bluuebunny/crossref_metadata_2025 huggingface dataset:
Critical Reflections on Technology to Support Physical Activity among Older Adults
Authors: Gerling Kathrin , Ray Mo , Abeele Vero Vanden , Evans Adam B.
Published: May 2020

Contemporary policy on ageing overwhelmingly focuses on active ageing and achieving a sustainable increase in disability-free years, leading to an agenda that promotes interventions that often focus on deficits of older persons with little consideration of their perspectives on physical activity. As the integration of technology to support physical activity routines becomes more common, this trend also becomes relevant to the Human-Computer Interaction (HCI) research community.

In this article, we present findings from a structured search of technical systems addressing physical activity among older adults that were published at the most cited HCI venues. Drawing from Thematic Analysis, we explore how the model of active ageing informs existing research, and how it is operationalized in technology design. We find that the deficit-focused perspective on ageing is reflected in many technology solutions published at the most visible HCI venues, and discuss shortcomings and strengths of present research to help guide discourse and future work in HCI.
Gender and Age Influences on Interpretation of Emoji Functions
Authors: Herring Susan C. , Dainas Ashley R.
Published: May 2020

An online survey, the
Understanding Emoji Survey
, was conducted to assess how English-speaking social media users interpret the pragmatic functions of emoji in examples adapted from public Facebook comments, based on a modified version of [15]’s taxonomy of functions. Of the responses received (N = 519; 351 females, 120 males, 48 “other”; 354 under 30, 165 over 30, age range 18--70+),
tone modification
was the preferred interpretation overall, followed by
virtual action
, although interpretations varied significantly by emoji type. Female and male interpretations were generally similar, while “other” gender respondents differed significantly in dispreferring
tone
and preferring
multiple functions
. Respondents over 30 often did not understand the functions or interpreted the emoji literally, while younger users interpreted them in more conventionalized ways. Older males were most likely, and younger females were least likely, to not understand emoji functions and to find emoji confusing or annoying, consistent with previously reported gender and age differences in attitudes toward, and frequency of, emoji use.
Understand Dynamic Regret with Switching Cost for Online Decision Making
Authors: Zhao Yawei , Zhao Qian , Zhang Xingxing , Zhu En , Liu Xinwang , Yin Jianping
Published: May 2020

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in much previous research, we still have little knowledge about the relation between the
dynamic regret
and the
switching cost
. In the article, we investigate the relation for two classic online settings: Online Algorithms (OA) and Online Convex Optimization (OCO). We provide a new theoretical analysis framework that shows an interesting observation; that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO. Specifically, the switching cost has significant impact on the dynamic regret in the setting of OA. But it does not have an impact on the dynamic regret in the setting of OCO. Furthermore, we provide a lower bound of regret for the setting of OCO, which is same with the lower bound in the case of no switching cost. It shows that the switching cost does not change the difficulty of online decision making problems in the setting of OCO.
Should I Stay or Should I Go
Authors: Plakia Maria , Tzamousis Evripides , Asvestopoulou Thomais , Pantermakis Giorgos , Filippakis Nick , Schulzrinne Henning , Kane-Esrig Yana , Papadopouli Maria
Published: May 2020

To improve the user engagement, especially under moderate to high traffic demand, it is important to understand the impact of the network and application QoS on user experience. This article comparatively evaluates the impact of impairments, with emphasis on rebufferings, startup delay, and bitrate changes, and their intensity and temporal dynamics, on user engagement in the context of video streaming. The analysis employed two large YouTube datasets. To characterize the user engagement and the impact of impairments, several new metrics were defined. We assessed whether or not there is astatistically significant relationshipbetween different types of impairments and user engagement metrics, taking into account not only the characteristics of the impairments but also the covariates of the session (e.g., video duration, mean data rate). After observing the relationships across the entire dataset, we tested whether these relationships also persist under specific conditions with respect to the covariates. The introduction of several new metrics and of various covariates in the analysis are two innovative aspects of this work. We found that the presence of negative bitrate changes (BR-) is a stronger predictor of abandonment than rebufferrings (RB). Positive bitrate changes (BR+) in low resolution sessions are not well received. High rebufferring ratio has a prominent impact on the video watching percentage. These results can be used to guide the video streaming adaptation as well as suggest which parameters should be varied in controlled field studies.
A Causal Dirichlet Mixture Model for Causal Inference from Observational Data
Authors: Lin Adi , Lu Jie , Xuan Junyu , Zhu Fujin , Zhang Guangquan
Published: May 2020

Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today’s data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects—average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.
Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities
Authors: Arora Udit , Dutta Hridoy Sankar , Joshi Brihi , Chetan Aditya , Chakraborty Tanmoy
Published: May 2020

With the rise in popularity of social media platforms like Twitter, having higher influence on these platforms has a greater value attached to it, since it has the power to influence many decisions in the form of brand promotions and shaping opinions. However, blackmarket services that allow users to inorganically gain influence are a threat to the credibility of these social networking platforms. Twitter users can gain inorganic appraisals in the form of likes, retweets, and follows through these blackmarket services either by paying for them or by joining syndicates wherein they gain such appraisals by providing similar appraisals to other users. These customers tend to exhibit a mix of organic and inorganic retweeting behavior, making it tougher to detect them.

In this article, we investigate these blackmarket customers engaged in collusive retweeting activities. We collect and annotate a novel dataset containing various types of information about blackmarket customers and use these sources of information to construct multiple user representations. We adopt Weighted Generalized Canonical Correlation Analysis (WGCCA) to combine these individual representations to derive user embeddings that allow us to effectively classify users as: genuine users, bots, promotional customers, and normal customers. Our method significantly outperforms state-of-the-art approaches (32.95% better macro F1-score than the best baseline).
TDEFSI
Authors: Wang Lijing , Chen Jiangzhuo , Marathe Madhav
Published: May 2020

Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present
T
heory-guided
D
eep Learning-based
E
pidemic
F
orecasting with
S
ynthetic
I
nformation (TDEFSI),
1
an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time-series data.

During the training phase, TDEFSI uses high-resolution simulations of epidemics that explicitly model spatial and social heterogeneity inherent in urban regions as one component of training data. We train a two-branch recurrent neural network model to take both within-season and between-season low-resolution observations as features and output high-resolution detailed forecasts. The resulting forecasts are not just driven by observed data but also capture the intricate social, demographic, and geographic attributes of specific urban regions and mathematical theories of disease propagation over networks.

We focus on forecasting the incidence of ILI and evaluate TDEFSI’s performance using synthetic and real-world testing datasets at the state and county levels in the USA. The results show that, at the state level, our method achieves comparable/better performance than several state-of-the-art methods. At the county level, TDEFSI outperforms the other methods. The proposed method can be applied to other infectious diseases as well.
Privacy Games
Authors: Chen Yiling , Sheffet Or , Vadhan Salil
Published: May 2020

The problem of analyzing the effect of privacy concerns on the behavior of selfish utility-maximizing agents has received much attention lately. Privacy concerns are often modeled by altering the utility functions of agents to consider also their privacy loss [4, 14, 20, 28]. Such privacy-aware agents prefer to take a randomized strategy even in very simple games in which non-privacy-aware agents play pure strategies. In some cases, the behavior of privacy-aware agents follows the framework of Randomized Response, a well-known mechanism that preserves differential privacy.

Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent
A
, in an attempt to discover the secret type of agent
B
, offers
B
a gift that one type of
B
agent likes and the other type dislikes. As opposed to previous works,
B
’s incentive to keep her type a secret isn’t the result of “hardwiring”
B
’s utility function to consider privacy, but rather takes the form of a payment between
B
and
A
. We investigate three different types of payment functions and analyze
B
’s behavior in each of the resulting games. As we show, under some payments,
B
’s behavior is very different than the behavior of agents with hardwired privacy concerns and might even be deterministic. Under a different payment, we show that
B
’s BNE strategy does fall into the framework of Randomized Response.
Accurate Rendering of Liquid-Crystals and Inhomogeneous Optically Anisotropic Media
Authors: Steinberg Shlomi
Published: May 2020

We present a novel method for devising a closed-form analytic expression to the light transport through the bulk of inhomogeneous optically anisotropic media. Those optically anisotropic materials, e.g., liquid-crystals and elastic fluids, arise in a plethora of established applications and exciting new research; however, current state-of-the-art methods of visually deducing their optical properties or rendering their appearance are either lacking or non-existent. We formulate our light transport problemunder the context of electromagnetism and derive, from first principles, a differential equation of the transmitted complex wave fields that fully account for the complicated interference phenomena that arise. At the core of our proposed rendering framework is a powerful mathematical representation, carefully crafted to enable us to produce highly accurate analytic approximative solutions for the light transport. This approach is previously unused in computer rendering, and our framework is capable of accurately rendering optically anisotropic materials with spatially varying optical properties at orders-of-magnitude better performance compared to existing methods. We demonstrate a few practical applications of our method, and we validate it against polarized photos of liquid-crystals as well as numerically against numerical solvers and qualitatively against brute-force renderings.
The Association of High School Computer Science Content and Pedagogy with Students’ Success in College Computer Science
Authors: Burgiel Heidi , Sadler Philip M. , Sonnert Gerhard
Published: May 2020

The number of computer science (CS) courses has been dramatically expanding in U.S. high schools (HS). In comparison with well-established courses in mathematics and science, little is known about how the decisions made by HS CS teachers regarding how and what to teach impact student performance later in introductory college CS courses. Drawing on a large sample of 2,871 introductory college CS students at 115 U.S. institutions who had taken a CS course in HS, we examined the topic coverage and prevailing instructional methods in the HS course and investigated how these experiences influenced student performance in college CS. Controlling for differences in student background, we find two predictors of higher grades in college CS: greater frequency of coding-related activities in HS (programming, debugging, studying algorithms) and lower frequency of “non-coding” computer use (e.g., data analysis, computer security). Interaction models revealed a more complex story. Coding-related activity more heavily benefited students who did not have coding help available at home. In the 28% of college CS courses in which instructors employed innovative pedagogies, students with higher ACT or SAT mathematics scores had a greater advantage than in traditionally taught courses. Finally, in the innovative college courses, students whose HS CS exams had typically included testing on vocabulary did worse than students whose exams had not included such tests.
Enlighten Me
Authors: Wisessing Pisut , Zibrek Katja , Cunningham Douglas W. , Dingliana John , McDonnell Rachel
Published: May 2020

Lighting has been used to enhance emotion and appeal of characters for centuries, from paintings in the Renaissance to the modern-day digital arts. In VFX and animation studios, lighting is considered as important as modelling, shading, or rigging. Most existing work focuses on either empirical best-practice created by artists of the centuries or on lighting perception with basic shapes. In contrast, our work focuses on the effect of lighting on emotional characters. Our study presents an extensive set of novel perceptual experiments designed to investigate the effects of brightness levels (key light brightness) and the proportion of light intensity illuminating the two sides of a character’s face (key-to-fill ratio). We are particularly interested in the effect of lighting on the recognition of emotion, emotion intensity, and the overall appeal, as these are crucial factors for audience engagement. Our results have implications for artists and developers wishing to increase the appeal and emotional expression of their characters, ranging from cartoon to realistic styles. Our key finding is that lighting can be used to effectively alter the intensity of emotion of a character and that brighter conditions increased appeal across all of our experiments.
Named Entity Recognition and Classification for Punjabi Shahmukhi
Authors: Ahmad Muhammad Tayyab , Malik Muhammad Kamran , Shahzad Khurram , Aslam Faisal , Iqbal Asif , Nawaz Zubair , Bukhari Faisal
Published: May 2020

Named entity recognition (NER) refers to the identification of proper nouns from natural language text and classifying them into named entity types, such as person, location, and organization. Due to the widespread applications of NER, numerous NER techniques and benchmark datasets have been developed for both Western and Asian languages. Even though Shahmukhi script of the Punjabi language has been used by nearly three fourths of the Punjabi speakers worldwide, Gurmukhi has been the main focus of research activities. Specifically, a benchmark NER corpus for Shahmukhi is non-existent, which has thwarted the commencement of NER research for the Shahmukhi script. To this end, this article presents the development and specifications of the first-ever NER corpus for Shahmukhi. The newly developed corpus is composed of 318,275 tokens and 16,300 named entities, including 11,147 persons, 3,140 locations, and 2,013 organizations. To establish the strength of our corpus, we have compared the specifications of our corpus with its Gurmukhi counterparts. Furthermore, we have demonstrated the usability of our corpus using five supervised learning techniques, including two state-of-the-art deep learning techniques. The results are compared, and valuable insights about the behaviors of the most effective technique are discussed.
Sign Language Generation System Based on Indian Sign Language Grammar
Authors: Sugandhi  , Kumar Parteek , Kaur Sanmeet
Published: May 2020

Sign Language (SL), also known as gesture-based language, is used by people with hearing loss to convey their messages. SL interpreters are required for people who do not have the knowledge of SL, but interpreters are not readily available. Thus, a machine-based translation system is required to translate the text into SL. In this article, a system is implemented for translating English text into Indian Sign Language (ISL). It acts as a tool for human-computer interaction and eliminates the need for an ISL human interpreter for communicating with people who have hearing loss. The system features a rich corpus of English words and commonly used sentences. It consists of components such as an ISL parser, the Hamburg Notation System, the Signing Gesture Mark-up Language, and 3D avatar animation for generating SL according to ISL grammar. The proposed system has been tested rigorously by SL users. The results proved that the proposed system is highly efficient and achieves an average score of accuracy (i.e., 4.2 for English words and 3.8 for sentences on a scale from 1 to 5). The performance of proposed system has also been evaluated using the BiLingual Evaluation Understudy score, which results in 0.95 accuracy. The proposed system and mobile application together has the potential to bring individuals with hearing loss and their entourage together.
Mining the Local Dependency Itemset in a Products Network
Authors: Ni Li , Luo Wenjian , Lu Nannan , Zhu Wenjie
Published: May 2020

Many studies have been conducted on market basket analysis such as association rules and dependent patterns. These studies mainly focus on mining all significant patterns or patterns directly associated with a given item in a dataset. The problem that has not been addressed is how to mine patterns associated with a given item from the local view. This problem becomes very meaningful when the market basket dataset is huge. To address this problem, in this study, first, a new idea called “local dependency itemset” is put forward, which refers to patterns associated with the given item. Second, a framework of mining the local dependency itemset is presented. The framework has two steps, which are executed iteratively. One is expanding the local dependency itemset that initially consists of only the given item; the other is updating the local products network. Third, this framework is implemented by three different dependence indicators and a typical local community detection algorithm. The experimental results confirm that the local dependency itemset is meaningful.
Medial Elastics
Authors: Lan Lei , Luo Ran , Fratarcangeli Marco , Xu Weiwei , Wang Huamin , Guo Xiaohu , Yao Junfeng , Yang Yin
Published: May 2020

We propose a framework for the interactive simulation of nonlinear deformable objects. The primary feature of our system is the seamless integration of deformable simulation and collision culling, which are often independently handled in existing animation systems. The bridge connecting them is the medial axis transform (MAT), a high-fidelity volumetric approximation of complex 3D shapes. From the physics simulation perspective, MAT leads to an expressive and compact reduced nonlinear model. We employ a semireduced projective dynamics formulation, which well captures high-frequency local deformations of high-resolution models while retaining a low computation cost. Our key observation is that the most compelling (nonlinear) deformable effects are enabled by the local constraints projection, which should not be aggressively reduced, and only apply model reduction at the global stage. From the collision detection (CD)/collision culling (CC) perspective, MAT is geometrically versatile using linear-interpolated spheres (i.e., the so-called medial primitives (MPs)) to approximate the boundary of the input model. The intersection test between two MPs is formulated as a quadratically constrained quadratic program problem. We give an algorithm to solve this problem exactly, which returns the deepest penetration between a pair of intersecting MPs. When coupled with spatial hashing, collision (including self-collision) can be efficiently identified on the GPU within a few milliseconds even for massive simulations. We have tested our system on a variety of geometrically complex and high-resolution deformable objects, and our system produces convincing animations with all of the collisions/self-collisions well handled at an interactive rate.
Polynomial Counting in Anonymous Dynamic Networks with Applications to Anonymous Dynamic Algebraic Computations
Authors: Kowalski Dariusz R. , Mosteiro Miguel A.
Published: May 2020

Starting with with work of Michail et al., the problem of
Counting
the number of nodes in Anonymous Dynamic Networks has attracted a lot of attention. The problem is challenging because nodes are indistinguishable (they lack identifiers and execute the same program), and the topology may change arbitrarily from round to round of communication, as long as the network is connected in each round. The problem is central in distributed computing, as the number of participants is frequently needed to make important decisions, including termination, agreement, synchronization, among others. A variety of distributed algorithms built on top of
mass-distribution
techniques have been presented, analyzed, and experimentally evaluated; some of them assumed additional knowledge of network characteristics, such as bounded degree or given upper bound on the network size. However, the question of whether Counting can be solved deterministically in sub-exponential time remained open. In this work, we answer this question positively by presenting M
ethodical
C
ounting
, which runs in polynomial time and requires no knowledge of network characteristics. Moreover, we also show how to extend M
ethodical
C
ounting
to compute the sum of input values and more complex functions without extra cost. Our analysis leverages previous work on random walks in evolving graphs, combined with carefully chosen alarms in the algorithm that control the process and its parameters. To the best of our knowledge, our Counting algorithm and its extensions to other algebraic and Boolean functions are the first that can be implemented in practice with worst-case guarantees.
OutdoorSent
Authors: Oliveira Wyverson Bonasoli de , Dorini Leyza Baldo , Minetto Rodrigo , Silva Thiago H.
Published: May 2020

Opinion mining in outdoor images posted by users during different activities can provide valuable information to better understand urban areas. In this regard, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures and one specifically designed for sentiment analysis. We also evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification and cross-dataset generalization performance. The evaluation explores a novel dataset—namely, OutdoorSent—and other publicly available datasets. We observe that the incorporation of knowledge about semantic attributes improves the accuracy of all ConvNet architectures studied. Besides, we found that exploring only images related to the context of the study—outdoor, in our case—is recommended, i.e., indoor images were not significantly helpful. Furthermore, we demonstrated the applicability of our results in the United States city of Chicago, Illinois, showing that they can help to improve the knowledge of subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment, which are also correlated with median income, opening up opportunities in different fields.
Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform
Authors: Li Yangguang , Jiang Zhen Ming (Jack) , Li Heng , Hassan Ahmed E. , He Cheng , Huang Ruirui , Zeng Zhengda , Wang Mian , Chen Pinan
Published: May 2020

Many software services today are hosted on cloud computing platforms, such as Amazon EC2, due to many benefits like reduced operational costs. However, node failures in these platforms can impact the availability of their hosted services and potentially lead to large financial losses. Predicting node failures before they actually occur is crucial, as it enables DevOps engineers to minimize their impact by performing preventative actions. However, such predictions are hard due to many challenges like the enormous size of the monitoring data and the complexity of the failure symptoms. AIOps (
A
rtificial
I
ntelligence for IT
Op
eration
s
), a recently introduced approach in DevOps, leverages data analytics and machine learning to improve the quality of computing platforms in a cost-effective manner. However, the successful adoption of such AIOps solutions requires much more than a top-performing machine learning model. Instead, AIOps solutions must be trustable, interpretable, maintainable, scalable, and evaluated in context. To cope with these challenges, in this article we report our process of building an AIOps solution for predicting node failures for an ultra-large-scale cloud computing platform at Alibaba. We expect our experiences to be of value to researchers and practitioners, who are interested in building and maintaining AIOps solutions for large-scale cloud computing platforms.
A Sequential Importance Sampling Algorithm for Counting Linear Extensions
Authors: Jensen Alathea , Beichl Isabel
Published: May 2020

In recent decades, a number of profound theorems concerning approximation of hard counting problems have appeared. These include estimation of the permanent, estimating the volume of a convex polyhedron, and counting (approximately) the number of linear extensions of a partially ordered set. All of these results have been achieved using probabilistic sampling methods, specifically Monte Carlo Markov Chain (MCMC) techniques. In each case, a rapidly mixing Markov chain is defined that is guaranteed to produce, with high probability, an accurate result after only a polynomial number of operations.

Although of polynomial complexity, none of these results lead to a practical computational technique, nor do they claim to. The polynomials are of high degree and a non-trivial amount of computing is required to get even a single sample. Our aim in this article is to present practical Monte Carlo methods for one of these problems, counting linear extensions. Like related work on estimating the coefficients of the reliability polynomial, our technique is based on improving the so-called Knuth counting algorithm by incorporating an importance function into the node selection technique giving a sequential importance sampling (SIS) method. We define and report performance on two importance functions.
The ACM SIGSOFT Paper and Peer Review Quality Initiative
Authors: Ralph Paul , Robbes Romain
Published: May 2020

Scholarly peer review is crucial to science: it not only determines what is published where, but also, indirectly, who is hired, funded and promoted. Yet, virtually every academic has peer review horror stories. Empirical evidence suggests that "peer review is prejudiced, capricious, inefficient, ineffective, and generally unscientific" [1]. An experiment at a major machine learning conference found that peer review was unreliable highlighted that the outcome of peer review can be very noisy [2, 3].

In May 2019, ACM SIGSOFT launched an initiative to improve the quality of research papers and peer reviews at software engineering venues. It has two main components: empirical standards and recommendations for improving review processes.
Software Engineering for Infrastructure and Configuration (SEConfig) - Workshop Report
Authors: Cito Jürgen , Santolucito Mark
Published: May 2020

Errors in con gurations, rather than source code, have become one of the major causes of system failures, resulting in security vulnerabilities, application outages, and incorrect program execu- tions. We report on the structure and results of the rst Interna- tional Workshop on Software Engineering for Infrastructure and Con guration Code. Our aim in organizing this jworkshop was to
PatternRank+NN
Authors: Xiao Zhijun , Li Cuiping , Chen Hong
Published: May 2020

We propose a ranking framework, called PatternRank+NN, for expanding a set of seed entities of a particular class (i.e., entity set expansion) from Web search queries. PatternRank+NN consists of two parts: PatternRank and NN. Unlike the traditional methods, PatternRank brings user behaviors into entity set expansion from Web search queries. PatternRank is a Markov chain which simulates the Web search query process of users on the graph model for Web search query log, and ranks the features of the class. The features in the front rank are used to generate candidate entities of the class. NN, a ranking strategy called Nearest Neighbor, ranks these candidate entities such that the set of seed entities can be expanded from the candidate entities in the front rank. Our experiments demonstrate the superior performance of PatternRank+NN in comparison with the state-of-the-art methods.
What role for antitrust in regulating platforms?
Authors: Hemphill C. Scott
Published: May 2020

Using regulation to protect competition and innovation.
Member spotlight
Authors: Miller Jeffrey
Published: May 2020

In this feature of the Bulletin, we highlight members of the SIGCSE community. In this issue, Bulletin co-editor Jeffrey Miller interviewed Briana Morrison.
Verification column
Authors: Lazić Ranko
Published: May 2020

This quarter we have an expert introduction and survey about verification motivated problems on weighted automata, with max-plus and cost register models in focus. Starting gently, and by means of examples, we are quickly led to the state of the art.
Identifying Unique Power System Signatures for Determining Vulnerability of Critical Power System Assets
Authors: Padhee Malhar , Biswas Reetam Sen , Pal Anamitra , Basu Kaustav , Sen Arunabha
Published: May 2020

In this paper, the finer granularity of phasor measurement unit (PMU) data is exploited to develop a data-driven ap- proach for accurate health assessment of large power trans- formers(LPTs). There research demonstrates how variations in signal-to-noiseratios (SNRs) of PMU measurements can be used as a reliable metric for health assessment. However, a single PMU device maybe affected by multiple equipment located in its neighborhood. To address the challenge of identifying the equipment that is primarily responsible for the degradation inquality of the PMU measurements,an in- telligent sensor selection scheme is proposed,which ensures that every critical equipment is associated with a unique identifying signature. The proposed algorithm is based on a real LPT failure event that occurred in the US Southwest. The inferences drawn from the proposed PMU-based health monitoring scheme can be easily supplemented with other LPT sensors to facilitate proactive intervention before the point-of-no-return is reached.
Advanced Persistent Threats in Autonomous Driving
Authors: Kant Krishna
Published: May 2020

In this paper, we consider the problem of detecting performance related advanced persistent threats on a system of connected automated vehicles (CAVs) possibly mixed with connected, but manually driven vehicles (CMVs), operating over an urban area or highway and its feeder roads. We show how the emerging edge computing infrastructure deployed on the roadside for traffic monitoring can be exploited for this task. We briefly propose an approach that estimates an "abnormality" measure of each vehicle and performance anomalies for each type of attack in order to better track the vehicles that are affected early on in a performance attack and thereby facilitate root cause analysis. We also note several challenges in using such an approach.
Immune modulating effects of additional supplementation of estradiol combined with testosterone in murine testosterone-deficient NAFLD model
Authors: Okamura Takuro , Hamaguchi Masahide , Bamba Ryo , Nakajima Hanako , Yoshimura Yuta , Kimura Tomonori , Nishida Kensuke , Hashimoto Yoshitaka , Fukuda Takuya , Senmaru Takafumi , Fukui Michiaki
Published: May 2020

The progression of nonalcoholic fatty liver disease (NAFLD) is associated with testosterone deficiency. NAFLD patients generally do not respond to treatment with testosterone alone. In animal studies, treatment with testosterone and estrogen reduced the ratios of ILC2:CD45 and ILC3:CD45 and increased M2 macrophages in liver. Our study suggests, based on our immunological data, that a combination of estrogen and testosterone may be clinically relevant for the treatment of NAFLD in patients with male menopause.
Dynamical System Perspective of Cosmological Models Minimally Coupled with Scalar Field
Authors: Singh S. Surendra , Sonia Chingtham
Published: May 2020

The stability criteria for the dynamical system of a homogeneous and isotropic cosmological model are investigated with the interaction of a scalar field in the presence of a perfect fluid. In this paper, we depict the dynamical system perspective to study qualitatively the scalar field cosmology under two special cases, with and without potential. In the absence of potential, we get a two-dimensional dynamical system, and we study the analytical as well as geometrical behavior. For the dynamical system with potential, we analyze different potential forms: simple exponential potential form (Vϕ=Voe−λϕ), double exponential potential form Vϕ=Voexp−Aexp2αϕ, and inverse power law potential form (Vϕ=Voϕ−α). We generate an autonomous system of ordinary differential equations (ASODE) for each case by introducing new dimensionless variables and obtain respective fixed points. We also analyze the type, nature, and stability of the fixed points and how their behavior reflects towards the cosmological scenarios. Throughout the whole work, the investigation of this model has shown us the deep connection between these theories and cosmic acceleration phenomena. The phase plots of the system at different conditions and different values of γ have been analyzed in detail, and their geometrical interpretations have been studied. The perturbation plots of the dynamical system have been analyzed with emphasis on our analytical findings. We have evaluated the total energy density (Ωϕ) at the fixed points and also found out the suitable range of γ and λ for a stable model.
Splenectomy for Solitary Splenic Metastasis in Recurrent Papillary Thyroid Cancer. A Case Report and Literature Review
Authors: Maffuz-Aziz Antonio , Garnica Gabriel , López-Hernández Silvia , Pineda-Diaz Janet , Baquera-Heredia Javier , López-Jiménez Patricia
Published: May 2020

Thyroid cancer is the most common endocrine malignancy, presenting with 23 500 new cases per year in the United States. About 7-23% of the patients will present recurrent metastases disease during follow-up. The classic variant of papillary carcinoma is less aggressive compared to its other variants like diffuse sclerosing, tall cell or columnar cell, and insular variants, and the sites to which this metastasizes is already well identified. Metastasis to the spleen is an extremely rare manifestation of papillary thyroid cancer. To date, only 3 cases have been reported in the literature. Herein, we present a 52-year-old male, who developed spleen metastases, 2.4 years after total thyroidectomy and central neck dissection followed by radioactive iodine ablation and seven months after treatment with sorafenib for lung metastases. The splenic lesion was detected in surveillance studies. This case highlights that splenic metastasis, although rare, may occur even in a patient with a locoregional and systemic controlled thyroid cancer and that it can be treated safely with surgical resection.
Flavonoids in Ageratum conyzoides L. Exert Potent Antitumor Effects on Human Cervical Adenocarcinoma HeLa Cells In Vitro and In Vivo
Authors: Lin Zeyan , Lin Yanyan , Shen Jinxing , Jiang Meijiao , Hou Youming
Published: May 2020

The Ageratum conyzoides L. (A. conyzoides) is commonly used as a traditional medicine, and its antitumor effects have also been studied. However, the functional roles of flavonoids in A. conyzoides in antitumor activities have not been clarified. The present study is aimed at investigating the biological effects of flavonoids in A. conyzoides on human cervical adenocarcinoma. Firstly, we detected that flavonoids in A. conyzoides significantly inhibited the proliferation, invasion, migration, and clonality of human cervical adenocarcinoma HeLa cells in vitro. Furthermore, we found that flavonoids in A. conyzoides induced significant S phase arrest and apoptosis and obviously decreased the intracellular reactive oxygen species (ROS) level in HeLa cells. Finally, we found that flavonoids in A. conyzoides significantly inhibited the HeLa xenograft tumor growth and epithelial‐mesenchymal transition (EMT) in vivo. In conclusion, our results demonstrated the obvious antitumor effects of flavonoids in A. conyzoides on HeLa cells, suggesting that flavonoids in A. conyzoides could be provided as a novel therapeutic compound for human cervical adenocarcinoma.
Low‐Frequency Repetitive Transcranial Magnetic Stimulation for the Treatment of Chronic Tinnitus: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Authors: Dong Changhong , Chen Cheng , Wang Teng , Gao Chunjiu , Wang Yidan , Guan Xinying , Dong Xin
Published: May 2020

Background. Chronic tinnitus affects approximately 10‐15% of the population. Low‐frequency repetitive transcranial magnetic stimulation (rTMS) has been considered as a promising and well‐tolerated therapeutic strategy for chronic tinnitus. However, a recent large‐scale multicenter clinical trial showed a negative result. Objective. This systematic review is aimed at assessing the efficacy and safety of low‐frequency rTMS in chronic tinnitus. Methods. We searched PubMed, Embase, and Cochrane Library for randomized controlled studies of rTMS treatment of chronic tinnitus. A pooled analysis of standardized mean difference (SMD) was performed with 95% confidence intervals (CI). Results. Ten RCTs involving 567 participants were included in this review. Compared with sham stimulation, rTMS showed no significant efficacy in tinnitus severity and disability measured by Tinnitus Handicap Inventory (THI) in short‐term (SMD = −0.04, 95% CI ‐0.23 to 0.16, P = 0.72), medium‐term (SMD = −0.13, 95% CI ‐0.43 to 0.17, P = 0.41), and long‐term (SMD = −0.16, 95% CI ‐0.38 to 0.05, P = 0.14) follow‐up. Tinnitus severity and disability measured by Tinnitus Questionnaire (TQ) also showed no significant improvement in short‐term (SMD = −0.11, 95% CI ‐0.31 to 0.10, P = 0.30), medium‐term (SMD = −0.10, 95% CI ‐0.37 to 0.16, P = 0.44), and long‐term (SMD = −0.20, 95% CI ‐0.40 to 0.01, P = 0.06) follow‐up. Additionally, no statistically significant difference was shown in the changes of tinnitus loudness assessed by a visual analogue scale (VAS) between rTMS and sham groups in the short‐term (SMD = −0.28, 95% CI ‐0.59 to 0.02, P = 0.07), medium‐term (SMD = −0.26, 95% CI ‐0.59 to 0.07, P = 0.13), and long‐term (SMD = −0.20, 95% CI ‐0.53 to 0.13, P = 0.24) follow‐up. Few mild or moderate adverse events were observed in both the rTMS and sham groups. Conclusion. Low‐frequency rTMS is well tolerated but not effective in treating chronic tinnitus based on the current analysis of pooled data. Further studies with modified and uniform protocols are required to investigate the potential benefit of rTMS in chronic tinnitus.
The Attenuation of Traumatic Brain Injury via Inhibition of Oxidative Stress and Apoptosis by Tanshinone IIA
Authors: Huang Yongpan , Long Xian , Tang Jiayu , Li Xinliang , Zhang Xiang , Luo Chunyan , Zhou Yan , Zhang Pan
Published: May 2020

Traumatic brain injury (TBI) is a major source of mortality and long-term disability worldwide. The mechanisms associated with TBI development are poorly understood, and little progress has been made in the treatment of TBI. Tanshinone IIA is an effective agent to treat a variety of disorders; however, the mechanisms of Tanshinone IIA on TBI remain unclear. The aim of the present study was to investigate the therapeutic potential of Tanshinone IIA on TBI and its underlying molecular mechanisms. Changes in microvascular permeability were examined to determine the extent of TBI with Evans blue dye. Brain edema was assessed by measuring the wet weight to dry weight ratio. The expression levels of CD11, interleukin- (IL-) 1β, and tumor necrosis factor- (TNF-) α mRNA were determined by reverse transcription-quantitative PCR. Aquaporin-4 (AQP4), glial fibrillary acidic protein (GFAP), and p47phox protein expression levels were detected by western blotting. Superoxide dismutase (SOD), catalase and glutathione peroxidase (GSH-PX) activities, and malondialdehyde (MDA) content were determined using commercial kits. Cell apoptosis was detected by western blotting and TUNEL staining. Tanshinone IIA (10 mg/kg/day, intraperitoneal administration) significantly reduced brain water content and vascular permeability at 12, 24, 48, and 72 h after TBI. Tanshinone IIA downregulated the mRNA expression levels of various factors induced by TBI, including CD11, IL-1β, and TNF-α. Notably, CD11 mRNA downregulation suggested that Tanshinone IIA inhibited microglia activation. Further results showed that Tanshinone IIA treatment significantly downregulated AQP4 and GFAP expression. TBI-induced oxidative stress and apoptosis were markedly reversed by Tanshinone IIA, with an increase in SOD and GSH-PX activities and a decrease in the MDA content. Moreover, Tanshinone IIA decreased TBI-induced NADPH oxidase activation via the inhibition of p47phox. Tanshinone IIA attenuated TBI, and its mechanism of action may involve the inhibition of oxidative stress and apoptosis.
Increasing Oxygen Mass Fraction in Blind Headings of a Plateau Metal Mine by Oxygen Supply Duct Design: A CFD Modelling Approach
Authors: Li Zijun , Zhao Shuqi , Li Rongrong , Huang Yilong , Xu Yu , Song Pinfang
Published: May 2020

Hypoxia problem has always been a difficult point in plateau tunneling projects. To solve this problem, a blind heading face in a plateau metal mine in western China was taken as the physical model, and the computational fluid dynamics was used to analyze the oxygen mass fraction distribution and oxygen-increasing effect in 1 m, 3 m, and 5 m roadway sections from the heading face. The optimal ventilation system was first built to obtain the optimum height and length of the airflow ducts. Then different cases with various oxygen supply duct designs were built in 2 scenarios. The results found that different oxygen supply duct design has significant influence on the oxygen distribution in the heading face. Also, each design has different optimal height of oxygen outlet. The oxygen supply effect is best when some small holes are made in the oxygen supply duct to diffuse oxygen to the working surface. The finding of this paper is helpful for effective and economical oxygen supply in roadway excavation of plateau metal mine and tunnel.
Fluid Structure Interaction on Paravalvular Leakage of Transcatheter Aortic Valve Implantation Related to Aortic Stenosis: A Patient-Specific Case
Authors: Basri Adi A. , Zuber Mohammad , Basri Ernnie I. , Zakaria Muhammad S. , Aziz Ahmad F. A. , Tamagawa Masaaki , Ahmad Kamarul A.
Published: May 2020

This study investigated the impact of paravalvular leakage (PVL) in relation to the different valve openings of the transcatheter aortic valve implantation (TAVI) valve using the fluid structure interaction (FSI) approach. Limited studies were found on the subject of FSI with regards to TAVI-PVL condition, which involves both fluid and structural responses in coupling interaction. Hence, further FSI simulation with the two-way coupling method is implemented to investigate the effects of hemodynamics blood flow along the patient-specific aorta model subjected to the interrelationship between PVL and the different valve openings using the established FSI software ANSYS 16.1. A 3D patient-specific aorta model is constructed using MIMICS software. The TAVI valve identical to Edward SAPIEN XT 26 (Edwards Lifesciences, Irvine, California), at different Geometrical Orifice Areas (GOAs), is implanted into the patient’s aortic annulus. The leaflet opening of the TAVI valve is drawn according to severity of GOA opening represented in terms of 100%, 80%, 60%, and 40% opening, respectively. The result proved that the smallest percentage of GOA opening produced the highest possibility of PVL, increased the recirculatory flow proximally to the inner wall of the ascending aorta, and produced lower backflow velocity streamlines through the side area of PVL region. Overall, 40% GOA produced 89.17% increment of maximum velocity magnitude, 19.97% of pressure drop, 65.70% of maximum WSS magnitude, and a decrement of 33.62% total displacement magnitude with respect to the 100% GOA.