bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of systemic bisphosphonate administration on patients with periodontal disease: a systematic review and meta-analysis protocol
Authors: Koide Yoko , Kataoka Yu , Hasegawa Takeshi , Ota Erika , Noma Hisashi
Published: March 2022

Introduction

Periodontal disease is a chronic oral infectious disease affecting adults worldwide as well as a lifestyle-related disease related to diabetes. Bisphosphonate is a drug often taken by patients with osteoporosis; however, it reportedly can cause jawbone necrosis. Due to its mechanism of action on bone tissue, bisphosphonate has been used topically on periodontal tissue to treat periodontal disease. However, the long-term systemic effects of bisphosphonates on periodontal tissues are unclear. This paper describes a protocol evaluating the effects of systemic bisphosphonate administration to prevent periodontal tissue destruction in patients with periodontal disease. No systematic review has attempted to summarise the evidence for systemic bisphosphonates in periodontal therapy. The results of the proposed systematic review will inform the practice and design of future clinical trials.

Methods and analysis

This paper describes a protocol for a systematic review of the relevant published analytic research using an aggregative thematic approach according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. Two authors will perform a comprehensive search for studies on Medline/PubMed, Scopus, Embase, LILACS and the Cochrane Central Register of Controlled Trials databases. Abstract screening, full-text screening and data extraction will be performed independently by two authors. A meta-analysis will be conducted as appropriate.

Ethics and dissemination

The protocol of this systematic review will be provided in a peer-reviewed journal. Formal ethics approval is not necessary because researchers will not identify individuals in the report.

PROSPERO registration number

CRD42020212698 (http://www.crd.york.ac.uk/PROSPERO/).
Familial risk of atrioventricular block in first-degree relatives
Authors: Dyssekilde Johnni Resdal , Christiansen Morten Krogh , Johansen Jens Brock , Nielsen Jens Cosedis , Bundgaard Henning , Jensen Henrik Kjaerulf
Published: March 2022

Objective

Rare cases of genetically inherited atrioventricular block (AVB) have been reported; however, the heredity of AVB remains unknown. We aimed to assess the heredity of AVB.

Design, setting and participants

Using data from the Danish Civil Registration Registry, we established a nationwide cohort of individuals with parental links. Data were merged with information from the Danish Pacemaker and Implantable Cardioverter Defibrillator Registry, containing information on all pacemaker implantations performed in Denmark during the study period, to identify patients who received a first-time pacemaker because of AVB.

Results

A total of 4 648 204 individuals had parental links and a total of 26 880 consecutive patients received a first-time pacemaker due to AVB. Overall, the adjusted rate ratio (RR) of pacemaker implantation due to AVB was 2.1 (95% CI 1.8 to 2.5) if a father, mother or sibling had AVB compared with the risk in the general population. The adjusted RR was 2.2 (1.7–2.9) for offspring of mothers with AVB, 1.9 (1.5–2.4) for offspring of fathers with AVB and 3.5 (2.3–5.4) for siblings to a patient with AVB. The risk increased inversely proportionally with the age of the index case at the time of pacemaker implantation. The corresponding adjusted RRs were 15.8 (4.8–52.3) and 10.0 (3.3–30.4) if a mother or father, respectively, had a pacemaker implantation before 50 years.

Conclusion and relevance

First-degree relatives to a patient with AVB carry an increased risk of AVB with the risk being strongly inversely associated with the age of the index case at pacemaker implantation. These findings indicate a genetic component in the development of AVB in families with an early-onset disease.
The relationship between udder skin surface temperature and milk production and composition in dairy cattle (Bos taurus Linnaeus, 1758)
Authors: Zaborski Daniel , Soroko-Dubrovina Maria , Grzesiak Wilhelm , Parafiniuk Mirosław , Modrzejewski Andrzej , Klym Oleh , Stadnytska Olha , Wójcik Jerzy
Published: March 2022

The aim of the present study was to determine correlations between udder skin surface temperatures and milk yield and estimated composition in dairy cows. The thermographic images of 34 Polish Holstein–Friesian black-and-white cows were taken in a milking parlor before and after milking. Partial correlation coefficients were calculated between the surface temperatures of the udder hind quarters and milk production traits controlling for age, parity, year, and milking time. Daily milk yield was weakly and nonsignificantly correlated with surface temperatures ( rp ranging from −0.19 to 0.21), except for the mean and maximum temperatures of the left hind quarter after milking ( rp = 0.40 and rp = 0.38, respectively). There were significant correlations of skin surface temperature with estimated fat content ( rp = −0.55 to 0.48), protein content ( rp = −0.39 to 0.42), fat yield ( rp = −0.42 to 0.54), and protein yield ( rp = 0.37 to 0.54). The estimated somatic cell count was significantly correlated with the minimum temperature ( rp = −0.54 to −0.36). The estimated urea content was significantly correlated with the minimum temperature ( rp = 0.52). A larger sample size is required in future research to confirm these preliminary results.
Colonization of Brachypodium distachyon by Gluconacetobacter diazotrophicus and its effect on plant growth promotion
Authors: Yang Xuan , Allen Hill Kathleen , Vessey J. Kevin , Tian Lining
Published: March 2022

Gluconacetobacter diazotrophicus is a nitrogen (N) fixing bacterium originally isolated from sugarcane in Brazil. Understanding the interactions between this bacterium and plants is important to exploit the nitrogen-fixing feature of G. diazotrophicus in different crops. Research was conducted to establish, monitor and optimize methods for introducing G. diazotrophicus into Brachypodium distachyon, a model for molecular research involving monocot plants. Colonization was studied in three plant growth media including liquid, semi-solid, and solid media. The effect of mineral nitrogen on colonization was also studied in each of these three media. The colonization of B. distachyon by G. diazotrophicus was analyzed by a histochemical β-glucuronidase (GUS) assay, PCR using G. diazotrophicus specific primers and bacterial re-isolation. Colonization was established in all tested plant growth conditions. The highest level of colonization was achieved in the liquid media without mineral nitrogen. Microscopic observations showed that G. diazotrophicus colonizes B. distachyon through the lateral root emergence sites, root hairs, and root tips. The colonization was found to be limited to root tissues and not found in other organs. These results indicate that B. distachyon roots are the primary organ of interaction and colonization by the bacterium. Plant fresh weight and lateral root numbers were significantly increased in inoculated plants indicating that G. diazotrophicus can stimulate growth of B. distachyon. This study indicates that B. distachyon is a useful model to study the mechanisms of G. diazotrophicus colonization and growth enhancement in monocot plants.
The plastome of ArcticOxytropis arctobia(Fabaceae) is significantly different from that ofO. splendensand other related species
Authors: Tavares Juan C. Camargo , Achakkagari Sai Reddy , Archambault Annie , Strömvik Martina V.
Published: March 2022

Anatomical and physiological specializations for plant adaptation to harsh climates result from molecular mechanisms that can be encoded in the nucleus or organelle. In this study, the complete plastomes of an arctic species, Oxytropis arctobia Bunge (Fabaceae), and a closely related temperate species, O. splendens Douglas ex Hook., were assembled, annotated, and analyzed to identify differences that might help explain their adaptation to different environments. This is consistent with the previously sequenced O. bicolor DC. and O. glabra plastomes, O. arctobia and O. splendens plastomes both have the common features of the inverted repeat-lacking clade (IRLC), as well as atpF intron loss, which is unique to the genus. However, significant differences were observed between the plastomes of O. arctobia and O. splendens and other closely related species (Oxytropis spp. and Astragalus spp.), including a 3 kb inversion, two large insertions (>1 kb), significant modifications of the accD gene, and an overall larger size.
The ADAMO project for the dark matter directionality approach
Authors: Belli P. , Bernabei R. , Caracciolo V. , Cerulli R. , Merlo V. , Cappella F. , Incicchitti A. , Cherubini N. , Piccinelli E. , Danevich F. A. , Kasperovych D. V. , Polischuk O. G. , Tretyak V. I.
Published: March 2022

Anisotropic scintillators can offer a unique possibility to exploit the so-called directionality approach to investigate the presence of those Dark Matter (DM) candidates inducing nuclear recoils. Their use can overcome the difficulty of detecting extremely short nuclear recoil tracks. In this paper, we present recent measurements performed on the anisotropic response of a ZnWO4 crystal scintillator to nuclear recoils in the framework of the ADAMO project. The anisotropic features of the ZnWO4 crystal scintillators were initially measured with [Formula: see text] particles; those results have also been confirmed by the additional measurements presented here. The experimental nuclear recoil data were obtained using a neutron generator at ENEA-Casaccia and neutron detectors to tag the scattered neutrons; in particular, the quenching factor values for nuclear recoils along different crystallographic axes have been determined for three different neutron scattering angles (i.e. nuclear recoils energies). The anisotropy of the light response for nuclear recoils in the ZnWO4 crystal scintillator has been determined at 5.4 standard deviations. These results open the possibility to realize a pioneering experiment to investigate the above-mentioned DM candidates by means of the directionality.
Tribochemical action on the tribochemical mechanical lapping (0001) C plane of the SiC single-crystal substrate
Authors: Jianguo Yao , Jiejing Li , Haixu Liu , Zhankui Wang , Yongwei Zhu , Jianxiu Su
Published: March 2022

The SiC single-crystal substrate with ultrasmooth and damage-free properties has potential applications in the field of optoelectronics and microelectronics. Lapping is one of the important processes for ultraprecision machining of the SiC single-crystal substrate. The lapping quality has an important impact on the surface quality, processing time, processing cost and processing efficiency of the subsequent chemical mechanical polishing (CMP) of the SiC single-crystal substrate. In this paper, four kinds of lapping pastes with different ingredients were prepared. The tribochemical action and mechanism of the oxidant in the lapping paste were studied. The results show that NaOH can be used as an oxidant for the tribochemical mechanical lapping of the SiC single crystal. Under the catalysis of iron oxide and frictional force, the tribochemical reaction between the SiC single-crystal substrate and the oxidant NaOH occurred and the silicon oxides (such as SiO[Formula: see text], gaseous carbon oxides and other reactants were produced. The surface energy of the SiC substrate was reduced and the material removal rate was improved effectively. These results can provide a reference for further research on the mechanism of tribochemical mechanical lapping.
Visual Normalization of Handwritten Chinese Characters Based on Generative Adversarial Networks
Authors: Zhu Yuanping , Zhang Hongrui , Huang Xin , Liu Zhuang
Published: March 2022

Compared with printed Chinese characters, handwritten Chinese characters are affected by writing coherence and anthropic factors, resulting in differences in stroke deformation, displacement, tilt, length, and thickness. This consequently affects the recognition of handwritten Chinese characters. A method based on generative adversarial networks (GANs) of Chinese character handwriting-to-printing font conversion is proposed herein, where Chinese handwriting-to-printing font conversion is represented as a font style conversion and regarded as the optimal state of normalization of handwritten Chinese characters. First, an encoder–decoder is integrated into a generative adversarial network, and a symmetric network extracts handwritten font multi-scale information. Subsequently, U-Net with skip connection is used to extract deep features and reduce the large amount of low-level information shared between the input and output. Furthermore, the skip connection reduces information loss during down-sampling. Finally, the integration loss measures the differences in the character structure and font style between the generated Chinese character image and the target font image by combining font style, encoding consistency, and [Formula: see text] losses. An experiment to convert Chinese character handwriting to multiple printing fonts is performed on the CASIA and CASIA-HWDB1.1 datasets, in which image pixel difference and character recognition accuracy are used as evaluation metrics. The obtained results show a more normalized visual writing style and contribute to recognition improvement, thereby verifying the effectiveness of this method.
BONE TURNOVER IMBALANCE IN RHEUMATOID ARTHRITIS: RELATIONSHIP BETWEEN TUMOR NECROSIS FACTOR-α AND DICKKOPF-1 WITH BONE TURNOVER MARKERS
Authors: Parlindungan Faisal , Hidayat Rudy , Sumariyono Sumariyono , Koesnoe Sukamto
Published: March 2022

Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease that leads to bone loss due to an imbalance of the bone turnover process that could be evaluated using bone turnover markers. Inflammation in RA is mainly mediated by Tumor necrosis factor-[Formula: see text] (TNF-[Formula: see text]) that will increase osteoclastogenesis. In recent studies, Dickkopf-1 (DKK-1) is a key regulatory pathway for bone formation that affects osteoblastogenesis. Objective: This study aims to examine the imbalance of the bone turnover process in RA patients. Methods: This was a cross-sectional study with 38 subjects of premenopausal women with RA. Serum levels of TNF-[Formula: see text], DKK-1, and bone turnover markers (CTx and P1NP) were investigated. Result: The median duration of RA in this study was 5 years with 60.5% of subjects in remission or with low disease activity. The median value of TNF-[Formula: see text] was 10.6 pg/mL, the mean value of DKK-1 was 4027[Formula: see text]pg/mL, the mean value of CTx was 2.74[Formula: see text]ng/mL, and the mean value of P1NP was 34[Formula: see text]pg/mL. This study revealed a weak positive correlation between TNF-[Formula: see text] and P1NP ([Formula: see text]; [Formula: see text]). Conclusion: This study found a low level of TNF-[Formula: see text], a high level of DKK-1, a high level of CTx and a low level of P1NP that indicates an imbalance of bone turnover process in RA patients that is in favor of bone resorption.
Thermal Buckling Analysis of Cracked Functionally Graded Plates
Authors: Van Do Thom , Hong Doan Duc , Chi Tho Nguyen , Dinh Duc Nguyen
Published: March 2022

Because structures made of FG material often operate in high temperature environments, it is critical to understand the mechanical behavior of these structures while taking temperature into consideration. As a result, the phase-field model is employed in this work to investigate the thermal buckling of fractured functionally graded material (FGM) plates. Additionally, this study demonstrates the difference between the plate’s static stability response to thermal load in the case of temperature-dependent (TD) material mechanical properties and in the case of temperature-independent (TID) material mechanical properties, illustrating that the calculation for these two cases will appear to be highly skewed. This provides a scientifically valid foundation for scientists to adopt a more realistic computation model. The equations are based on the third-order shear deformation plate theory (TSDT) of Reddy. This work proves that this discrepancy strongly depends on the material, boundary condition, crack length as well as thickness of the crack plate, it has never been explored in published work, the results give very useful and needful information for scientists to choose the best accuracy case to calculate and apply in practice.
Planetary geomorphology
Authors: Conway Susan J.
Published: March 2022

With the advent of the space age, planetary geomorphology has become a stand-alone discipline. This contribution provides a summary of the different processes that have been identified to form landscapes and landforms on planetary bodies in our Solar System, including rocky planets, icy planets and moons, dwarf planets, comets and asteroids. I highlight the insights these landforms have provided into the workings of these bodies and how what has been learnt in space has often taught us new lessons about the Earth. Finally, I conclude that despite the limitations imposed by remote sensing, planetary geomorphology has a bright future in planning future missions to explore our Solar System as well as understanding the data that will be returned.
Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone’s Blind Source Separation
Authors: Khosravy Mahdi , Gupta Neeraj , Dey Nilanjan , Crespo Rubén González
Published: March 2022

Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) have great potential for efficient application to a network of Internet of Things (IoT) at a high data rate. When the IoT network is among the underwater sensory devices known as the Internet of Underwater Things (IoUT), the electromagnetic wave cannot play the role of baseband signal due to rapid fall-off inside the water. Thus, acoustic OFDM is a reliable replacement for conventional OFDM inside the water. A blind structure for MIMO acoustic OFDM using Independent Component Analysis (ICA) brings even further advantages in data rate and energy consumption by avoiding the required pilot and preamble data. This research work presents a blind MIMO Acoustic OFDM blind transceiver for IoUT based on Probabilistic Stone’s Blind Source Separation (PS-BSS). The proposed technique has multiple times lower complexity compared to the ICA-based technique while maintaining a comparable efficiency. As observed in the results carried out with 100 Monte Carlo runs of transmission of random data bits over a highly sparse channel that is the common case of an underwater environment, the proposed PS-BSS-based technique dominates the ICA-based one, and as the sparseness of the channel decreases, its efficiency is comparable to the ICA-based technique. Thus, in the case of a highly sparse channel, the proposed technique is superior in both aspects of efficiency and complexity, while over lower sparseness, due to its comparative efficiency, it can be hired as an optimum technique fulfilling a fair tradeoff between efficiency and complexity.
IP Geolocation through Geographic Clicks
Authors: Dan Ovidiu , Parikh Vaibhav , Davison Brian D.
Published: March 2022

IP geolocation databases map IP addresses to their physical locations. They are used to determine the location of online users when their precise location is unavailable. These databases are vital for a number of online services, including search engine personalization, content delivery, local ads, and fraud detection. However, IP geolocation databases are often inaccurate. In this work we present two novel approaches to improving IP geolocation by mining search engine click logs. First, we show that we can derive which URLs have local affinity by clustering clicks from IPs with known locations. We demonstrate that we can further propagate these URL locations to IP addresses with unknown locations. Our approach significantly outperforms two state-of-the-art commercial IP geolocation databases by 25 and 36 percentage points at a distance error of 10 kilometers, respectively. Second, we present an alternative method of assigning locations to URLs when IP location training data is not available, by instead extracting locations from the body of web documents. This second approach also outperforms the baselines by 7 and 17 percentage points, respectively, and has higher coverage than the first method. Finally, we also demonstrate that our two approaches outperform the academic state of the art based on mining query logs.
Adaptive Cognitive Training with Reinforcement Learning
Authors: Zini Floriano , Le Piane Fabio , Gaspari Mauro
Published: March 2022

Computer-assisted cognitive training can help patients affected by several illnesses alleviate their cognitive deficits or healthy people improve their mental performance. In most computer-based systems, training sessions consist of graded exercises, which should ideally be able to gradually improve the trainee’s cognitive functions. Indeed, adapting the difficulty of the exercises to how individuals perform in their execution is crucial to improve the effectiveness of cognitive training activities. In this article, we propose the use of reinforcement learning (RL) to learn how to automatically adapt the difficulty of computerized exercises for cognitive training. In our approach, trainees’ performance in performed exercises is used as a reward to learn a policy that changes over time the values of the parameters that determine exercise difficulty. We illustrate a method to be initially used to learn difficulty-variation policies tailored for specific categories of trainees, and then to refine these policies for single individuals. We present the results of two user studies that provide evidence for the effectiveness of our method: a first study, in which a student category policy obtained via RL was found to have better effects on the cognitive function than a standard baseline training that adopts a mechanism to vary the difficulty proposed by neuropsychologists, and a second study, demonstrating that adding an RL-based individual customization further improves the training process.
Tribe or Not? Critical Inspection of Group Differences Using TribalGram
Authors: Ahn Yongsu , Yan Muheng , Lin Yu-Ru , Chung Wen-Ting , Hwa Rebecca
Published: March 2022

With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains, including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group’s shared characteristics; in others, the group-level analysis can lead to problems, including stereotyping and systematic oppression. How can analytic tools facilitate a more conscientious process in group analysis? In this work, we identify a set of
accountable group analytics
design guidelines to explicate the needs for group differentiation and preventing overgeneralization of a group. Following the design guidelines, we develop

TribalGram

, a visual analytic suite that leverages interpretable machine learning algorithms and visualization to offer inference assessment, model explanation, data corroboration, and sense-making. Through the interviews with domain experts, we showcase how our design and tools can bring a richer understanding of “groups” mined from the data.
Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA
Authors: Pan Yonghua , Li Zechao , Zhang Liyan , Tang Jinhui
Published: March 2022

Recently, many Visual Question Answering (VQA) models rely on the correlations between questions and answers yet neglect those between the visual information and the textual information. They would perform badly if the handled data distribute differently from the training data (i.e., out-of-distribution (OOD) data). Towards this end, we propose a two-stage unbiased VQA approach that addresses the unbiased issue from a causal perspective. In the causal inference stage, we mark the spurious correlation on the causal graph, explore the counterfactual causality, and devise a causal target based on the inherent correlations between the conventional and counterfactual VQA models. In the distillation stage, we introduce the causal target into the training process and leverages distilling as well as curriculum learning to capture the unbiased model. Since Causal Inference with Knowledge Distilling and Curriculum Learning (CKCL) reinforces the contribution of the visual information and eliminates the impact of the spurious correlation by distilling the knowledge in causal inference to the VQA model, it contributes to the good performance on both the standard data and out-of-distribution data. The extensive experimental results on VQA-CP v2 dataset demonstrate the superior performance of the proposed method compared to the state-of-the-art (SotA) methods.
Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation
Authors: Li Jinfeng , Liu Weifeng , Zhou Yicong , Yu Jun , Tao Dapeng , Xu Changsheng
Published: March 2022

Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. However, these algorithms will be infeasible when only a few labeled data exist in the source domain, thus the performance decreases significantly. To address this challenge, we propose a
Domain-invariant Graph Learning (DGL)
approach for domain adaptation with only a few labeled source samples. Firstly, DGL introduces the Nyström method to construct a plastic graph that shares similar geometric property with the target domain. Then, DGL flexibly employs the Nyström approximation error to measure the divergence between the plastic graph and source graph to formalize the distribution mismatch from the geometric perspective. Through minimizing the approximation error, DGL learns a domain-invariant geometric graph to bridge the source and target domains. Finally, we integrate the learned domain-invariant graph with the semi-supervised learning and further propose an adaptive semi-supervised model to handle the cross-domain problems. The results of extensive experiments on popular datasets verify the superiority of DGL, especially when only a few labeled source samples are available.
In-IDE Code Generation from Natural Language: Promise and Challenges
Authors: Xu Frank F. , Vasilescu Bogdan , Neubig Graham
Published: March 2022

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning
concept
into
code
, especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from
natural language queries
, but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges?” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models.
CRAR: Accelerating Stereo Matching with Cascaded Residual Regression and Adaptive Refinement
Authors: Zeng Linghua , Tian Xinmei
Published: March 2022

Dense stereo matching estimates the depth for each pixel of the referenced images. Recently, deep learning algorithms have dramatically promoted the development of stereo matching. The state-of-the-art result is achieved by models adopting deep convolutional neural networks. However, a considerable computational burden is also introduced, which slows the inference. To solve this problem, previous works down-sampled the input images to decrease the spatial size. However, down-sampling increases the error rate and its lower bound. In this article, we accelerate stereo matching algorithms through the improvement of network structure. Inspired by network compression, we conduct decomposition and sparsification to squeeze the computationally expensive cost optimization network. It is sparsified and then decomposed into smaller networks, which are designed and trained in a cascaded manner to reach the nearest possible performance of the larger network. Previous methods have utilized numerous refinement methods to adjust the coarse disparity. We integrate refinement methods to create an unified algorithm to utilize parallelism for running devices to further accelerate the inference. The extensive experiments on Kitti2015, Kitti2012, and Middlebury datasets demonstrate the efficiency of our method.
Learning GUI Completions with User-defined Constraints
Authors: Brückner Lukas , Leiva Luis A. , Oulasvirta Antti
Published: March 2022

A key objective in the design of
graphical user interfaces (GUIs)
is to ensure consistency across screens of the same product. However, designing a compliant layout is time-consuming and can distract designers from creative thinking. This paper studies layout recommendation methods that fulfill such consistency requirements using machine learning. Given a desired element type and size, the methods suggest element placements following real-world GUI design processes. Consistency requirements are given implicitly through previous layouts from which patterns are to be learned, comparable to existing screens of a software product. We adopt two recently proposed methods for this task, a
Graph Neural Network (GNN)
and a Transformer model, and compare them with a custom approach based on sequence alignment and
nearest neighbor search (kNN)
. The methods were tested on handcrafted datasets with explicit layout patterns, as well as large-scale public datasets of diverse mobile design layouts. Our results show that our instance-based learning algorithm outperforms both neural network approaches. Ultimately, this work contributes to establishing smarter design tools for professional designers with explainable algorithms that increase their efficacy.
GraSP: Local Grassmannian Spatio-Temporal Patterns for Unsupervised Pose Sequence Recognition
Authors: Buckchash Himanshu , Raman Balasubramanian
Published: March 2022

Many applications of action recognition, especially broad domains like surveillance or anomaly-detection, favor unsupervised methods considering that exhaustive labeling of actions is not possible. However, very limited work has happened in this domain. Moreover, the existing self-supervised approaches suffer from their dependency upon labeled data for finetuning. To this end, this paper puts forward a manifold based unsupervised pose-sequence recognition approach that leverages only the natural biases present in the data. It works by clustering the projections of temporal derivatives of the fragmented data on the Grassmann manifold. Temporal derivatives are formed by the inter-frame gradients with local and global metrics. To commensurate with this, a dynamic view-invariant pose representation is proposed. Additionally, a variable aggregation step is introduced for better feature vector quantization. Extensive empirical evaluation and ablations on several challenging datasets under three categories confirm the superiority of the proposed approach in contrast to current methods.
Distributed Gateway Selection for Video Streaming in VANET Using IP Multicast
Authors: Chowdhury Debanjan Roy , Nandi Sukumar , Goswami Diganta
Published: March 2022

The volume of video traffic as infotainment service over vehicular ad hoc network (VANET) has rapidly increased for past few years. Providing video streaming as VANET infotainment service is very challenging because of high mobility and heterogeneity of vehicular networks. While the number of mobile gateways (vehicles connected to the Internet) needs to be minimized to reduce service cost, the streaming quality also needs to be satisfactory for end-users. Existing works either focus on gateway minimization or focus on enhancing user satisfaction. We propose a video streaming solution, namely, DGSVS, which does gateway minimization with the constrained time data delivery to end-users. We formulate our constrained gateway minimization problem as
minimum set covering
(MSC) problem and solve with a distributed approximation method for MSC. We assume that only a subset of vehicles in VANET run DGSVS application. Therefore, instead of application layer cooperation for gateway-client association, network layer cooperation is proposed. We propose a novel multicast protocol DSS-CAST for this purpose, which is specialized in streaming data distribution for dynamic scenarios. We compare the performance of DGSVS with other existing protocols and found that DGSVS is most effective in service cost minimization while it is able to achieve competitive QoE performance.
Multilayer Video Encoding for QoS Managing of Video Streaming in VANET Environment
Authors: Alaya Bechir , Sellami Lamaa
Published: March 2022

Efficient delivery and maintenance of the quality of service (QoS) of audio/video streams transmitted over VANETs for mobile and heterogeneous nodes are one of the major challenges in the convergence of this network type and these services. In this context, we propose an inter-layer approach for multimedia stream transmission in a VANET environment (VSMENET). The main idea of our work is based on the dynamic adaptation of the transmission rate according to the physical rate available in the VANET. VSMENET is all about eliminating downtime during video playback by vehicle users. This involves adapting the quality of the video to the actual performance of the VANETs, intelligent encoding of video on the
Road Side Units (RSU)
side, and finally continuous maintenance of the calculation tasks on the RSU side and sufficient video data on the vehicle node side. Thus, we are interested in the process of evaluating the strict parameters of the VANETs, influencing the video transmission. For example, we propose, on the one hand, an architecture for intelligent data selection and good clock synchronization, and, on the other hand, efficient management of the availability and consumption of video data. We used the NetSim simulator to test the proposed approach performance. To this end, several algorithms such as OCLFEC, MAC, ShieldHEVC, and AntArmour have been implemented for such a performance comparison. Our work suggests that VSMENET is well concerning the average lifetime of the video packets and their delivery rate (more than 9% gain compared with other approaches).
Blockchain-Based Audio Watermarking Technique for Multimedia Copyright Protection in Distribution Networks
Authors: Natgunanathan Iynkaran , Praitheeshan Purathani , Gao Longxiang , Xiang Yong , Pan Lei
Published: March 2022

Copyright protection in multimedia protection distribution is a challenging problem. To protect multimedia data, many watermarking methods have been proposed in the literature. However, most of them cannot be used effectively in a multimedia distribution network (MDN) as they are not designed to support multi-layer watermark embedding. Multi-layer watermarking mechanisms were developed to protect multimedia data across different layers in an MDN. However, in those mechanisms, we need to trust the entities in the MDN, such as regional and country distributors. To overcome this potential drawback, in this article, we propose a novel privacy protection mechanism for MDNs by combining the advantages of both blockchain and watermarking technologies. A specifically designed watermarking algorithm is used to link the copyright information with the audio file, while a novel blockchain-based smart contract mechanism is developed to enforce the proper functioning of each entity in the distribution network. Moreover, the new audio mechanism is computationally efficient. Although audio signals are used to show the effectiveness of the proposed mechanism, the proposed approach can easily be extended to other multimedia objects, such as an image. The validity of the proposed mechanism is demonstrated by our simulation results. The proposed mechanism can benefit multimedia production companies and other entities in the MDN.
Fully Dynamic (Δ +1)-Coloring in
O
(1) Update Time
Authors: Bhattacharya Sayan , Grandoni Fabrizio , Kulkarni Janardhan , Liu Quanquan C. , Solomon Shay
Published: March 2022

The problem of (Δ +1)-vertex coloring a graph of maximum degree Δ has been extremely well studied over the years in various settings and models. Surprisingly, for the dynamic setting, almost nothing was known until recently. In SODA’18, Bhattacharya, Chakrabarty, Henzinger and Nanongkai devised a randomized algorithm for maintaining a (Δ +1)-coloring with
O
(log Δ) expected amortized update time. In this article, we present an improved randomized algorithm for (Δ +1)-coloring that achieves
O
(1) amortized update time and show that this bound holds not only in expectation but also with high probability.

Our starting point is the state-of-the-art randomized algorithm for maintaining a maximal matching (Solomon, FOCS’16). We carefully build on the approach of Solomon, but, due to inherent differences between the maximal matching and (Δ +1)-coloring problems, we need to deviate significantly from it in several crucial and highly nontrivial points.

1
4 vs 7 Sparse Undirected Unweighted Diameter Is SETH-hard at Time
n
4/3
Authors: Bonnet Édouard
Published: March 2022

We show, assuming the Strong Exponential Time Hypothesis, that for every ε > 0, approximating undirected unweighted
Diameter
on
n
-vertex
m
-edge graphs within ratio 7/4 - ε requires
m

4/3 -
o
(1)

time, even when
m
= Õ(
n
). This is the first result that conditionally rules out a near-linear time 5/3-approximation for undirected
Diameter
.
Constant-time Dynamic (Δ +1)-Coloring
Authors: Henzinger Monika , Peng Pan
Published: March 2022

We give a fully dynamic (Las-Vegas style) algorithm with
constant expected amortized
time per update that maintains a proper (Δ +1)-vertex coloring of a graph with maximum degree at most Δ. This improves upon the previous
O
(log Δ)-time algorithm by Bhattacharya et al. (SODA 2018). Our algorithm uses an approach based on assigning random ranks to vertices and does not need to maintain a hierarchical graph decomposition. We show that our result does not only have optimal running time but is also optimal in the sense that already deciding whether a Δ-coloring exists in a dynamically changing graph with maximum degree at most Δ takes Ω (log
n
) time per operation.
Densely Enhanced Semantic Network for Conversation System in Social Media
Authors: Li Yongrui , Wang Zengfu , Yu Jun
Published: March 2022

The human–computer conversation system is a significant application in the field of multimedia. To select an appropriate response, retrieval-based systems model the matching between the dialogue history and response candidates. However, most of the existing methods cannot fully capture and utilize varied matching patterns, which may degrade the performance of the systems. To address the issue, a densely enhanced semantic network (DESN) is proposed in our work. Given a multi-turn dialogue history and a response candidate, DESN first constructs the semantic representations of sentences from the word perspective, the sentence perspective, and the dialogue perspective. In particular, the dialogue perspective is a novel one introduced in our work. The dependencies between a single sentence and the whole dialogue are modeled from the dialogue perspective. Then, the response candidate and each utterance in the dialogue history are made to interact with each other. The varied matching patterns are captured for each utterance–response pair by using a dense matching module. The matching patterns of all the utterance–response pairs are accumulated in chronological order to calculate the matching degree between the dialogue history and the response. The responses in the candidate pool are ranked with the matching degree, thereby returning the most appropriate candidate. Our model is evaluated on the benchmark datasets. The experimental results prove that our model achieves significant and consistent improvement when compared with other baselines.
Area-Optimal Simple Polygonalizations: The CG Challenge 2019
Authors: Demaine Erik D. , Fekete Sándor P. , Keldenich Phillip , Krupke Dominik , Mitchell Joseph S. B.
Published: March 2022

We give an overview of theoretical and practical aspects of finding a simple polygon of minimum (Min-Area) or maximum (Max-Area) possible area for a given set ofnpoints in the plane. Both problems are known to beNP-hard and were the subject of the 2019 Computational Geometry Challenge, which presented the quest of finding good solutions to more than 200 instances, ranging fromn= 10 all the way ton= 1, 000, 000.
A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View
Authors: Liu Caixia , Kong Dehui , Wang Shaofan , Li Jinghua , Yin Baocai
Published: March 2022

Recovering the geometry of an object from a single depth image is an interesting yet challenging problem. While previous learning based approaches have demonstrated promising performance, they don’t fully explore spatial relationships of objects, which leads to unfaithful and incomplete 3D reconstruction. To address these issues, we propose a
Spatial Relationship Preserving Adversarial Network (SRPAN)
consisting of
3D Capsule Attention Generative Adversarial Network (3DCAGAN)
and
2D Generative Adversarial Network (2DGAN)
for coarse-to-fine 3D reconstruction from a single depth view of an object. Firstly, 3DCAGAN predicts the coarse geometry using an encoder-decoder based generator and a discriminator. The generator encodes the input as latent capsules represented as stacked activity vectors with local-to-global relationships (i.e., the contribution of components to the whole shape), and then decodes the capsules by modeling local-to-local relationships (i.e., the relationships among components) in an attention mechanism. Afterwards, 2DGAN refines the local geometry slice-by-slice, by using a generator learning a global structure prior as guidance, and stacked discriminators enforcing local geometric constraints. Experimental results show that SRPAN not only outperforms several state-of-the-art methods by a large margin on both synthetic datasets and real-world datasets, but also reconstructs unseen object categories with a higher accuracy.
A Novel Multi-Sample Generation Method for Adversarial Attacks
Authors: Duan Mingxing , Li Kenli , Deng Jiayan , Xiao Bin , Tian Qi
Published: March 2022

Deep learning models are widely used in daily life, which bring great convenience to our lives, but they are vulnerable to attacks. How to build an attack system with strong generalization ability to test the robustness of deep learning systems is a hot issue in current research, among which the research on black-box attacks is extremely challenging. Most current research on black-box attacks assumes that the input dataset is known. However, in fact, it is difficult for us to obtain detailed information for those datasets. In order to solve the above challenges, we propose a multi-sample generation model for black-box model attacks, called MsGM. MsGM is mainly composed of three parts: multi-sample generation, substitute model training, and adversarial sample generation and attack. Firstly, we design a multi-task generation model to learn the distribution of the original dataset. The model first converts an arbitrary signal of a certain distribution into the shared features of the original dataset through deconvolution operations, and then according to different input conditions, multiple identical sub-networks generate the corresponding targeted samples. Secondly, the generated sample features achieve different outputs through querying the black-box model and training the substitute model, which are used to construct different loss functions to optimize and update the generator and substitute model. Finally, some common white-box attack methods are used to attack the substitute model to generate corresponding adversarial samples, which are utilized to attack the black-box model. We conducted a large number of experiments on the MNIST and CIFAR-10 datasets. The experimental results show that under the same settings and attack algorithms, MsGM achieves better performance than the based models.
Predicting the Impact of Disruptions to Urban Rail Transit Systems
Authors: Mo Xiaoyun , Cao Chu , Li Mo , Wang David Z. W.
Published: March 2022

Service disruptions of rail transit systems have become more frequent in the past decade in urban cities, due to various reasons, such as power failures, signal errors, and so on. Smart transit cards provide detailed tapping records of commuters, which enable us to infer their trajectories under both normal and disruptive circumstances. In this article, we study and predict the impact of disruptions on commuters and further evaluate the vulnerability of the rail system. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact, and we derive the predictor of each metric based on the inferred alternative route choices of commuters under disruptive circumstances. We demonstrate that the alternative route choices contribute to more similar feature distribution among different disruptions, which is crucial to tackling the main challenge of abnormal data scarcity and is beneficial for obtaining more reliable predictors for future disruptions. We evaluate our approach with a real-world transit card dataset. The result demonstrates the effectiveness of our method. Based on the predictors, we further analyse the vulnerability of the rail system. An evaluation with cross validation from taxi GPS trajectory data indicates its efficacy in discovering vulnerable rail stations as well as Origin-Destination pairs.