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2022 ◽  
Vol 29 (1) ◽  
pp. 1-28
Eunice Jun ◽  
Melissa Birchfield ◽  
Nicole De Moura ◽  
Jeffrey Heer ◽  
René Just

Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into statistical models, a process we refer to as hypothesis formalization . In a formative content analysis of 50 research papers, we find that researchers highlight decomposing a hypothesis into sub-hypotheses, selecting proxy variables, and formulating statistical models based on data collection design as key steps. In a lab study, we find that analysts fixated on implementation and shaped their analyses to fit familiar approaches, even if sub-optimal. In an analysis of software tools, we find that tools provide inconsistent, low-level abstractions that may limit the statistical models analysts use to formalize hypotheses. Based on these observations, we characterize hypothesis formalization as a dual-search process balancing conceptual and statistical considerations constrained by data and computation and discuss implications for future tools.

2023 ◽  
Vol 55 (1) ◽  
pp. 1-36
Yupeng Hu ◽  
Wenxin Kuang ◽  
Zheng Qin ◽  
Kenli Li ◽  
Jiliang Zhang ◽  

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed.

2022 ◽  
Vol 14 (2) ◽  
pp. 1-24
Bin Wang ◽  
Pengfei Guo ◽  
Xing Wang ◽  
Yongzhong He ◽  
Wei Wang

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.

2022 ◽  
Vol 141 ◽  
pp. 308-320
Lamprinakos Grigorios ◽  
Solon Magrizos ◽  
Ioannis Kostopoulos ◽  
Dimitrios Drossos ◽  
David Santos

2022 ◽  
Vol 18 (1) ◽  
pp. 1-27
Yu Liu ◽  
Joshua Comden ◽  
Zhenhua Liu ◽  
Yuanyuan Yang

Wireless data collection requires a sequence of resource provisioning decisions due to the limited battery capacity of wireless sensors. The corresponding online resource provisioning problem is challenging. Recently, many prediction methods have been proposed that can be used to benefit the performance of various systems through their incorporation. Therefore, in this article, we focus on online resource provisioning problems with short-term predictions motivated by the wireless data collection problem. Specifically, we design separate online algorithms for systems in which the state evolves in either a stationary manner or an arbitrarily determined manner and prove their performance bounds where their bounds improve as the amount of available predictions increases. Additionally, we design a meta-algorithm that can choose which online algorithm to implement at each point in time, depending on the recent behavior of the system environment. The practical performances of the proposed algorithms are corroborated in trace-driven numerical simulations of data collection of shared bikes. Additionally, we show that the performance of our meta-algorithm in various system environments can be better than that of the single best algorithm chosen in hindsight.

2028 ◽  
Vol 4 (2) ◽  
pp. 34-47
Dirwan Dirwan ◽  
Bunyamin Bunyamin ◽  
St Umrah

Reading is a commandment from Allah. commanded through al-Qur'an Surah al-Alaq verse 1 which was revealed through the intermediary angel Gabriel to the Prophet Muhammad. Which will be preached to all the human Ummah specifically to the Ummah who always follows the teachings of Islam. Therefore, we must read the Koran, both reading directly (written) and reading with signs of Allah's power. (implied). Which has a variety of discussions ranging from reading the Koran, mentadabburi, practice and practice so as to create a generation of Qur'ani. In this paper using library research with the method of using library data collection techniques (Library Research) that uses various kinds of existing literature such as books, books of hadith, books of interpretation and the Qur'an that will help in using methods, deductive, inductive and comparative. And it was concluded that the command to read in the Koran so that it can be seen that reading is not just a job, not a mere hobby but as a very important command that will underlie all our activities in this world, let alone a prosecutor of knowledge, reading also is a foundation to go to the path that is blessed by Allah.

Hamda A. Laouini ◽  

The present study was conducted to assess and investigate the attitudes of the Preparatory Year students towards leaning English at rural branch of the University of Jeddah in Saudi Arabia. The author endeavours to examine and measure the University students’ opinions and perceptions regarding the importance of Learning English. He also attempts to explore the areas of difficulties in foreign language Learning within the rural context of AlKamil College of Sciences and Arts (Makkah, Saudi Arabia). 75 randomly selected students (40 male and 35 female) participated in this study project. In this study, the researcher opted for a mixed research method. For quantitative data collection a five-point Likert scale questionnaire was adapted from Gardner’s ‘Attitude Motivation Test Battery (AMTB) along with a silent interview for a qualitative data collection in order to assess the participants’ attitudes and perceptions regarding learning English. Overall, the results reveal that students in rural university branches in Saudi Arabia hold positive attitudes towards learning English and they are constantly attempting to improve their language proficiency. This study also explores the different obstacles impeding the students’ sought progress in language learning along with the possible solutions that may enable them to use and practise English in a more spontaneous way.

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