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Attribute-based encryption (ABE) has been widely used to provide fine-grained access control to encrypted data in cloud computing. However, in practice, since the attributes are shared by multiple users in the ABE system, it is difficult to identify malicious users which intentionally but implicitly reveal their own ABE secret keys for some purpose. In recent years, a substantial amount of work focused on designing efficient tracing mechanisms to identify such misbehaving users on ABE. In this paper, we propose an efficient generic construction of black-box traceable ABE to identify the traitors. Our construction supports (public) adaptive black-box traceability and has short keys and short ciphertexts that are independent of the number of users. Technically, our construction relies on a generic transformation from any attribute-based inner-product functional encryption (ABIPFE) scheme to black-box traceable ABE system. Furthermore, to instantiate our generic construction from standard lattices, we propose the first lattice-based ABIPFE scheme, which is proven weakly selective secure in the standard model.

Attribute based signature (ABS) is a novel cryptographic primitive, which permits users to sign a message over attributes without revealing other information. A signature only reveals that it is signed by a signer whose some attributes meet an access policy. However, some ABS schemes only support the threshold access policy, where the signing algorithms are limited by the threshold. The threshold access policy cannot express precise access control well. In addition, the computation cost of the verification algorithm is heavy since pairing operations are required. Pairing is costly operation comparing to exponentiation. Therefore, existing ABS schemes are not suitable to resource-limited devices, such as RFID tags and smart cards. In order to solve the issues above, we present a novel ABS scheme by using the attribute tree as access policy that expresses flexible access control. We utilize server-aid technique to help the verifier to verify signatures and reduce the computation burden. Our scheme is proved secure against unforgeable and anonymous under chosen-policy selective-message attack in the standard model. Compared with existing schemes, our scheme is more efficient in terms of private key generation and verification. The proposed scheme reduces users calculation burden and expresses more flexible access policy.

Homomorphic Proxy Re-Encryption (HPRE) is an extension of Proxy Re-Encryption (PRE) which combines the advantages of both Homomorphic Encryption (HE) and PRE. A HPRE scheme allows arbitrary evaluations to be performed on ciphertexts under one (the delegator's) public key and, using a re-encryption key, it transforms the resulting ciphertext to a new ciphertext under another (the delegatee's) public key. Prior HPRE schemes are either CPA-secure or CCA-secure but only support partial homomorphic operations. We propose a generic construction of single-hop HPRE scheme which supports fully homomorphic operations. The proposed scheme is proven secure in our new index-based CCA-HPRE model. Our technique is to give a generic transformation that turns any multi-identity identity-based FHE (IBFHE) scheme with key switching into Fully Homomorphic Encryption (FHE) with key switching from which we can obtain the proposed single-hop HPRE scheme. We also present a concrete instantiation of multi-identity IBFHE with key switching from learning with errors (LWE) in the standard model.

Transformer-based language models have become the de-facto standard model for various NLP applications given the superior algorithmic performances. Processing a transformer-based language model on a conventional accelerator induces the memory wall problem, and the ReRAM-based accelerator is a promising solution to this problem. However, due to the characteristics of the self-attention mechanism and the ReRAM-based accelerator, the pipeline hazard arises when processing the transformer-based language model on the ReRAM-based accelerator. This hazard issue greatly increases the overall execution time. In this paper, we propose a framework to resolve the hazard issue. Firstly, we propose the concept of window self-attention to reduce the attention computation scope by analyzing the properties of the self-attention mechanism. After that, we present a window-size search algorithm, which finds an optimal window size set according to the target application/algorithmic performance. We also suggest a hardware design that exploits the advantages of the proposed algorithm optimization on the general ReRAM-based accelerator. The proposed work successfully alleviates the hazard issue while maintaining the algorithmic performance, leading to a 5.8× speedup over the provisioned baseline. It also delivers up to 39.2×/643.2× speedup/higher energy efficiency over GPU, respectively.

Searchable symmetric encryption (SSE) enables a user to outsource a private dataset to a cloud server in encrypted form while retaining the ability to search over the encrypted outsourced data. The existing SSE schemes improve the search and safety performances from different perspectives. However, almost none of the existing SSE schemes considers the data distribution issues. We find that when the dataset is not distributed uniformly, the search quality based on the conventional methods decreases. Therefore, the existing SSE schemes cannot guarantee high search quality when faced with non-uniform datasets. In addition, most existing SSE solutions cannot hide the distribution of the query set. In this paper, we design a Secure similarity search over Encrypted Non-uniform and high-dimensional Datasets (SEND) with a novel way to enhance security. The basic idea is to combine SSE with locality-sensitive hashing (LSH). Unlike earlier schemes, SEND uses selective hashing, which has better performance for non-uniform datasets. Also, we present a novel approach to hide the distribution of the query set, which makes SEND more secure. Our experimental results indicate SEND achieves a high search quality of recall and precision, and it is proven secure against adaptively chosen query attacks in the standard model.

In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients' data at any point of time. Secure cloud storage protocols enable a client to check integrity of outsourced data. In this work, we explore the possibility of constructing a secure cloud storage for dynamic data by leveraging the algorithms involved in secure network coding. We show that some of the secure network coding schemes can be used to construct efficient secure cloud storage protocols for dynamic data, and we construct such a protocol (DSCS I) based on a secure network coding protocol. To the best of our knowledge, DSCS I is the first secure cloud storage protocol for dynamic data constructed using secure network coding techniques which is secure in the standard model. Although generic dynamic data support arbitrary insertions, deletions and modifications, append-only data find numerous applications in the real world. We construct another secure cloud storage protocol (DSCS II) specific to append-only data --- that overcomes some limitations of DSCS I. Finally, we provide prototype implementations for DSCS I and DSCS II in order to evaluate their performance.

Software-defined networking (SDN) has become an attractive solution to carry out centralized and efficient control in industrial internet of things (IIoT). However, its security has received little attention when applied to IIoT, and no comprehensive consideration has been given to attacks against forwarding nodes (FNs), the basic elements of the data plane. Therefore, in this paper, we aim to investigate attacks against FNs from multiple perspectives in software-defined IIoT. To the best of our knowledge, we are the first to systematically consider this kind of attacks. Since it is difficult to pre-deploy all defence methods against various attacks, we propose a deep reinforcement learning (DRL)-based general attack tolerance scheme to guide the benign traffic flow bypass the attacked FNs. Furthermore, in view of the situation that the real dataset is rare and the standard model-based dataset is likely to be impractical, we use generative adversarial network (GAN), a representative deep generative model (DGM), to flexibly generate real-like network traffic for more sufficient and effective experimental verification on the attack tolerance scheme. Experimental results show that our proposed scheme can significantly improve the successful arrival rate of IIoT traffic and achieve near-optimal results.

Ring signatures allow a user to sign messages as a member of a set of users, which is called the ring. This primitive ensures that nobody can detect which member in the ring signs the message. Libert, Peters, and Qian (2018) [24] proposed the first tightly secure ring signature scheme with O ( log ⁡ n ) signature size in the random oracle model, where n is the size of a ring. To our knowledge, a tightly secure ring signature scheme has never been reported without depending on the random oracle methodology. In this paper, we propose two generic constructions of tightly secure ring signatures in the standard model. Our first (resp., second) construction is secure in the common reference string model (resp., the plain model). Both of our constructions are secure under the decisional linear assumption over the pairing groups. Our first generic construction has a more efficient instantiation than our second one. While our second generic construction does not have an efficient instantiation, its signature size achieves O ( log ⁡ n ) asymptotically, which is the same as one of the Libert et al.'s scheme.

The trend towards the Internet of Things (IoT) technology promises the growth of home and organizational automation in the world. Due to the large amount of IoT data stored in the semi-trust cloud server, the IoT is vulnerable to cyberattacks. Therefore, the privacy violation of outsourced data is an obstacle to efficient IoT data storage. Searchable encryption is a promising solution for secure storage and selective data retrieval. However, achieving desired privacy, performance, and attributes such as dynamics, access control, scalability, and fair arbitration is still a challenge in searchable encryption schemes. In this paper, we propose a fair and dynamic multi-owner searchable encryption scheme wherein dynamics does not leak the sensitive information. In the proposed scheme, the verifiability and fair arbitration, respectively, prevent the malicious cloud servers and malicious users from having any motivation to disrupt the accuracy of the exchanged data. Generating a single trapdoor corresponding to the conjunctive keyword query, a client can search over all documents stored in the cloud, the owner of which has allowed the client to access. We also prove that our scheme is secure against the keyword guessing attack in the standard model. Finally, evaluating the performance of the proposed scheme on a real dataset, we demonstrate its efficiency as well.

Asylum-related migration is highly complex, uncertain, and volatile, which precludes using standard model-based predictions to inform policy and operational decisions. At the same time, asylum's potentially high societal impacts on receiving countries and the resource implications of asylum processes call for more proactive approaches for assessing current and future migration flows. In this article, we propose an alternative approach to asylum modeling, based on the detection of early warning signals by using models originating from statistical control theory. Our empirical analysis of several asylum flows into Europe in 2010–2016 demonstrates the approach's utility and potential in aiding the management of mixed migration flows, while also shedding more light on the work needed to make better use of the “big data” and scenario-based methods for comprehensive and systematic examination of risk, uncertainty, and emerging trends.

Outsourced attribute-based signatures (OABS) enable users to sign messages without revealing specific identity information and are suitable for scenarios with limited computing power. Recently, Mo et al. proposed an expressive outsourced attribute-based signature scheme (Peer-to-Peer Networking and Applications, 11, 2017). In this paper, we show that Mo et al.’s scheme does not achieve any of the three security properties. Their scheme is incorrect. The adversary can collude with the malicious signing-cloud service provider (S-CSP) to forge valid signatures on any message and any attribute set. And the S-CSP could trace the access structures used to generate the signatures. Then, we treat the S-CSP as an adversary and present more accurate unforgeability and anonymity models for OABS to remedy the drawbacks of the previous ones. Finally, we propose a simple but significant improvement to fix our attacks. The improved scheme achieves correctness, unforgeability, and perfect anonymity while keeping the efficiency almost unchanged. We also prove the security of the improved scheme under the standard model.

Many identity-based digital signature schemes are proved secure in the random oracle model. However, the application of the random oracle may lead to security risks. The used hash function is specific and the response result of the query is not always random, hence it may cause the insecurity of the scheme. To solve the above issues, this paper presents an efficient identity-based signature scheme which is proved secure under the standard model. The security of the proposed scheme is reduced to the well-known computational Diffie–Hellman (CDH) assumption. Furthermore, compared with the related identity-based signature schemes, the scheme proposed in this paper has great advantages in the computation cost of signing and verification.

Despite some gender-related bumps in the road, the author had the good fortune that her career spanned the evolution of the Standard Model from its inception in the late 1960s and early 1970s to its final confirmation with the discovery of the Higgs boson in 2012. Her major contributions to these developments and other facets of her career are described.

Multi-head self-attention (attention mechanism) has been employed in a variety of fields such as machine translation, language modeling, and image processing due to its superiority in feature extraction and sequential data analysis. This is benefited from a large number of parameters and sophisticated model architecture behind the attention mechanism. To efficiently deploy attention mechanism on resource-constrained devices, existing works propose to reduce the model size by building a customized smaller model or compressing a big standard model. A customized smaller model is usually optimized for the specific task and needs effort in model parameters exploration. Model compression reduces model size without hurting the model architecture robustness, which can be efficiently applied to different tasks. The compressed weights in the model are usually regularly shaped (e.g. rectangle) but the dimension sizes vary (e.g. differs in rectangle height and width). Such compressed attention mechanism can be efficiently deployed on CPU/GPU platforms as their memory and computing resources can be flexibly assigned with demand. However, for Field Programmable Gate Arrays (FPGAs), the data buffer allocation and computing kernel are fixed at run time to achieve maximum energy efficiency. After compression, weights are much smaller and different in size, which leads to inefficient utilization of FPGA on-chip buffer. Moreover, the different weight heights and widths may lead to inefficient FPGA computing kernel execution. Due to the large number of weights in the attention mechanism, building a unique buffer and computing kernel for each compressed weight on FPGA is not feasible. In this work, we jointly consider the compression impact on buffer allocation and the required computing kernel during the attention mechanism compressing. A novel structural pruning method with memory footprint awareness is proposed and the associated accelerator on FPGA is designed. The experimental results show that our work can compress Transformer (an attention mechanism based model) by 95x. The developed accelerator can fully utilize the FPGA resource, processing the sparse attention mechanism with the run-time throughput performance of 1.87 Tops in ZCU102 FPGA.

The standard model for developing AI systems assumes a fixed, known objective that the AI system is required to optimize through its actions. Systems developed within the standard model have been increasingly successful. I briefly summarize the state of the art and its likely evolution over the next decade. Substantial breakthroughs leading to general-purpose AI are much harder to predict, but they will have an enormous impact on the global economy and on human roles therein. At the same time, I expect that the standard model will become increasingly untenable in real-world applications because of the difficulty of specifying objectives completely and correctly. I propose a new model for AI development in which the machine’s uncertainty about the true objective leads to qualitatively new modes of behaviour that are more robust, controllable, and deferential.

In this paper, we propose the notion of adaptive all-but-one lossy trapdoor functions (aABO-LTFs), a variant of all-but-one lossy trapdoor functions. An aABO-LTF is parameterised by a set of branches. Given the lossy branch, the function statistically loses the information of its inputs. Given injective branches, the function is injective, and there is a trapdoor that enables efficient function inversion. What differentiates an aABO-LTF and an ABO-LTF is that for an aABO-LTF, the lossy branch is indistinguishable from the other branches even if the adversary gets to ask for function inversions on any injective branches apart from the lossy branch. We demonstrate the usefulness of the adaptivity of aABO-LTFs by providing generic and efficient constructions of an adaptively chosen-ciphertext secure (CCA-secure) public-key encapsulation mechanism (KEM) and an adaptive deterministic public-key encryption (DPKE) without random oracles using aABO-LTFs in a very simple black-box way. Our constructions are direct in the sense of that it avoids generic transformations using one-time signatures or message authentication codes typically found in standard model CCA-secure constructions. Moreover, we show that aABO-LTFs can be instantiated generically by lossy trapdoor primitives, including lossy trapdoor functions (LTFs) and identity-based (lossy) trapdoor functions (IB-LTFs). We also demonstrate that the lattice-based ABO-LTFs proposed by Alwen et al. (CRYPTO'13) are aABO-LTFs. Several existing CCA-secure KEM and DPKE schemes can be described by our generic constrictions. Therefore, our work unifies these seemingly unrelated schemes and explains the design principles behind these schemes.

We study career concerns in italian academia. We mold our empirical analysis on the standard model of contests, formalized in the multiunit all-pay auction. The number of posts, the number of applicants, and the relative importance of the criteria for promotion determine academics' effort and output. In Italian universities, incentives operate only through promotion, and all appointment panels are drawn from strictly separated and relatively narrow scientific sectors. Thus, the parameters affecting payoffs can be measured quite precisely, and we take the model to a newly constructed data set that collects the journal publications of all Italian university professors. Our identification strategy is based on a reform introduced in 1999, parts of which affected different academics differently. We find that individual researchers respond to incentives in the manner described by the theoretical model: roughly, more capable researchers respond to increases in the importance of the publications for promotion and in the competitiveness of the scientific sector by exerting more effort; less able researchers are discouraged by competition and do the opposite.

Since Isaac Newton the understanding of the physical world is more and more complex. The Euclidean space of three dimensions , independent of time is replaced in Enstein’s vision by the Lorentzian space-time at first, then by four dimensions manifold to unify space and matter. String theorists add to space more dimensions to make their theory consistent. Complex topological invariants which characterize different kind of spaces are developed. Space is discretized at the quantum scale in the loop quantum gravity theory. A non-commutative and spectral geometry is defined from the theory of operator algebra by Alain Connes. In this review, our goal is to enumerate different approaches implementing algebra and topology in order to understand the standard model of particles and beyond

The commonly adopted value at risk (VaR) model assumes the distribution of portfolio values that are normally distributed with stationary and independent increment, which restricts its usage for cases of arbitrary distributions. In this study, the current assumption is improved by constructing theoretical development of VaR model based on fractional Brownian motion, with Hurst parameter, H, as its underlying distribution. Empirical investigation is conducted to compare the standard model with the proposed model. The findings indicated that higher value of H provided higher value in VaR, thus suggesting greater probability to lose.

A reversible Turing machine (RTM) is a standard model of reversible computing that reflects physical reversibility. So far, to describe an RTM the quadruple formulation and the quintuple formulation have been used. In this paper, we propose the program form as a new formulation for RTMs. There, an RTM is described by a sequence of only five kinds of instructions. It is shown that any RTM in the quintuple form is converted to an RTM in the program form, and vice versa. We also show each instruction is implemented by a particular reversible logic element with memory called a rotary element (RE) very simply. Hence, a circuit that simulates a given RTM is easily and systematically constructed out of REs.

Purpose: The purpose of this project is to provide basic data that can be used as a reference for designing and constructing small-scale evacuation facilities that can keep people safe from various disasters in Korea. Method: In the process of constructing a mock-up based on the small-scale evacuation facility planning criteria derived from previous studies, this study is to verify the planning criteria presented in previous studies and suggest complementary points. Result: First, since relatively detailed structure planning standards are presented, it was not too difficult to reflect it on the actual design and build the structure. Second, since it is an underground structure, it is necessary to apply a thorough waterproofing plan and construction method to prevent water and moisture from entering the room. Third, it is necessary to consider sealing materials that can structurally withstand the pressure of explosion and block the inflow of radioactive and biochemical substances. It is considered to be a method of Fourth, in the process of building evacuation facilities, there are no high-performance special facilities such as CBRN ventilation systems and explosion-proof valves in Korea, so we have to a lot of research and development in this field.

Purpose: The final purpose of this study is to minimize the cost and time required in the green remodeling decision making and further promote the green remodeling market, and in this work, a standard model for each type of public building was developed as a first step to develop judgement tools to support pre-decision making. Method: The standard model refers to a series of databases with pre-set input variables for each type of building in order to minimize existing complex energy simulation input variables and increase accessibility to non-specialists. The database was established using existing public building licensing data, building energy efficiency certification cases, legal standards, and related research. Result: Based on the standard model database, we will develop support tools for pre-decision making of green remodeling and develop a web-based system that can easily compare energy requirements before and after green remodeling improvement and calculate expected construction costs.

From the World Health Organization (WHO) Thailand has reported from Wuhan, China, regarding the outbreak of novel coronavirus (COVID-19). Therefore, a novel wireless automatic body temperature sensing system (WA-BTSS) is proposed. WA-BTSS consists of three main functions, which are 1) sensing mode-2) embedded microcontroller-WiFi chip, and 3) IoT communication network. The proposed novelty WABTSS screen the people who enter the building with long-distance detection of 50 cm. The infrared sensor used is high quality with medical accuracy. It works together with NodeMCU-ESP8266 Microcontroller, which module is embedded with a WiFi internet chip. The proposed IoTinnovation is to facilitate users in any organization due to the high security of personnel screening personnel entering and exiting the building. The performance of both body temperature devices was tested in terms of accuracy test based on the actual experiment, and then analyze the obtained results using the statistics program. The obtained results have shown the Cronbach's Alpha value of .752, and it can be summarized that the proposed WA-BTSS performance in terms of accuracy test is positively consistent with the standard model at a significant 95 % confidence level. © 2021 Wydawnictwo SIGMA-NOT. All rights reserved.

Low savings are an important factor in low economic growth rates. Saudi Arabia faces many future challenges, e.g., maintaining the gross domestic product, improving economic growth rates, providing job opportunities, as well as decreasing unemployment and nationalization rates. Therefore, the present research paper aims to identify the most important factors affecting domestic savings in Saudi Arabia by building a simultaneous equations model to measure interactions and interrelations between variables using 3SLS. The results showed a significant positive interaction between variables. Increasing domestic savings by 1% increased local investment by 0.957%, whereas increasing the investment coverage ratio by 1% increased local investment by 0.971%. Moreover, increasing local investment by 1% increased gross domestic product by 0.136%, while decreasing the rate by 1% increased gross domestic product by 0.334%. Increasing population by 1% increased gross domestic product by 1.520%. In short, these factors conveyed high rates of response.

Multireceiver identity-based encryption is a cryptographic primitive, which allows a sender to encrypt a message for multiple receivers efficiently and securely. In some applications, the receivers may not want their identities to be revealed. Motivated by this issue, in 2010, Fan et al. first proposed the concept of anonymous multireceiver identity-based encryption (AMRIBE). Since then, lots of literature studies in this field have been proposed. After surveying the existing works, however, we found that most of them fail to achieve provable anonymity with tight reduction. A security proof with tight reduction means better quality of security and better efficiency of implementation. In this paper, we focus on solving the open problem in this field that is to achieve the ANON-IND-CCA security with tight reduction by giving an AMRIBE scheme. The proposed scheme is proven to be IND-MID-CCA and ANON-MID-CCA secure with tight reduction under a variant of the DBDH assumption. To the best of our knowledge, this is the first scheme proven with tight reducible full CCA security in the standard model.

We consider the macroeconomic implications of the interaction between nominal rigidities and labor market frictions in a framework where firms jointly make pricing and hiring decisions. In our New Keynesian model, workers randomly search for jobs and are matched with firms that post take-it-or-leave-it contracts and are subject to sticky prices. Relative to the typical model that separates search frictions and nominal frictions into wholesale and retail firms, respectively, our model implies smoother wages because firms can affect their marginal costs by adjusting prices. A consequence of smoother wages is that the vacancy-to-unemployment ratio responds much more than under the standard model. Additionally, our baseline model shows larger effects of changing the inflation target and smaller effects of changing unemployment benefits. We also show that combining search and price frictions in one firm leads to less volatility after technology shocks but more volatility after monetary policy shocks.

Vehicular ad hoc network(VANET) is the application of mobile ad hoc networks (MANETs) on the road of traffic. It is an important issue to achieve secure sharing of information among vehicles. Authenticated key agreement (AKA) is a good option to solve the problem. The security proofs of all known identity based two-party authenticated key agreement (IB2PAKA) schemes are given in the random oracle model(ROM). A cryptographic scheme, that is proven to be secure in ROM, is not necessarily safe in real life. Recently, Dang et al. presented an IB2PAKA scheme for VANET and claimed that it is provably secure in the extended Canetti-Krawczyk (eCK) model and ROM. In this paper, we indicate that Dang et al.’s scheme is not secure by showing two concrete attacks, then put forward a new IB2PAKA scheme and provide the security proofs in eCK model and the standard model (SM). Our scheme is suitable for VANET due to it does not require pairing operations and requires only four scale multiplication operations.

Manufacturing systems in the present industry must be flexible and allow fast reconfiguration to supply increasing demands for diversified products. Thus, a standard model for data exchange should ...

Blind signcryption (BSC) can guarantee the blindness and untrackability of signcrypted messages, and moreover, it provides simultaneous unforgeability and confidentiality. Most traditional BSC schemes are based on the number theory. However, with the rapid development of quantum computing, traditional BSC systems are faced with severe security threats. As promising candidate cryptosystems with the ability to resist attacks from quantum computing, lattice-based cryptosystems have attracted increasing attention in academic fields. In this paper, a post-quantum blind signcryption scheme from lattice (PQ-LBSCS) is devised by applying BSC to lattice-based cryptosystems. PQ-LBSCS inherits the advantages of the lattice-based cryptosystem and blind signcryption technique. PQ-LBSCS is provably secure under the hard assumptions of the learning with error problem and small integer solution problem in the standard model. Simulations are carried out using the Matlab tool to analyze the computational efficiency, and the simulation results show that PQ-LBSCS is more efficient than previous schemes. PQ-LBSCS has extensive application prospects in e-commerce, mobile communication, and smart cards.

In “Scientific Realism and the Issue of Variability in Behavior,” Arocha (2021) develops an acute critique of “the standard model of current research practice in psychology” (p. 376), sharply dissecting five unwarranted assumptions behind it. To address these issues, the author proposes adopting a nonpositivist philosophical basis for behavioral research: scientific realism. Behind this argumentation, however, it is implied that scientific realism is fit for becoming the metatheoretical framework for psychology because it addresses the shortcomings of the current positivist model. In this commentary, I argue that scientific realism is not fit for becoming that philosophical basis, because it is open to reducing the discipline’s subject matter—the human person—to make it fit with models that have been fruitful in other sciences. Three historical examples are presented to show the risks of adopting models from disciplines devoted to explaining other phenomena to tackle the complexity of psychology’s subject matter.

This comment on “Scientific Realism and the Issue of Variability in Behavior” (Arocha, 2021) succinctly summarizes the features of the positivist background that are found in the standard model of research implemented in psychology; moreover, this comment emphasizes that this model is unable to move the psychological sciences toward discoveries. Arocha (2021) put the realist paradigm forward as an alternative for psychology researchers. This paradigm changes the ontological, epistemological, and methodological assumptions of psychological sciences. It emphasizes the interconnectedness of the sociocultural and mental mechanisms of psychological functioning. The comment concludes with an outline of some objectives of realist psychological research.

Numerical simulations of atmospheric flow were carried out in this study in order to evaluate the neighborhood effects on the wind loading over standard model-scale tall buildings. The computational models were developed by solving the steady-state Reynolds Averaged Navier-Stokes equations (RANS equations) with turbulence treated by a k-ε model. Two building positioning scenarios were simulated: scenario-1 consisted of the isolated configuration of a standard model-scale building and scenario-2 was composed of the standard building with a selected neighborhood. Both scenarios were analyzed for wind incidence angles of zero, 45, and 90 degrees. The numerical results were obtained in terms of pressure and force coefficients which allowed the determination of neighborhood factors. The simulations showed that the neighborhood influences the mean wind loading on the faces of the standard building, sometimes amplifying the load (in the case of incident winds at zero and 90 degrees), sometimes attenuating the acting forces (in the case of incident winds at zero and 45 degrees). The numerical results were compared with experimental data and showed similar orders of magnitude suggesting that the simulations correctly describe the physical behavior of the wind action. Nesse trabalho foram realizadas simulações numéricas do escoamento atmosférico com o principal objetivo de avaliar os efeitos de vizinhança em edifícios altos. Os modelos computacionais foram desenvolvidos mediante o uso do método RANS, sendo o fechamento da turbulência tratado por um modelo do tipo k-ε. Foram simulados dois cenários de posicionamento de edifícios: o cenário-1 consistiu na configuração isolada de um edifício padrão e o cenário-2 foi composto pelo edifício padrão com uma vizinhança selecionada. Ambos os cenários foram analisados para ângulos de incidência de ventos de zero, 45 e 90 graus. Os resultados numéricos foram obtidos em termos de coeficientes de pressão e força, os quais permitiram a determinação dos fatores de vizinhança. As simulações mostraram que a vizinhança influencia de forma significativa a atuação do vento sobre as fachadas do edifício padrão, ora amplificando o carregamento, ora atenuando as forças atuantes. Os resultados numéricos foram comparados com experimentos e mostraram ordens de grandeza semelhantes, sugerindo que as simulações descrevem de forma adequada o comportamento físico da ação dos ventos.

This paper examines the computational feasibility of the standard model of learning in economic theory. It is shown that the information update technique at the heart of this model is impossible to compute in all but the simplest scenarios. Specifically, using tools from theoretical machine learning, the paper first demonstrates that there is no polynomial implementation of the model unless the independence structure of variables in the data is publicly known. Next, it is shown that there cannot exist a polynomial algorithm to infer the independence structure, consequently, the overall learning problem does not have a polynomial implementation. Using the learning model when it is computationally infeasible carries risks, and some of these are explored in the latter part of the paper in the context of financial markets. Especially in rich, high-frequency environments, it implies discarding a lot of useful information, and this can lead to paradoxical outcomes in interactive game-theoretic situations. This is illustrated in a trading example where market prices can never reflect an informed trader’s information, no matter how many rounds of trade. The paper provides new theoretical motivation for the use of bounded rationality models in the study of financial asset pricing—the bound on rationality arising from the computational hardness in learning.

This paper presents two functional encryption schemes for quadratic functions (or degree-2 polynomials) achieving simulation-based security in the semi-adaptive model with constant-size secret keys. Prior constructions in the standard model either achieve weaker security model [CRYPTO 17] or require linear-size secret keys (in the message length) [PKC 20]. One of our proposed schemes is comparable to existing schemes in the generic group model in terms of ciphertext size. Technically, we combine Wee’s compiler [TCC 17] with Gay’s paradigm [PKC 20]. However, we avoid (partially) function-hiding inner-product functional encryption used in Gay’s paradigm which makes our work conceptually simpler.

In this study, we analyze whether model complexity improves accuracy of CoCo pricing models. We compare the out-of-sample pricing ability of four models using a broad dataset that contains all CoCos which were issued between January 1, 2013 and May 31, 2016 in euros. The regarded models include the standard model from De Spiegeleer and Schoutens (J Deriv 20:27–36, 2012), a modified version enriched by credit risk, an extended model that accounts for the effective lifetime of the CoCo, and a trading model, solely based on historic market prices but no pricing theory at all. For a normal market environment, the simple trading model provides a higher pricing accuracy than the theory-based models. Under distress, however, a theory-based model with a sufficiently high complexity is required.

Common-pool resource (CPR) users often face two types of problems to solve: provision and appropriation. Provision problems are often solved through the voluntary or mandatory contribution of users. This paper uses a laboratory experiment to explore how the provision scheme shapes extraction choices by CPR users who are heterogeneous in wealth. Users’ choices are compared under three conditions. In the progressive- and regressive-tax conditions, users’ contribution to the provision of CPR is determined exogenously, and in the third condition, users endogenously choose their contribution level. The standard model of own-payoff-maximization and the behavioral models of inequality aversion and equity theory predict different patterns of choices for rich and poor users under each condition. Experimental results are consistent with the predictions of the inequality-aversion model and clearly refute the predictions of equity theory. Importantly, inequality aversion appears to be motivating choices differently in the exogenous- and endogenous-provision conditions: under the regressive- and progressive-tax conditions, extraction choices are consistent with inequality aversion motives, whereas in the endogenous treatment, contribution decisions are consistent with inequality aversion but extraction choices are not.

In this paper I discuss a standard model of life cycle consumption behavior when the discount rate depends on the state of health and health deteriorates with increasing age. I show that this feature allows the introduction of time-consistent discounting at a non-constant rate and to model, in a convenient way, the notion that individuals discount future payoffs at higher rates when the risk of death increases. I show that the model generates an empirically plausible age-consumption pattern even when perfect annuity markets exist.

Indifferentiability of iterated hash functions is seen as evidence that there are no structural flaws in the iteration structure of the algorithm. However, it is often overlooked that such considerations only hold in the random oracle model and do not give any guarantee in the standard model. In this article, we show the following separation result: there is a hash function that is indifferentiable from a random oracle, but is totally insecure in the standard model. In particular, we show that it does not satisfy collision or multicollision-resistance, second preimage-resistance or preimage-resistance for any family of compression functions. Therefore, at least in theory, hash function indifferentiability does not guarantee the structural integrity of the hash algorithm in the standard model. Results in the random oracle model are not affected.

Reward prediction error, the difference between the expected and obtained reward, is known to act as a reinforcement learning neural signal. In the current study, we propose a model fitting approach that combines behavioral and neural data to fit computational models of reinforcement learning. Briefly, we penalized subject-specific fitted parameters that moved away too far from the group median, except when that deviation led to an improvement in the model’s fit to neural responses. By means of a probabilistic monetary learning task and fMRI, we compared our approach with standard model fitting methods. Q-learning outperformed actor-critic at both behavioral and neural level, although the inclusion of neuroimaging data into model fitting improved the fit of actor-critic models. We observed both action-value and state-value prediction error signals in the striatum, while standard model fitting approaches failed to capture state-value signals. Finally, left ventral striatum correlated with reward prediction error while right ventral striatum with fictive prediction error, suggesting a functional hemispheric asymmetry regarding prediction-error driven learning.

Fog Computing is a region of Computer Science that is under steady construction and development, and related to data security, the worldview turns out to be more solid and secure for IoT's edge stages. The verification of limited memory devices has serious issues since memory utilization is high when applied with different models that have the motivation behind shared confirmation. In this paper, we propose the Novel cipher text-based encryption model (NCEM) which has an information access control plot dependent on Ciphertext-Policy it give information privacy, fine-grained control, and mysterious validation in a multi-authority fog computing framework. The sign cryption and plan cryption overhead for the client is altogether diminished by redistributing the bothersome calculation tasks to fog hubs. The proposed conspire is demonstrated to be secure in the standard model and can give trait repudiation and public unquestionable status. The security analysis, asymptotic multifaceted nature examination, and implementation results demonstrate that our construction can offset the security objectives with useful effectiveness in calculation.

This paper characterizes mechanisms satisfying incentive compatibility and individual rationality in the classical public good provision problem. Many papers in the literature obtain the results in the so-called standard model of ex ante identical agents with a continuous, closed interval of types. The main contribution of this paper is the characterization of the budget-surplus maximizing mechanism satisfying incentive compatibility and individual rationality (Theorem 1 for Bayesian implementation and Theorem 3 for dominant strategy implementation) that applies to a finite discretization over the standard model. Making use of the proposed budget-surplus maximizing mechanisms, we show that some known results do not need the agents’ risk neutrality, whereas some others do rely on the agents’ risk neutrality in a subtle manner. Furthermore, we improve upon some known results and obtain new results which do not exist in the standard model.

Notwithstanding the prominence of the so-called Standard Model of Military Group Cohesion (SMMGC), important parts of the model are understudied: both conceptually and empirically. In this article we, first, synthesize previous research to conceptualize and measure the overlooked institutional cohesion dimension. Second, we test the validity of the proposed full four-dimensional SMMGC model using a survey of an Italian Alpini battalion, and more rigorous methods than in previous research. Results are supportive of our proposed measurements and the validity of the four-dimensional model. We thus make a methodological and an empirical contribution to further the ongoing debate on military cohesion.

In many accident contexts, the expected accident harm depends on observable as well as unobservable dimensions of the precaution exercised by the parties involved. The observable dimensions are commonly referred to as the ‘care’ levels and the unobservable aspects as the ‘activity’ levels. In a seminal contribution, Shavell, S (1980). Strict liability versus negligence. J. Leg. Stud. 9: 1–25 extended the scope of the economic analysis of liability rules by providing a model that allows for the care as well as activity level choices. Subsequent works have used and extended Shavell’s model to predict outcomes under various liability rules, and also to compare their efficiency properties. These works make several claims about the existence and efficiency of equilibria under different liability rules, without providing any formal proof. In this paper, we re-examine the prevalent claims in the literature using the standard model itself. Contrary to these prevalent claims, we show that the standard negligence liability rules do not induce equilibrium for all of the accident contexts admissible under the model. Under the standard model, even the ‘no-fault’ rules can fail to induce a Nash equilibrium. In the absence of an equilibrium, it is not plausible to make a claim about the efficiency of a rule per-se or vis-a-vis other rules. We show that even with commonly used utility functions that meet all of the requirements of the standard model, the social welfare function may not have a maximum. In many other situations fully compatible with the standard model, a maximum of the social welfare function is not discoverable by the first order conditions. Under the standard model, even individually optimum choices might not exist. We analyze the underlying problems with the standard model and offer some insights for future research on this subject.

Binary tree encryption is an intriguing primitive that enables many practical applications to achieve an increasing important security feature, forward security. However, the public key size of existing constructions grows linearly with the depth of the underlying binary tree in the standard model. To support more secret keys associated with nodes, it is often expected that the tree has a sufficiently large depth. This places a burden on employing it implicitly or explicitly in real world. In this work, we show how to compress linear-size public key down to constant-size public key and give our construction featuring constant-size public key in the standard model. We prove that our construction achieves an improved security, adaptive security, under the matrix decision Diffie–Hellman assumption, which is a generalization of standard $k$-Lin assumption. Moreover, our key-generation, key-derivation and encryption algorithms have lower time complexity than that of the prior construction, leading to further efficiency improvements. To illustrate these improvements in practice, we give an implementation of our construction and the prior one and then evaluate the performance in the tree depth.

Behavioural economics is a thriving field that offers descriptive models of human decision-making that deviate from the standard model of decision-making in economics. This article presents insights from behavioural economics that can help address dynamic inconsistency, that is, time-inconsistency problems of employees and inform incentive design strategies. The author argues that lessons from behavioural economics can be applied to design solutions that can transform HR practices. HR managers and leaders stand to benefit from the emerging evidence from the lab and field in behavioural economics that calls for a rethinking of the conventional understanding of human behaviour.

We reformulate the standard New Keynesian model to include heterogeneity in price stickiness suggested by microevidence on price changes and to allow for positive trend inflation. In the new model, higher trend inflation leads to a relatively greater long‐run output loss and, consequently, a smaller determinacy region than in the standard model. When trend inflation is 4%, the determinacy region of the new model is almost nonexistent, cautioning against increasing the inflation target to 4% as a means to avoid the zero lower bound in the future and pointing to the costs that high inflation may have had in the past.

Identity-based proxy re-encryption (IB-PRE) can convert the ciphertext encrypted under Alice’s identity to Bob’s ciphertext of the same message by a semi-trusted proxy with the proper transformation key. The main purpose of our work is to enhance the security of IB-PRE. For outside attacks, all existing IB-PRE constructions from lattices have only achieved a limited or weak security model called IND-sID-CPA security. Therefore, by embedding re-encryption key generation and re-encryption algorithms appropriately in Agrawal et al.’s identity-based encryption scheme from lattices, we construct an IND-ID-CPA secure IB-PRE scheme over decisional learning with errors (LWE) under the standard model. For inside attacks, we propose a new primitive IB-VPRE by extending the basic IB-PRE scheme with a new functionality called re-encryption verifiability, meaning that a re-encrypted ciphertext receiver or a third party can verify whether the received ciphertext is correctly transformed from an original ciphertext or not, and thus can detect illegal activities of the proxy. We realize re-encryption verifiability using the homomorphic signature technique as a black box, making the resulting scheme non-interactive and quantum-immune after instanced by a lattice-based homomorphic signature scheme.

In model-based testing, we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution paths, depending on some internal decisions made by the software. Consequently, performing precise test analyses, e.g. to calculate the test coverage, are not possible.. This can be mitigated if developers can annotate the model with estimated probabilities for taking each transition. A probabilistic model checking algorithm can subsequently be used to do simple probabilistic coverage analysis. However, in practice developers often want to know what the achieved aggregate coverage is, which unfortunately cannot be re-expressed as a standard model checking problem. This paper presents an extension to allow efficient calculation of probabilistic aggregate coverage, and also of probabilistic aggregate coverage in combination withk-wise coverage.

Key elements of John Searle’s articulation of the Standard Model of Social Ontology can be found within Max Weber’s ideal type of legal-rational authority. However, the fact that, for Weber, legal-rational authority is just one of three types of legitimate authority, along with traditional and charismatic authority, suggests limitations to the Standard Model’s scope of applicability. Where Searle takes himself to have provided an account of “the structure of human civilization,” Weber’s taxonomy suggests that Searle has only given us an account of a way of being a civilization. This understanding of traditional authority also reveals why the Standard Model misconstrues the structure of ordinary, informal statuses, such as friendship.