pwc-archive/papers-with-abstracts huggingface dataset:
Balanced Knowledge Distribution among Software Development Teams -- Observations from Open-Source and Closed-Source Software Development
Authors: Saad Shafiq, Christoph Mayr-Dorn, Atif Mashkoor, Alexander Egyed
Date: 2022-07-26 00:00:00

In software development teams, developer turnover is among the primary reasons for project failures as it leads to a great void of knowledge and strain for the newcomers. Unfortunately, no established methods exist to measure how knowledge is distributed among development teams. Knowing how this knowledge evolves and is owned by key developers in a project helps managers reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300k issues and 1.3M comments from 518 open-source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the developers' concepts are with the team over time. We also investigate the impact of leaving members on the frequency of concepts. Finally, we evaluate the soundness of our approach to closed-source software, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the high-level domain knowledge within a team and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers harm the team's concept familiarity.
Using clarification questions to improve software developers' Web search
Authors: Mia Mohammad Imran, Kostadin Damevski
Date: 2022-07-26 00:00:00
Tasks: Learning-To-Rank, valid

Context: Recent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to general purpose Web queries. Most approaches up to this point have addressed this problem with software engineering-specific automated query reformulation techniques, which work without developer involvement but are limited by the content of the original query. In other words, these techniques automatically improve the existing query but can not contribute new, previously unmentioned, concepts. Objective: In this paper, we propose a technique to guide software developers in manually improving their own Web search queries. We examine a conversational approach that follows unsuccessful queries with a clarification question aimed at eliciting additional query terms, thus providing to the developer a clear dimension along which the query could be improved. Methods: We describe a set of clarification questions derived from a corpus of software developer queries and a neural approach to recommending them for a newly issued query. Results: Our evaluation indicates that the recommendation technique is accurate, predicting a valid clarification question 80% of the time and outperforms simple baselines, as well as, state-of-the-art Learning To Rank (LTR) baselines. Conclusion: As shown in the experimental results, the described approach is capable at recommending appropriate clarification questions to software developers and considered useful by a sample of developers ranging from novices to experienced professionals.
Adaptive Asymmetric Label-guided Hashing for Multimedia Search
Authors: Yitian Long
Date: 2022-07-26 00:00:00
Tasks: Cross-Modal Retrieval, Quantization, Retrieval

With the rapid growth of multimodal media data on the Web in recent years, hash learning methods as a way to achieve efficient and flexible cross-modal retrieval of massive multimedia data have received a lot of attention from the current Web resource retrieval research community. Existing supervised hashing methods simply transform label information into pairwise similarity information to guide hash learning, leading to a potential risk of semantic error in the face of multi-label data. In addition, most existing hash optimization methods solve NP-hard optimization problems by employing approximate approximation strategies based on relaxation strategies, leading to a large quantization error. In order to address above obstacles, we present a simple yet efficient Adaptive Asymmetric Label-guided Hashing, named A2LH, for Multimedia Search. Specifically, A2LH is a two-step hashing method. In the first step, we design an association representation model between the different modality representations and semantic label representation separately, and use the semantic label representation as an intermediate bridge to solve the semantic gap existing between different modalities. In addition, we present an efficient discrete optimization algorithm for solving the quantization error problem caused by relaxation-based optimization algorithms. In the second step, we leverage the generated hash codes to learn the hash mapping functions. The experimental results show that our proposed method achieves optimal performance on all compared baseline methods.
A Survey on Trustworthy Recommender Systems
Authors: Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang
Date: 2022-07-25 00:00:00
Tasks: Decision Making, Explainable Recommendation, Fairness, Recommendation Systems, Survey

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user-controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices
Authors: Amit Chaulwar, Lukas Malik, Maciej Krajewski, Felix Reichel, Leif-Nissen Lundbæk, Michael Huth, Bartlomiej Matejczyk
Date: 2022-06-29 00:00:00
Tasks: Dimensionality Reduction, Knowledge Distillation, Sentence

Modern search systems use several large ranker models with transformer architectures. These models require large computational resources and are not suitable for usage on devices with limited computational resources. Knowledge distillation is a popular compression technique that can reduce the resource needs of such models, where a large teacher model transfers knowledge to a small student model. To drastically reduce memory requirements and energy consumption, we propose two extensions for a popular sentence-transformer distillation procedure: generation of an optimal size vocabulary and dimensionality reduction of the embedding dimension of teachers prior to distillation. We evaluate these extensions on two different types of ranker models. This results in extremely compressed student models whose analysis on a test dataset shows the significance and utility of our proposed extensions.
Pairwise sequence alignment at arbitrarily large evolutionary distance
Authors: Brandon Legried, Sebastien Roch
Date: 2022-07-25 00:00:00
Tasks: Multiple Sequence Alignment

Ancestral sequence reconstruction is a key task in computational biology. It consists in inferring a molecular sequence at an ancestral species of a known phylogeny, given descendant sequences at the tip of the tree. In addition to its many biological applications, it has played a key role in elucidating the statistical performance of phylogeny estimation methods. Here we establish a formal connection to another important bioinformatics problem, multiple sequence alignment, where one attempts to best align a collection of molecular sequences under some mismatch penalty score by inserting gaps. Our result is counter-intuitive: we show that perfect pairwise sequence alignment with high probability is possible in principle at arbitrary large evolutionary distances - provided the phylogeny is known and dense enough. We use techniques from ancestral sequence reconstruction in the taxon-rich setting together with the probabilistic analysis of sequence evolution models involving insertions and deletions.
Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense
Authors: Deyin Liu, Lin Wu, Haifeng Zhao, Farid Boussaid, Mohammed Bennamoun, Xianghua Xie
Date: 2022-07-09 00:00:00
Tasks: Adversarial Defense

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce a mis-classification, and thus threatens the reliability of deep learning based deployment systems. Adversarial training (AT) is often adopted to improve robustness through training a mixture of corrupted and clean data. However, most of AT based methods are ineffective in dealing with transferred adversarial examples which are generated to fool a wide spectrum of defense models, and thus cannot satisfy the generalization requirement raised in real-world scenarios. Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN. In this work, we propose a novel approach based on Jacobian norm and Selective Input Gradient Regularization (J-SIGR), which suggests the linearized robustness through Jacobian normalization and also regularizes the perturbation-based saliency maps to imitate the model's interpretable predictions. As such, we achieve both the improved defense and high interpretability of DNNs. Finally, we evaluate our method across different architectures against powerful adversarial attacks. Experiments demonstrate that the proposed J-SIGR confers improved robustness against transferred adversarial attacks, and we also show that the predictions from the neural network are easy to interpret.
Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population
Authors: Al-Akhir Nayan, Boonserm Kijsirikul, Yuji Iwahori
Date: 2022-07-12 00:00:00
Tasks: Prediction, regression

During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Color Coding of Large Value Ranges Applied to Meteorological Data
Authors: Daniel Braun, Kerstin Ebell, Vera Schemann, Laura Pelchmann, Susanne Crewell, Rita Borgo, Tatiana von Landesberger
Date: 2022-07-13 00:00:00

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel "nested" color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.
Joint Proportional Fairness Scheduling Using Iterative Search for mmWave Concurrent Transmission
Authors: Ahmed M. Nor
Date: 2022-07-21 00:00:00
Tasks: Fairness, Scheduling

Millimeter wave (mmWave) will play a significant role as a 5G candidate in facing the growing demand of enormous data rate in the near future. The conventional mmWave standard, IEEE 802.11ad, considers establishing only one mmWave link in wireless local area network (WLAN) to provide multi Gbps data rate. But, mmWave has a tenuous channel which hinders it from providing such rate. Hence, it's necessary to establish multiple mmWave links simultaneously by deploying a multiple number of mmWave access points (APs) in 5G networks. Unfortunately, applying conventional standard without any modifications for mmWave concurrent transmission impedes mmWave APs from selecting optimum mmWave concurrent links. Because IEEE 802.11ad standard associates the user equipment (UEs) to mmWave APs using the link that has the maximum received power without considering mutual interference between simultaneous links. In this paper, a joint proportional fairness scheduling (JPFS) optimization problem for establishing optimum mmWave concurrent transmission links is formulated. And, to find a solution to this non-polynomial (NP) time problem, we use exhaustive search (ES) scheme. Numerical simulation proves the effectiveness of using the ES scheme to improve the system performance.
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text
Authors: Yoonhyung Lee, Seunghyun Yoon, Kyomin Jung
Date: 2022-07-26 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

In this paper, we propose a novel speech emotion recognition model called Cross Attention Network (CAN) that uses aligned audio and text signals as inputs. It is inspired by the fact that humans recognize speech as a combination of simultaneously produced acoustic and textual signals. First, our method segments the audio and the underlying text signals into equal number of steps in an aligned way so that the same time steps of the sequential signals cover the same time span in the signals. Together with this technique, we apply the cross attention to aggregate the sequential information from the aligned signals. In the cross attention, each modality is aggregated independently by applying the global attention mechanism onto each modality. Then, the attention weights of each modality are applied directly to the other modality in a crossed way, so that the CAN gathers the audio and text information from the same time steps based on each modality. In the experiments conducted on the standard IEMOCAP dataset, our model outperforms the state-of-the-art systems by 2.66% and 3.18% relatively in terms of the weighted and unweighted accuracy.
Assessment of a cost-effective headphone calibration procedure for soundscape evaluations
Authors: Bhan Lam, Kenneth Ooi, Zhen-Ting Ong, Karn N. Watcharasupat, Trevor Wong, Woon-Seng Gan
Date: 2022-07-24 00:00:00
Tasks: Attribute, Audio Emotion Recognition, Soundscape evaluation

To increase the availability and adoption of the soundscape standard, a low-cost calibration procedure for reproduction of audio stimuli over headphones was proposed as part of the global ``Soundscape Attributes Translation Project'' (SATP) for validating ISO/TS~12913-2:2018 perceived affective quality (PAQ) attribute translations. A previous preliminary study revealed significant deviations from the intended equivalent continuous A-weighted sound pressure levels ($L_{\text{A,eq}}$) using the open-circuit voltage (OCV) calibration procedure. For a more holistic human-centric perspective, the OCV method is further investigated here in terms of psychoacoustic parameters, including relevant exceedance levels to account for temporal effects on the same 27 stimuli from the SATP. Moreover, a within-subjects experiment with 36 participants was conducted to examine the effects of OCV calibration on the PAQ attributes in ISO/TS~12913-2:2018. Bland-Altman analysis of the objective indicators revealed large biases in the OCV method across all weighted sound level and loudness indicators; and roughness indicators at \SI{5}{\%} and \SI{10}{\%} exceedance levels. Significant perceptual differences due to the OCV method were observed in about \SI{20}{\%} of the stimuli, which did not correspond clearly with the biased acoustic indicators. A cautioned interpretation of the objective and perceptual differences due to small and unpaired samples nevertheless provide grounds for further investigation.
Confirmation Bias in Social Networks
Authors: Marcos R. Fernandes
Date: 2022-07-26 00:00:00

In this study, I present a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and relative magnitude of the state and confirmation biases. Hence, long-run learning is not attained even when people impartially interpret ambiguity. Finally, analytically confirming the probability of emergence of the less-biased consensus when people are connected and have different priors is difficult. Hence, I used simulations to analyze its determinants and found three main results: i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness (i.e. when partisans agree to exchange opinions with opposing partisans) might inhibit efficiency in some cases.
On the Stability of Electromechanical Switching Devices
Authors: Edgar Ramirez-Laboreo, Carlos Sagues, Eduardo Moya-Lasheras, Eloy Serrano-Seco
Date: 2022-07-26 00:00:00

Electromagnetic relays and solenoid actuators are commonly used for their bistable behavior, which allows for switching between two states in electrical, pneumatic, or hydraulic circuits, among other applications. Although there has been extensive research on modeling, estimation, and control of these electromechanical systems, a gap remains in the analysis area. This paper addresses this gap by presenting an equilibrium and stability analysis to gain deeper insight into their bistability. This analysis leverages a hybrid dynamical model to obtain analytical expressions that relate the physical parameters to the switching conditions. These expressions are useful, e.g., for fundamental understanding, quick analyses, or design optimization. The results are discussed in depth and potential practical applications are explored. Finally, the analysis is validated with experimental results from a real device.
Noise reduction in Laguerre-domain discrete delay estimation
Authors: Mohamed Abdalmoaty, Alexander Medvedev
Date: 2022-07-26 00:00:00

This paper introduces a stochastic framework for a recently proposed discrete-time delay estimation method in Laguerre-domain, i.e. with the delay block input and output signals being represented by the corresponding Laguerre series. A novel Laguerre domain disturbance model is devised, which allows the involved signals to be square-summable sequences and is suitable in a number of important applications. The relation to two commonly used time-domain disturbance models is clarified. Furthermore, by forming the input signal in a certain way, the signal shape of an additive output disturbance can be estimated and utilized for noise reduction. It is demonstrated that a significant improvement in the delay estimation error is achieved when the noise sequence is correlated. The noise reduction approach is applicable to other Laguerre-domain problems than pure delay estimation.
Modeling mandatory and discretionary lane changes using dynamic interaction networks
Authors: Yue Zhang, Yajie Zou, Yuanchang Xie, Lei Chen
Date: 2022-07-26 00:00:00
Tasks: Autonomous Vehicles, Decision Making

A quantitative understanding of dynamic lane-changing (LC) interaction patterns is indispensable for improving the decision-making of autonomous vehicles, especially in mixed traffic with human-driven vehicles. This paper develops a novel framework combining the hidden Markov model and graph structure to identify the difference in dynamic interaction networks between mandatory lane changes (MLC) and discretionary lane changes (DLC). A hidden Markov model is developed to decompose LC interactions into homogenous segments and reveal the temporal properties of these segments. Then, conditional mutual information is used to quantify the interaction intensity, and the graph structure is used to characterize the connectivity between vehicles. Finally, the critical vehicle in each dynamic interaction network is identified. Based on the LC events extracted from the INTERACTION dataset, the proposed analytical framework is applied to modeling MLC and DLC under congested traffic with levels of service E and F. The results show that there are multiple heterogeneous dynamic interaction network structures in an LC process. A comparison of MLC and DLC demonstrates that MLC are more complex, while DLC are more random. The complexity of MLC is attributed to the intense interaction and frequent transition of the interaction network structure, while the random DLC demonstrate no obvious evolution rules and dominant vehicles in interaction networks. The findings in this study are useful for understanding the connectivity structure between vehicles in LC interactions, and for designing appropriate and well-directed driving decision-making models for autonomous vehicles and advanced driver-assistance systems.