bluuebunny/crossref_metadata_2025 huggingface dataset:
$$K^{0}_{S}$$ meson production in inelastic p+p interactions at 158 $$\text{ GeV }/c$$ beam momentum measured by NA61/SHINE at the CERN SPS
Authors: Acharya A. , Adhikary H. , Allison K. K. , Amin N. , Andronov E. V. , Antićić T. , Babkin V. , Baszczyk M. , Bhosale S. , Blondel A. , Bogomilov M. , Bondar Y. , Brandin A. , Bravar A. , Bryliński W. , Brzychczyk J. , Buryakov M. , Busygina O. , Bzdak A. , Cherif H. , Ćirković M. , Csanad M. , Cybowska J. , Czopowicz T. , Damyanova A. , Davis N. , Deliyergiyev M. , Deveaux M. , Dmitriev A. , Dominik W. , Dorosz P. , Dumarchez J. , Engel R. , Feofilov G. A. , Fields L. , Fodor Z. , Garibov A. , Gaździcki M. , Golosov O. , Golovatyuk V. , Golubeva M. , Grebieszkow K. , Guber F. , Haesler A. , Igolkin S. N. , Ilieva S. , Ivashkin A. , Johnson S. R. , Kadija K. , Kargin N. , Kashirin E. , Kiełbowicz M. , Kireyeu V. A. , Klochkov V. , Kolesnikov V. I. , Kolev D. , Korzenev A. , Kovalenko V. N. , Kowalski S. , Koziel M. , Kozłowski B. , Krasnoperov A. , Kucewicz W. , Kuich M. , Kurepin A. , Larsen D. , László A. , Lazareva T. V. , Lewicki M. , Łojek K. , Lyubushkin V. V. , Maćkowiak-Pawłowska M. , Majka Z. , Maksiak B. , Malakhov A. I. , Marcinek A. , Marino A. D. , Marton K. , Mathes H.-J. , Matulewicz T. , Matveev V. , Melkumov G. L. , Merzlaya A. O. , Messerly B. , Mik Ł. , Morozov S. , Nagai Y. , Naskręt M. , Ozvenchuk V. , Panova O. , Paolone V. , Petukhov O. , Pidhurskyi I. , Płaneta R. , Podlaski P. , Popov B. A. , Porfy B. , Posiadała-Zezula M. , Prokhorova D. S. , Pszczel D. , Puławski S. , Puzović J. , Ravonel M. , Renfordt R. , Röhrich D. , Rondio E. , Rumberger B. T. , Rumyantsev M. , Rustamov A. , Rybczynski M. , Rybicki A. , Sadhu S. , Sadovsky A. , Schmidt K. , Selyuzhenkov I. , Seryakov A. Yu. , Seyboth P. , Słodkowski M. , Staszel P. , Stefanek G. , Stepaniak J. , Strikhanov M. , Ströbele H. , Šuša T. , Taranenko A. , Tefelska A. , Tefelski D. , Tereshchenko V. , Toia A. , Tsenov R. , Turko L. , Unger M. , Uzhva D. , Valiev F. F. , Veberič D. , Vechernin V. V. , Wickremasinghe A. , Wójcik K. , Wyszyński O. , Zaitsev A. , Zimmerman E. D. , Zwaska R. ,   NA61/SHINE Collaboration
Published: January 2022

The production of $$K^{0}_{S}$$

K
S
0

mesons in inelastic p+p collisions at beam momentum 158 $$\text{ GeV }/c$$

GeV

/
c

($$\sqrt{s_{NN}}=17.3$$

s

NN

=
17.3

 $$\text{ GeV }$$

GeV

) was measured with the NA61/SHINE spectrometer at the CERN Super Proton Synchrotron. Double-differential distributions were obtained in transverse momentum and rapidity. The mean multiplicity of $$K^{0}_{S}$$

K
S
0

was determined to be $$0.162 \pm 0.001 (stat.) \pm 0.011 (sys.)$$

0.162
±
0.001
(
s
t
a
t
.
)
±
0.011
(
s
y
s
.
)

. The results on $$K^{0}_{S}$$

K
S
0

production are compared with model predictions (EPOS 1.99, SMASH 2.0, PHSD and UrQMD 3.4 models) as well as with published world data.
Measuring close proximity interactions in summer camps during the COVID-19 pandemic
Authors: Leoni Elia , Cencetti Giulia , Santin Gabriele , Istomin Timofei , Molteni Davide , Picco Gian Pietro , Farella Elisabetta , Lepri Bruno , Murphy Amy L.
Published: January 2022

Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic.

Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses.

By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps’ organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors.

Overall, we offer an insight into the educator-child and child-child social interactions during the pandemic, thus providing a valuable tool for schools, summer camps, and policy makers to (re)structure educational activities safely.
The role of baryon structure in neutron stars
Authors: Motta Theo F. , Thomas Anthony W.
Published: January 2022

Understanding the equation of state of dense nuclear matter is a fundamental challenge for nuclear physics. It is an especially timely and interesting challenge as we have reached a period where neutron stars, which contain the most dense nuclear matter in the Universe, are now being studied in completely new ways, from gravitational waves to satellite-based telescopes. We review the theoretical approaches to calculating this equation of state which involve a change in the structure of the baryons, along with their predictions for neutron star properties.
Real-Time Leaf Recognition Method Based on Image Segmentation and Feature Extraction
Authors: Cai Xingquan , Huo Yuqing , Chen Yunbo , Xi Mengyao , Tu Yuxin , Sun Chen , Sun Haiyan
Published: January 2022

Leaf recognition has been an important research field of image recognition in the recent past. However, traditional leaf recognition methods can be easily affected by environments and cannot realize multi-leaf recognition under a complex background in real time. In this work, we present a real-time leaf recognition method based on image segmentation and feature recognition. First, we denoise the input of a leaf image, performing a leaf segmentation with an improved FCN network model, and then optimize the contour edge with a CRF algorithm to get a leaf segmentation image. Second, we extract the content features of the segmented leaf image with an Inception-V2 network model to get a feature map of the leaf image. Third, we input the feature map into an RPN network to obtain a set of regional candidate frames and then integrate the feature map and the information of candidate frames in a RoI Pooling layer, which can extract the feature map of a candidate frame area and scale it to a fixed-size feature map. Finally, we send the feature map to a fully connected layer to classify each preselection box content through the calculation of preselection feature maps, and then obtain the final accurate position of the prediction box by utilizing a bounding box regression. The experimental results show that the proposed method can achieve multi-leaf recognitions with high accuracy and fast speed under complex environments in real time.
MODELING STOCK PRICE MOVEMENTS PREDICTION BASED ON NEWS SENTIMENT ANALYSIS AND DEEP LEARNING
Authors: TAJMAZINANI MAEDEH , HASSANI HOSSEIN , RAEI REZA , ROUHANI SAEED
Published: January 2022

Nowadays, with the rapid growth of information spread, investors involve news and sentiments in their financial decision more than before. This paper investigates the effect of technical and fundamental analysis in the form of technical indicators and sentiments of news on Iranian stocks. Several packages and technologies are developed for English semantic; in this regard, most previous works are done on English, especially Twitter. On the other hand, there are rare attempts about the effect of Persian semantics on Iranian stocks due to the lack of uniform packages and technologies. This study collects news articles in Iran that are related to stocks. After data preprocessing, the polarity of news is discerned by the HESNEGAR lexicon. It is the first to consider a semantic Persian lexicon on Iranian stocks. Three models are proposed based on the deep learning approach-convolutional neural networks; price only, news sentiments and hybrid models. Experimental results showed that hybrid model considering both technical indicators and news sentiments using the HESNEGAR lexicon could significantly improve the prediction accuracy compared to price only and news sentiments models. This study can be the reference model to plan a trading strategy.
APPROXIMATE SERIES SOLUTIONS OF A ONE-FACTOR TERM STRUCTURE MODEL FOR BOND PRICING
Authors: EDEKI SUNDAY ONOS , OKOLI DEBORAH CHIKWADO , AHMAD HIJAZ , WONG WING-KEUNG
Published: January 2022

One-factor term structure model in finance and economic setups conveys the opinion that there exists only one Brownian process in the formulation of the short rate model as one source of randomness. To extend the theory, in this paper, we develop the approximate solution of a one-factor bond pricing model that can be used to obtain solutions of both nonlinear and multi-factor models and get applications of two proposed solution methods: Elzaki Adomian decomposition method (EADM) and Laplace Adomian decomposition solution method (LADM). We first obtain an efficient, reliable and approximate-analytical solution for a one-factor bond pricing model. We provide illustrative examples that are in good agreement when compared with those already in literature. The methods are very effective in the application being the modified version of the classical Elzaki, and Laplace transforms. Since our proposed methods are effective and efficient, we recommend academics and practitioners apply our proposed model to obtain solutions for some financial models, including both the multi-factor and nonlinear models.
Virial theorems and virial stresses of micropolar media
Authors: Ostoja-Starzewski Martin
Published: January 2022

A generalization of virial theorems and virial stresses to micropolar continuum mechanics is explored. The linear momentum balance in dyadic product with translation leads to (i) the first virial theorem of micropolar continuum mechanics involving the infinite-time limit of the kinetic translational energy and (ii) a classical formula for computing the virial force-stress known in molecular dynamics. The angular momentum balance in dyadic product with rotation leads to (i) the second virial theorem of micropolar continuum mechanics involving the infinite-time limit of the kinetic rotational energy and (ii) a virial couple-stress along with a formula for its computation. The latter stress is also uncovered in the dyadic product of linear momentum balance with rotation. The virial force-stress and virial couple-stress contain, respectively, the Reynolds force-stress and turbulent couple-stress.
NPC:

N

euron

P

ath

C

overage via Characterizing Decision Logic of Deep Neural Networks
Authors: Xie Xiaofei , Li Tianlin , Wang Jian , Ma Lei , Guo Qing , Juefei-Xu Felix , Liu Yang
Published: January 2022

Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in the practical operational environment, which calls for systematic testing, especially in safety-critical scenarios. Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs. However, due to the blackbox nature of DNN, the existing structural coverage criteria are difficult to interpret, making it hard to understand the underlying principles of these criteria. The relationship between the structural coverage and the decision logic of DNNs is unknown. Moreover, recent studies have further revealed the non-existence of correlation between the structural coverage and DNN defect detection, which further posts concerns on what a suitable DNN testing criterion should be.

In this article, we propose the
interpretable
coverage criteria through constructing the decision structure of a DNN. Mirroring the control flow graph of the traditional program, we first extract a decision graph from a DNN based on its interpretation, where a path of the decision graph represents a decision logic of the DNN. Based on the control flow and data flow of the decision graph, we propose two variants of path coverage to measure the adequacy of the test cases in exercising the decision logic. The higher the path coverage, the more diverse decision logic the DNN is expected to be explored. Our large-scale evaluation results demonstrate that: The path in the decision graph is effective in characterizing the decision of the DNN, and the proposed coverage criteria are also sensitive with errors, including natural errors and adversarial examples, and strongly correlate with the output impartiality.
Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?
Authors: Calefato Fabio , Lanubile Filippo
Published: January 2022

Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition. However, self-assessment questionnaires are not a practical solution for collecting multiple observations on a large scale. Instead, automatic personality detection, while overcoming these limitations, is based on off-the-shelf solutions trained on non-technical corpora, which might not be readily applicable to technical domains like software engineering. In this article, we first assess the performance of general-purpose personality detection tools when applied to a technical corpus of developers’ e-mails retrieved from the public archives of the Apache Software Foundation. We observe a general low accuracy of predictions and an overall disagreement among the tools. Second, we replicate two previous research studies in software engineering by replacing the personality detection tool used to infer developers’ personalities from pull-request discussions and e-mails. We observe that the original results are not confirmed, i.e., changing the tool used in the original study leads to diverging conclusions. Our results suggest a need for personality detection tools specially targeted for the software engineering domain.
Low-power Near-data Instruction Execution Leveraging Opcode-based Timing Analysis
Authors: Athanasios Tziouvaras , Georgios Dimitriou , Georgios Stamoulis
Published: January 2022

Traditional processor architectures utilize an external DRAM for data storage, while they also operate under worst-case timing constraints. Such designs are heavily constrained by the delay costs of the data transfer between the core pipeline and the DRAM, and they are incapable of exploiting the timing variations of their pipeline stages. In this work, we focus on a near-data processing methodology combined with a novel timing analysis technique that enables the adaptive frequency scaling of the core clock and boosts the performance of low-power designs. We propose a near-data processing and better-than-worst-case co-design methodology to efficiently move the instruction execution to the DRAM side and, at the same time, to allow the pipeline to operate at higher clock frequencies compared to the worst-case approach. To this end, we develop a timing analysis technique, which evaluates the timing requirements of individual instructions and we dynamically scale the clock frequency, according to the instructions types that currently occupy the pipeline. We evaluate the proposed methodology on six different RISC-V post-layout implementations using an HMC DRAM to enable the processing-in-memory (PIM) process. Results indicate an average speedup factor of 1.96× with a 1.6× reduction in energy consumption compared to a standard RISC-V PIM baseline implementation.
Register-Pressure-Aware Instruction Scheduling Using Ant Colony Optimization
Authors: Shobaki Ghassan , Gordon Vahl Scott , McHugh Paul , Dubois Theodore , Kerbow Austin
Published: January 2022

This paper describes a new approach to register-pressure-aware instruction scheduling, using
Ant Colony Optimization (ACO)
. ACO is a nature-inspired optimization technique that researchers have successfully applied to NP-hard sequencing problems like the
Traveling Salesman Problem (TSP)
and its derivatives. In this work, we describe an ACO algorithm for solving the long-standing compiler optimization problem of balancing
Instruction-Level Parallelism (ILP)
and
Register Pressure (RP)
in pre-allocation instruction scheduling. Three different cost functions are studied for estimating RP during instruction scheduling. The proposed ACO algorithm is implemented in the LLVM open-source compiler, and its performance is evaluated experimentally on three different machines with three different instruction-set architectures: Intel x86, ARM, and AMD GPU. The proposed ACO algorithm is compared to an exact
Branch-and-Bound (B&B)
algorithm proposed in previous work. On x86 and ARM, both algorithms are evaluated relative to LLVM's generic scheduler, while on the AMD GPU, the algorithms are evaluated relative to AMD's production scheduler. The experimental results show that using SPECrate 2017 Floating Point, the proposed algorithm gives geometric-mean improvements of 1.13% and 1.25% in execution speed on x86 and ARM, respectively, relative to the LLVM scheduler. Using PlaidML on an AMD GPU, it gives a geometric-mean improvement of 7.14% in execution speed relative to the AMD scheduler. The proposed ACO algorithm gives approximately the same execution-time results as the B&B algorithm, with each algorithm outperforming the other on a substantial number of hard scheduling regions. ACO gives better results than B&B on many large instances that B&B times out on. Both ACO and B&B outperform the LLVM algorithm on the CPU and the AMD algorithm on the GPU.
An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks
Authors: Liao Lizhi , Li Heng , Shang Weiyi , Ma Lei
Published: January 2022

Deep neural network (DNN)
models typically have many hyperparameters that can be configured to achieve optimal performance on a particular dataset. Practitioners usually tune the hyperparameters of their DNN models by training a number of trial models with different configurations of the hyperparameters, to find the optimal hyperparameter configuration that maximizes the training accuracy or minimizes the training loss. As such hyperparameter tuning usually focuses on the model accuracy or the loss function, it is not clear and remains under-explored how the process impacts other performance properties of DNN models, such as inference latency and model size. On the other hand, standard DNN models are often large in size and computing-intensive, prohibiting them from being directly deployed in resource-bounded environments such as mobile devices and
Internet of Things (IoT)
devices. To tackle this problem, various model optimization techniques (e.g., pruning or quantization) are proposed to make DNN models smaller and less computing-intensive so that they are better suited for resource-bounded environments. However, it is neither clear how the model optimization techniques impact other performance properties of DNN models such as inference latency and battery consumption, nor how the model optimization techniques impact the effect of hyperparameter tuning (i.e., the compounding effect). Therefore, in this paper, we perform a comprehensive study on four representative and widely-adopted DNN models, i.e.,
CNN image classification
,
Resnet-50
,
CNN text classification
, and
LSTM sentiment classification
, to investigate how different DNN model hyperparameters affect the standard DNN models, as well as how the hyperparameter tuning combined with model optimization affect the optimized DNN models, in terms of various performance properties (e.g., inference latency or battery consumption). Our empirical results indicate that tuning specific hyperparameters has heterogeneous impact on the performance of DNN models across different models and different performance properties. In particular, although the top tuned DNN models usually have very similar accuracy, they may have significantly different performance in terms of other aspects (e.g., inference latency). We also observe that model optimization has a confounding effect on the impact of hyperparameters on DNN model performance. For example, two sets of hyperparameters may result in standard models with similar performance but their performance may become significantly different after they are optimized and deployed on the mobile device. Our findings highlight that practitioners can benefit from paying attention to a variety of performance properties and the confounding effect of model optimization when tuning and optimizing their DNN models.
DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators
Authors: Lin Wei-Ting , Cheng Hsiang-Yun , Yang Chia-Lin , Lin Meng-Yao , Lien Kai , Hu Han-Wen , Chang Hung-Sheng , Li Hsiang-Pang , Chang Meng-Fan , Tsou Yen-Ting , Nien Chin-Fu
Published: January 2022

Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. In addition, due to the hardware constraints, the way to deploy neural network models on memristor crossbar arrays affects the computation parallelism and communication overheads. To enable reliable and energy-efficient memristor-based accelerators, a simulation platform is needed to precisely analyze the impact of non-ideal circuit/device properties on the inference accuracy and the influence of different deployment strategies on performance and energy consumption. In this paper, we propose a flexible simulation framework, DL-RSIM, to tackle this challenge. A rich set of reliability impact factors and deployment strategies are explored by DL-RSIM, and it can be incorporated with any deep learning neural networks implemented by TensorFlow. Using several representative convolutional neural networks as case studies, we show that DL-RSIM can guide chip designers to choose a reliability-friendly design option and energy-efficient deployment strategies and develop optimization techniques accordingly.
Spatial Isolation Implies Zero Knowledge Even in a Quantum World
Authors: Chiesa Alessandro , Forbes Michael A. , Gur Tom , Spooner Nicholas
Published: January 2022

Zero knowledge plays a central role in cryptography and complexity. The seminal work of Ben-Or et al. (STOC 1988) shows that zero knowledge can be achieved unconditionally for any language in
NEXP
, as long as one is willing to make a suitable
physical assumption
: if the provers are spatially isolated, then they can be assumed to be playing independent strategies.

Quantum mechanics, however, tells us that this assumption is unrealistic, because spatially-isolated provers could share a quantum entangled state and realize a non-local correlated strategy. The MIP
*
model captures this setting.

In this work, we study the following question:
Does spatial isolation still suffice to unconditionally achieve zero knowledge even in the presence of quantum entanglement?

We answer this question in the affirmative: we prove that every language in
NEXP
has a 2-prover
zero knowledge
interactive proof that is sound against entangled provers; that is,

NEXP ⊆ ZK-MIP
*

.

Our proof consists of constructing a zero knowledge interactive probabilistically checkable proof with a strong algebraic structure, and then lifting it to the MIP
*
model. This lifting relies on a new framework that builds on recent advances in low-degree testing against entangled strategies, and clearly separates classical and quantum tools.

Our main technical contribution is the development of new algebraic techniques for obtaining unconditional zero knowledge; this includes a zero knowledge variant of the celebrated sumcheck protocol, a key building block in many probabilistic proof systems. A core component of our sumcheck protocol is a new algebraic commitment scheme, whose analysis relies on algebraic complexity theory.
Welcome to AI matters 7(3)
Authors: Leite Iolanda , Karpatne Anuj
Published: January 2022

Welcome to the third issue of this year's AI Matters Newsletter. We open with the annual report of SIGAI by our leadership team. We then bring you a brief report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis. In our regular Education column, Lisa Zhang, Pouria Fewzee, and Charbel Feghali describe a Model AI Assignment where students combine various techniques from a deep learning course to build a denoising autoencoder for news headlines. We end the issue with two article contributions, one by Richard Freedman that describes the 2023 EAAI Mentored Undergraduate Research Challenge, and another by Ulle Endriss, Ann Nowé, Maria Gini, Victor Lesser, Michael Luck, Ana Paiva, and Jaime Sichman, which provides perspectives on the completion of 20 years of AAMAS.
SIGAI annual report
Authors: Das Sanmay , Mattei Nicholas , Dickerson John P. , Koenig Sven , Dennis Louise , Medsker Larry , Neller Todd , Leite Iolanda , Karpatne Anuj , Tsang Alan
Published: January 2022

We have continued adjusting to a "new normal" in the Covid era. In addition to the significant socio-economic challenges of the pandemic, for us as a scientific organization, we continue to grapple with a world with few, if any, in-person conferences for a second year in a row, and continued virtual interactions for the community. We are, however, proud of what we have been able to accomplish in the past year. As part of transparent communication with our membership, we share here the annual report that we provide to ACM each summer. You may notice a slight change in format this year, to focus on areas that ACM is particularly interested in hearing from us about. Also note that we include the report without modifications, so the information is a few months old!
Conference reports
Authors: Dennis Louise A.
Published: January 2022

This section is compiled from reports of recent events sponsored or run in cooperation with ACM SIGAI. In general these reports were written and submitted by the conference organisers.
Koli Calling 2021 report
Authors: Petersen Andrew , Seppälä Otto
Published: January 2022

Koli Calling is a single-track conference covering a range of topics related to education in the computing disciplines. The 21st Koli Calling International Conference on Computing Education Research was held virtually from November 18-21, 2021. An affiliated Doctoral Consortium, "AI Education for All," was held in-person immediately prior to the conference. This year, the conference was held in cooperation with the ACM and SIGCSE and was organized by the University of Eastern Finland, Aalto University, and the University of Toronto Mississauga.
SIGCSE Technical Symposium 2022 call for participation
Authors: Merkle Larry , Doyle Maureen
Published: January 2022

The 2022 Technical Symposium is scheduled for March 2-5, 2022 at the Rhode Island Convention Center in Providence (RICC), RI, USA, and we are excited to offer the conference for the first time ever in a hybrid modality. The theme is computing education's role in contributing to society's hope for a better future. There are a number of hotels within 0.1 miles (160 m) of the RICC, including the Omni Providence Hotel, which has skybridge access to the convention center.
SIGCSE 2022 board election
Authors: Settle Amber
Published: January 2022

ACM's Policy and Procedures on SIG Elections require that those SIGs holding elections notify their membership of candidates for elected offices. Additional candidates may be placed on the ballot by petition. All candidates must be ACM Professional Members, as well as members of the SIG. Anyone interested in petitioning must inform ACM Headquarters, Pat Ryan (ryanp@hq.acm.org), and the Secretary of SIGCSE of their intent to petition by 15 March 2022. Petitions must be submitted to ACM Headquarters for verification by 1 April 2022.
CSEdResearch.org announcement
Authors: McGill Monica
Published: January 2022

In this issue, we're highlighting two survey instruments with evidence of reliability and validity that might help you in your next study.
Member spotlight
Authors: Miller Jeffrey , Wallace Charles , Vahid Frank
Published: January 2022

In this feature of the Bulletin, we highlight members of the SIGCSE community. This issue's spotlight is Frank Vahid, a professor of computer science and engineering at the University of California at Riverside.
INODE
Authors: Amer-Yahia Sihem , Koutrika Georgia , Braschler Martin , Calvanese Diego , Lanti Davide , Lücke-Tieke Hendrik , Mosca Alessandro , Mendes de Farias Tarcisio , Papadopoulos Dimitris , Patil Yogendra , Rull Guillem , Smith Ellery , Skoutas Dimitrios , Subramanian Srividya , Stockinger Kurt
Published: January 2022

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise.

We introduce INODE - an end-to-end data exploration system - that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.
Al2O3/MoOx Hole-Selective Passivating Contact for Silicon Heterojunction Solar Cell
Authors: Chowdhury Sanchari , Khokhar Muhammad Quddammah , Pham Duy Phong , Yi Junsin
Published: January 2022

Carrier selective contact (CSC) layers have been extensively studied to realize high passivation effect in solar cells. Excellent passivation properties of Al2O3 and a-Si:H(i) as passivating interlayers between the hole-selective contact (HSC) MoOx and p-type c-Si wafer surface are reported herein. MoOx single layer exhibits a high work function value (≥5.0 eV), which can cause sufficient band bending in the band structure for HSC. An Al2O3/MoOx contact exhibits a significantly higher transmittance and surface passivation compared with that of an a-Si:H(i)/MoOx contact. The passivation results for Al2O3/MoOx contact are a carrier lifetime (τeff) of 830 μs and implied open circuit voltage (iVOC) of 726 mV, whereas for conventional a-Si:H(i)/MoOx contact, the corresponding values are 770 μs and 716 mV. Delicate thickness optimization was performed using experimental and simulation results for Al2O3/MoOx and a-Si:H(i)/MoOx stacks to achieve high performance in p-type c-Si solar cells.
Role of Strain in Surface Segregation of La
1-X
SrxCo0.2Fe0.8O3
Authors: Yu Yang , Ludwig Karl F , Gopalan Srikanth , Pal Uday Bhanu , Basu Soumendra Nath
Published: January 2022

Strontium doped lanthanum cobalt ferrite (LSCF), a widely used cathode material in solid oxide fuel cells (SOFC), can have long-term stability issues that can adversely affect electrochemical performance. Heteroepitaxial thin films of La1-x
SrxCo0.2Fe0.8O3 with varying Sr content (x = 0.4, 0.3, 0.2) were deposited on single crystal NdGaO3, SrTiO3 and GdScO3 substrates by pulsed laser deposition. The lattice mismatch between the films and the substrate led to different strains in the films. The extent of Sr-rich precipitate formation on the film surface was quantified using total reflection X-ray fluorescence (TXRF), and atomic force microscopy (AFM). The microstructure and the nature of the bonding of the surface Sr-rich phases were investigated by scanning/transmission electron microscopy (S/TEM) and hard x-ray photoelectron spectroscopy (HAXPES), respectively. The strain in the thin films was measured by high-resolution x-ray diffraction (HRXRD). The combined effects of the strontium content and strain on the extent of surface phase formation will be discussed.
In-Situ Electrochemical Stiffness in Li-ion batteries
Authors: Gewirth Andrew A , Lundberg Kimberly E.
Published: January 2022

Mechanical deformation in battery electrodes incites degradation and capacity loss by causing cracks in the active material, electrical isolation of particles from the current collector, and additional surface area available for electrolyte decomposition. Surface (stress) and bulk (strain) mechanical changes occur asynchronously, and increasing our understanding of their individual contributions to deformation can aid in the design of more robust and longer-lasting batteries. One method with which to compare the complex interplay between stress and strain is electrochemical stiffness. Electrochemical stiffness is the voltage-dependent ratio of the stress derivative to strain derivative. It offers insight into the rate at which competing surface and bulk processes are occurring during electrochemical cycling.

In this talk we discuss electrochemical stiffness measurements obtained for graphite, LiMn2O4 (LMO) and LiFePO4 (LFP), anode and cathode materials chosen because of their wide-spread commercial use and differing intercalation mechanisms and crystal structures. Electrochemical stiffness measurements for LMO reveals asynchronous stress and strain behavior. An electrochemical stiffness peak shows that stress dominates prior to LMO delithiation, which suggests the existence of surface morphological changes as delithiation begins. Likewise, the stress derivative with respect to potential for LFP also shows an increased rate of stress change just prior to delithiation. The origin of asynchronous behavior in stress and strain measurements will be discussed.
Phase-Field Simulation of Stress Evolution in Sodium Ion Battery Electrode Particles
Authors: Zhang Tao , Kamlah Marc
Published: January 2022

Sodium-ion batteries have been considered as a promising alternative to Lithium-ion batteries. The cathode material NaxFePO4 of sodium-ion batteries shows phase changes during intercalation. In this work, a phase-field model for NaxFePO4 is studied for the first time. A coupled phase-field model for the Cahn-Hilliard diffusion equation and finite deformation elasticity is derived. For the mechanical part, two finite deformation elasticity formulations based on elastic Green strain and logarithmic elastic strain, respectively, are introduced and compared. The material parameters for NaxFePO4 are determined. We implemented the model in COMSOL Multiphysics® for a spherically symmetric problem of sodium insertion into a cathodic particle made of NaxFePO4. Furthermore, we compare the two cathode materials NaxFePO4 and LixFePO4 to each other in terms of phase changes and stresses, and show that the stresses in the cathode material NaxFePO4 are larger at the phase segregated state.
The Discovery of Calcite Intrinsic Wettability by the First-Ever Optical Illumination Inside Dark Fluid using IRIDW Apparatus
Authors: Alfarisi Omar , Raza Aikifa , Zhang TieJun
Published: January 2022

Wettability is one of the critical physical-chemical properties controlling multiphase flow in porous media. Therefore, it is vital to identify the wettability for each rock type when building 3D geological models for predicting the fluid flow behavior using a numerical simulator. Wettability-unique relative permeability curves are part of each flow simulator's rock type for proper simulation predictions. The reference approach for wettability determination is contact angle measurement. The literature recoded the wettability of contact angle measurement inside transparent fluid like water and decane. However, we need to visualize and measure the water-rock contact angle inside dark fluid like the hydrocarbon. We propose visualizing and measuring the wettability contact angle for rock-water inside dark hydrocarbon fluid. We use the Illumination through Rock Inside Dark fluid for Wettability measurement (IRIDW) apparatus.
Influence of Sublevel Unloading Excavation with Deep Consideration of the Superposition Effect on Deformation of an Existing Tunnel under an Intelligent Geotechnical Concept
Authors: Tao Xiangling , Luan Pinzhi , Ma Jinrong , Song Weihua
Published: January 2022

The construction of a deep and large foundation will inevitably impose stress and deformation on the existing shield tunnel structure. Based on the two-stage analysis and the Pasternak foundation beam model, the analytical solution for the existing tunnel deformation is derived by taking the grouting pressure and water buoyancy into account in the total tunnel deformation. Using FLAC 3D software, based on the soil-structure interaction model, the variation law of the tunnel uplift value under partition excavation of the foundation pit is studied and the variation law of the stress field and displacement field of the tunnel under the MJS method is analyzed and compared. The results of this paper are worthy of reference for similar projects.
Zinc-Blende GeC Stabilized on GaN (001): An Ab Initio Study
Authors: Camacho-García J. H. , Ruiz-Peralta Ma L. , Hernández-Cocoletzi G. , Bautista-Hernández A. , Salazar-Villanueva M. , Escobedo-Morales A. , Chigo-Anota E. , Moreno-Hernández J. C.
Published: January 2022

First-principle calculations have been performed to explore the initial stages of the zinc blende-like germanium carbide epitaxial growth on the gallium nitride (001)-(2 × 2) surface. First, we studied the Ge/C monolayer adsorption and incorporation at high symmetry sites. Results show that the adsorptions at the top and hcp1 sites are the most stable structures of C and Ge, respectively. Different terminated surfaces were used on the GeC epitaxial growth. According to the surface formation energies, only the first two bilayers are stable; therefore, the GeC epitaxial growth is favorable only under N-rich conditions on a Ge-terminated surface and with Ge bilayers terminated. In addition, it is demonstrated that GeC bilayers on the C-terminated surfaces are unstable and preclude the epitaxial growth. Electronic properties have been investigated by calculating the density of states (DOS) and the projected density of states (PDOS) of the most favorable structures.
Reversible Data Hiding Based on Multiple Pairwise PEE and Two-Layer Embedding
Authors: He Wenguang , Xiong Gangqiang , Wang Yaomin
Published: January 2022

Recent reversible data hiding (RDH) work tends to realize adaptive embedding by discriminately modifying pixels according to image content. However, further optimization and computational complexity remain great challenges. By presenting a better incorporation of pixel value ordering (PVO) prediction and pairwise prediction-error expansion (PEE) technologies, this paper proposes a new RDH scheme. The largest/smallest three pixels of each block are utilized to generate error-pairs. To achieve optimization of the distribution of error pairs, two-layer embedding is introduced such that full-enclosed pixels of each block can be used to determine how to optimally define the spatial location of pixels within block. Then, to modify error pairs with less distortion introduced, the shifted pairing error is involved in the separable utilization of the other one; i.e., it serves as the context for recalculating the other one. Since the recalculation is equivalent to expansion bins selection, various extensions of original pairwise PEE are designed, parameterized, and combined into the so-called multiple pairwise PEE, with which the 2D histogram can be divided into a set of sub-ones for more accurate modification. The experimental results verify the superiority of the proposed scheme over several PVO-based schemes. On the Kodak image database, the average PSNR gains over original PVO-based pairwise PEE are 0.83 and 0.99 dB for capacities of 10,000 and 20,000 bits, respectively.
Marshall–Olkin Extended Gumbel Type‐II Distribution: Properties and Applications
Authors: Willayat Farwa , Saud Naz , Ijaz Muhammad , Silvianita Anita , El-Morshedy Mahmoud
Published: January 2022

Due to the advance computer technology, the use of probability distributions has been raised up to solve the real life problems. These applications are found in reliability engineering, computer sciences, economics, psychology, survival analysis, and some others. This study offers a new probability model called Marshall–Olkin Extended Gumbel Type‐II (MOEGT‐II) which can model various shapes of the failure rate function. The proposed distribution is capable to model increasing, decreasing, reverse J‐shaped, and upside down bathtub shapes of the failure rate function. Various statistical properties of the proposed distribution are derived such as alternate expressions for the density and distribution function, special cases of MOEGT‐II distribution, quantile function, Lorenz curve, and Bonferroni curve. Estimation of the unknown parameters is carried out by the method of maximum likelihood. A simulation study is conducted using three different iterative methods with different samples of sizes n. The usefulness and potentiality of the MOEGT‐II distribution have been shown using three real life data sets. The MOEGT‐II distribution has been demonstrated as better fit than Exponentiated Gumbel Type‐II (EGT‐II), Marshall–Olkin Gumbel Type‐II (MOGT‐II), Gumbel Type‐II (GT‐II), Marshall–Olkin–Frechet (MOF), Frechet (F), Burr III, Log Logistic (LL), Beta Inverse Weibull (BIW), and Kumaraswamy Inverse Weibull (KIW) distributions.
Hybrid Collaborative Filtering Algorithm Based on Sparse Rating Matrix and User Preference
Authors: Wang Hengtao , Wang Hongman , Yang Fangchun , Li Jinglin
Published: January 2022

This study presents a hybrid collaborative filtering recommendation algorithm for sparse data (HCFDS) to increase the recommendation impact by addressing the problem of data sparsity in standard collaborative filtering methods. To begin, the similarity calculation divergence is evident in a data sparse environment due to the difference in user scoring standards and the rise in weight of the same score in the overall score. The user similarity algorithm IU-CS and item similarity algorithm II-CS are suggested in this work by incorporating the score difference threshold and the same score penalty factor, in order to address the deviation of similarity computation caused by the excessive dilation. Second, this work offers a filling optimization technique for score prediction to address the issue of missing score matrix data. The II-CS algorithm presented in this work is used to forecast the missing items in the scoring matrix first, and then, the user’s preference score in the item category dimension is utilized to correct the score prediction value and fill the matrix. Finally, the IU-CS method presented in this work is used in this study to provide recommendations on the filled score matrix. Experiments indicate that, when compared to the preoptimization method and other algorithms, the optimized algorithm successfully solves the problem of data sparsity and the recommendation accuracy is considerably increased.
Compound Attitude Maneuver and Collision Avoiding Control for a Novel Noncontact Close-Proximity Formation Satellite Architecture
Authors: Liao He , Xie Jinjin , Zhou Xiaodong , Yao Chuang , Tang Zhongxing , Zhao Yanbin , Qi Jirong
Published: January 2022

In order to achieve the attitude maneuver performance of the noncontact close-proximity formation satellite architecture, this paper presents a compound control strategy with variable-parameter sliding mode control and disturbance observer-based feedforward compensation. Firstly, the variable-parameter sliding mode control is proposed to guarantee the attitude maneuver performance of the payload module. Secondly, the collision avoiding control with disturbance observer-based feedforward compensation is proposed to guarantee the synchronization of the two separated modules within the small air clearance constraint of the noncontact Lorentz actuator. Finally, a physical air-floating platform is established to verify the effectiveness of the proposed approach.
Vibration Analysis and Experimental Study of GIS Busbar Enclosure under Electric Force
Authors: Yang Dingge , Li Yuzhao , Han Yanhua , Ding Bin , Li Liangshu , Wu Jingfeng
Published: January 2022

To explore the vibration response of the GIS busbar enclosure in a strong electric field, the electric force on the busbar enclosure was solved by the voltage in the circuit and the principle of virtual work. The vibration mode was obtained by finite element technology. Accordingly, the vibration response of the busbar enclosure was obtained by vibration analysis in the frequency domain, and the vibration acceleration of the busbar was monitored in the substation. The simulation results showed that the busbar enclosure was subjected to the electric force because of the fundamental voltage and harmonic voltages in the conductor. By coupling the radial electric force and the vibration mode, it was found that the vibration responses of the busbar enclosure at 100 Hz and 2900 Hz were greater than those at other frequencies. The experimental result showed that the frequency with the highest vibration amplitude was the same as that of the simulation result, and the vibration acceleration amplitude in the experiment and simulation at 2900 Hz was basically consistent, which verified the accuracy of the simulation result. The study shows that the GIS enclosure shell will produce vibration at the two times power frequency and high frequency, which provides an explanation for the high-frequency vibration of busbar shell structure in the ultra-high-voltage substation and provides an objective basis for the design of the busbar enclosure in the ultra-high-voltage substation.