Miracle-dz/UniFAR-FTUs huggingface dataset:
A behavior model for improving realism in a VR team sport

Personality, moods and emotions play a vital role in real-life interactions, especially for team activities like sports. Diverse player personalities lead to distinct emotional reactions impacting game play. One of the major challenges while developing a realistic virtual environment is simulating human-like behaviors and imitations. We aim to improve the believability of virtual players by incorporating personality, moods and emotions for them. We propose a novel framework for a multi-player VR Volleyball game by integrating various behavioral changes during game play. This is achieved by simulating player reactions and rendering changes in the player’s body postures, which are dependent on game environment parameters and scenarios. We showcase visual analysis of individual players’ mood changes during game play to further corroborate the presented VR framework. Incorporating the proposed model, we exhibit the progression and variations in moods for players with different personalities when presented with varied game situations. We conducted a user study with 10 participants, which consisted of seven males and three females. We used two versions of the VR Volleyball game. One version of the game is without any behavioral changes, and the other version included the proposed behavioral model. Our results shows that behavioral changes setting had significantly higher ratings compared to the version with non-behavioral changes setting.
Robust Rock Detection and Clustering with Surface Analysis for Robotic Rock Breaking Systems

A reliable and robust visual perception system is often a key-enabler for autonomous robotic systems working in at least partially unknown environment. For autonomous robotic rock breaking system, boulder detection and localization system is an essential part that must be able to operate under challenging environments. The problem can be summarized as detecting unstructured objects in a structured environment. A key to robust boulder detection is an effective clustering algorithm, that can segment a point cloud, captured by e.g., a Time-of-Flight (TOF) camera, into clusters of individual boulders. This information is crucial for an autonomous robotic secondary breaking system, that uses it to break each individual boulder by a hydraulic impact hammer. This study proposes a novel algorithm for point cloud segmentation that can be used for the boulder detection application. The method is designed using features of the Point Cloud Library (PCL), and it is benchmarked against other readily available algorithms in the PCL. The results indicate robust performance with an impressive 97.4% accuracy on our dataset.
POP Cart: Product Recommendation System by an Agent on a Shopping Cart

In this study, we developed POP Cart that uses a shopping cart and an agent to recommend products. The agent on POP Cart is designed to call the customer by name, chatting and making recommendations to the customer casually like a friend. POP Cart has two advantages: first, the agent can form a positive relationship with the customer is in the store, which could be beneficial for a high recommendation success rate, as found in previous studies. Second, the agent can make multiple recommendations for different products according to the position of the customer while they are shopping in a large store. To evaluate the effectiveness of the recommendations made by the POP Cart agent, we conducted a field experiment in a real supermarket in Japan, where 49 participants shopped under three cart conditions. The results revealed that having an agent on a shopping cart is an effective way to recommend and sell products.
A Flexible Strain Sensor for Detecting Pressure based on Metamaterial Structure

Pressure pulsation is the key problem causing pipeline vibration and reducing pipeline system reliability and service life. Detection technology plays a significant role in preventing pipeline malfunction. This work provides a cost-effective and realizable sensor for pressure detection. We combine the metamaterials structure with conductive fabric to monitor tube state. When the tube’s inner pressure changes, the metamaterials will bend. A resistive flexible conductive fabric is installed to measure inner pressure. This sensor has the efficiency of easy fabrication. We described the relationship between inner pressure and the bending property of the sensor. The experimental results show that the sensor detects pipeline inner pressure accurately. And the experimental data is consistent with the simulation results. The authors provide a convenient and economical approach to pressure detection.
Challenges in Automatic License Plate Recognition System: An Indian Scenario

Automatic License Plate Recognition (ALPR) system is very useful in many applications. There are a lot of challenges in designing and implementing an ALPR system concerning different conditions. In the literature, most of the techniques available are restricted to few conditions of the ALPR system. Before working on any technique of ALPR system researchers should have an idea about different challenges in this field. In this paper, various challenges in the ALPR system in the Indian scenario are discussed.
Pt–Graphene Contacts Fabricated by Plasma Functionalization and Atomic Layer Deposition

Pt–graphene contacts are fabricated by atomic layer deposition (ALD) on H2 and O2 plasma functionalized graphene. The plasma functionalization of graphene enables the growth of uniform Pt layers on graphene by ALD and improves the Pt–graphene interaction, which results in a reduced Pt–graphene contact resistance. Devices created using a H2 plasma treatment before Pt ALD perform considerably better than the ones created using O2 plasma treatments (Rc = 0.4 ± 0.2 kΩ µm and Rc = 1.2 ± 0.1 kΩ µm, respectively). The lower performance of the O2 plasma treatments is attributed to a less favorable Pt–graphene oxide interaction and the amorphization of graphene due to the O2 plasma interaction. Supporting the latter, density functional theory calculations indicate that the CH groups created by a H2 plasma treatment leave the π‐conjugation (by C‐pz orbitals) of graphene largely intact, resulting in good in‐plane and out‐of‐plane conductivity. The CO groups formed by an O2 plasma treatment however disturb the C‐pz character, deteriorating the in‐plane conduction of graphene, despite the good out‐of‐plane conduction. The results indicate that the treatment of graphene by H2 plasma is a straightforward approach to improve the ALD growth on graphene and reduce the Pt–graphene contact resistance.
The Friendship Bench as a brief psychological intervention with peer support in rural Zimbabwean women: a mixed methods pilot evaluation

Abstract Background There is a large treatment gap for common mental disorders in rural areas of low-income countries. We tested the Friendship Bench as a brief psychological intervention delivered by village health workers (VHWs) in rural Zimbabwe. Methods Rural women identified with depression in a previous trial received weekly home-based problem-solving therapy from VHWs for 6 weeks, and joined a peer-support group. Depression was assessed using the Edinburgh Postnatal Depression Scale (EPDS) and Shona Symptom Questionnaire (SSQ). Acceptability was explored through in-depth interviews and focus group discussions. The proportion of women with depression pre- and post-intervention was compared using McNemar's test. Results Ten VHWs delivered problem-solving therapy to 27 women of mean age 33 years; 25 completed six sessions. Women valued an established and trustful relationship with their VHW, which ensured confidentiality and prevented gossip, and reported finding individual problem-solving therapy beneficial. Peer-support meetings provided space to share problems, solutions and skills. The proportion of women with depression or suicidal ideation on the EPDS declined from 68% to 12% [difference 56% (95% confidence interval (CI) 27.0–85.0); p = 0.001], and the proportion scoring high (>7) on the SSQ declined from 52% to 4% [difference 48% (95% CI 24.4–71.6); p < 0.001] after the 6-week intervention. Conclusion VHW-delivered problem-solving therapy and peer-support was acceptable and showed promising results in this pilot evaluation, leading to quantitative and qualitative improvements in mental health among rural Zimbabwean women. Scale-up of the Friendship Bench in rural areas would help close the treatment gap for common mental disorders.
Engagement Intention Estimation in Multiparty Human-Robot Interaction

As the applications of intelligent agents (IAs) are gradually increasing in daily life, they are expected to have reasonable social intelligence to interact with people by appropriately interpreting human behavior and intention. This paper presents a method to estimate whether people have willingness to join in a conversation, which helps to endow IAs with the capability of detecting potential participants. The method is built on the CNN-LSTM network, which takes image features and social signals as input, making use of general information conveyed in images, semantic social cues proven by social psychology studies, and temporal information in the sequence of inputs. The network is designed to have a multi-branch structure with the flexibility of accommodating different types of inputs. We also discuss the signal transition in multiparty human-robot interaction scenarios. The method is evaluated on three datasets with social signals and/or images as inputs. The results show that the proposed method can infer human engagement intention well.
Kinetic energy cascade in stably stratified open-channel flows

Abstract In this paper, the kinetic energy cascade in stably stratified open-channel flows is investigated. A mathematical framework to incorporate vertical scales into the conventional kinetic energy spectrum and its budget is introduced. This framework defines kinetic energy density in horizontal spectral and vertical scale space. The energy cascade is studied by analysing the evolution of kinetic energy density. It is shown that energetic streamwise scales ($\lambda _x$) become larger with increasing vertical scale. For the strongest stratification, for which the turbulence becomes intermittent, the energetic streamwise scales are suppressed, and energy density resides in $\lambda _x$ of the size of the domain. It is shown that, in an unstratified case, vertical scales of the size comparable to the height of the logarithmic layer connect viscous regions to the outer layer. By contrast, in stratified cases, such a connection is not observed. Moreover, it is shown that nonlinear transfer for streamwise scales is dominated by in-plane triad interactions and inter-plane transfer is more active in transferring energy density among small vertical scales of the size comparable to the height of viscous sublayer. The vertical scales of size comparable to the height of the viscous sublayer and buffer layer are the most active scales in the viscous term and the production term in the energy density budget, respectively.
Enabling Robots to Adhere to Social Norms by Detecting F-Formations

Robot navigation in environments shared with humans should take into account social structures and interactions. The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups (FSCS), utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.
Wavelet Scattering Transform for Multiclass Support Vector Machines in Audio Devices Classification System

This paper presents an acoustic device classification system using multiclass support vector machines (MCSVM) on wavelet features. Using the MCSVM classifier that includes different binary SVMs, device-wise classification accuracy is achieved with shorter computational time than a convolutional neural network (CNN). Other types of kernels for the MCSVM classifier are compared with the k-nearest neighbors (kNN) algorithm, multiclass Naive Bayes, and Decision Tree. The experiment results demonstrate that the classification accuracy of MCSVM is better than Naive Bayes, kNN, and Decision Tree when tested on Detection and Classification of Acoustic Scenes and Events for 2020 (DCASE2020) dataset. The device-wise classification accuracy for the proposed MCSVM classifier exhibits approximately 15.6% better than the baseline (via CNN) results in DCASE2020-Task1A. Hence, it has good potential in robotic and drone systems for acoustic device detection.
Normative roboticists: the visions and values of technical robotics papers

Visions have an important role in guiding and legitimizing technical research, as well as contributing to expectations of the general public towards technologies. In this paper we analyze technical robotics papers published between 1998 and 2019 to identify themes, trends and issues with the visions and values promoted by robotics research. In particular, we identify the themes of robotics visions and implicitly normative visions; and we quantify the relative presence of a variety of values and applications within technical papers. We conclude with a discussion of the language of robotics visions, marginalized visions and values, and possible paths forward for the robotics community to better align practice with societal interest. We also discuss implications and future work suggestions for Responsible Robotics and HRI research.
An Unsupervised Model for Identifying and Characterizing Dark Web Forums

Dark Web forums are significantly exploited to trade confidential information and illicit products by criminals. This paper addresses the problem of how to identify the cluster of discussion forums and their characteristics on the Dark Web. Exiting methods are mostly dependent on the continuous labeled contents, which are expensive and not feasible due to the nature of Dark Web data. Therefore, an approach that does not need a continuous availability of labeled forum and related knowledge is required. To this end, we propose an unsupervised model to identify and characterize Dark Web forums by combining clustering algorithm and decision tree algorithm. The proposed method presents the characteristics in an explainable form that can be used by the cyber threat intelligence system and law enforcement as scientific evidence to analyze any data breach or illicit activities in the Dark Web forums. To evaluate the performance of our model comprehensive experiments were conducted using real Dark Web forum data. The proposed approach achieves 98% accuracy and F1 score of 98% validating the efficacy of our proposed model to successfully characterize Dark Web forums. The experimental results suggest that the proposed model could be useful to the cyber threat intelligence and law enforcement community for building an intelligent source of knowledge that can be used for detecting data breach and illicit activities happening in the Dark Web forums.
Towards Edge Computing as a Service: Dynamic Formation of the Micro Data-Centers

Edge computing brings cloud services closer to the edge of the network, where data originates, and dramatically reduces the network latency of the cloud. It is a bridge linking clouds and users making the foundation for novel interconnected applications. However, edge computing still faces many challenges like remote configuration, well-defined native applications model, and limited node capacity. It lacks geo-organization and a clear separation of concerns. As such edge computing is hard to be offered as a service for future real-time user-centric applications. This paper presents the dynamic organization of geo-distributed edge nodes into micro data-centers to cover any arbitrary area and expand capacity, availability, and reliability. A cloud organization is used as an influence with adaptations for a different environment, and a model for edge applications utilizing these adaptations is presented. It is argued that the presented model can be integrated into existing solutions or used as a base for the development of future systems. Furthermore, a clear separation of concerns is given for the proposed model. With the separation of concerns setup, edge-native applications model, and a unified node organization, we are moving towards the idea of edge computing as a service, like any other utility in cloud computing.
Drivetrain Architectures for a Mechanically Decoupled Contra-Rotating Reversible Pump-Turbine

With the rise of renewable energy production in the pan-European grid, the need for flexible energy storage is experiencing a rapid increase. Pumped hydropower storage has proven viability due to its long lifespan and cost-effectiveness. The ALPHEUS project will implement pumped hydropower storage for flat topographies to augment grid stability in adjacent regions. To ensure optimal efficiency and fast switching times in these low head applications, a contra-rotating axial Reversible Pump-Turbine (RPT) is designed. The runners will be driven by two separate Axial-Flux Permanent Magnet Synchronous Motors (AF-PMSM) to ensure optimal efficiency and flexibility at variable speed and flow rate. In this new setup, great attention is needed for the drivetrain architecture. The AF-PMSMs can be placed either outside or inside the water tube, using respectively tube elbows or bulbs. Furthermore, coaxial shafts allow the machines to be placed together, on one side of the RPT. This paper proposes four drivetrain architecture concepts, which are evaluated qualitatively based on their influence on RPT and AFPMSM performance as well as bearing arrangement.
A Sensory Feedback System with Pneumatic Dual-Structure Tactile Display for Softness Assessment during Laparoscopic Surgery

In laparoscopic surgery, the surgeon’s tactile sense is restricted. We aim to enable softness assessment by providing tactile feedback to the surgeon for adenomyosis of the uterus. Tactile feedback is effective for not only accurate softness discrimination but also adequate operation. This paper proposes a sensory feedback system with a pneumatic dual-structure tactile display, which presents information on the pressure distribution obtained by a softness sensing probe. The tactile display has central and peripheral stimulation areas, which expand due to air pressure to present, for the concentration and dispersion of pressure on the finger pad. This display is lightweight, cost-effective, sterilizable, and disposable. It is mounted on a sensing probe handle, which we previously developed and is available for clinical use by the acoustic sensing principle. To emphasize the pressure distribution by the dual structure, a gain adjustment was proposed. Fundamental test on the tactile display and the psychophysical experiment on the feedback system with confidence rating and pressing force measurement were conducted. The discrimination sensitivity and confidence were significantly improved, and the pressing force was significantly reduced using feedback. The results indicated that the proposed system is effective for softness assessment and safe operation during laparoscopic surgery.
Presentation of Tactile Pleasantness Using Airborne Ultrasound

In this study, we investigate the physical condition to produce a pleasant feeling by airborne ultrasound stimulus. Since the midair haptic stimulus can precisely control its position and force amplitude, we can evaluate the effect on pleasantness by various spatiotemporal patterns with high reproducibility. In the experiment, we presented a stroke tactile stimulus on a forearm and a palm. We changed the vibration frequency from 0 to 200 Hz while changing the movement speed from 10 to 300 mm/s, evaluating the pleasantness using Likert scale. For objective evaluation, we measured the skin potential level. The experimental result shows that non-modulated stimuli with their speed of 300 mm/s are felt the most pleasant stimulus, while the objective index showed no significant differences.
Electro-Tactile Display Kit for Fingertip*

An electro-tactile display is a compact, lightweight, and low-energy device that provides distributed tactile feedback. In this study, we reviewed the components of an electro-tactile display, such as the high-voltage source and switching circuit, to realize sufficient miniaturization to be embedded in other haptic devices.
Robust Resource Allocation for Two-Tier HetNets: An Interference-Efficiency Perspective

The heterogeneous network (HetNet) is a promising network for beyond 5G to improve network capacity and spectral efficiency by developing low-power small cells within macro networks. Therefore, appropriate resource allocation schemes should be designed to balance between data rates and the cross-tier interference among different cells. To improve system robustness and also reduce the harmful interference to macrocell users (MUs), in this paper, we maximize the total interference efficiency (IE) (i.e., the ratio of the sum data rates of femtocell users (FUs) to the total cross-tier interference from femtocell base stations (FBSs) to MUs) with imperfect channel state information in a two-tier orthogonal frequency division multiple access based HetNet by jointly optimizing the transmit power of the FBS and the subcarrier allocation factor. Meanwhile, the outage rate constraint of each FU, the outage interference constraint of each MU, the maximum transmit power constraint of the FBS, and the subcarrier assignment constraint are considered simultaneously. The considered problem belongs to a mixed-integer non-linear programming problem and thus is NP-hard. The original problem with uncertain parameters is transformed into a deterministic and convex one solved by using the quadratic transformation approach, the variable relaxation approach, and Lagrange dual theory. The feasible region analysis and robust sensitivity are provided. Simulation results demonstrate that the proposed scheme has a lower interference to the MU and higher IE by comparing it with the traditional schemes.
A Reconfigurable Accelerator for Generative Adversarial Network Training Based on FPGA

In recent years, generative adversarial networks (GANs) have been widely applied in various tasks, demonstrating outstanding performance, such as image generation, style transfer, and video generation. However, due to their high computation complexity and large amount of intermediate data to be stored, the on-device learning that trains GANs on embedded platforms remains a very challenging problem. In this work, we propose an FPGA-based reconfigurable accelerator for efficient GAN training. Firstly, the cascaded fast FIR algorithm (CFFA) is opti-mized towards GAN training, and a fast convolution processing element (FCPE) based on the optimized algorithm is introduced to support various computation patterns during GAN training. Secondly, a well optimized architecture on the basis of FCPEs is presented, which is flexible to support forward, backward, and weight gradient phases of GAN training. Finally, training of a prevailing network (DCGAN) is implemented on Xilinx VCU108 platform with our methods. Experimental results show that our design achieves 315.18 GOPS and 83.87 GOPS/W in terms of throughput and energy efficiency, respectively. Our accelerator achieves 4.0× improvement over the state-of-the-art design in energy efficiency.