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نتیجه جستجو - elections

تعداد مقالات یافته شده: 37
ردیف عنوان نوع
1 Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
روش ترکیبی کوانتومی-کلاسیک برای تشخیص تقلب با انتخاب ویژگی کوانتومی-2022
This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine’s feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum-based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.
INDEX TERMS: Fraud Detection | Quantum | Feature Selection | QSVM | Quantum Kernel Alignment
مقاله انگلیسی
2 Towards a pragmatic detection of unreliable accounts on social networks
به سوی تشخیص عملی حسابهای غیر قابل اعتماد در شبکه های اجتماعی-2021
In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).
Keywords: Unreliable accounts detection | Social networks | Machine learning | Data mining | Volume and time adaptive methodology
مقاله انگلیسی
3 An analysis of Twitter users’ long term political view migration using cross-account data mining
تجزیه و تحلیل از مهاجرت دیدگاه های طولانی مدت کاربران توییتر با استفاده از داده های متقابل حسابداری-2021
During the 2016 US presidential election, we witnessed a polarized population and an election outcome that defied the predictions of many media sources. In this study, we conducted a follow-up on political view migration through tracking Twitter users’ account activity. The study was conducted by following a set of Twitter users over a four year period. Each year, Twitter user activities were collected and analyzed by our novel cross-account data mining algorithm. This algorithm through multiple iterations computes a numerical political score for each user based on their connection to other users and hashtags. We identified a set of seed users and hashtags using prominent political figures and movements to bootstrap the algorithm. The political score distribution demonstrates a divided population on political views. We also observed that users are more moderate in years close to elections (2017 and 2020) compared to years of none election (2018 and 2019). There is an overall migration trend from conservatives to progressives during the four years. This change in scores across the four year time frame suggests a unique political cycle exclusive to Donald Trump’s unprecedented presidential term. Our results in a broad sense portray the potential capabilities of a data collection and scoring algorithm that detected a noticeable political migration and describes the broad social characteristics of certain politically aligned users on social media platforms.
keywords: شبکه های اجتماعی | سیاست | توییتر | داده کاوی | Social networks | Politics | Twitter | Datamining
مقاله انگلیسی
4 What accounts for Duvergers law? The behavioral mechanisms underpinning two-party convergence in India
چه قانونی برای قانون دوروگر؟مکانیزم های رفتاری همگرایی دو حزب در هند-2021
Duverger’s Law states the single-member district plurality rules should produce two-party competition. In district-level election races where this expectation holds, what political behaviors—ranging from elites’ strategic formation of political parties to voters’ strategic abandonment of losing candidates—account for these outcomes? Using data from state elections in India, this article demonstrates that no single mechanism accounts for most electoral outcomes consistent with Duverger’s Law. However, mechanisms related to the behavior elites, far more than voters, produce convergence on two-party competition. This article uncovers relatively little evidence of outcomes driven by strategic voting, instead finding that much of the convergence on two parties is attributable to various forms of strategic entry in which parties selectively field candidates in certain races. In particular, elite collusion—when multiple parties coordinate on where to field candidates—is especially important. Data from other countries confirm that these findings are not unique to India.
keywords: قوانین انتخاباتی | قانون دوروگر | هماهنگی انتخابات | رای گیری استراتژیک | هندوستان | Electoral rules | Duverger’s law | Electoral coordination | Strategic voting | India
مقاله انگلیسی
5 Regulation and purchase diversity: Empirical evidence from the U:S: alcohol market
مقررات و تنوع خرید: شواهد تجربی از بازار الکل ایالات متحده-2020
The repeal of the Prohibition Act in 1933 introduced state-level regulations on the retail availability of alcoholic beverages. Recently there has been much debate among industry stakeholders on how changes to these laws will affect consumer choices. We develop an index to measure purchase diversity for alcoholic beverages that considers similarities in product attributes. Following a set of households that moved between regulatory environments during the 2004 to 2016 period, we examine the effect of alcohol availability on purchase diversity. Our key finding shows that consumers further diversify their product selections in states that allow alcohol sales in grocery stores.
Keywords: Alcoholic beverages | Consumer behavior | Diversification index | Regulation | Retail availability
مقاله انگلیسی
6 Similarity query support in big data management systems
پشتیبانی پرس و جوی شباهت ها در سیستم های مدیریت داده های بزرگ-2020
Similarity query processing is becoming increasingly important in many applications such as data cleaning, record linkage, Web search, and document analytics. In this paper we study how to provide end-to-end similarity query support natively in a parallel database system. We discuss how to express a similarity predicate in its query language, how to build indexes, how to answer similarity queries (selections and joins) efficiently in the runtime engine, possibly using indexes, and how to optimize similarity queries. One particular challenge is how to incorporate existing similarity join algorithms, which often require a series of steps to achieve a high efficiency, including collecting token frequencies, finding matching record id pairs, and reassembling result records based on id pairs. We present a novel approach that uses existing runtime operators to implement such complex join algorithms without reinventing the wheel; doing so positions the system to automatically benefit from future improvements to those operators. The approach includes a technique to transform a similarity join plan into an efficient operator-based physical plan during query optimization by using a template expressed largely in the system’s user-level query language; this technique greatly simplifies the specification of such a transformation rule. We use Apache AsterixDB, a parallel Big Data management system, to illustrate and validate our techniques. We conduct an experimental study using several large, real datasets on a parallel computing cluster to assess the similarity query support. We also include experiments involving three other parallel systems and report the efficacy and performance results.
Keywords: Similarity query | Parallel database | Optimization
مقاله انگلیسی
7 Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections
آیا تحلیل های توییتر می توانند نتیجه انتخابات را پیش بینی کنند؟ بینشی از انتخابات مجلس پنجم 2017-2020
Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.
Keywords: Analytics | Election prediction | Social media | Natural language processing | Machine learning | Sentiment analysis | Twitter
مقاله انگلیسی
8 Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle
مطالعه پارامتری در مورد یادگیری تقویت استراتژی مدیریت انرژی بهینه شده برای یک وسیله نقلیه الکتریکی هیبریدی-2020
An efficient energy split among different source of energy has been a challenge for existing hybrid electric vehicle (HEV) supervisory control system. It requires an optimized energy use of internal combustion engine and electric source such as battery, fuel cell, ultracapacitor, etc. In recent years, Reinforcement Learning (RL) based energy management strategy (EMS) has emerged as one of the efficient control strategies. The effectiveness Reinforcement Learning method largely depends on optimized parameter selections. However, a thorough parametric study still lacks in this field. It is a fundamental step for efficient implementation of the RLbased EMS. Different from existing RL-based EMS literature, this study conducts a parametric study on several key factors during the RL-based EMS development, including: (1) state types and number of states, (2) states and action discretization, (3) exploration and exploitation, and (4) learning experience selection. The main results show that learning experience selection can effectively reduce the vehicle fuel consumption. The study of the states and action discretization show that the vehicle fuel consumption reduces as action discretization increases while increasing the states discretization is detrimental to the fuel consumption. Moreover, the increasing number of states improves fuel economy. With the help of the proposed parametric analysis, the RL-based EMS can be easily adapted to other power split problems in a HEV application.
Keywords: Reinforcement learning | Q-learning | Energy management strategy | Hybrid electric vehicle
مقاله انگلیسی
9 Do FOI laws and open government data deliver as anti-corruption policies? Evidence from a cross-country study
آیا قوانین FOI و داده های دولت آزاد به عنوان سیاست های ضد فساد ارائه می شود؟ شواهدی از یک مطالعه متقابل کشور-2020
In election times, political parties promise in their manifestos to pass reforms increasing access to government information to root out corruption and improve public service delivery. Scholars have already offered several fascinating explanations of why governments adopt transparency policies that constrain their choices. However, knowledge of their impacts is limited. Does greater access to information deliver on its promises as an anticorruption policy? While some research has already addressed this question in relation to freedom of information laws, the emergence of new digital technologies enabled new policies, such as open government data. Its effects on corruption remain empirically underexplored due to its novelty and a lack of measurements. In this article, I provide the first empirical study of the relationship between open government data, relative to FOI laws, and corruption. I propose a theoretical framework, which specifies conditions necessary for FOI laws and open government data to affect corruption levels, and I test it on a novel cross-country dataset. The results suggest that the effects of open government data on corruption are conditional upon the quality of media and internet freedom. Moreover, other factors, such as free and fair elections, independent and accountable judiciary, or economic development, are far more critical for tackling corruption than increasing access to information. These findings are important for policies. In particular, digital transparency reforms will not yield results in the anti-corruption fight unless robust provisions safeguarding media and internet freedom complement them.
Keywords: freedom of information | open government data | transparency | accountability | corruption | media and internet freedom | cross-country analysis
مقاله انگلیسی
10 Return migration, crime, and electoral engagement in Mexico
بازگشت مهاجرت ، جرم و مشغله انتخاباتی در مکزیک-2020
Since 2006, the Great Recession and tighter migration policies in the U.S. have increased the rates of return migration to Mexico. Scholars debate whether high rates of return motivate greater electoral engagement via the democratic norms returnees may bring back with them. An alternative account holds that returnees are seen as dissimilar by their non-migrant co-nationals, causing returnees to disengage from politics. We contribute to this debate using municipal data on voter turnout and on rates of return migration for the case of Mexico from 2000 to 2010. Relying on an instrumental strategy that exploits migrants’ exposure to changes in unemployment rates as an exogenous predictor for return, we find robust evidence that high rates of return result in less electoral participation in presidential and local elections. Besides, electoral disengagement seems to be intensified by the presence of criminal violence, which surged during our period of analysis. Return migration may have a positive impact on other modes of political participation; but at least when it comes to voting, our research aligns with the pessimistic camp of the debate in that return migration increases electoral apathy.
مقاله انگلیسی
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