Reviewerly/exHarmony huggingface dataset:
Spatial-frequency HEVC multiple description video coding with adaptive perceptual redundancy allocation

Multiple description coding (MDC) approaches improve the error-resilient performance of video transmission by introducing redundancy. The existing multiple description video coding (MDVC) schemes are rarely designed for particular coding structure of HEVC, and the characteristics of the human visual system (HVS) are seldom considered. In this paper, a spatial-frequency multiple description video coding with adaptive perceptual redundancy allocation framework, named SF-PMDVC, is proposed for HEVC. For descriptions generation, after polyphase down-sampling in spatial domain, a transformation based on integer discrete cosine transform (DCT) is expended to adapt to the flexible coding unit partitioning process in HEVC, and the frequency coefficients are segmented and mapped to reduce the bitrate of each description. To further improve the performance of MDVC, an adaptive perceptual redundancy allocation strategy based on visual saliency is proposed, which improves the coding efficiency adapting to the visual perception. Experimental results show that the proposed scheme improves the error resiliency of HEVC by achieving superior objective and subjective reconstructed video quality as compared to the state-of-the-art MDVC methods for HEVC.
A dynamic graph representation learning based on temporal graph transformer

The graph neural network has received significant attention in recent years because of its unique role in mining graph-structure data and its ubiquitous application in various fields, such as social networking and recommendation systems. Although most work focuses on learning low-dimensional node representation in static graphs, the dynamic nature of real-world networks makes temporal graphs more practical and significant. In this paper, we propose a dynamic graph representation learning method based on a temporal graph transformer (TGT), which can efficiently preserve high-order information and temporally evolve structural properties by incorporating an update module, an aggregation module, and a propagation module in a single model. The experimental results on three real-world networks demonstrate that the TGT outperforms state-of-the-art baselines for dynamic link prediction and edge classification tasks in terms of both accuracy and efficiency.
The Value of Open Data in HCI: A Case Report from Mobile Text Entry Research

For many years, HCI research has been known to suffer from a replication crisis, due to the lack of openly available datasets and accompanying code. Recent research has identified several barriers that prevent the wider sharing of primary research materials in HCI, but such material does, in fact, exist. Interested in the field of mobile text entry research, and largely hindered by the lack of access to participants due to the COVID-19 pandemic, the exploration of a recently published open gaze and touch dataset became an appealing prospect. This paper demonstrates the numerous problems and the extent of required effort related to understanding, sanitising and utilising open data in order to produce meaningful outcomes from it, through a detailed account of working with this dataset. Despite these issues, the paper demonstrates the value of open data as a means to produce novel contributions, without the need for additional new data (in this case, an unsupervised learning pipeline for the robust detection of gaze clusters in vertically distinct areas of interest). Framing the experience of this case study under a dataset lifecycle model intended for ML open data, a set of useful guidelines for researchers wishing to exploit open data is derived. A set of recommendations is also proposed, about the handling of papers accompanied by data, by conferences and journals in the future, Finally, the paper proposes a set of actions for the mobile text entry community, in order to facilitate data sharing across its members.
Heterogeneous sensor network for the measurement of dementia progression and well-being: preliminary study

This paper presents the development of a sensor network for measuring the well-being of people with dementia (PwD) and assessing the progression of the disease throughout the overall course of the dementia. To gain an insight into the overall well-being of a PwD, sensors can provide information about multiple aspects, such as the level of social, cognitive and physical activities and abilities. The proposed measurement system is minimally invasive and can be adapted to different built environments and allows to monitor human behavior under multiple aspects such as lifestyle monitoring, sleep analysis, social interaction, and human localization. The core technology of the chosen sensor network is made of a GPS tracker, a lifestyle monitoring sensor network, a social tablet and a smart mattress for sleep monitoring. These sensors collect data that built a heterogeneous dataset, that can be used in combination with artificial intelligence (AI) algorithms that can be trained to predict PwD well-being and extract more useful information, such as the progression of the dementia disease. The proposed solution is intended to reduce and support the workload of formal carers, since the progression of the dementia decreases PwD well-being, increases the caregiver burden and possibly decreases the quality of care.
Random Patrol Path Planning for Unmanned Surface Vehicles in Shallow Waters

To solve the problem of surface patrol in small and medium-sized shallow waters, a multi-point path planning scheme for unmanned surface vehicle (USV) is proposed. The random patrol path planning method is designed as follows: 1) randomly select target points 2) combine genetic algorithm to solve Traveling Salesman Problem (TSP) 3) use Rapidly-exploring Random Trees Star algorithm (RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) and Dynamic Window Approach algorithm (DWA) to plan the path. Through the cooperation of global path planning and local path planning, under the premise of ensuring the safety of the USV, the random search of the water surface can be more efficiently carried out. Through simulation experiments, it is proved that the path planning algorithm designed in this paper can complete the patrol task of the water surface under the condition that the USV is safe in shallow waters.
Collaborative Hunting Strategy for Multi-Amphibious Spherical Robots in Obstacle Environments

Aiming at the problem of capturing dynamic targets in obstacle environment, this paper proposes the multi-robot collaborative hunting strategy based on artificial potential field, which can herd targets to a designated volume of space with a smaller number of robots required and lower computational cost. The strategy is composed of trajectory prediction of dynamic targets, resultant force generation mechanism based on artificial potential field, and cooperative path planning based on dynamic targets. In the proposed strategy, some obstacles in ideal positions can also be selected as defenders to form the hunting circle, which can reduce the number of robots required to complete the hunting task. In addition, robots near the target will actively drive the target to other robots to reduce the hunting time. The simulation results verified the effectiveness of the proposed multi-robot collaborative hunting strategy.
Design and Realization of Underwater Target Vision 3D Reconstruction System

In order to meet the needs of high precision and fine 3D digitization of underwater targets, a visual 3D reconstruction system for underwater targets is designed. The system consists of underwater image sequence acquisition, underwater image preprocessing and 3D reconstruction. The acquisition of image sequences is based on the dynamic positioning platform with visual positioning and underwater camera. The underwater image preprocessing adopts the underwater image enhancement algorithm based on dark channel prior and color balance fusion to process the underwater image, improve the clarity and contrast of the sampled image, and correct the color deviation problem of the underwater image. The motion structure recovery and multi-view stereo vision algorithm are used to recover the three-dimensional point cloud information of spatial objects from two-dimensional images. The underwater image sequence is reconstructed by COLMAP + OpenMVS + Meshlab to obtain the reconstructed model data. The experimental results show that the designed underwater target visual three-dimensional reconstruction system has good three-dimensional reconstruction accuracy. After the underwater image enhancement method proposed in this paper, the measurement error of the reconstructed model is reduced from 5.3 % to 3.0 %, and the details of the model are more abundant. The accuracy and completeness of the reconstructed model are improved. This lays a foundation for large-scale underwater geological exploration and the establishment of large-scale underwater three-dimensional digital map.
IoT technologies for Active Ageing: an Overview of the Elderly Dehydration Case

Dehydration is a condition that involves severe complications for the health status of a subject and could cause death if not timely compensated, especially in the elderly. While such a state is one of the primary causes for elderly patients hospitalization, this paper tries to identify a general structure of a wearable device in charge of continuously monitoring the hydration level of the user, in order to identify possible critical conditions and noticing in real time the occurrence of this situation to caregivers. To this aim, this paper reviews the state of the art in terms of wearable sensing devices, processing techniques and data transmission technologies: the available sensing techniques are discussed analyzing their applicability in the context of low power wearable devices. Similarly, the performances of the most common Internet of Things (IoT)-based data transmission technologies are presented focusing on their usability in this specific context. While no unique solution can be identified, the outcomes of this paper can be exploited for the definition of different typologies of devices according to specific operating conditions. This represents a fundamental starting point required for the development of a fully operating device to be tested and employed in a real application scenario.
ASIC-Based Implementation of Random Spray Retinex Algorithm for Image Enhancement

This paper presents a detailed digital design for the Random Spray Retinex (RSR) algorithm’s main functionality. The proposed hardware architecture supports parallel computing and provides an efficient design in terms of speed, area, and power consumption compared to traditional designs. The implementation results show that the proposed parallel Application Specific Integrated Circuit (ASIC) design is highly efficient in reducing the computational complexity resulting from the data-intensive algorithm while greatly accelerating the RSR algorithm. The proposed method is seen as a step toward a low-complexity, real-time hardware architecture for the popular retinex algorithm used for image enhancement. Lastly, this architecture was implemented using standard ASIC design flow with 22nm foundry technology; it occupied an area of $430.24 \mu m^{2}$ and consumed a total power of $66.6 \mu W$, for 4 points per spray, making it very suitable for integrated System on Chips (SoC). Furthermore, the design can be scaled to a higher number of points per spray.
Investigating Small Device Implementation of FRET-based Optical Reservoir Computing

This work aims to develop a reservoir computing (RC) device based on the phenomenon of FRET between quantum dots (QDs), where FRET is a phenomenon in which excited states are transferred between QDs, and its properties change depending on the distance between QDs. We propose a compact structure in which optical input/output and quantum dots are adjacently placed without lenses or delays. The proposed structure exploits the QD-based optical reservoir as an intermediate layer and adopts memory to enable recurrent inputs. We evaluate the feasibility of the proposed structure by applying tasks that require nonlinearity. Simulation-based experimental results show that the proposed device can perform logistic map, time-series XOR, and NARMA10.
VLSI Architecture of Saturation Based Image Dehazing Algorithm and its FPGA Implementation

Image dehazing performs a crucial role in various real-time applications such as remote sensing, Advance Driver Assistance System (ADAS), surveillance systems etc. Therefore, a hardware implementation of an image dehazing system with high efficiency and low hardware cost is very much desirable. In this paper, we have proposed a nine-stage pipelined hardware architecture for haze removal which uses saturation information of the hazy image as the basis to derive local airlight (atmospheric light) and transmission of the individual pixels of the dehazed image. Since we have used pixel-based approach, our method does not require any edge detection unit as in the patch-based approach to decide the pixel in a patch is on the edge or not. The proposed method can operate at 94.7 MHz and require 970 logic elements (LEs) when implemented on the FPGA platform.
Motion Sickness Minimization Alerting System Using The Next Curvature Topology

Current intelligent car prototypes increasingly move to become autonomous where no driver is required.If an automated vehicle has rearward and forward facing seats and none of the passengers pay attention to the road, they increasingly experience the motion sickness because of the inability of passengers to anticipate the future motion trajectory.In this paper, we focus on anticipatory audio and video cues using pleasant sounds and a Human Machine Interface to display and inform the passengers about the upcoming trajectories that may lead to make the passengers sick.To be able to anticipate the next moves, we require an evaluation system of the next 1 kilometer of the road using the map.The road is investigated based on the amount of the turns and the maximum speed allowed that lead to lateral accelerations that is high enough based on Motion Sickness Dose Value to make the passengers sick.The system alerts the passengers through a Human Machine Interface to focus on the road for prevention of the Motion Sickness.We evaluate our method by using Motion Sickness Dose Value.Based on this work, we can prevent the sickness due to lateral accelerations by making the passengers to focus on the road and decrease the vestibular conflict.
Research on vehicle target detection technology based on UAV aerial images

This paper investigates the application of deep learning based target detection algorithms for UAV aerial photography to identify vehicle targets. In order to achieve the goal of real-time detection, several optimisations are made on the basis of the YOLOv5 target detection algorithm.1) The feature fusion structure of the algorithm is adjusted to assign weights to the input features, effectively improving the detection accuracy of the target. 2) Varifocal Loss is chosen as the loss function of the algorithm to solve the problem of imbalance between positive and negative samples. 3) Combined with the multi-target tracking technology DeepSORT, the target can be tracked normally even when the target encounters a long period of occlusion, and effectively reduces the number of ID conversions. The experimental results show that the improved target detection algorithm can improve the detection accuracy of UAV aerial images and achieve the demand of real-time target detection for UAV aerial photography.
Vectorized and performance‐portable quicksort

Abstract Recent works showed that implementations of quicksort using vector CPU instructions can outperform the non‐vectorized algorithms in widespread use. However, these implementations are typically single‐threaded, implemented for a particular instruction set, and restricted to a small set of key types. We lift these three restrictions: our proposed vqsort algorithm integrates into the state‐of‐the‐art parallel sorter , with a geometric mean speedup of 1.59. The same implementation works on seven instruction sets (including SVE and RISC‐V V) across four platforms. It also supports floating‐point and 16–128 bit integer keys. To the best of our knowledge, this is the fastest sort for large arrays of non‐tuple keys on CPUs, up to 20 times as fast as the sorting algorithms implemented in standard libraries. This article focuses on the practical engineering aspects enabling the speed and portability, which we have not yet seen demonstrated for a quicksort implementation. Furthermore, we introduce compact and transpose‐free sorting networks for in‐register sorting of small arrays, and a vector‐friendly pivot sampling strategy that is robust against adversarial input.
The Educational Use of WhatsApp

The mobile phone has become the preferred means of communication, especially among young people, and there are many teachers who are committed to introducing it as a resource in their classrooms. Therefore, the objective of this work was to describe the educational uses of WhatsApp. The methodology was a systematic review of published work on the subject, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and similar phases in the process. The sample was made up of 192 papers, which shows the large amount of research that has gone into the subject. WhatsApp’s educational use comes from the creation of groups—students alone or including the teacher. It is applied in both formal and informal education in all educational stages and in numerous subjects. It is also related to improvements in active learning, decision-making, and motivation, in addition to skills related to efficiency, planning, and time organization. Although the application was not designed for the educational environment, its advantages have shown it to be an adequate educational and support resource in teaching and learning at all educational stages.
Wandering Notification System for Caregivers of People with Dementia

This article describes a prototype design of a system dedicated for wandering prevention of people with Alzheimer, dementia, or other form of memory loss. The design integrates proximity sensors, BLE beacon and telecommunication technologies for location monitoring of a person with dementia, alerting the caregiver when the person leaves home and positioning the person outdoors. The experiments show that the prototype is robust, inexpensive and works in real-time.
YOLO Object Detection Algorithm with Hybrid Atrous Convolutional Pyramid

Aiming at the problems of insufficient detection accuracy of small targets and insufficient receptive field of feature points in the field of target detection, a YOLO target detection algorithm with hybrid atrous convolution pyramid is proposed. The algorithm firstly introduces atrous convolutions with different dilation rates into the feature pyramid network, and builds a hybrid receptive field module (HRFM), which enhances the ability to obtain global information by increasing the receptive field, and solves the problem of target occlusion; Secondly, design a pyramid network of atrous path aggregation, fusion of shallow feature information and high-level semantic information, improve the global detail information and representation ability of feature maps, and enhance the multi-scale adaptability of the model. Three progressive schemes are designed for testing on the VOC dataset. The experimental results show that the algorithm can effectively solve the problem of target occlusion and improve the detection accuracy of small targets.
Age-related differences in the kinematics of aimed movements in immersive virtual reality: a preliminary study

Immersive virtual reality (VR) represents a viable technology to support rehabilitation and promote the recovery of upper limb functions after stroke. Nonetheless, it has not been determined yet if VR can elicit movements that share the same kinematic characteristics of those occurring in the real world (RW), thus positively impacting arm use in daily life. A previous study enrolling young adults showed promising results: joints' ranges of motion were preserved, although movement times were longer and peak velocity lower in VR. Starting from these results, this work aimed at comparing young and older adults' (i.e., an age-matched sample to stroke survivors) upper limb kinematics while performing aimed movements in RW and immersive VR. The presented study was a within-subject repeated-measures design in which participants had to reach, grasp and transport grocery items from a simplified supermarket shelf unit. The VR condition was performed using an HTC Vive head-mounted display; its controller was used to interact with virtual objects. Three conditions were tested: VR, RW, and RW while holding the controller (RWC, to account for carrying a weight). Ten healthy young adults <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(26.7\pm 5.46$</tex> and three older adults <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(69.0\pm 2.0)$</tex> were enrolled. The collected data showed that older adults moved slower, more curved, and reached lower peak velocity during both reaching and transfer in VR compared to young adults. Arm ranges of motion seemed to be preserved, whereas thorax movements were different. We hypothesized that these differences might be dependent on age-related vision and cognitive decline, lack of familiarity with VR technology, and lack of force feedback. Further studies are needed to address these issues and confirm or reject our hypotheses.
Ship Target Detection Algorithm Based on Improved YOLOX_s

As an important part of ship intelligent navigation, improving the speed and accuracy of target detection can ensure the safe navigation of ships. Aiming at the low accuracy of ship target detection, an improved YOLOX_s network ship target detection algorithm is proposed. First, the Spatial Attention Module (SAM) is integrated into the YOLOX_s backbone network to focus on the target to be detected from the spatial dimension and improve the detection accuracy. Then, the Focal Loss loss function is used to replace the traditional BCE Loss loss function, which effectively alleviates the problem of unbalanced positive and negative samples of the single-stage target detector. The experimental results show that the detection accuracy of the improved YOLOX_s algorithm is 3.50% higher than the original algorithm, and the detection speed is only 1. 62ms lower. Without significantly reducing the detection speed, the ship target detection accuracy is effectively improved, which proves the effectiveness of the improved YOLOX_s algorithm.