bluuebunny/crossref_metadata_2025 huggingface dataset:
IdeateRelate: An Examples Gallery That Helps Creators Explore Ideas in Relation to Their Own
Authors: Xu Xiaotong (Tone) , Xiong Rosaleen , Wang Boyang , Min David , Dow Steven P.
Published: October 2021

Creating truly original ideas requires extensive knowledge of existing ideas. Navigating prior examples can help people to understand what has already been done and to assess the quality of their own ideas through comparison. The creativity literature has suggested that the conceptual distance between a proposed solution and a potential inspiration can influence one's thinking. However, less is known about how creators might use data about conceptual distance when exploring a large repository of ideas. To investigate this, we created a novel tool for exploring examples called IdeateRelate that visualizes 600+ COVID-related ideas, organized by their similarity to a new idea. In an experiment that compared the IdeateRelate visualization to a simple list of examples, we found that users in the Viz condition leveraged both semantic and categorical similarity, curated a more similar set of examples, and adopted more language from examples into their iterated ideas (without negatively affecting the overall novelty). We discuss implications for creating adaptive interfaces that provide creative inspiration in response to designers' ideas throughout an iterative design process.
The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions
Authors: Ferreira Isabella , Cheng Jinghui , Adams Bram
Published: October 2021

Code review is an important quality assurance activity for software development. Code review discussions among developers and maintainers can be heated and sometimes involve personal attacks and unnecessary disrespectful comments, demonstrating, therefore, incivility. Although incivility in public discussions has received increasing attention from researchers in different domains, the knowledge about the characteristics, causes, and consequences of uncivil communication is still very limited in the context of software development, and more specifically, code review. To address this gap in the literature, we leverage the mature social construct of incivility as a lens to understand confrontational conflicts in open source code review discussions. For that, we conducted a qualitative analysis on 1,545 emails from the Linux Kernel Mailing List (LKML) that were associated with rejected changes. We found that more than half (66.66%) of the non-technical emails included uncivil features. Particularly, frustration, name calling, and impatience are the most frequent features in uncivil emails. We also found that there are civil alternatives to address arguments, while uncivil comments can potentially be made by any people when discussing any topic. Finally, we identified various causes and consequences of such uncivil communication. Our work serves as the first study about the phenomenon of in(civility) in open source software development, paving the road for a new field of research about collaboration and communication in the context of software engineering activities.
Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents
Authors: Stuart Michael T. , Kneer Markus
Published: October 2021

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question - also explored in the experiment - whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook.
Reflexivity of Account, Professional Vision, and Computer-Supported Cooperative Work: Working in the Maritime Domain
Authors: Pan Yushan
Published: October 2021

Computer-supported cooperative work (CSCW) researchers have plenty to say about designing through texts. However, the relationships between implementation and design are often misunderstood by engineering project systems developers who design collaborative systems, among others. This problem is not new; how to utilize CSCW insights effectively and correctly in engineering projects has long been a concern in the CSCW community. By reviewing year-long, multiple-site ethnographic studies in the maritime domain conducted since 2015, this paper reflects on the "reflexivity of account" and "professional vision" as complementary concepts for ensuring that actors' actions and reasonings are designed in such a way that actors-in this case, maritime operators, systems developers, educators, policymakers, and shipowners-are accountable in and through their member groups. Rather than generalizing my findings and my role in the maritime domain as explicit knowledge to help other CSCW researchers in studying engineering projects, the goal of this paper is to seek intersubjective knowledge of my fieldwork to trigger the utilization of CSCW insights as a fundamental basis for facilitating systems design. The aim is to shape and reshape systems in line with what those actors do in reality.
Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?
Authors: Xiao Ziang , Mennicken Sarah , Huber Bernd , Shonkoff Adam , Thom Jennifer
Published: October 2021

Voice assistants offer users access to an increasing variety of personalized functionalities. Researchers and engineers who build these experiences rely on various signals from users to create the machine learning models powering them. One type of signal is explicit feedback. While collecting explicit user feedback in situ via voice assistants would help improve and inspect the underlying models, from a user perspective it can be disruptive to the overall experience, and the user might not feel compelled to respond. However, careful design can help alleviate the friction in the experience. In this paper, we explore the opportunities and the design space for voice assistant explicit feedback elicitation. First, we present four usage categories of explicit feedback in situ for model evaluation and improvement, derived from interviews with machine learning practitioners. Then, using realistic scenarios generated for each category, we conducted an online study to evaluate multiple voice assistant designs. Our results reveal that when the voice assistant is introduced as a learner or a collaborator, users were more willing to respond to its request for feedback and felt less disruptive. In addition, giving users instructions on how to initiate feedback themselves can reduce the perceived disruptiveness compared to asking users for feedback directly. Based on our findings, we discuss the implications and potential future directions for designing voice assistants to elicit user feedback for personalized voice experiences.
The Long Road Ahead: Ongoing Challenges in Contributing to Large OSS Organizations and What to Do
Authors: Guizani Mariam , Chatterjee Amreeta , Trinkenreich Bianca , May Mary Evelyn , Noa-Guevara Geraldine J. , Russell Liam James , Cuevas Zambrano Griselda G. , Izquierdo-Cortazar Daniel , Steinmacher Igor , Gerosa Marco A. , Sarma Anita
Published: October 2021

Open source communities hosted in large foundations operate in a complex socio-technical ecosystem, which includes a heterogeneous mix of projects and stakeholders. Previous work has thus far investigated the challenges faced in OSS communities from the point of view of specific stakeholders, primarily at the level of individual projects. None have yet studied the challenges faced within a large, federated open source organization. In this paper, we aim to bridge this gap to identify ongoing challenges contributors face in a mature OSS organization. To do so, we surveyed 624 contributors at the Apache Software Foundation (ASF) and ran 11 semi-structured follow up interviews. We validated our findings through member checking with the interviewees as well as the ASF Diversity and Inclusion (D&I) committee. The contributions of this paper include: (1) an empirically-evidenced conceptual model of the 88 challenges that contributors face in a mature OSS foundation and (2) a set of 48 community-recommended strategies for alleviating these challenges. Our results show that even well-established and mature organizations still face a variety of individual and project-specific challenges and that it is difficult to design a comprehensive set of processes and guidelines to match the needs and expectations of a diverse and large federated community. Our conceptual challenges model and associated strategies to mitigate them can provide guidance to other OSS foundations and projects helping them in building better support processes and tools to create a successful, thriving community of contributors.
The Design of Reciprocal Learning Between Human and Artificial Intelligence
Authors: Zagalsky Alexey , Te'eni Dov , Yahav Inbal , Schwartz David G. , Silverman Gahl , Cohen Daniel , Mann Yossi , Lewinsky Dafna
Published: October 2021

The need for advanced automation and artificial intelligence (AI) in various fields, including text classification, has dramatically increased in the last decade, leaving us critically dependent on their performance and reliability. Yet, as we increasingly rely more on AI applications, their algorithms are becoming more nuanced, more complex, and less understandable precisely at a time we need to understand them better and trust them to perform as expected. Text classification in the medical and cybersecurity domains is a good example of a task where we may wish to keep the human in the loop. Human experts lack the capacity to deal with the high volume and velocity of data that needs to be classified, and ML techniques are often unexplainable and lack the ability to capture the required context needed to make the right decision and take action. We propose a new abstract configuration of Human-Machine Learning (HML) that focuses on reciprocal learning, where the human and the AI are collaborating partners. We employ design-science research (DSR) to learn and design an application of the HML configuration, which incorporates software to support combining human and artificial intelligences. We define the HML configuration by its conceptual components and their function. We then describe the development of a system called Fusion that supports human-machine reciprocal learning. Using two case studies of text classification from the cyber domain, we evaluate Fusion and the proposed HML approach, demonstrating benefits and challenges. Our results show a clear ability of domain experts to improve the ML classification performance over time, while both human and machine, collaboratively, develop their conceptualization, i.e., their knowledge of classification. We generalize our insights from the DSR process as actionable principles for researchers and designers of 'human in the learning loop' systems. We conclude the paper by discussing HML configurations and the challenge of capturing and representing knowledge gained jointly by human and machine, an area we feel has great potential.
A Model of Socially Sustained Self-Tracking for Food and Diet
Authors: Lu Xi , Chen Yunan , Epstein Daniel A.
Published: October 2021

Studies of personal informatics systems primarily examine people's use or non-use, but people often leverage other technology towards their long-term behavior change processes such as social platforms. We explore how tracking technologies and social platforms together help people build healthy eating behaviors by interviewing 18 people who use Chinese food journaling apps. We contribute a Model of Socially Sustained Self-Tracking in personal informatics, building on the past model of Personal Informatics and the learning components of Social Cognitive Theory. The model illustrates how people get advice from social platforms on when and how to track, transfer data to and apply knowledge from social platforms, evolve to use social platforms after tracking, and occasionally resume using tracking tools. Observational learning and enactive learning are central to these processes, with social technologies helping people to gain deeper and more reliable domain knowledge. We discuss how lapsing and abandoning of tracking can be viewed as evolving to social platforms, offering recommendations for how technology can better facilitate this evolution.
Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology
Authors: Lu Alex Jiahong , Dillahunt Tawanna R. , Marcu Gabriela , Ackerman Mark S.
Published: October 2021

Today, teachers have been increasingly relying on data-driven technologies to track and monitor student behavior data for classroom management. Drawing insights from interviews with 20 K--8 teachers, in this paper we unpack how teachers enacted both care and control through their data work in collecting, interpreting, and using student behavior data. In this process, teachers found themselves subject to surveilling gazes from parents, school administrators, and students. As a result, teachers had to manipulate the student behavior data to navigate the balance between presenting a professional image to surveillants and enacting care/control that they deemed appropriate. In this paper we locate two nuanced forms of teachers' data work that have been under-studied in CSCW: (1) data work as recontextualizing meanings and (2) data work as resisting surveillance. We discuss teachers' struggle over (in)visibility and their negotiation of autonomy and subjectivity in these two forms of data work. We highlight the importance of foregrounding and making space for informal data workers' (in our case, teachers') resistance and negotiation of autonomy in light of datafication.
"The Network Is an Excuse": Hardware Maintenance Supporting Community
Authors: Garrison Philip , Jang Esther Han Beol , Lithgow Michael A. , Pace Nicolás Andrés
Published: October 2021

The global community networking movement promotes locally-managed network infrastructure as a strategy for affordable Internet connectivity. This case study investigates a group of collectively managed WiFi Internet networks in Argentina and the technologists who design the networking hardware and software. Members of these community networks collaborate on maintenance and repair and practice new forms of collective work. Drawing on Actor-Network Theory, we show that the networking technologies play a role in the social relations of their maintenance and that they are intentionally configured to do so. For technology designers and deployers, we suggest a path beyond designing for easy repair: since every breakdown is an opportunity to learn, we should design for accessible repair experiences that enable effective collaborative learning.
Efficiency and Care in Community-led Initiatives
Authors: Rossitto Chiara , Korsgaard Henrik , Lampinen Airi , Bødker Susanne
Published: October 2021

This paper illustrates the multifaceted aspects of caring practices, and the ways they are entangled with the organizing of community-driven initiatives. Highlighting the situated inter-dependencies between concerns for care and efficiency, and considering caring practices as essential to the practical work that makes communities work, we reflect on how caring and efficiency rationalities frame the use, and scope the design of digital technologies. Drawing on two cases, the analysis shows the ways in which digital technologies oftentimes overshadow communities' key concerns for care, and how attempts to design for community settings can result in anti-designs, that is sociotechnical configurations that can disrupt caring practices. The contribution of the paper is twofold: first, an analysis of the different configurations of caring and efficiency and, second, a focus on care in the design and appropriation of technologies into this space.
Promises are made to be broken: migrating R to strict semantics
Authors: Goel Aviral , Ječmen Jan , Krynski Sebastián , Flückiger Olivier , Vitek Jan
Published: October 2021

Function calls in the R language do not evaluate their arguments, these are passed to the callee as suspended computations and evaluated if needed. After 25 years of experience with the language, there are very few cases where programmers leverage delayed evaluation intentionally and laziness comes at a price in performance and complexity. This paper explores how to evolve the semantics of a lazy language towards strictness-by-default and laziness-on-demand. To provide a migration path, it is necessary to provide tooling for developers to migrate libraries without introducing errors. This paper reports on a dynamic analysis that infers strictness signatures for functions to capture both intentional and accidental laziness. Over 99% of the inferred signatures were correct when tested against clients of the libraries.
Label dependent lambda calculus and gradual typing
Authors: Fu Weili , Krause Fabian , Thiemann Peter
Published: October 2021

Dependently-typed programming languages are gaining importance, because they can guarantee a wide range of properties at compile time. Their use in practice is often hampered because programmers have to provide very precise types. Gradual typing is a means to vary the level of typing precision between program fragments and to transition smoothly towards more precisely typed programs. The combination of gradual typing and dependent types seems promising to promote the widespread use of dependent types.

We investigate a gradual version of a minimalist value-dependent lambda calculus. Compile-time calculations and thus dependencies are restricted to labels, drawn from a generic enumeration type. The calculus supports the usual Pi and Sigma types as well as singleton types and subtyping. It is sufficiently powerful to provide flexible encodings of variant and record types with first-class labels.

We provide type checking algorithms for the underlying label-dependent lambda calculus and its gradual extension. The gradual type checker drives the translation into a cast calculus, which extends the original language. The cast calculus comes with several innovations: refined typing for casts in the presence of singletons, type reduction in casts, and fully dependent Sigma types. Besides standard metatheoretical results, we establish the gradual guarantee for the gradual language.
SpecSafe: detecting cache side channels in a speculative world
Authors: Brotzman Robert , Zhang Danfeng , Kandemir Mahmut Taylan , Tan Gang
Published: October 2021

The high-profile Spectre attack and its variants have revealed that speculative execution may leave secret-dependent footprints in the cache, allowing an attacker to learn confidential data. However, existing static side-channel detectors either ignore speculative execution, leading to false negatives, or lack a precise cache model, leading to false positives. In this paper, somewhat surprisingly, we show that it is challenging to develop a speculation-aware static analysis with precise cache models: a combination of existing works does not necessarily catch all cache side channels. Motivated by this observation, we present a new semantic definition of security against cache-based side-channel attacks, called Speculative-Aware noninterference (SANI), which is applicable to a variety of attacks and cache models. We also develop SpecSafe to detect the violations of SANI. Unlike other speculation-aware symbolic executors, SpecSafe employs a novel program transformation so that SANI can be soundly checked by speculation-unaware side-channel detectors. SpecSafe is shown to be both scalable and accurate on a set of moderately sized benchmarks, including commonly used cryptography libraries.
Gauss: program synthesis by reasoning over graphs
Authors: Bavishi Rohan , Lemieux Caroline , Sen Koushik , Stoica Ion
Published: October 2021

While input-output examples are a natural form of specification for program synthesis engines, they can be imprecise for domains such as table transformations. In this paper, we investigate how extracting readily-available information about the user intent behind these input-output examples helps speed up synthesis and reduce overfitting. We present Gauss, a synthesis algorithm for table transformations that accepts partial input-output examples, along with user intent graphs. Gauss includes a novel conflict-resolution reasoning algorithm over graphs that enables it to learn from mistakes made during the search and use that knowledge to explore the space of programs even faster. It also ensures the final program is consistent with the user intent specification, reducing overfitting. We implement Gauss for the domain of table transformations (supporting Pandas and R), and compare it to three state-of-the-art synthesizers accepting only input-output examples. We find that it is able to reduce the search space by 56×, 73× and 664× on average, resulting in 7×, 26× and 7× speedups in synthesis times on average, respectively.
Not so fast: understanding and mitigating negative impacts of compiler optimizations on code reuse gadget sets
Authors: Brown Michael D. , Pruett Matthew , Bigelow Robert , Mururu Girish , Pande Santosh
Published: October 2021

Despite extensive testing and correctness certification of their functional semantics, a number of compiler optimizations have been shown to violate security guarantees implemented in source code. While prior work has shed light on how such optimizations may introduce semantic security weaknesses into programs, there remains a significant knowledge gap concerning the impacts of compiler optimizations on non-semantic properties with security implications. In particular, little is currently known about how code generation and optimization decisions made by the compiler affect the availability and utility of reusable code segments called gadgets required for implementing code reuse attack methods such as return-oriented programming.

In this paper, we bridge this gap through a study of the impacts of compiler optimization on code reuse gadget sets. We analyze and compare 1,187 variants of 20 different benchmark programs built with two production compilers (GCC and Clang) to determine how their optimization behaviors affect the code reuse gadget sets present in program variants with respect to both quantitative and qualitative metrics. Our study exposes an important and unexpected problem; compiler optimizations introduce new gadgets at a high rate and produce code containing gadget sets that are generally more useful to an attacker than those in unoptimized code. Using differential binary analysis, we identify several undesirable behaviors at the root of this phenomenon. In turn, we propose and evaluate several strategies to mitigate these behaviors. In particular, we show that post-production binary recompilation can effectively mitigate these behaviors with negligible performance impacts, resulting in optimized code with significantly smaller and less useful gadget sets.
QuickSilver: modeling and parameterized verification for distributed agreement-based systems
Authors: Jaber Nouraldin , Wagner Christopher , Jacobs Swen , Kulkarni Milind , Samanta Roopsha
Published: October 2021

The last decade has sparked several valiant efforts in deductive verification of distributed agreement protocols such as consensus and leader election. Oddly, there have been far fewer verification efforts that go beyond the core protocols and target applications that are built on top of agreement protocols. This is unfortunate, as agreement-based distributed services such as data stores, locks, and ledgers are ubiquitous and potentially permit modular, scalable verification approaches that mimic their modular design. We address this need for verification of distributed agreement-based systems through our novel modeling and verification framework, QuickSilver, that is not only modular, but also fully automated. The key enabling feature of QuickSilver is our encoding of abstractions of verified agreement protocols that facilitates modular, decidable, and scalable automated verification. We demonstrate the potential of QuickSilver by modeling and efficiently verifying a series of tricky case studies, adapted from real-world applications, such as a data store, a lock service, a surveillance system, a pathfinding algorithm for mobile robots, and more.
Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering
Authors: Pham Tuan Anh
Published: October 2021

Soil liquefaction is a dangerous phenomenon for structures that lose their shear strength and soil resistance, occurring during seismic shocks such as earthquakes or sudden stress conditions. Determining the liquefaction and nonliquefaction capacity of soil is a difficult but necessary job when constructing structures in earthquake zones. Usually, the possibility of soil liquefaction is determined by laboratory tests on soil samples subjected to dynamic loads, and this is time‐consuming and costly. Therefore, this study focuses on the development of a machine learning model called a Forward Neural Network (FNN) to estimate the activation of soil liquefaction under seismic condition. The database is collected from the published literature, including 270 liquefaction cases and 216 nonliquefaction case histories under different geological conditions and earthquakes used for construction and confirming the model. The model is built and optimized for hyperparameters based on a technique known as random search (RS). Then, the L2 regularization technique is used to solve the overfitting problem of the model. The analysis results are compared with a series of empirical formulas as well as some popular machine learning (ML) models. The results show that the RS‐L2‐FNN model successfully predicts soil liquefaction with an accuracy of 90.33% on the entire dataset and an average accuracy of 88.4% after 300 simulations which takes into account the random split of the datasets. Compared with the empirical formulas as well as other machine learning models, the RS‐L2‐FNN model shows superior performance and solves the overfitting problem of the model. In addition, the global sensitivity analysis technique is used to detect the most important input characteristics affecting the activation prediction of liquefied soils. The results show that the corrected SPT resistance (N1)60 is the most important input variable, affecting the determination of the liquefaction capacity of the soil. This study provides a powerful tool that allows rapid and accurate prediction of liquefaction based on several basic soil properties.
Construction and Evaluation of a Tumor Mutation Burden-Related Prognostic Signature for Thyroid Carcinoma
Authors: Lu Haodong , Liu Qian , Chang Qing , Du Jinghai , Zhang Chunying , Chang Wang , Guo Xin , Hu Yaojie , Liu Shiguang , Tang Guoshuai , Chen Chunyou
Published: October 2021

Thyroid carcinoma is a type of prevalent cancer. Its prognostic evaluation depends on clinicopathological features. However, such conventional methods are deficient. Based on mRNA, single nucleotide variants (SNV), and clinical information of thyroid carcinoma from The Cancer Genome Atlas (TCGA) database, this study statistically analyzed mutational signature of patients with this disease. Missense mutation and SNV are the most common variant classification and variant type, respectively. Next, tumor mutation burden (TMB) of sample was calculated. Survival status of high/low TMB groups was analyzed, as well as the relationship between TMB and clinicopathological features. Results revealed that patients with high TMB had poor survival status, and TMB was related to several clinicopathological features. Through analysis on DEGs in high/low TMB groups, 381 DEGs were obtained. They were found to be mainly enriched in muscle tissue development through enrichment analysis. Then, through Cox regression analysis, a 5-gene prognostic signature was established, which was then evaluated through survival curves and receiver operation characteristic (ROC) curves. The result showed that the signature was able to effectively predict patient’s prognosis and to serve as an independent prognostic risk factor. Finally, through Gene Set Enrichment Analysis (GSEA) on high/low-risk groups, DEGs were found to be mainly enriched in signaling pathways related to DNA repair. Overall, based on the TCGA-THCA dataset, we constructed a 5-gene prognostic signature through a trail of bioinformatics analysis.
Lysophosphatidylcholine Offsets the Protective Effects of Bone Marrow Mesenchymal Stem Cells on Inflammatory Response and Oxidative Stress Injury of Retinal Endothelial Cells via TLR4/NF-κB Signaling
Authors: Zhao Haijun , He Yanhui
Published: October 2021

Diabetic retinopathy (DR), as a major cause of blindness worldwide, is one common complication of diabetes mellitus. Inflammatory response and oxidative stress injury of endothelial cells play significant roles in the pathogenesis of DR. The study is aimed at investigating the effects of lysophosphatidylcholine (LPC) on the dysfunction of high glucose- (HG-) treated human retinal microvascular endothelial cells (HRMECs) after being cocultured with bone marrow mesenchymal stem cells (BMSCs) and the underlying regulatory mechanism. Coculture of BMSCs and HRMECs was performed in transwell chambers. The activities of antioxidant-related enzymes and molecules of oxidative stress injury and the contents of inflammatory cytokines were measured by ELISA. Flow cytometry analyzed the apoptosis of treated HRMECs. HRMECs were further treated with 10-50 μg/ml LPC to investigate the effect of LPC on the dysfunction of HRMECs. Western blotting was conducted to evaluate levels of TLR4 and p-NF-κB proteins. We found that BMSCs alleviated HG-induced inflammatory response and oxidative stress injury of HRMECs. Importantly, LPC offsets the protective effects of BMSCs on inflammatory response and oxidative stress injury of HRMECs. Furthermore, LPC upregulated the protein levels of TLR4 and p-NF-κB, activating the TLR4/NF-κB signaling pathway. Overall, our study demonstrated that LPC offsets the protective effects of BMSCs on inflammatory response and oxidative stress injury of HRMECs via TLR4/NF-κB signaling.
Clinical Outcomes of Aberration-Free All Surface Laser Ablation (ASLA) vs. Aberration-Free ASLA Assisted by Smart Pulse Technology in High Myopia: A One-Year Follow-Up Study
Authors: Du XiaoHao , Zhang Jia , Su Meng , Cao WenJia , Zeng Shuang , Wang QinMei , Aslanides Ioannis M. , Chen ShiHao
Published: October 2021

Purpose. To compare the clinical outcomes of aberration-free all surface laser ablation (ASLA) with and without the use of smart pulse technology (SPT) in high myopia. Methods. This study retrospectively analyzed 138 eyes (138 patients, only the right eye was selected) treated for high myopia (spherical equivalent ≥−6.00 diopters) using aberration-free ASLA (non-SPT group; 85 eyes) and aberration-free ASLA assisted by SPT (SPT group; 53 eyes). Examinations such as visual acuity, refraction, and haze were performed before the 12-month follow-up. Corneal epithelial healing time was assessed in the first postoperative day. Visual acuity and refraction examination were performed at 7 days and 1, 3, 6, and 12 months postoperatively. Corneal haze was evaluated in 1, 3, 6, and 12 months. Safety, efficacy, and corneal wavefront aberrations were assessed 12 months after the treatment. Results. At 12 months postoperatively, 60% versus 40% of eyes achieved 20/16 Snellen lines or better, and 92% versus 82% of eyes achieved 20/20 Snellen lines or better visual acuity in the SPT and the non-SPT groups, respectively. The average postoperative epithelial healing time was 3.75 ± 1.00 days in the SPT group and 3.73 ± 1.30 days in the non-SPT group (

P
≥
0.05

). The safety and the efficacy index of the SPT group were better than those of the non-SPT group in the follow-ups. The attempted spherical equivalent before the surgery and the achieved spherical equivalent at 12 months were comparable between the two groups. Regarding the aberrations, the results of Coma 90° in the SPT group were better than those in the non-SPT group (

P
≤
0.05

), but the increase of RMS HOAs (root mean square higher order aberrations), Coma 0°, and spherical aberration postoperatively had no statistical difference between the two groups (

P
≥
0.05

). Conclusions: Both aberration-free ASLA with and without SPT showed favorable safety, effectiveness, and predictability within 12 months for high myopia. And, ASLA using SPT might have potential advantages in the long-term visual quality.
Genetic Algorithm in Data Mining of Colorectal Images
Authors: Chen Shou-Ming , Zhang Jun-Hui
Published: October 2021

There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image processing technology to perform image analysis. At the same time, combined with the actual requirements of image detection, the gray theory model is used as the basic theory of image processing, and the image detection prediction model is constructed to predict the data. In addition, in order to study the effectiveness of the algorithm, the experiment is carried out to analyze the validity of the data of the study, and the predicted value is compared with the actual value. The research shows that the proposed algorithm has certain accuracy and can provide theoretical reference for subsequent related research.
Construction of Ecological Environment Information System Based on Big Data: A Case Study on Dongting Lake Ecological Area
Authors: Wan Diandi , Yin Shaohua
Published: October 2021

With the rapid development of cloud computing, Internet of Things, and other technologies, the information technology trend led by “big data” has an impact on all fields. The application of big data technology in the field of ecological environmental protection enables accurate and comprehensive ecological information collection, data analysis, and mining, accurate ecological problem identification, and effective solution. Taking Dongting Lake Ecological Area as an example, this paper constructs an ecological environment information system based on big data and expounds its specific application in water, atmosphere, soil environment monitoring, and pollution control, aiming to provide a reference for the application of big data technology in the field of ecological environment protection in Dongting Lake Ecological Area and more effectively maintain the ecological environmental quality and safety in the area.
Research on Behavior Monitoring of Elderly Living Alone Based on Wearable Devices and Sensing Technology
Authors: Yin Mulin
Published: October 2021

Human behavior recognition and status monitoring are current research hotspots, especially in the fields of medical monitoring, smart home, and elderly care. With the development of sensor technology, low-power IC chips, and wireless body sensors, miniature sensor networks can be popularized and applied in daily life. Since the energy consumption of sensor networks is a bottleneck problem that limits its development, this paper designs a multimodal collaborative sensing method for the application scenarios of elderly people living alone to reduce energy consumption in the process of daily behavior perception of the elderly. This method subdivides behavior perception into behavior recognition and status monitoring, determines the optimal sensor combination for identifying monitoring different behaviors based on information theory, and then uses a behavior recognition model modeled by a multiclassifier and a status model modeled by a plurality of two classifiers that are used to perceive user behavior. A large number of experimental results show that compared with the traditional sensor network method, our proposed solution can achieve effective sensing while reducing the energy consumption in the process of data transmission and model calculation, thereby prolonging the working life of the sensing network and realizing long-term and reliable daily behavior perception.
An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment
Authors: Chiu Po-Chih , Su Kuo-Wei , Ou Tsung-Yin , Yu Chih-Lung , Cheng Chen-Yang , Hsiao Wei-Chieh , Shu Ming-Hung , Lin Guan-Yu
Published: October 2021

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.
A Novel BBS9 Mutation Identified via Whole‐Exome Sequencing in a Chinese Family with Bardet‐Biedl Syndrome
Authors: Zhang Yue , Xu Manhong , Zhang Minglian , Yang Guoxing , Li Xiaorong
Published: October 2021

Bardet‐Biedl syndrome (BBS) is a genetically heterogeneous disorder characterized by polydactyly, obesity, rod‐cone dystrophy, and mental retardation. Twenty‐one genes have been identified as causing BBS. This study collected a BBS pedigree from two patients and performed whole‐exome sequencing on one patient. We identified a novel homozygous variant c.1114C>T (p.Q372X) in the BBS9 of the two siblings. This variant was confirmed and completely cosegregated with the disease of this family by Sanger sequencing. We report a novel homozygous variant c.1114C>T in the BBS9 gene in a Chinese family.
An Improved Form of 2D SPH Method for Modeling the Excavation Damage of Tunnels Containing Random Fissures
Authors: Ren Xuhua , Yu Shuyang , Zhang Jixun , Wang Haijun , Sun Zhaohua
Published: October 2021

The excavation damage of deep tunnels is one of the most important factors contributing to the failure of tunnel structures. In order to investigate the influence of tunnel shapes and fissure geometries, the kernel function in the traditional SPH method has been improved, which can realize the brittle fracture characteristics of particles and can be called the Improved Kernel of Smoothed Particle Hydrodynamics (IKSPH-2D). Meanwhile, the random fissure generation method in IKSPH has been put forward. Different tunnel shapes, fissure geometries, and locations are considered during the simulation of tunnel excavation, and results show that (1) the typical “V”-shaped shear damage zones appear after the tunnel excavation, which is consistent with engineering practice. Meanwhile, tunnel excavation also has an “activating” effect on the preexisting fissures. (2) The stability of circular-shaped tunnel is the best, while horseshoe shaped tunnel is worse, and the “U”-shaped tunnel is the worst. (3) Fissures with small and large dip angles have the greatest influence on the stability of tunnel excavation. With the increase of fissure numbers and lengths, the tunnel tends to be instable. (4) The IKSPH method gets free from traditional grids in FEM, which can dynamically reflect the fracture processes of tunnel excavation. Meanwhile, developing 3D IKSPH parallel program will be the future directions.
Influence of Gestational Hormones on the Bacteria-Induced Cytokine Response in Periodontitis
Authors: Ortiz-Sánchez Betsaida J. , Legorreta-Herrera Martha , Rodriguez-Sosa Miriam
Published: October 2021

Periodontitis is an inflammatory disease that affects the supporting structures of teeth. The presence of a bacterial biofilm initiates a destructive inflammatory process orchestrated by various inflammatory mediators, most notably proinflammatory cytokines, which are upregulated in the gingival crevicular fluid, leading to the formation of periodontal pockets. This represents a well-characterized microbial change during the transition from periodontal health to periodontitis; interestingly, the gestational condition increases the risk and severity of periodontal disease. Although the influence of periodontitis on pregnancy has been extensively reviewed, the relationship between pregnancy and the development/evolution of periodontitis has been little studied compared to the effect of periodontitis on adverse pregnancy outcomes. This review is aimed at summarizing the findings on the pregnancy-proinflammatory cytokine relationship and discussing its possible involvement in the development of periodontitis. We address (1) an overview of periodontal disease, (2) the immune response and possible involvement of proinflammatory cytokines in the development of periodontitis, (3) how bone tissue remodelling takes place with an emphasis on the involvement of the inflammatory response and metalloproteinases during periodontitis, and (4) the influence of hormonal profile during pregnancy on the development of periodontitis. Finally, we believe this review may be helpful for designing immunotherapies based on the stage of pregnancy to control the severity and pathology of periodontal disease.
An Update on Assessment, Therapeutic Management, and Patents on Insomnia
Authors: Porwal Amit , Yadav Yogesh Chand , Pathak Kamla , Yadav Ramakant
Published: October 2021

Insomnia is an ordinary situation related to noticeable disability in function and quality of life, mental and actual sickness, and mishappenings. It represents more than 5.5 million appointments to family doctors every year. Nonetheless, the ratio of insomniacs who are treated keeps on being low, demonstrating the requirement for proceeding with advancement and dispersal of effective treatments. Accordingly, it becomes significant to provide a compelling treatment for clinical practice. It indicates a need for the determination of various critical viewpoints for the evaluation of insomnia along with various accessible alternatives for treatment. These alternatives incorporate both nonpharmacological therapy, specifically cognitive behavioural therapy for insomnia, and a number of pharmacological treatments like orexin antagonists, “z‐drugs,” benzodiazepines, selective histamine H1 antagonists, nonselective antihistamines, melatonin receptor agonists, antipsychotics, antidepressants, and anticonvulsants. Besides in individuals whose insomnia is due to restless leg syndrome, depression/mood disorder, or/and circadian disturbance, there is insignificant proof favouring the effectiveness of different prescriptions for the treatment of insomnia though they are widely used. Other pharmacological agents producing sedation should be prescribed with care for insomnia therapy because of greater risk of next‐day sleepiness along with known adverse effects and toxicities. This review is also aimed at providing an update on various patents on dosage forms containing drugs for insomnia therapy.
Enlightenment of Physical Education Teaching Experiment Based on Cloud Computing to the Current Physical Education Reform
Authors: Wang Feng
Published: October 2021

Our country has a large land area, and the development of physical education is not balanced. In daily teaching activities, teachers and students use computers and networks to teach, which generates massive amounts of data. Schools are limited by funds and cannot meet the growing demand for storage of teaching resources. It is also unable to realize the sharing of teaching resources. In order to solve the problems existing in the existing education and teaching platform of the school, especially in the teaching reform, to meet the requirements of all parties facing physical education, the concept of cloud computing was proposed, and the services and methods provided by the cloud computing-based teaching resource platform were discussed. Through the questionnaire survey method of college students and teachers, statistical methods and logical analysis methods were used to analyze the data collected in the questionnaire. Summary and analysis are as follows. The survey results show that more than 50% of the people are dissatisfied with the current physical education and believe that it has not played its due role, and more than 70% of the people agree with the reform of physical education. The experimental results also show that interesting and diverse physical education courses can attract students to participate and increase their interest. From the overall survey results, the problem of college physical education courses is more serious, and it is urgent to optimize teaching from the cloud computing level. On the one hand, it is necessary to improve the relevant cloud computing and other technical platform facilities; on the other hand, it is necessary to improve the teaching level of teachers and change the current educational concept to make it livelier and more interesting.