6G therefore an eye on the security and privacy domain is a must. In this paper, the main focus is on Malware and Ransomware attacks, and based on this, dealing with both of the attacks through Machine Learning and Deep Learning models and also by comparing both of them based on their accuracy. The collaboration of Machine and Deep Learning with the upcoming network 6G achieves the betterment in the security domain as it is the major field to work upon.
Multi-objective optimization of Force Transmission Quality and Joint Misalignment of a 5-Bar Knee Exoskeleton

The ability to follow a normal anatomical motion is one of the fundamental design requirements of wearable exoskeleton. Human knee motion is a combination of sliding and rolling actions, which can not be replicated by a simple kinematic pair with a fixed rotation axis. In this paper, a polycentric robotic knee exoskeleton is designed based on the 5-bar mechanism (5- BM) to reproduce knee motion. The Genetic Algorithm finds the optimum parameters of the 5-BM by minimizing a weighted cost function which consists of the average Joint Force Index (JFI) and the misalignment between the centre of rotation of the exoskeleton and the user’s knee. An average and maximum ICR error of 0. 16mm and 0. 43mm is obtained by the optimized 5-BM. It yielded an average actuation toque of 7. 33N.m and JFI of 4.88 compared to 217. 73N.m and 6.04 obtained by the widely used 4-BM.
Speed Classification of Upper Limb Movements Through EEG Signal for BCI Application

Brain-Computer Interface (BCI) systems have obtained remarkable results in rehabilitation and robot control processes by converting brain signals into control commands. The quantity of movement speed is the fundamental issue in BCI that requires additional research. This paper investigated the classification of the slow and fast speeds of eight different upper limb movements through electroencephalogram (EEG) signals and information about the values of speed and maximum angle of movements from the MPU6050 module. Datasets were obtained by recording the EEG signals from 10 subjects and the module information connected on their right hand during movements. This study used Filter Bank Common Spatial Pattern (FBCSP) and Wavelet-Common Spatial Pattern (W-CSP) methods to extract speed features of movements. In both methods, features selected by the Mutual Information (MI) were sent to the Convolution Neural Network (CNN) and various machine learning classifiers. Due to the results of subject-independent speed classification, the FBCSP-CNN method obtained the highest accuracy of 90% with a Kappa coefficient of 0.8 for flexion/extension of the shoulder. Results from our proposed method demonstrate the ability to introduce a refined set of control commands into the BCI system by recognizing the features associated with the speed of movement parameters.
Multi-Objective Optimal Scheduling for Multi-Renewable Energy Power System Considering Flexibility Constraints

Along with the connection of renewable energy sources to the power system, the economy and the reliability of the power system may be significantly affected due to the uncertainty and ran-domness of the renewable energy if the multi-renewable energy power system (MREPS) lacks flexibility. Therefore, minimizing the operation cost, curtailment rate of the renewable energy and power fluctuations on tie-line are considered in this paper. Moreover, an optimal day-ahead scheduling model of MREPS is also presented with involving the flexibility constraints for the distributed generations. Using the multi-objective particle swarm optimization (MOPSO) algorithm, the model can achieve a set of Pareto non-dominated solutions for the day-ahead scheduling strategy. Finally, the best compromise solution in the set is determined by fuzzy comprehensive evaluation method. The experimental results show the effectiveness of the presented method.