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نتیجه جستجو - انتساب

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
1 Evaluating the effectiveness of biometric sensors and their signal features for classifying human experience in virtual environments
ارزیابی اثربخشی سنسورهای بیومتریک و ویژگی های سیگنال آنها برای طبقه بندی تجربه انسان در محیط های مجازی-2021
Built environments play an essential role in our day-to-day lives since people spend more than 85% of their times indoors. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on varying human experience, which propelled researchers to study the improvement of human experience in built environments using quantitative methods such as biometric sensing. However, a notable gap in the knowledge persists as researchers are faced with sensors that are commonly used in the neuroscience domain, resulting in a disconnect regarding the selection of effective sensors that can be used to measure human experience in designed spaces. This issue is magnified when considering the variety of sensor signal features that have been proposed and used in previous studies. This study builds on data captured during a series of user studies conducted to measure subjects’ physiological responses in designed spaces using the combination of virtual environments and biometric sensing. This study focuses on the data analysis of the collected sensor data to identify effective sensors and their signal features in classifying human experience. To that end, we used a feature attribution model (i.e., SHAP), which calculates the importance of each signal feature in terms of Shapley values. Results show that electroencephalography (EEG) sensors are more effective as compared to galvanic skin response (GSR) and photoplethysmogram (PPG) (i.e., achieving the highest SHAP values among the three at 3.55 as compared to 0.34 for GSR and 0.21 for PPG) when capturing human experience in alternate designed spaces. For EEG, signal features calculated from the back channels (occipital and parietal areas) were found to possess comparable effectiveness as the frontal channel (i.e., have similar mean SHAP values per channel). In addition, frontal and occipital asymmetry were found to be effective in identifying human experience in designed spaces.
Keywords: Architectural design | Feature attribution | Data-driven methods | Human experience | Virtual environments
مقاله انگلیسی
2 AI as a moral crumple zone: The effects of AI-mediated communication on attribution and trust
هوش مصنوعی به عنوان یک منطقه مچاله کننده اخلاقی: تأثیرات ارتباطات واسطه هوشمصنوعی در انتساب و اعتماد-2020
AI-mediated communication (AI-MC) represents a new paradigm where communication is augmented or generated by an intelligent system. As AI-MC becomes more prevalent, it is important to understand the effects that it has on human interactions and interpersonal relationships. Previous work tells us that in human interactions with intelligent systems, misattribution is common and trust is developed and handled differently than in interactions between humans. This study uses a 2 (successful vs. unsuccessful conversation) x 2 (standard vs. AI-mediated messaging app) between subjects design to explore whether AI mediation has any effects on attribution and trust. We show that the presence of AI-generated smart replies serves to increase perceived trust between human communicators and that, when things go awry, the AI seems to be perceived as a coercive agent, allowing it to function like a moral crumple zone and lessen the responsibility assigned to the other human communicator. These findings suggest that smart replies could be used to improve relationships and perceptions of conversational outcomes between interlocutors. Our findings also add to existing literature regarding perceived agency in smart agents by illustrating that in this type of AI-MC, the AI is considered to have agency only when communication goes awry.
Keywords: Artificial intelligence (AI) | Communication | AI-Mediated Communication (AI-MC) | Computer-Mediated Communication (CMC) | Trust | Attribution
مقاله انگلیسی
3 Eigenvalue assignment enabled control law for multivariable nonlinear systems with mismatched uncertainties
انتساب مقادیر ویژه قانون کنترل فعال برای سیستم های غیر خطی چند متغیره با عدم قطعیت عدم تطابق-2020
This paper presents an adaptive filtering output feedback control architecture for multivariable nonlinear systems with mismatched uncertainties enabled by an eigenvalue assignment method. A piecewise con- stant adaptive law updates the adaptive parameters which represent the uncertainty estimates by solving the error dynamics between the output predictor and the real system with the neglection of unknowns. By employing a computationally efficient eigenvalue assignment method, the multivariable nonlinear sys- tem is transformed into Frobenius canonical form. A novel filtering control law which allows the desired system to be nonminimum-phase and does not require dynamic inversion of the desired system is de- signed to compensate the nonlinear uncertainties and track a given trajectory, following a performance determined by the eigenvalues assigned to the controller. The uniform performance bounds are derived for the system state and control input as compared to the corresponding signals of a bounded virtual reference system, which defines the best performance that can be achieved by the closed-loop system. Numerical examples are provided to illustrate the effectiveness of the eigenvalue assignment enabled control law, comparisons between the proposed controller and funnel controller are carried out.
Keywords: Eigenvalue assignment | Mismatched uncertainties | Multivariable nonlinear system | Nonminimum phase | Output feedback | Tracking
مقاله انگلیسی
4 Static malware detection and attribution in android byte-code through an end-to-end deep system
شناسایی بدافزارهای استاتیکی و انتساب در بایت کد اندرویدی از طریق یک سیستم عمیق انتها به انتها-2020
Android reflects a revolution in handhelds and mobile devices. It is a virtual machine based, an open source mobile platform that powers millions of smartphone and devices and even a larger no. of applications in its ecosystem. Surprisingly in a short lifespan, Android has also seen a colossal expansion in application malware with 99% of the total malware for smartphones being found in the Android ecosystem. Subsequently, quite a few techniques have been proposed in the literature for the analysis and detection of these malicious applications for the Android platform. The increasing and diversified nature of Android malware has immensely attenuated the usefulness of prevailing malware detectors, which leaves Android users susceptible to novel malware. Here in this paper, as a remedy to this problem, we propose an anti-malware system that uses customized learning models, which are sufficiently deep, and are ’End to End deep learning architectures which detect and attribute the Android malware via opcodes extracted from application bytecode’. Our results show that Bidirectional long short-term memory (BiLSTMs) neural networks can be used to detect static behavior of Android malware beating the state-of-the-art models without using handcrafted features. For our experiments in our system, we also choose to work with distinct and independent deep learning models leveraging sequence specialists like recurrent neural networks, Long Short Term Memory networks and its Bidirectional variation as well as those are more usual neural architectures like a network of all connected layers(fully connected), deep convnets, Diabolo network (autoencoders) and generative graphical models like deep belief networks for static malware analysis on Android. To test our system, we have also augmented a bytecode dataset from three open and independently maintained state-of-the-art datasets. Our bytecode dataset, which is on an order of magnitude large, essentially suffice for our experiments. Our results suggests that our proposed system can lead to better design of malware detectors as we report an accuracy of 0.999 and an F1-score of 0.996 on a large dataset of more than 1.8 million Android applications.
Keywords: End-to-end architecture | Malware analysis | Deep neural networks | Android and big data
مقاله انگلیسی
5 Language models and fusion for authorship attribution
مدل های زبان و همجوشی برای انتساب نویسندگی-2019
We deal with the task of authorship attribution, i.e. identifying the author of an unknown document, proposing the use of Part Of Speech (POS) tags as features for language modeling. The experimentation is carried out on corpora untypical for the task, i.e., with documents edited by non-professional writers, such as movie reviews or tweets. The former corpus is homogeneous with respect to the topic making the task more challenging, The latter corpus, puts language models into a framework of a continuously and fast evolving language, unique and noisy writing style, and limited length of social media messages. While we find that language models based on POS tags are competitive in only one of the corpora (movie reviews), they generally provide efficiency benefits and robustness against data sparsity. Furthermore, we experiment with model fusion, where language models based on different modalities are combined. By linearly combining three language models, based on characters, words, and POS trigrams, respectively, we achieve the best generalization accuracy of 96% on movie reviews, while the combination of language models based on characters and POS trigrams provides 54% accuracy on the Twitter corpus. In fusion, POS language models are proven essential effective components.
Keywords: Authorship attribution | Language models | Computational linguistics | Text classification | Machine learning
مقاله انگلیسی
6 Understanding outcome bias
درک تعصب نتیجه-2019
Disentangling effort and luck is critical when evaluating outcomes. In a principal-agent experiment, we demonstrate that principals’ judgments of agents are biased by luck, despite perfectly observable effort. This erodes the power of incentives to stimulate effort. We explore two potential solutions to this “outcome bias”–information control, and outsourcing judgment to independent third parties. Both are ineffective. When principals control information about luck, they do not avoid it. When agents control information, they manipulate principals’ outcome bias to minimize punishments. We also find that even independent third parties exhibit outcome bias. These findings suggest that outcome bias cannot be driven solely by disappointment nor distributional preferences. Instead, we hypothesize that luck directly affects principals’ inference about agent type even though effort is observed. We elicit the beliefs of third parties and principals and find that lucky agents are believed to be harder workers than identical, unlucky agents.
Keywords: Experiment | Reciprocity | Outcome bias | Attribution bias | Blame
مقاله انگلیسی
7 Linguistic data mining with complex networks: A stylometric-oriented approach
داده کاوی زبانی با شبکه های پیچیده: یک رویکرد استایلومتری گرا-2019
By representing a text by a set of words and their co-occurrences, one obtains a word- adjacency network being a reduced representation of a given language sample. In this pa- per, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative charac- teristics of the networks and applying machine learning algorithms, it is possible to dis- tinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attri- bution accuracy over 90%. A correspondence between the text authorship and the word- adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features. Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed.
Keywords: Complex networks | Natural language | Data mining | Stylometry | Authorship attribution
مقاله انگلیسی
8 Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining
شناسایی اهمیت نسبی ویژگی های خود اسیب غیر خودکشی درطبقه بندی ایده های خودکشی، برنامه ها و رفتار با استفاده از داده کاوی اکتشافی -2018
Individuals with a history of non-suicidal self-injury (NSSI) are at alarmingly high risk for suicidal ideation (SI), planning (SP), and attempts (SA). Given these findings, research has begun to evaluate the features of this multi faceted behavior that may be most important to assess when quantifying risk for SI, SP, and SA. However, no studies have examined the wide range of NSSI characteristics simultaneously when determining which NSSI features are most salient to suicide risk. The current study utilized three exploratory data mining techniques (elastic net regression, decision trees, random forests) to address these gaps in the literature. Undergraduates with a history of NSSI (N = 359) were administered measures assessing demographic variables, depression, and 58 NSSI characteristics (e.g., methods, frequency, functions, locations, scarring) as well as current SI, current SP, and SA history. Results suggested that depressive symptoms and the anti-suicide function of NSSI were the most important features for predicting SI and SP. The most important features in predicting SA were the anti-suicide function of NSSI, NSSI-related medical treatment, and NSSI scarring. Overall, results suggest that NSSI functions, scarring, and medical lethality may be more important to assess than commonly regarded NSSI severity indices when ascertaining suicide risk.
Keywords: Non-suicidal self-injury ، Suicidal ideation ، Suicide plan ، Suicide attempt ، Exploratory data mining ، Elastic net regression ، Decision trees
مقاله انگلیسی
9 A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines
یک روش بهینه سازی قوی برای یک سیستم یکپارچه پویای سلولی تولید و برنامه ریزی تولید با ماشین آلات غیر قابل اعتماد-2016
In this study, a robust optimization approach is developed for a new integrated mixed-integer linear programming (MILP) model to solve a dynamic cellular manufacturing system (DCMS) with unreliable machines and a production planning problem simultane- ously. This model is incorporated with dynamic cell formation, inter-cell layout, machine reliability, operator assignment, alternative process routings and production planning concepts. To cope with the parts processing time uncertainty, a robust optimization approach immunized against even worst-case is adopted. In fact, this approach enables the system’s planner to assess different levels of uncertainty and conservation throughout planning horizon. This study minimizes the costs of machine breakdown and relocation, operator training and hiring, inter-intra cell part trip, and shortage and inventory. To verify the performance of the presented model and proposed approach, some numerical examples are solved in hypothetical limits using the CPLEX solver. The experimental results demon- strate the validity of the presented model and the performance of the developed approach in finding an optimal solution. Finally, the conclusion is presented.© 2015 Elsevier Inc. All rights reserved.
Keywords:Robust optimization | Dynamic cellular manufacturing system | Production planning | Uncertainty | Machine reliability | Operator assignment
مقاله انگلیسی
10 A rescheduling and cost allocation mechanism for delayed arrivals
مکانیزم تغییر زمان و تخصیص هزینه برای تاخیر ورود-2016
We propose a solution to the problem of rescheduling a sequence of arrivals that are subject to a delay event at a common destination. Such situations include jobs arriving at a single production facility, aircraft whose landings are postponed, and ships that are inbound to a dock or lightering facility. Each arrival faces a nonlinear cost due to the delay, but the delay costs can be mitigated by allowing the arrivals to be reordered. We optimize the reordering process by designing a Vickrey–Clarke–Groves (VCG) mechanism to construct a payoff matrix describing the amounts necessary to move the currently assigned arrival slots either earlier or later. Using this payoff matrix, we compute the optimal reordering of the arrivals by utilizing the well-known solution to the assignment problem, which maximizes the benefit in a computationally efficient fashion. The VCG mechanism is strategyproof, that is, no arrival has an incentive to misreport the value of moving up or down in the sequence. We also show that participating in the centralized process is to no arrival's disadvantage. Because VCG procedures in general are subject to budget deficits, we provide alternative mechanisms to overcome this difficulty. Finally, we carry out computational experiments demonstrating that the VCG mechanism can be implemented for realistically-sized problem sets and that the cost savings are significant.& 2015 Elsevier Ltd. All rights reserved.
Production scheduling | Vickrey–Clarke–Groves mechanism | Assignment problem | Combinatorial exchange
مقاله انگلیسی
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