bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of AI-Manipulated Fake Faces via Mining Generalized Features
Authors: Yu Yang , Ni Rongrong , Li Wenjie , Zhao Yao
Published: March 2022

Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society. Although existing detection methods consider different categories of fake faces, the performance on detecting the fake faces with “unseen” manipulation techniques is still poor due to the distribution bias among cross-manipulation techniques. To solve this problem, we propose a novel framework that focuses on mining intrinsic features and further eliminating the distribution bias to improve the generalization ability. First, we focus on mining the intrinsic clues in the channel difference image (CDI) and spectrum image (SI) view of two different aspects, including the camera imaging process and the indispensable step in AI manipulation process. Then, we introduce the Octave Convolution and an attention-based fusion module to effectively and adaptively mine intrinsic features from CDI and SI view of these two different but intrinsic aspects. Finally, we design an alignment module to eliminate the bias of manipulation techniques to obtain a more generalized detection framework. We evaluate the proposed framework on four categories of fake faces datasets with the most popular and state-of-the-art manipulation techniques and achieve very competitive performances. We further conduct experiments on cross-manipulation techniques, and the results of our method show the superior advantages on improving generalization ability.
Cross-modal Graph Matching Network for Image-text Retrieval
Authors: Cheng Yuhao , Zhu Xiaoguang , Qian Jiuchao , Wen Fei , Liu Peilin
Published: March 2022

Image-text retrieval is a fundamental cross-modal task whose main idea is to learn image-text matching. Generally, according to whether there exist interactions during the retrieval process, existing image-text retrieval methods can be classified into independent representation matching methods and cross-interaction matching methods. The independent representation matching methods generate the embeddings of images and sentences independently and thus are convenient for retrieval with hand-crafted matching measures (e.g., cosine or Euclidean distance). As to the cross-interaction matching methods, they achieve improvement by introducing the interaction-based networks for inter-relation reasoning, yet suffer the low retrieval efficiency. This article aims to develop a method that takes the advantages of cross-modal inter-relation reasoning of cross-interaction methods while being as efficient as the independent methods. To this end, we propose a graph-based
Cross-modal Graph Matching Network (CGMN)
, which explores both intra- and inter-relations without introducing network interaction. In CGMN, graphs are used for both visual and textual representation to achieve intra-relation reasoning across regions and words, respectively. Furthermore, we propose a novel graph node matching loss to learn fine-grained cross-modal correspondence and to achieve inter-relation reasoning. Experiments on benchmark datasets MS-COCO, Flickr8K, and Flickr30K show that CGMN outperforms state-of-the-art methods in image retrieval. Moreover, CGMM is much more efficient than state-of-the-art methods using interactive matching. The code is available at
https://github.com/cyh-sj/CGMN
.
Area Optimal Polygonization Using Simulated Annealing
Authors: Goren Nir , Fogel Efi , Halperin Dan
Published: March 2022

We describe a practical method to find near-optimal solutions for the area-optimal simple polygonization problem: Given a set of points
S
in the plane, the objective is to find a simple polygon of minimum or maximum area defined by
S
. Our approach is based on the celebrated metaheuristic Simulated Annealing. The method consists of a modular pipeline of steps, where each step can be implemented in various ways and with several parameters controlling it. We have implemented several different algorithms and created an application that computes a polygon with minimal (or maximal) area. We experimented with the various algorithmic options and with the controlling parameters of each algorithm to tune up the pipeline. Then, we executed the application on each of the benchmark instances, exploiting a grid of servers, to obtain near optimal results.
Supporting People with Acquired Brain Injury to Use a Reminding App; Narrow-deep vs. Broad-shallow User Interfaces
Authors: Jamieson Matthew , Lennon Marilyn , Cullen Breda , Brewster Stephen , Evans Jonathan
Published: March 2022

People with memory impairments following an acquired brain injury stand to benefit from smartphone apps as memory aids. Due, in part, to usability issues they use smartphone-based reminding less than the general population. Evidence suggests this group may benefit from user interface (UI) designs with more screens with less information per screen (narrow-deep UI) rather than fewer screens with more information per screen (broad-shallow UI). This study compared the difference in speed, accuracy, guidance needed, and task load for 32 people with acquired brain injury when setting reminders using narrow-deep and broad-shallow UI. They were also given cognitive assessments (measuring selective attention, executive functioning, and overall executive and memory ability) and interviewed about their UI preference. There was a significant difference in accuracy; participants were less accurate (they made two more errors on average for every three reminders set) using a broad-shallow compared to narrow-deep UI. The reason for this difference was that participants omitted more information when using broad-shallow UI. There were no differences in speed, guidance required, and overall task-load. Participants with better selective attention and more experience with smartphones benefited the most from narrow-deep UI compared to broad-shallow UI. Most participants preferred one UI over the other. Those who preferred narrow-deep found it easier to use, that they missed less information and liked having one piece of information at a time. Those who preferred broad-shallow found it easier to review the information and felt less likely to lose track. The findings can inform that implementation of UI choices to make apps more accessible for those with cognitive impairments.
Computing the Inverse Geodesic Length in Planar Graphs and Graphs of Bounded Treewidth
Authors: Cabello Sergio
Published: March 2022

The inverse geodesic length of a graph
G
is the sum of the inverse of the distances between all pairs of distinct vertices of
G
. In some domains, it is known as the Harary index or the global efficiency of the graph. We show that, if
G
is planar and has
n
vertices, then the inverse geodesic length of
G
can be computed in roughly
O
(
n
9/5
) time. We also show that, if
G
has
n
vertices and treewidth at most
k
, then the inverse geodesic length of
G
can be computed in
O
(
n
log

O
(
k
)

n
) time. In both cases, we use techniques developed for computing the sum of the distances, which does not have “inverse” component, together with batched evaluations of rational functions.
Quasipolynomial Multicut-mimicking Networks and Kernels for Multiway Cut Problems
Authors: Wahlström Magnus
Published: March 2022

We show the existence of an exact mimicking network of

k
O

(

log
k

) edges for minimum multicuts over a set of terminals in an undirected graph, where
k
is the total capacity of the terminals, i.e., the sum of the degrees of the terminal vertices. Furthermore, using the best available approximation algorithm for
Small Set Expansion
, we show that a mimicking network of

k
O

(

log3
k

) edges can be computed in randomized polynomial time. As a consequence, we show quasipolynomial kernels for several problems, including
Edge Multiway Cut
,
Group Feedback Edge Set
for an arbitrary group, and
Edge Multicut
parameterized by the solution size and the number of cut requests. The result combines the matroid-based irrelevant edge approach used in the kernel for

s
-Multiway Cut

with a recursive decomposition and sparsification of the graph along sparse cuts. This is the first progress on the kernelization of
Multiway Cut
problems since the kernel for

s
-Multiway Cut

for constant value of
s
(Kratsch and Wahlström, FOCS 2012).
Generation of Realistic Synthetic Financial Time-series
Authors: Dogariu Mihai , Ştefan Liviu-Daniel , Boteanu Bogdan Andrei , Lamba Claudiu , Kim Bomi , Ionescu Bogdan
Published: March 2022

Financial markets have always been a point of interest for automated systems. Due to their complex nature, financial algorithms and fintech frameworks require vast amounts of data to accurately respond to market fluctuations. This data availability is tied to the daily market evolution, so it is impossible to accelerate its acquisition. In this article, we discuss several solutions for augmenting financial datasets via synthesizing realistic time-series with the help of generative models. This problem is complex, since financial time series present very specific properties, e.g., fat-tail distribution, cross-correlation between different stocks, specific autocorrelation, cluster volatility and so on. In particular, we propose solutions for capturing cross-correlations between different stocks and for transitioning from fixed to variable length time-series without resorting to sequence modeling networks, and adapt various network architectures, e.g., fully connected and convolutional GANs, variational autoencoders, and generative moment matching networks. Finally, we tackle the problem of evaluating the quality of synthetic financial time-series. We introduce qualitative and quantitative metrics, along with a portfolio trend prediction framework that validates our generative models’ performance. We carry out experiments on real-world financial data extracted from the US stock market, proving the benefits of these techniques.
Importance Sampling for a Simple Markovian Intensity Model Using Subsolutions
Authors: Djehiche Boualem , Hult Henrik , Nyquist Pierre
Published: March 2022

This article considers importance sampling for estimation of rare-event probabilities in a specific collection of Markovian jump processes used for, e.g., modeling of credit risk. Previous attempts at designing importance sampling algorithms have resulted in poor performance and the main contribution of the article is the design of efficient importance sampling algorithms using subsolutions. The dynamics of the jump processes cause the corresponding Hamilton-Jacobi equations to have an intricate state-dependence, which makes the design of efficient algorithms difficult. We provide theoretical results that quantify the performance of importance sampling algorithms in general and construct asymptotically optimal algorithms for some examples. The computational gain compared to standard Monte Carlo is illustrated by numerical examples.
Word Sense Disambiguation using Cooperative Game Theory and Fuzzy Hindi WordNet based on ConceptNet
Authors: Jain Goonjan , Lobiyal D. K.
Published: March 2022

Natural Language is fuzzy in nature. The fuzziness of Hindi language was captured in the
Fuzzy Hindi WordNet (FHWN)
. FHWN assigned membership values to fuzzy relationships by consulting experts from various domains. However, these membership values need to be corrected. In the proposed work, we compute the membership values of fuzzy semantic relations using ConceptNet. Later, we perform WSD of Hindi text using cooperative game theoretic approach. We used the Shapley Value centrality measure where we predict which coalition of players (word senses) proves to be the most beneficial. We tested and compared our algorithm with the existing state-of-the-art approaches of Hindi on three datasets and results are better on all the three datasets. One more notable aspect is that the results are quite stable even if the fuzzy membership values of fuzzy graphs changes.
Konkani WordNet: Corpus-Based Enhancement using Crowdsourcing
Authors: Manerkar Sanjana , Asnani Kavita , Khorjuvenkar Preeti Ravindranath , Desai Shilpa , Pawar Jyoti D.
Published: March 2022

Konkani is one of the languages included in the eighth schedule of the Indian constitution. It is the official language of Goa and is spoken mainly in Goa and some places in Karnataka and Kerala. Konkani WordNet or Konkani Shabdamalem (kōṁkanī śabdamālēṁ) as it has been referred to, was developed under the Indradhanush WordNet Project Consortium during the period from August 2010 to October 2013. This project was funded by Technology Development for Indian Languages (TDIL), Department of Electronics & Information Technology (Deity), and Ministry of Communication and Information Technology (MCIT). The work on Konkani WordNet has halted since the end of the project. Currently, the Konkani WordNet contains around 32,370 synsets. However, to make it a powerful resource for NLP applications in the Konkani language, a need is felt for research work toward enhancement of the Konkani WordNet via community involvement. Crowdsourcing is a technique in which the knowledge of the crowd is utilized to accomplish a particular task.

In this article, we have presented the details of the crowdsourcing platform named “Konkani Shabdarth” (kōṁkanī śabdārth). Konkani Shabdarth attempts to use the knowledge of Konkani speaking people for creating new synsets and perform the quantitative enhancement of the wordnet. It also intends to work toward enhancing the overall quality of the Konkani WordNet by validating the existing synsets, and adding the missing words to the existing synsets. A text corpus named “Konkani Shabdarth Corpus”, has been created from the Konkani literature while implementing the Konkani Shabdarth tool. Using this corpus, 572 root words that are missing from the Konkani WordNet have been identified which are given as input to Konkani Shabdarth. As of now, total 94 users have registered on the platform, out of which 25 users have actually played the game. Currently, 71 new synsets have been obtained for 21 words. For some of the words, multiple entries for the concept definition have been received. This overlap is essential for automating the process of validating the synsets. Due to the pandemic period, it has been difficult to train and get players to actually play the game and contribute. We studied the impact of adding missing words from other existing Konkani text corpus on the coverage of Konkani WordNet. The expected increase in the percentage coverage of Konkani WordNet has been found to be in the range 20–27 after adding the missing words from the Konkani Shabdarth corpus in comparison to the other corpora for which the increase is in the range 1–10.
A Method Using Generative Adversarial Networks for Robustness Optimization
Authors: Feldkamp Niclas , Bergmann Soeren , Conrad Florian , Strassburger Steffen
Published: March 2022

The evaluation of robustness is an important goal within simulation-based analysis, especially in production and logistics systems. Robustness refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has minimal effect on a given output. In this paper, we present an approach for optimizing robustness based on deep generative models, a special method of deep learning. We propose a method consisting of two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors in a competitive, turn-based game. In a case study, the proposed method is tested and compared to traditional methods for robustness analysis including Taguchi method and Response Surface Method.
Enabling Morally Sensitive Robotic Clarification Requests
Authors: Jackson Ryan Blake , Williams Tom
Published: March 2022

The design of current natural language-oriented robot architectures enables certain architectural components to circumvent moral reasoning capabilities. One example of this is reflexive generation of clarification requests as soon as referential ambiguity is detected in a human utterance. As shown in previous research, this can lead robots to (1) miscommunicate their moral dispositions and (2) weaken human perception or application of moral norms within their current context. We present a solution to these problems by performing moral reasoning on each potential disambiguation of an ambiguous human utterance and responding accordingly, rather than immediately and naively requesting clarification. We implement our solution in the Distributed Integrated Cognition Affect and Reflection robot architecture, which, to our knowledge, is the only current robot architecture with both moral reasoning and clarification request generation capabilities. We then evaluate our method with a human subjects experiment, the results of which indicate that our approach successfully ameliorates the two identified concerns.
On Memory Traffic and Optimisations for Low-order Finite Element Assembly Algorithms on Multi-core CPUs
Authors: Trotter James D. , Cai Xing , Funke Simon W.
Published: March 2022

Motivated by the wish to understand the achievable performance of finite element assembly on unstructured computational meshes, we dissect the standard cellwise assembly algorithm into four kernels, two of which are dominated by irregular memory traffic. Several optimisation schemes are studied together with associated lower and upper bounds on the estimated memory traffic volume. Apart from properly reordering the mesh entities, the two most significant optimisations include adopting a lookup table in adding element matrices or vectors to their global counterparts, and using a row-wise assembly algorithm for multi-threaded parallelisation. Rigorous benchmarking shows that, due to the various optimisations, the actual volumes of memory traffic are in many cases very close to the estimated lower bounds. These results confirm the effectiveness of the optimisations, while also providing a recipe for developing efficient software for finite element assembly.
Minimizing Stack Memory for Partitioned Mixed-criticality Scheduling on Multiprocessor Platforms
Authors: Zhao Qingling , Qu Mengfei , Gu Zonghua , Zeng Haibo
Published: March 2022

A Mixed-Criticality System (MCS) features the integration of multiple subsystems that are subject to different levels of safety certification on a shared hardware platform. In cost-sensitive application domains such as automotive E/E systems, it is important to reduce application memory footprint, since such a reduction may enable the adoption of a cheaper microprocessor in the family. Preemption Threshold Scheduling (PTS) is a well-known technique for reducing system stack usage. We consider partitioned multiprocessor scheduling, with Preemption Threshold Adaptive Mixed-Criticality (PT-AMC) as the task scheduling algorithm on each processor and address the optimization problem of finding a feasible task-to-processor mapping with minimum total system stack usage on a resource-constrained multi-processor. We present the Extended Maximal Preemption Threshold Assignment Algorithm (EMPTAA), with dual purposes of improving the taskset’s schedulability if it is not already schedulable, and minimizing system stack usage of the schedulable taskset. We present efficient heuristic algorithms for finding sub-optimal yet high-quality solutions, including Maximum Utilization Difference based Partitioning (MUDP) and MUDP with Backtrack Mapping (MUDP-BM), as well as a Branch-and-Bound (BnB) algorithm for finding the optimal solution. Performance evaluation with synthetic task sets demonstrates the effectiveness and efficiency of the proposed algorithms.
An End-to-end Heterogeneous Restraint Network for RGB-D Cross-modal Person Re-identification
Authors: Wu Jingjing , Jiang Jianguo , Qi Meibin , Chen Cuiqun , Zhang Jingjing
Published: March 2022

The RGB-D cross-modal person re-identification (re-id) task aims to identify the person of interest across the RGB and depth image modes. The tremendous discrepancy between these two modalities makes this task difficult to tackle. Few researchers pay attention to this task, and the deep networks of existing methods still cannot be trained in an end-to-end manner. Therefore, this article proposes an end-to-end module for RGB-D cross-modal person re-id. This network introduces a cross-modal relational branch to narrow the gaps between two heterogeneous images. It models the abundant correlations between any cross-modal sample pairs, which are constrained by heterogeneous interactive learning. The proposed network also exploits a dual-modal local branch, which aims to capture the common spatial contexts in two modalities. This branch adopts shared attentive pooling and mutual contextual graph networks to extract the spatial attention within each local region and the spatial relations between distinct local parts, respectively. Experimental results on two public benchmark datasets, that is, the BIWI and RobotPKU datasets, demonstrate that our method is superior to the state-of-the-art. In addition, we perform thorough experiments to prove the effectiveness of each component in the proposed method.
Pansharpening Scheme Using Bi-dimensional Empirical Mode Decomposition and Neural Network
Authors: Saxena Nidhi , Raman Balasubramanian
Published: March 2022

The pansharpening is a combination of multispectral (MS) and panchromatic (PAN) images that produce a high-spatial-spectral-resolution MS images. In multiresolution analysis–based pansharpening schemes, some spatial and spectral distortions are found. It can be reduced by adding spatial detail images of the PAN image into MS images. In the convolution neural network– (CNN) based method, the lowpass filter image extracted by the CNN model when MS and PAN images are directly applied into the input. The feature values are very high and reduce the conversion efficiency. In the proposed scheme, bi-dimensional empirical mode decomposition is used to extract the spatial detail information of the PAN image to reduce the feature values of the input. This extracted PAN image information is applied to the CNN to produce the non-linear changes in the image pixels and transformed into the perfect spatial detail image. It identifies the spatial and spectral detail quantity for the proposed scheme and it also varies with the different datasets automatically of the same satellite images. Simulation results in the context of qualitative and quantitative analysis demonstrate the effectiveness of proposed scheme applied on datasets collected by different satellites.
The Influence of Robot Designs on Human Compliance and Emotion: A Virtual Reality Study in the Context of Future Public Transport
Authors: Herzog Olivia , Forchhammer Niklas , Kong Penny , Maruhn Philipp , Cornet Henriette , Frenkler Fritz
Published: March 2022

As robots enter everyday environments, they start performing tasks originally performed by humans. One field of application is the public transport sector. The deployment of autonomous transport systems comes with a lack of human contact persons for help, guidance, and crowd management. This elicits challenges regarding redirecting and managing passengers. Current solutions on platforms can be replaced or enriched with service robots whose task includes crowd management as well as social interaction. This study investigates how the human-likeness of a robot influences the compliance and emotions of public transport users. A Virtual Reality experiment was conducted (N=33) to evaluate two different robot designs in a bus stop boarding scenario. The two robot designs differ in terms of humanoid appearance. In different experimental trials, participants had to perform a given task that was nullified by instructions from one of the two robots. Additionally, the dissonance of the situation was altered so that the environment either justified the robot's interference or not. Compliant behavior, pleasure, and arousal ratings, as well as task processing times were recorded. The experiment included an individual interview and a post-study questionnaire. The results suggest that future deployment of service robots has the potential to redirect passengers. In dissonant situations, clear reasoning must be given to make the robot effective. However, the robot's visual appearance has a more substantial impact on arousal and subjective preferences than on evoked behavior. The study implies that the presence of a service robot can influence peoples’ choices and gives hints about the importance of giving a reason. However, objectively, the level of the robot's humanoid appearance did not make a difference.
What's in an ALT Tag? Exploring Caption Content Priorities through Collaborative Captioning
Authors: Muehlbradt Annika , Kane Shaun K.
Published: March 2022

Evaluating the quality of accessible image captions with human raters is difficult, as it may be difficult for a visually impaired user to know how comprehensive a caption is, whereas a sighted assistant may not know what information a user will need from a caption. To explore how image captioners and caption consumers assess caption content, we conducted a series of collaborative captioning sessions in which six pairs, consisting of a blind person and their sighted partner, worked together to discuss, create, and evaluate image captions. By making captioning a collaborative task, we were able to observe captioning strategies, to elicit questions and answers about image captions, and to explore blind users’ caption preferences. Our findings provide insight about the process of creating good captions and serve as a case study for cross-ability collaboration between blind and sighted people.
Development and Evaluation of a Tool for Assisting Content Creators in Making PDF Files More Accessible
Authors: Pradhan Debashish , Rajput Tripti , Rajkumar Aravind Jembu , Lazar Jonathan , Jain Rajiv , Morariu Vlad I. , Manjunatha Varun
Published: March 2022

Most PDF documents are inaccessible for people with disabilities, creating barriers in education, science, commerce, e-government, and recreation. Documents in PDF format are considered harder to make accessible than documents in other formats, primarily due to the insufficient tools available to assist content creators. In this article, we present the research and development of Ally, a new tool to assist content creators in remediating their PDF files to improve accessibility. Ally utilizes best practices from other areas of HCI research to create a more efficient and effective interaction for remediating regions, headers, reading order, and tables in a PDF document for improved accessibility. Twenty participants attempted to complete the same PDF accessibility remediation tasks using both Ally and a standard industry tool, Adobe Acrobat Pro. Ally was almost twice as fast and three times as accurate compared to Acrobat Pro, with participants reporting a strong preference for and a much higher level of satisfaction with Ally. The approaches taken in Ally improve the ability to create accessible PDFs efficiently and accurately for the four important aspects studied, but future work will need to incorporate additional functionality, related to remediating alt text, forms, and other aspects of PDF accessibility.
Accelerating Transform Algorithm Implementation for Efficient Intra Coding of 8K UHD Videos
Authors: Guo Yang , Gao Wei , Ma Siwei , Li Ge
Published: March 2022

Real-time ultra-high-definition (UHD) video applications have attracted much attention, where the encoder side urgently demands the high-throughput two-dimensional (2D) transform hardware implementation for the latest video coding standards. This article proposes an effective acceleration method for transform algorithm in UHD intra coding based on the third generation of audio video coding standard (AVS3). First, by conducting detailed statistical analysis, we devise an efficient hardware-friendly transform algorithm that can reduce running cycles and resource consumption remarkably. Second, to implement multiplierless computation for saving resources and power, a series of shift-and-add unit (SAU) hardwares are investigated to have much less adoptions of shifters and adders than the existing methods. Third, different types of hardware acceleration methods, including calculation pipelining, logical-loop unrolling, and module-level parallelism, are designed to efficaciously support the data-intensive high frame-rate 8K UHD video coding. Finally, due to the scarcity of 8K video sources, we also provide a new dataset for the performance verification. Experimental results demonstrate that our proposed method can effectively fulfill the real-time 8K intra encoding at beyond 60 fps, with very negligible loss on rate-distortion (R-D) performance, which is averagely 0.98% Bjontegaard-Delta Bit-Rate (BD-BR).
A Secure and Anonymous Communicate Scheme over the Internet of Things
Authors: Sun Qindong , Lin Kai , Si Chengxiang , Xu Yanyue , Li Shancang , Gope Prosanta
Published: March 2022

Anonymous exchange of data has a strong demand in many scenarios. With the development of IoT and wireless networks, plenty of smart devices are interconnected through wireless technologies such as 5G and Wi-Fi, making it possible to use them for information exchanging. The authors find a P2P network model for secure and anonymous communication, which is a typical Crowds system and the operating mechanism meets the characteristics of limited-resources of IoT devices. Based on this network model, the authors design a lightweight communication scheme for the remote-control system in this work, using two kinds ofVirtual-Spaces to achieve the purpose of identity announced and data exchanged. The authors implemented a prototype system of the scheme and tested it over theFreenet, proving that the scheme can effectively resist the impact of flow analysis on the anonymity of communication while ensuring communication data security. By analyzing the scheme’s performance, the author believes that the scheme is practical and is suitable for scenarios that are not time-sensitive but require high anonymity.
EGM: An Efficient Generative Model for Unrestricted Adversarial Examples
Authors: Xiang Tao , Liu Hangcheng , Guo Shangwei , Gan Yan , Liao Xiaofeng
Published: March 2022

Unrestricted adversarial examples allow the attacker to start attacks without given clean samples, which are quite aggressive and threatening. However, existing works for generating unrestricted adversary examples are quite inefficient and cannot achieve a high success rate. In this article, we explore an end-to-end and effective solution for unrestricted adversary example generation. To stabilize the training process and make our generative model converge to satisfactory results, we design a novel decoupled two-step efficient generative model (EGM), which contains a conditional reference generator and a conditional adversarial transformer. The former is responsible for generating reference samples from noises and source classes. The latter is responsible for converting the reference sample into adversarial examples corresponding to target classes. To improve the success rate, we design a new strategy, augmentation of adversarial labels to produce dynamic target labels and enhance the exploration ability of EGM. Such a strategy can be also applied to existing attacks to improve their attack success rates, which is of independent interest. We conduct extensive experiments to evaluate our proposed model and demonstrate the necessity of decoupling the generation process in EGM. Experimental results show our EGM is much faster and achieves a higher success rate than the state-of-the-art attacks.
Concurrent Low-power Listening: A New Design Paradigm for Duty-cycling Communication
Authors: Liu Daibo , Cao Zhichao , Jiang Hongbo , Zhou Siwang , Xiao Zhu , Zeng Fanzi
Published: March 2022

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.
Which Conference Is That? A Case Study in Computer Science
Authors: Demetrescu Camil , Finocchi Irene , Ribichini Andrea , Schaerf Marco
Published: March 2022

Conferences play a major role in some disciplines such as computer science and are often used in research quality evaluation exercises. Differently from journals and books, for which ISSN and ISBN codes provide unambiguous keys, recognizing the conference series in which a paper was published is a rather complex endeavor: There is no unique code assigned to conferences, and the way their names are written may greatly vary across years and catalogs. In this article, we propose a technique for the entity resolution of conferences based on the analysis of different semantic parts of their names. We present the results of an investigation of our technique on a dataset of 42,395 distinct computer science conference names excerpted from the DBLP computer science repository,

1

which we automatically link to different authority files. With suitable data cleaning, the precision of our record linkage algorithm can be as high as 94%. A comparison with results obtainable using state-of-the-art general-purpose record linkage algorithms rounds off the article, showing that our
ad hoc
solution largely outperforms them in terms of the quality of the results.
Federated Self-training for Semi-supervised Audio Recognition
Authors: Tsouvalas Vasileios , Saeed Aaqib , Ozcelebi Tanir
Published: March 2022

Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartphones and virtual assistants, labeling is entrusted to the clients or labels are extracted in an automated way. Specifically, in the case of audio data, acquiring semantic annotations can be prohibitively expensive and time-consuming. As a result, an abundance of audio data remains unlabeled and unexploited on users’ devices. Most existing federated learning approaches focus on supervised learning without harnessing the unlabeled data. In this work, we study the problem of semi-supervised learning of audio models via self-training in conjunction with federated learning. We propose 
FedSTAR
to exploit large-scale on-device unlabeled data to improve the generalization of audio recognition models. We further demonstrate that self-supervised pre-trained models can accelerate the training of on-device models, significantly improving convergence within fewer training rounds. We conduct experiments on diverse public audio classification datasets and investigate the performance of our models under varying percentages of labeled and unlabeled data. Notably, we show that with as little as 3% labeled data available, 
FedSTAR
 on average can improve the recognition rate by 13.28% compared to the fully supervised federated model.
GPER-dependent estrogen signaling increases cardiac GCN5L1 expression
Authors: Manning Janet R. , Thapa Dharendra , Zhang Manling , Stoner Michael W. , Sembrat John C. , Rojas Mauricio , Scott Iain
Published: March 2022

We demonstrate here for the first time that mitochondrial protein acetylation is increased in female hearts, associated with an increase in GCN5L1 levels through a GPER-dependent mechanism. These findings reveal a new potential mediator of divergent cardiac mitochondrial function between men and women.
Bile acids and salt-sensitive hypertension: a role of the gut-liver axis
Authors: Ishimwe Jeanne A. , Dola Thanvi , Ertuglu Lale A. , Kirabo Annet
Published: March 2022

Salt-sensitivity of blood pressure (SSBP) affects 50% of the hypertensive and 25% of the normotensive populations. Importantly, SSBP is associated with increased risk for mortality in both populations independent of blood pressure. Despite its deleterious effects, the pathogenesis of SSBP is not fully understood. Emerging evidence suggests a novel role of bile acids in salt-sensitive hypertension and that they may play a crucial role in regulating inflammation and fluid volume homeostasis. Mechanistic evidence implicates alterations in the gut microbiome, the epithelial sodium channel (ENaC), the farnesoid X receptor, and the G protein-coupled bile acid receptor TGR5 in bile acid-mediated effects on cardiovascular function. The mechanistic interplay between excess dietary sodium-induced alterations in the gut microbiome and immune cell activation, bile acid signaling, and whether such interplay may contribute to the etiology of SSBP is still yet to be defined. The main goal of this review is to discuss the potential role of bile acids in the pathogenesis of cardiovascular disease with a focus on salt-sensitive hypertension.
Irx5 and transient outward K+ currents contribute to transmural contractile heterogeneities in the mouse ventricle
Authors: Kim Kyoung-Han , Oh Yena , Liu Jie , Dababneh Saif , Xia Ying , Kim Ri Youn , Kim Dae-Kyum , Ban Kiwon , Husain Mansoor , Hui Chi-Chung , Backx Peter H.
Published: March 2022

Irx5 is a vital transcription factor that establishes the transmural heterogeneity of ventricular myocyte contractility, thereby ensuring proper contractile function in the healthy heart. Regional differences in excitation-contraction coupling in the ventricular myocardium are primarily mediated through the inverse relationship between Irx5 and the fast transient outward K+ current ( Ito,f) across the ventricular wall.
Axial Compression Behavior of Rubberized Concrete-Filled Square Steel Tubular Columns after Exposed to High Temperatures
Authors: Yan Lanlan , Liang Jiongfeng , Li Wei
Published: March 2022

The experimental studies on the axial compression behaviors of rubberized concrete-filled square steel tubular columns after exposed to high temperatures were investigated in this paper. Ten specimens, including nine rubberized concrete-filled square steel tubular columns with 5%, 10%, and 20% rubber aggregate replacement rate, respectively, and one natural concrete-filled square steel tubular column with 0% rubber aggregate replacement rate after exposed to 20°C, 200°C, 400°C, 600°C, and 800°C for 30 min, 90 min, and 180 min, respectively, were subjected to test. The failure mode and load-displacement curve of the specimens were displayed and analyzed. The test results showed that the higher the replacement rate was, the smaller the ultimate load the specimen could bear and the better the ductility exhibited. The stiffness change of the specimen was not obvious, and the stiffness of the specimen with 20% rubber aggregate replacement rate decreased significantly. With the increase of temperature, the ultimate load and stiffness of the specimen decreased, and the stiffness of the specimen decreased greatly at 800°C. Based on the experimental results, a formula for calculating the bearing capacity of rubberized concrete-filled square steel tubular column after exposed to high temperatures was proposed, and the calculated results were in good agreement with the experimental values.
Damage and Fatigue Life Evaluation for Laser Cladding Remanufactured Wheel
Authors: Hua Liang , Zhou Run-dong
Published: March 2022

Owing to high speeds and heavy loads, the wheels of trains and locomotives undergo gradual but significant damage. Conventionally, these damaged wheels are repaired using the rotatory repair method; however, this approach results in large amounts of material waste. To address this issue, in this study, laser cladding remanufacturing technology was employed to repair a damaged wheel. The contact stress and creep characteristics of the cladded wheel were determined using Simpack, a dynamic simulation software program. The existing damage function model, which is based on the wear number, was modified, and this modified model was used to perform damage assessments for the remanufactured wheel. Furthermore, a life evaluation model for wheels remanufactured using this laser cladding technology was established. The service life of the cladded wheel calculated using this model was in good agreement with the design life of the wheel.
Analysis of Cause-Specific Mortality in Patients with Retinoblastoma
Authors: Yang Lei , Fang Xiaolian , Jin Mei , Chen Jun , Zhang Chengyue , Mou Jianing , Zhao Junyang , Ni Xin
Published: March 2022

Background. Retinoblastoma (RB) is a rare pediatric tumor with a relatively favorable prognosis. However, RB is associated with cause-specific mortality, some of that should be of great importance to clinicians. In this study, we summarize the characteristics of cause-specific mortality from nontumor disease in patients with RB. Methods. This retrospective case series study identified and analyzed cause-specific mortality in patients with RB. The information of cause-specific mortality of RB patients, including detailed clinical characteristics, diagnosis, treatment process, cause-specific mortality classification, and lag time, was assessed. Results. A total of 12 eligible patients were selected from 264 patients who died among 3780 patients diagnosed with RB. The cause-specific mortality rate was 4.5% for all patients with RB who died and 0.3% for all patients with RB. The main nontumor cause-specific mortalities were diseases of the nervous, circulatory, and respiratory systems, which specifically included intracranial infection, cerebral hemorrhage, paraplegia, and respiratory failure. The longest lag time was 42 days from the last chemoradiotherapy or surgery. Conclusion. Nontumor cause-specific mortality is an essential outcome of RB. Thus, intensive care and differentiation during management need to be taken seriously.
Design of Branch Definition Algorithm for Top-
k
Inverse Queries for Image Processing
Authors: Zhang Haixia , Peng Qingxiu
Published: March 2022

Images are the main way for human beings to obtain and exchange information, and they play a crucial role in the process of human understanding and exploration of the world. Top-

k

inverse queries are widely used in real life. Currently, the most efficient algorithm for computing top-

k

inverse sets is the inverse top-

k

algorithm. Our algorithm is significantly limited when dealing with top-

k

inverse queries. To address these limitations, an intuitive branch-and-bound algorithm is proposed to efficiently handle top-

k

inverse queries, and novel optimization methods are discussed to mention its high performance. Experimental evaluation shows that the algorithm is far more efficient than the inverse top-

k

algorithm.
Clinical Effect of Revascularization Strategies and Pharmacologic Treatment on Long-Term Results in Patients with Advanced Peripheral Artery Disease with TASC C and D Femoropopliteal Lesions
Authors: Lee Chiu-Yang
Published: March 2022

Background. This study was to assess the clinical outcome and associated parameters of endovascular therapy (EVT group) and bypass surgery (bypass group) in patients with long femoropopliteal TransAtlantic Inter-Society Consensus II (TASC II) C and D peripheral artery disease (PAD). Methods. 187 patients who underwent successful EVT or bypass surgery were assessed. The endpoints included the events of cardiovascular disease (CVD) and lower-extremity amputation (LEA), 3-year primary patency, and 3-year amputation-free survival (AFS). Results. The 3-year primary and secondary patency rates were better in the bypass group (

P
=
0.007

and

P
=
0.039

, respectively), while the incidences of LEA, new CVD events, and mortality were comparable between groups. Weighted multivariate Cox analyses showed that cilostazol treatment (hazard ratio (HR): 0.46, 95% confidence interval (CI): 0.3–0.72,

P
=
0.001

), statin treatment (HR: 0.54, 95% CI: 0.33–0.9,

P
=
0.014

), and direct revascularization (DR) (HR: 0.47, 95% CI: 0.29–0.74,

P
=
0.001

) were predictive factors of 3-year primary patency. Kaplan–Meier curve analyses of time-to-primary cumulative AFS showed that nondiabetes mellitus, mild PAD, and cilostazol and statin treatment were correlated with a superior 3-year AFS (log rank test,

P
=
0.001

,

P
<
0.001

,

P
=
0.009

, and

P
=
0.044

, respectively). Conclusions. Endovascular stenting based on the angiosome concept and bypass surgery provide comparable benefits for the treatment of long, advanced femoropopliteal lesions after a short follow-up period, whereas cilostazol therapy for more than 3 months, aggressive treatment of dyslipidemia, and surgical revascularization were associated with higher primary patency.
Impact of Work‐Related Chronic Low Back Pain on Functional Performance and Physical Capabilities in Women and Men: A Sex‐Wise Comparative Study
Authors: Ahmed Hashim , Kishore Kamal , Bhat Priyadarshani , Alghadir Ahmad H. , Iqbal Amir
Published: March 2022

Purpose. This study is aimed at determining the impact of work‐related low back pain (LBP) on functional performance and physical capabilities. Methods. This cross‐sectional study included women (n = 25, mean age, 38.12 ± 4.59) and men (n = 25, mean age, 37.20 ± 5.38) with a history of work‐related mechanical chronic LBP who visited our university hospital’s outpatient department. All participants were assessed for primary outcomes, including the severity of LBP on rest and on activity, functional performance, and physical capabilities using a numeric pain rating scale (NPRS), Roland‐Morris disability questionnaire (RDQ), five‐time sit‐to‐stand test (FTSST), and fifty‐foot walk test (FFWT), respectively. Independent t‐tests compared the scores of the outcomes between groups while Pearson’s correlation coefficient identified the correlation between the outcomes’ measures at a significance level of 0.05. Results. With a response rate of 63.29%, a total of fifty participant’s data were obtained for the analysis. A comparison between women and men groups highlighted a significant difference in the scores of the FTSST and FFWT; however, there were insignificant differences in the scores of the NPRS at rest, NPRS on activity, and RDQ. The bivariate correlation revealed a highly significant, positive, and moderate correlation between the scores of NPRS at rest and FTSST, NPRS on activity and FTSST, NPRS at rest and FFWT, NPRS on activity and FFWT, FTSST and RDQ, and FFWT and RDQ in the women group. Similarly, there was a significant, positive, and low correlation between the scores of FTSST and RDQ and FFWT and RDQ in the men group. Conclusion. Work‐related chronic LBP affected the physical capabilities of women more than those of men. However, it equally affected the functional performance of all participants in the study. Furthermore, work‐related chronic LBP affected the physical capabilities (FTSST and FFWT) and functional performance (RDQ) of women more than those of men.