Reviewerly/exHarmony huggingface dataset:
Using Virtual Reality to Validate the Chinese Version of the Independent Television Commission-Sense of Presence Inventory

The English version of the Independent Television Commission-Sense of Presence Inventory (ITC-SOPI), which was developed in 2001 to measure how involved or present participants are when experiencing different media, has substantial psychometric evidence. This study was used to translate and validate the ITC-SOPI in interactive virtual environments among the Chinese population. We used the forward-backward translation procedure. An expert panel reviewed the translated ITC-SOPI until the Chinese version of the ITC-SOPI was finalized. A total of 210 participants (133 males and 77 females), with a mean age of 23.05 years ( SD = 3.56, range = 17–47), completed the Chinese ITC-SOPI. The following psychometric properties were examined: factor structure, internal consistency, test–retest reliability, and convergent validity. Confirmatory factor analysis (CFA) showed a good fit (χ 2 /df = 1.70, Tucker–Lewis Index [TLI] = 0.91, comparative fit index [CFI] = 0.92, root mean square error of approximation [RMSEA] = 0.058) of the four-factor model (spatial presence, engagement, ecological validity, and negative effects). For each factor, the Chinese ITC-SOPI had high internal consistency (Cronbach’s α ranging from 0.75 to 0.87) and test–retest reliability (intraclass correlation coefficient ranging from 0.82 to 0.91). Significant correlations were identified between all factors and the Interpersonal Reactivity Index-C (IRI-C) and the Generalized Anxiety Disorder-7 (GAD-7). The Chinese ITC-SOPI had good psychometric properties, suggesting that it is a reliable and valid tool for evaluating media users’ sense of presence in a Chinese-speaking context.
Sketching: A Method for Imagining Journalistic Tech

Internet-connected things, wearable computers and smart environments all promise new ways for publics to interact with information and with media professionals. In this environment, journalists and journalism educators have an opportunity to play a stronger role in designing stories and interactions that achieve social and democratic goals. Technical training, critical skills and entrepreneurial thinking are among the ways scholars and educators have grappled with this issue. In this article we add to these approaches, outlining a design-led initiative that introduces sketching as a method for exploring the use of technology in a journalistic context and imagining new possibilities. Rather than teach technological competency, we use sketching as a way to develop enterprise skills and foster creative thinking about how journalism might evolve in light of new and emerging technologies.
Video Saliency Prediction via Joint Discrimination and Local Consistency

While saliency detection on static images has been widely studied, the research on video saliency detection is still in an early stage and requires more efforts due to the challenge to bring both local and global consistency of salient objects into full consideration. In this article, we propose a novel dynamic saliency network based on both local consistency and global discriminations, via which semantic features across video frames are simultaneously extracted and a recurrent feature optimization structure is designed to further enhance its performances. To ensure that the generated dynamic salient map is more concentrated, we design a lightweight discriminator with a local consistency loss LC to identify subtle differences between predicted maps and ground truths. As a result, the proposed network can be further stimulated to produce more realistic saliency maps with smoother boundaries and simpler layer transitions. The added LC loss forces the network to pay more attention to the local consistency between continuous saliency maps. Both qualitative and quantitative experiments are carried out on three large datasets, and the results demonstrate that our proposed network not only achieves improved performances but also shows good robustness.
Monocular depth estimation with SPN loss

Understanding the 3D space is crucial for autonomous vehicles for planning and navigation. Traditionally autonomous vehicles use LiDAR sensor to 3D map its environment. LiDAR sensor data are often noisy and sparse making it not fully reliable for real-time applications like autonomous driving, thus redundant such sensors are used for the purpose. The array of cameras in an autonomous vehicle purposed for detection and tracking can be reused for depth estimation as well. In this paper, an unsupervised monocular depth estimation approach for autonomous vehicles which can be used as redundant depth estimators replacing multiple LiDAR sensors. Here, a deep learning based method is used with a multiscale encoder-decoder network to estimate depth. Target view among the stereo pairs is reconstructed by inverse warping the source view using geometric camera projection. The network is guided by the stereo positive–negative(SPN) loss which minimizes the loss between reconstructed view and corresponding stereo ground truth and, also maximizes the loss between reconstructed views and corresponding opposite stereo ground truth. The proposed approach shows state of the art accuracy in autonomous driving dataset KITTI.
UL-CNN: An Ultra-Lightweight Convolutional Neural Network Aiming at Flash-Based Computing-In-Memory Architecture for Pedestrian Recognition

Pedestrian recognition has achieved the state-of-the-art performance due to the progress of recent convolutional neural network (CNN). However, mainstream CNN models are too complicated to emerging Computing-In-Memory (CIM) architectures for hardware implementation, because enormous parameters and massive intermediate processing results may incur severe “memory bottleneck”. This paper proposed a design methodology of Parameter Substitution with Nodes Compensation (PSNC) to significantly reduce parameters of CNN model without inference accuracy degradation. Based on the PSNC methodology, an ultra-lightweight convolutional neural network (UL-CNN) was designed. The UL-CNN model is a specially optimized convolutional neural network aiming at a flash-based CIM architecture (Conv-Flash) and to apply for recognizing person. The implementation result of running UL-CNN on Conv-Flash shows that the inference accuracy is up to 94.7%. Compared to LeNet-5, on the premise of the similar operations and accuracy, the amounts of UL-CNN’s parameters are less than 37% of LeNet-5 at the same dataset benchmark. Such parameter reduction can dramatically speed up the training process and economize on-chip storage overhead, as well as save the power consumption of the memory access. With the aid of UL-CNN, the Conv-Flash architecture can provide the best energy efficiency compared to other platforms (CPU, GPU, FPGA, etc.), which consumes only 2.2 × 105J to complete pedestrian recognition for one frame.
Using a Hybrid Approach to Data Management in Relational Database and Blockchain: a Case Study on The E-health Domain

Relational Databases (RDBs) have been widely used for decades. However, new persistence technologies are emerging, such as Blockchain, which is disruptive and has relevant properties, such as immutability and no third parties. Therefore, applications that use RDB can benefit from these properties by migrating part of their data to Blockchains. This article presents the MOON, a hybrid approach to manage data in RDB and Blockchain, which receives SQL queries. A case study was performed with a real health dataset using three scenarios. The conclusion is that the MOON responds to requests correctly and provides RDB and Blockchain features. Moreover, its response time was intermediate between RDB and Blockchains.
Can design documentaries disrupt design for disability?

This paper shows how design documentaries can motivate new perspectives for design and disability. We critically consider the ways in which design documentaries can foreground children's lived experiences and priorities, in cases where it is not always possible to involve children early on in the design process. By presenting a design case for supporting communication that involves children with severe speech and physical impairments and their social peers, we discuss how this narrative method can evoke designer empathy and guide new interpretations. Our findings show that design documentaries can convey to designers rich and multifaceted accounts of children's communication experiences. Although this is found to be generative, we also identify a tension with a bodily impairment understanding of disability. Drawing on reflections from our case study, we propose new methodological implications for embedding design documentaries in the design process of technologies for disability.
Object Cosegmentation in Noisy Videos With Multilevel Hypergraph

With the target of simultaneously segmenting semantically related videos to identify the common objects, video object cosegmentation has attracted the attention of researchers in recent years. Existing methods are primarily based on pair-wise relations between adjacent pixels and regions, which are susceptible to performance degradation from object entries/exists or occlusions. Specifically, we refer these video frames without the common objects present as the “empty” frames. In this paper, we propose a multilevel hypergraph-based full Video object CoSegmentation (VCS) method, which incorporates high-level semantics and low-level appearance/motion/saliency to construct the hyperedge among multiple spatially and temporally adjacent regions. Specifically, the high-level semantic model fuses multiple object proposals from each frame instead of relying on a single object proposal per frame. A hypergraph cut is subsequently utilized to calculate the object cosegmentation. Experiments on four video object segmentation/cosegmentation datasets against state-of-the-art methods with both objective and subjective results manifest the effectiveness of the proposed VCS method, including the SegTrack and VCoSeg datasets without “empty” frames, the XJTU-Stevens dataset with 3.7% “empty” frames, and the Noisy-ViCoSeg dataset proposed together with our method with 30.3% “empty” frames.
Playing software: the role of the ludic in the software society

This article explores how the concept of play as a vision of software that establishes new relations with digital technologies. This article argues that play has a fundamental role in shaping human engagement with software, and the cultural phenomena that derive from that engagement.
Second-order extended particle filter with exponential family observation model

Particle filter is the most widely used Bayesian sequential state estimation method for nonlinear dynamic systems. When importance sampling is adopted, it is still a challenge to select an appropriate importance function for sampling to avoid particle degeneracy. This paper suggests a novel particle filter, called second-order extended particle filter, which uses conditional normal distribution to approximate the theoretical optimal importance function in sequential state estimation. The approximation is fulfilled through taking logarithm to the optimal importance function and implementing second-order Taylor expansion. This method is suitable for exponential family observation models, which have numerous applications in state estimation research field.
The Untold Secrets of WiFi-Calling Services: Vulnerabilities, Attacks, and Countermeasures

Since 2016, all of four major U.S. operators have rolled out Wi-Fi calling services. They enable mobile users to place cellular calls over Wi-Fi networks based on the 3GPP IMS technology. Compared with conventional cellular voice solutions, the major difference lies in that their traffic traverses untrusted Wi-Fi networks and the Internet. This exposure to insecure networks can cause the Wi-Fi calling users to suffer from security threats. Its security mechanisms are similar to the VoLTE, because both of them are supported by the IMS. They include SIM-based security, 3GPP AKA, IPSec, etc. However, are they sufficient to secure Wi-Fi calling services? Unfortunately, our study yields a negative answer. We conduct the first security study on the operational Wi-Fi calling services in three major U.S. operators networks using commodity devices. We disclose that current Wi-Fi calling security is not bullet-proof and uncover three vulnerabilities. By exploiting the vulnerabilities, we devise two proof-of-concept attacks: telephony harassment or denial of voice service and user privacy leakage; both of them can bypass the existing security defenses. We have confirmed their feasibility using real-world experiments, as well as assessed their potential damages and proposed a solution to address all identified vulnerabilities.
SDN-cloud: a power aware resource management system for efficient energy optimization

Purpose In modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC. Design/methodology/approach In this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller. Findings Resource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements. Research limitations/implications The main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process. Originality/value As a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.
Transfer-Gan: Multimodal Ct Image Super-Resolution Via Transfer Generative Adversarial Networks

Multimodal CT scans, including non-contrast CT, CT perfusion, and CT angiography, are widely used in acute stroke diagnosis and therapeutic planning. While each imaging modality has its advantage in brain cross-sectional feature visualizations, the varying image resolution of different modalities hinders the ability of the radiologist to discern consistent but subtle suspicious findings. Besides, higher image quality requires a high radiation dose, leading to increases in health risks such as cataract formation and cancer induction. In this work, we propose a deep learning-based method Transfer-GAN that utilizes generative adversarial networks and transfer learning to improve multimodal CT image resolution and to lower the necessary radiation exposure. Through extensive experiments, we demonstrate that transfer learning from multimodal CT provides substantial visualization and quantity enhancement compare to the training without learning the prior knowledge.
Camera calibration method for solid spheres based on triangular primitives

A new camera calibration method based on triangular primitives for solid grid sphere objects are proposed in this work to address the contradiction among the number of cameras, calibration accuracy, and algorithm stability in the current camera calibration process. In the proposed method, a sphere is used as the base, and the surface coverage curve of the sphere forms a calibration object as the grid based on triangular primitives. We use the absolute quadratic curve and vanishing point to calculate the intrinsic and extrinsic parameters of the camera, respectively. The proposed method can simplify the element correspondence in the calibration and calibrate multiple cameras in different positions simultaneously. Experimental results show that the new 3D calibrator exceeds the calibration accuracy and stability of traditional algorithms. Moreover, the comprehensive evaluation of the algorithm is improved significantly.
Adversarial Normalization for Multi Domain Image Segmentation

Image normalization is a critical step in medical imaging. This step is often done on a per-dataset basis, preventing current segmentation algorithms from the full potential of exploiting jointly normalized information across multiple datasets. To solve this problem, we propose an adversarial normalization approach for image segmentation which learns common normalizing functions across multiple datasets while retaining image realism. The adversarial training provides an optimal normalizer that improves both the segmentation accuracy and the discrimination of unrealistic normalizing functions. Our contribution therefore leverages common imaging information from multiple domains. The optimality of our common normalizer is evaluated by combining brain images from both infants and adults. Results on the challenging iSEG and MRBrainS datasets reveal the potential of our adversarial normalization approach for segmentation, with Dice improvements of up to 59.6% over the baseline.
App Design and Implementation for Learning Human Anatomy Through Virtual and Augmented Reality

The influence on the teaching of Augmented Reality (RA) and Virtual Reality (RV) techniques is analyzed in the process of teaching-learning of the Human Anatomy subject, in health science students. For this purpose, two own applications have been designed, for mobile devices and Virtual Reality glasses, with the purpose of incorporate these techniques in teaching, for the study of human anatomy, that facilitate the students a better learning of anatomical body contents through these technological procedures. In this way it is intended to achieve a better transmission of knowledge to students in an effective, visual, interactive and close the main contents related to human anatomy. We believe that these technological tools constitute an excellent complementary medium to the traditional atlases, facilitating the learning of the anatomical structures.
A Modified Method for Digital Modulation Recognition based on Instantaneous Signal Features

Digital modulation recognition is an import part of signal processing. On the basis of the instantaneous features and their statistics being introduced, the classical recognition method based on decision theory with fixed threshold is analyzed, and a classical method with low correct recognition rate at low SNR is found. This paper proposes a recognition method based on dynamic thresholds relying on SNR. In particular, a procedure of SNR estimation is added firstly, and then a new modulation recognition with dynamic thresholds is realized. Compared with the classical recognition method based on decision theory with fixed threshold, this modified method can achieve higher recognition rate for low SNR signal.
A Comprehensive Framework of Usability Issues Related to the Wearable Devices

Wearable devices have the potential to be used for monitoring, augmenting, assisting, delivering content, and tracking in both individual and organizational contexts. Despite this potential, previous studies indicate that the abandonment rate is quite high relative to the usage rate due to usability factors. This chapter provides a comprehensive systematic literature review on the usability issues related to wearable devices, as well as recommendations for overcoming the identified problems. It also investigates and presents a survey of the existing usability evaluation methods used to identify and evaluate the usability of wearable devices, including their strengths and limitations. As such, we present a categorization framework that gives an overview of the overall usability issues that act as the barriers to user adoption and a summary of which types of usability issues are associated with which type of device category. The chapter has the potential to inform and assist researchers, practitioners, and application developers as they work toward developing, implementing, and evaluating wearable devices and their associated interfaces, and this, in turn, may assist with sustained engagement among users.
Road traffic accident scene detection and mapping system based on aerial photography

Road traffic accident scenes provide useful information for understanding how accidents happen and calculating the speeds of the vehicles involved. Unmanned aerial vehicles can obtain photographs of accident scenes, but utilizing these photographs has problems such as low target resolution and scale changes. An improved Resnet–Single-Shot Multibox Detector (R-SSD) algorithm based on a deep residual network (Resnet) is presented to address these problems. A residual network with better characterisation ability is proposed to replace the basic network, and residual learning is employed to reduce difficulty in network training and improve target detection accuracy. The proposed aerial target detection algorithm, based on feature information fusion (I-SSD), addresses the problems of repeated detection and small-sample missed detection in the original SSD target detection algorithm. Based on the detection results, a road traffic accident scene mapping system using either AutoCAD or hand-drawing can be designed.
Generating Controllable Ultrasound Images of the Fetal Head

Synthesis of anatomically realistic ultrasound images could be potentially valuable in sonographer training and to provide training images for algorithms, but is a challenging technical problem. Generating examples where different image attributes can be controlled may also be useful for tasks such as semi-supervised classification and regression to augment costly human annotation. In this paper, we propose using an information maximizing generative adversarial network with a least-squares loss function to generate new examples of fetal brain ultrasound images from clinically acquired healthy subject twenty-week anatomy scans. The unsupervised network succeeds in disentangling natural clinical variations in anatomical visibility and image acquisition parameters, which allows for user-control in image generation. To evaluate our method, we also introduce an additional synthetic fetal ultrasound specific image quality metric called the Fréchet SonoNet Distance (FSD) to quantitatively evaluate synthesis quality. To the best of our knowledge, this is the first work that generates ultrasound images with a generator network trained on clinical acquisitions where governing parameters can be controlled in a visually interpretable manner.
Asymmetric twin-field quantum key distribution with both statistical and intensity fluctuations

Twin-field quantum key distribution (TF-QKD) is a disruptive innovation which is able to overcome the rate-distance limit of QKD without trusted relays. Since the proposal of the first TF-QKD protocol, theoretical and experimental breakthroughs have been made to enhance its ability. However, there still exist some practical issues waiting for settlement. In this paper, we examine the performances of asymmetric TF-QKD protocol with unstable light sources and limited data sizes. The statistical fluctuations of the parameters are estimated employing Azuma's inequality. Through numerical simulations, we compare the secret key rates of the asymmetric TF-QKD protocol with different data sizes and variant intensity fluctuation magnitudes. Our results demonstrate that both statistical and intensity fluctuations have significant impacts on the performance of asymmetric TF-QKD.