While the extent to which individuals engage in and enjoy cognitive abilities, commonly known as need for cognition (NFC), has been suggested to promote adaptive behaviors associated with well-being, there has not been a systematic examination of the strength of the relationship between NFC and well-being. This meta-analysis sought to examine the association between NFC and well-being. Based on 108 effect sizes extracted from 52 samples (50 records), a small to medium positive relationship (r = .20, 95% CI [.16, .23], p < .001) between NFC and well-being was observed. Sub-group analyses revealed that NFC was associated with different aspects of well-being, including life satisfaction, positive affect, negative affect, purpose in life, self-acceptance, personal growth, environmental mastery, positive relations with others, autonomy, depression, anxiety and stress (|r|s = [.07, .45]). Exploratory moderation analyses showed that age moderated the relationship between NFC and well-being, whereby the positive relationship was stronger in younger samples. The gender proportion of the sample also moderated the relationship between NFC and well-being for certain specific measures of well-being, whereby the positive relationship between NFC and well-being was stronger among females.
Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into decentralized platforms that run without intermediaries and centralized institutions. Smart contracts are programs that run on the blockchain, and by utilizing smart contracts, developers can more easily develop DeFi applications. Some key features of smart contracts -- self-executed and immutability -- ensure the trustworthiness, transparency and efficiency of DeFi applications, and have led to a fast-growing DeFi market. However, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are hard for contract users to discover. We call these code snippets in a DeFi smart contract as ''DeFi Contract Traps" (DCTs). In this paper, we identify five DeFi contract traps and introduce their behaviors, describe how attackers use them to make unfair profits, and analyse their prevalence in the Ethereum platform. We propose a symbolic execution tool, DeFiDefender, to detect such traps and use a manually labeled small-scale dataset that consists of 700 smart contracts to evaluate it. Our results show that our tool is not only highly effective but also highly efficient. DeFiDefender only needs 0.48s to analyze one DeFi smart contract and obtains a high average accuracy (98.17%), precision (99.74%), and recall (89.24%). Among the five DeFi contract traps introduced in this paper, four of them can be detected through contract bytecode without the need for source code. We also apply DeFiDefender to a large-scale dataset that consists of 20,679 real DeFi related Ethereum smart contracts. We found that 52.13% of these DeFi smart contracts contain at least one contract trap. Although a smart contract that contains contract traps is not necessarily malicious, our finding suggests that DeFi related contracts have many centralized issues in a zero-trust environment and in the absence of a trusted party.
Drawing on real options and resource dependence theories, this study examines how firms adjust their innovation investments to address trade policy effect uncertainty (TPEU), a type of firm‐specific, perceived environmental uncertainty capturing managers' difficulty in predicting the impacts of potential policy changes on business operations. To develop a context‐dependent, time‐varying measure of TPEU, we apply bidirectional encoder representations from transformers, an advanced deep learning technique. We analyze the texts of mandatory management discussion and analysis sections of annual reports from 3181 publicly listed Chinese firms. Our sample comprises 22,669 firm‐year observations spanning the years 2007 to 2019. The econometric analyses show that firms experiencing higher TPEU will reduce innovation investments. This effect is stronger for firms facing lower competition, involving more foreign sales, and not owned by the state. These findings provide clarity on previously inconclusive results by showcasing the significant influence of policy effect uncertainty, as opposed to policy state uncertainty, on firms' decisions regarding innovation investments. Additionally, these findings underscore the importance of resource dependence factors as crucial contextual factors in this decision‐making process.
The 16th ACM International Conference on Web Search and Data Mining (WSDM 2023) was held in Singapore. It was held as in-person conference that also featured rich virtual elements. This brief report provides an overview of WSDM 2023 with organization and program details and statistics from Conference Chairs and Program Committee Chairs, as well as a message from the WSDM Steering Committee Chair. Date: 27 February-3 March 2023. Website: https://www.wsdm-conference.org/2023.
While national parochialism is commonplace, individual differences explain more variance in it than cross-national differences. Global consciousness (GC), a multi-dimensional concept that includes identification with all humanity, cosmopolitan orientation, and global orientation, transcends national parochialism. Across six societies (N = 11,163), most notably the USA and China, individuals high in GC were more generous allocating funds to the other in a dictator game, cooperated more in a one-shot prisoner’s dilemma, and differentiated less between the ingroup and outgroup on these actions. They gave more to the world and kept less for the self in a multi-level public goods dilemma. GC profiles showed 80% test–retest stability over 8 months. Implications of GC for cultural evolution in the face of trans-border problems are discussed.
Person re-identification (Person Re-ID) is widely regarded as a promising technique to identify a target person through surveillance cameras in the wild. Nevertheless, person Re-ID leads to severe personal image privacy concerns as personal images are stipulated by laws and guidelines as private data. To address these concerns, this article explores the first solution for building a privacy-preserving person Re-ID system. Specifically, this article formulizes privacy-preserving person Re-ID as similarity metrics of encrypted feature vectors because the underlying operation of person Re-ID is to compute the similarity of feature vectors that are extracted from person images by a machine learning model. However, feature vectors are generally denoted by floating-point numbers. To this end, this article exploits a series of new encoding mechanisms and secure batch computing protocols to encrypt floating-point feature vectors and achieve the underlying operation of person Re-ID. Rigorous theoretical analyses demonstrate that this work achieves person Re-ID without compromising any personal image privacy. Furthermore, the proposed secure batch protocols significantly enhance the performance of privacy-preserving person Re-ID while outputting the same precision as the previous method.
This paper presents a systematic and comprehensive survey on blockchain interoperability, where interoperability is defined as the ability of blockchains to flexibly transfer assets, share data, and invoke smart contracts across a mix of public, private, and consortium blockchains without any changes to the underlying blockchain systems. Analyzing the vast landscape of both research papers and industry projects, we classify the existing works into five categories, namely, (1) sidechains, (2) notary schemes, (3) hashed time lock contracts (HTLC), (4) relays, and (5) blockchain agnostic protocols. We analyze the existing works under a taxonomy that consists of system and safety characteristics, such as decentralization, direction of communication, locking mechanism, verification mechanism, trust, safety, liveness, and atomicity. Different from other surveys, we are the first to evaluate the performance of some representative interoperability approaches between Bitcoin and Ethereumcovering sidechains, notary schemes, and HTLCs. Even though the performance of cross-chain transactions is low (typically fewer than 10 transactions per second), the main reason is the underlying blockchain ( e.g. , Bitcoin and Ethereum) and not the interoperability approach. Finally, we discuss existing challenges and possible research directions inblockchain interoperability. For example, we identify challenges in interoperability acrosspermissioned and permissionless blockchains, in interacting with scripting blockchains, in security and privacy.
In Unmanned Aerial Vehicle (UAV) performing tasks, the UAV often faces electricity shortages. The traditional scheme to charge a UAV needs to return to the ground. Using the charging UAV (CUAV) can avoid the waste of electricity caused by the return. However, the existing works only consider a fixed charging location for electricity replenishment. Moreover, fewer works focus on the matching relationship between multi-CUAV and multi-UAV. It is challenging to complete the expected charging work due to the mismatch between the electricity demand and supply. To address this problem, we propose a two-stage electricity scheduling scheme. Specifically, in the charging location selection stage, we solve the Nash equilibrium (NE) of flight consumption between CUAVs and UAVs through the exact potential game, thereby determining the accessible charging position. Then, in the electricity transaction stage, we adopt the Stackelberg game model to determine the Stackelberg Equilibrium (SE) between the acceptance rate of CUAVs and the rejection rate of UAVs, ensuring that both CUAVs and UAVs are satisfied with the unit electricity prices and electricity demands. Based on the above two game stages, we propose a supply and demand scheduling (SDS) algorithm to achieve dynamic scheduling between CUAVs and UAVs. Theoretical analysis indicates the exits of NE and SE. Furthermore, the extensive experiments show that our scheme has significant advantages over the baselines in charging cost, charging price, and flight consumption.
With the rapid development of Intelligent Transportation System (ITS), a large number of spatial data are generated in ITS. Although outsourcing spatial data to the cloud server can reduce the high local computation and storage overheads, it will also lead to security and privacy issues. Therefore, it is necessary to have a survey to specifically summarize these advanced privacy-preserving spatial data query schemes. However, the existing surveys considering both location information and keywords of spatial data only summarize the spatial keyword query scheme in plaintext environment, they do not consider the privacy of spatial data. Although there are some surveys on privacy-preserving spatial data query, they only focus on the location information of spatial data without considering descriptive keywords. Therefore, to understand the progress and research trends in the field, we give a comprehensive survey on secure spatial data query in ITS to summarize and analyze the most advanced solutions. Then, we make a comprehensive and detailed comparison of existing solutions in terms of query function, index structure, time complexity, security, etc. Finally, we show some open challenges and potential research directions for privacy-preserving spatial data query.
The COVID-19 pandemic has caused a dramatic change in the demand composition of restaurants and, at the same time, catalyzed on-demand food delivery (OFD) services—such as DoorDash, Grubhub, and Uber Eats—to a large extent. With massive amounts of data on customers, drivers, and merchants, OFD platforms can achieve higher efficiency with better strategic and operational decisions; these include dynamic pricing, order bundling and dispatching, and driver relocation. Some of these decisions, and especially proactive decisions in real time, rely on accurate and reliable short-term predictions of demand ranges or distributions. In this paper, we develop a Poisson-based distribution prediction (PDP) framework equipped with a double-hurdle mechanism to forecast the range and distribution of potential customer demand. Specifically, a multi-objective function is designed to learn the likelihood of zero demand and approximate true demand and label distribution. An uncertainty-based multi-task learning technique is further employed to dynamically assign weights to different objective functions. The proposed model, evaluated by numerical experiments based on a real-world dataset collected from an OFD platform in Singapore, is shown to outperform several benchmarks by achieving more reliable demand range forecasting.
AI-enabled collaborative robots are designed to be used in close collaboration with humans, thus requiring stringent safety standards and quick response times. Adversarial attacks pose a significant threat to the deep learning models of these systems, making it crucial to develop methods to improve the models' robustness against them. Adversarial training is one approach to improve their robustness: it works by augmenting the training data with adversarial examples. This, unfortunately, comes with the cost of increased computational overhead and extended training times. In this work, we balance the need for additional adversarial data with the goal of minimizing the training costs by selecting the most ‘valuable’ data for adversarial training. In particular, we propose a robustness-oriented boundary data selection method, RAST-AT, which stands for robust and fast adversarial training. RAST-AT selects training data near to the boundary by considering adversarial perturbations. Our method improves the speed of model training on CIFAR-10 by 68.67%, and compared to other data selection methods, has 10% higher accuracy with 10% training data selected, and 7% higher robustness with 4% training data selected. Our method also significantly improves efficiency by at least 25% in adversarial training, with the same performance. Finally, we evaluate our method on a cobot system, generating adversarial patches as attacks, and adopting RAST-AT as the defense. We find that RAST-AT can defend against 60% of untargeted attacks and 20% of targeted attacks. Our work highlights the benefits of developing effective defenses against adversarial attacks to ensure the security and reliability of AI-powered safety-critical systems.
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