Search Results (50)

Ninety-five-year-old Doris Held, a great niece of Sigmund Freud, has been convening the Shakespeare Reading Group in Northampton, Massachusetts, my hometown, since she moved here in 2016. In the following essay, which is a personal response to my experience of this group of Shakespearean readers, to Doris Held, and to the work of Shakespeare in general, I attempt to chart the full impact of the Bard’s work on my life and on the world around me. I am neither a scholar nor a historian. In a true sense, I am a bystander Shakespearean, who has received deep reward and benefit from the experience, but it is Doris Held and her group who opened my eyes to the precise nature of this unexamined reward. Doris brought the spirit of the group from Cambridge, Massachusetts, where she had been a dues-paying member for decades of something called the Old Cambridge Shakespeare Association, which itself dates to 1880. My wife Debra and I attended the first meeting in Northampton more than a decade ago, and we have been receiving emails from Doris four times a year ever since. While these communications often induce guilt, they invariably lead to pleasures that I would never want to relinquish. That is a complicated dynamic in my routine, and I try to grapple with its ebb and flow in the pages that follow. Each time I get one, I have a version of the same conversation in my head. Is Doris still doing this? Haven’t they done all the plays by now? All things considered, wouldn’t they—and I—rather be home watching a true crime documentary about Gaby Petito on Netflix? What the hell is William Shakespeare to me anyway? At this point, if I’m honest, Shakespeare is Doris. The experience with this group led me in two directions. One took me back to my now long-ago history with Shakespeare’s work as an actor in college. The other took me via historical research into the prehistory of Doris Held’s previous Shakespeare group in Cambridge, Massachusetts. The two paths gave me a deeper grasp of the influence of his work across the world and on my own life. Full article

Firmware authenticity and integrity during upgrades are critical security factors in Internet of Things (IoT) applications in the age of edge artificial intelligence (AI). Data from IoT applications are vital for business decisions. Any unintended or malicious change in data can adversely impact the goals of an IoT application. Several studies have focused on using blockchain to ensure the authentication of IoT devices and the integrity of data once the data are in the blockchain. Firmware upgrades on IoT edge devices have also been investigated with blockchain applications, with a focus on eliminating external threats during firmware upgrades on IoT devices. In this paper, we propose a new IoT device design that works against internal threats by preventing malicious codes from device manufacturers. In IoT applications that monitor critical data, it is important to ensure that the correct firmware reporting honest data is running on the devices. As devices are owned and operated by a small group of application stakeholders, this multiprocessor design extracts the firmware periodically and checks whether it matches the signatures of the expected firmware designed for the business goals of the IoT applications. The test results show that there is no significant increase in code, disruption, or power consumption when implementing such a device. This scheme provides a hardware-oriented solution utilizing processor-to-processor communication protocols and is an alternative to running lightweight blockchain on IoT edge devices. Full article

Sustainability requirements pose complex challenges for the construction industry, requiring adaptation strategies, innovative solutions, and collaboration between stakeholders to build resilient structures for the future. Nevertheless, effective collaboration between stakeholders is hindered by information asymmetry, which can produce disputes, endanger honest communication between the participants, and in the long term, negatively affect industrial productivity. To improve the sustainability of construction businesses and construction projects, there is a need for the implementation of mitigation measures for information asymmetry risks. This study presents possible mitigation measures for information asymmetry between clients and contractors in the construction industry. Mitigation measures were identified through a literature review and semi-structured interviews with industry professionals. Furthermore, a questionnaire was designed and employed to prioritize mitigation measures and explore their utility for mitigating three types of risks: adverse selection, moral hazard, and hold-up. The results of this study indicate that building trust, regular supervision and monitoring of work, thorough verification of bidders, and fair distribution of risks between the client and the contractor have the most utility in mitigating information asymmetry in construction projects. The practical contribution of this research includes defining the most useful measures to mitigate risks caused by information asymmetry. Project managers can apply the results of this research for more successful achievement of their project goals. Implementing effective risk management strategies, such as building trust between stakeholders, can help mitigate the impacts of unforeseen events and build resilience in construction projects. Full article

Background: The COVID-19 pandemic has impacted nursing theatre staff, departmental activity, and delivery of services to patients. This work-based project aimed to investigate the challenges of nursing leadership in an elective orthopaedic department at current times. Methods: The study collected qualitative data exploring theatre staff’s expectations from leadership, offering insight on how the pandemic has influenced the way of working and exploring how the future in this unit may look. The answers from 20 practitioners to an anonymised open-ended survey were examined using thematic analysis. Results: The participants described a leader as a good communicator who focuses on empowering others and supporting the team, identified by the majority as a senior team member. From the findings, three topics were identified: immediate changes, delayed changes, and pre-existing conditions. The answers painted a reality that is complex and multifaceted, where numerous variables play a part in the physical and mental health of each candidate, impacting their performance as well as their work/life balance. Overall, the strongest subjects recurring in the findings were the need for nursing leadership to focus on supporting staff with training opportunities, to actively plan for a reduction in staffing shortages, and to be constantly mindful of staff well-being. Conclusions: This study pointed out that the need for constant communication with their staff, building honest relationships, and being a reliable leader, focused on empowering others and supporting the team were important factors for the nursing management during the COVID-19 pandemic and post-COVID-19 era. Full article

The secure computation of symmetric encryption schemes using Yao’s garbled circuits, such as AES, allows two parties, where one holds a plaintext block m and the other holds a key k, to compute $Enc(k,m)$ without leaking m and k to one another. Due to its wide application prospects, secure AES computation has received much attention. However, the evaluation of AES circuits using Yao’s garbled circuits incurs substantial communication overhead. To further improve its efficiency, this paper, upon observing the special structures of AES circuits and the symmetries of an S-box, proposes a novel ciphertext reduction scheme for garbling an S-box in the last SubBytes step. Unlike the idea of traditional Yao’s garbled circuits, where the circuit generator uses the input wire labels to encrypt the corresponding output wire labels, our garbling scheme uses the input wire labels of an S-box to encrypt the corresponding “flip bit strings”. This approach leads to a significant performance improvement in our garbling scheme, which necessitates only $2^8$ ciphertexts to garble an S-box and a single invocation of a cryptographic primitive for decryption compared to the best result in previous work that requires $8\times 2^8$ ciphertexts to garble an S-box and multiple invocations of a cryptographic primitive for decryption. Crucially, the proposed scheme provides a new idea to improve the performance of Yao’s garbled circuits. We analyze the security of the proposed scheme in the semi-honest model and experimentally verify its efficiency. Full article

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy. Full article

Federated edge learning (FEL) has recently attracted great interest due to its real-time response and energy-efficient characteristics. Most existing work focuses on designing algorithms to improve model performance, ignoring the malicious behavior and personal decision-making of self-interested edge servers. Although some efforts have been devoted to incentivizing honest edge server engagement by compensating training costs, this rarely considers resource efficiency and often assumes that edge servers provide complete information to the platform, which may lead to the risk of private attribute leakage. Hence, we aim to achieve an incentive mechanism that promotes secure and efficient model training between the platform and edge servers. However, edge servers’ multi-dimensional private attributes and training strategies make the optimization problem nonconvex, and incomplete information further increases the complexity of the analysis. In order to address these challenges and by integrating contract theory and exponential mechanism, we propose an efficiency-aware incentive mechanism, FedNow, which enables edge servers to personally determine their local training rounds while motivating their participation without giving access to their true training strategies and private attributes. Specifically, we enabld edge servers to add noise to their submitted training strategy to hide their true training rounds; then, we carefully designed an efficiency score function to select honest and efficient edge servers without disclosing their private attributes. In order to demonstrate that FedNow strictly outperforms existing schemes in terms of total costs, we theoretically derived sufficient conditions for making the total costs of FedNow lower than existing schemes and designed a greedy algorithm that uses the Monte Carlo method to find feasible near-optimal solutions in polynomial time. Our extensive experimental assessment using synthetic and real datasets shows the superiority of FedNow. Full article

British drug policies could underserve women with treatment needs, and this paper provides evidence that communication through the words and actions of professionals across drug and alcohol services, health and mental health, social work and the criminal justice sector can leave women feeling stigmatised and failed. Women live with the stigma of ‘the lying addict’; however, documents and courtroom statements provided by professionals can misrepresent women’s experiences, which exacerbates social harm. Data are drawn from feminist participatory action research, where female lived experience experts worked alongside academics to implement a qualitative study using interviews and focus groups with women using treatment services (n = 28) and an online world café with professionals working with these women (n = 9) and further professionals providing support at lived experience data collection events (n = 5). This data set is cross-referenced with one-to-one and small-group interviews with professionals in the field (n = 17) conducted by a third-sector partner. Findings establish that stigma negatively impacts the identification of treatment needs and access to timely and appropriate service delivery. Social harms to women with addictions could be significantly reduced with timely, authentic, honest, gender-informed and trauma-informed practices for girls and women using drugs and alcohol to self-medicate from traumatic experiences. Full article

Humans make decisions about their actions based on a combination of their objectives and their knowledge about the state of the world surrounding them. In social interactions, one prevalent goal is the ambition to be perceived to be an honest, trustworthy person in terms of having a reputation of frequently making true statements. Aiming for this goal requires the decision whether to communicate information truthfully or if deceptive lies might improve the reputation even more. The basis of this decision involves not only an individual’s belief about others, but also their understanding of others’ beliefs, described by the concept of Theory of Mind, and the mental processes from which these beliefs emerge. In the present work, we used the Reputation Game Simulation as an approach for modeling the evolution of reputation in agent-based social communication networks, in which agents treat information approximately according to Bayesian logic. We implemented a second-order Theory of Mind based message decision strategy that allows the agents to mentally simulate the impact of different communication options on the knowledge of their counterparts’ minds in order to identify the message that is expected to maximize their reputation. Analysis of the communication patterns obtained showed that deception was chosen more frequently than the truthful communication option. However, the efficacy of such deceptive behavior turned out to have a strong correlation with the accuracy of the agents’ Theory of Mind representation. Full article

In this article, we present the first proposal for contract signing based on blockchain that meets the requirements of fairness, hard-timeliness, and bc-optimism. The proposal, thanks to the use of blockchain, does not require the use of trusted third parties (TTPs), thus avoiding a point of failure and the problem of signatories having to agree on a TTP that is trusted by both. The presented protocol is fair because it is designed such that no honest signatory can be placed at a disadvantage. It meets the hard-timeliness requirement because both signatories can end the execution of the protocol at any time they wish. Finally, the proposal is bc-optimistic because blockchain functions are only executed in case of exception (and not in each execution of the protocol), with consequent savings when working with public blockchains. No previous proposal simultaneously met these three requirements. In addition to the above, this article clarifies the concept of timeliness, which previously has been defined in a confusing way (starting with the authors who used the term for the first time). We conducted a security review that allowed us to verify that our proposal meets the desired requirements. Furthermore, we provide the specifications of a smart contract designed for the Ethereum blockchain family and verified the economic feasibility of the proposal, ensuring it can be aligned with the financial requirements of different scenarios. Full article

Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners’ strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves. Full article

This study aims to explore a demoethical model for sustainable development in modern society. It proposes an approach that focuses on organizing activities to improve sustainable development. Specifically, it presents a demoethical model relevant to Society 5.0 and Industry 5.0 organizations. The objective is to identify demoethical values that can drive sustainable development in the era of digitalization. Through a literature review and analysis, this study identifies key components of the demoethical model and provides practical recommendations for stakeholders involved in digital transformation. The analysis of demoethical norms and phenomena, such as education, nurturing, mind, knowledge, science, and honest work, has enabled the identification of values that align with sustainable development in society. The results of the study demonstrate that the notion of a demoethical foundation for sustainability is rooted in the concept of spirituality as the basis for a new societal development scenario and its relationship with nature. The study shows that ideas about the demoethical basis of sustainability are based on the priority of spirituality as the basis of a new scenario for the development of society, as well as the integration of demographic, socio-economical, and ecological components in system-wide modeling. Full article

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution. Full article

Pregnancy anamnesis is a crucial part of child and adolescent psychiatry diagnostics. In previous works, the reliability of retrospective maternal self-report on perinatal characteristics was heterogeneous. This prospective longitudinal study aimed to evaluate women’s recall of prenatal events in a within-subject design. A sample of 241 women gave a self-report on prenatal alcohol, smoking, partnership quality, pregnancy satisfaction, and obstetric complications during the 3rd trimester (t0), childhood (t1, 6–10 y), and adolescence (t2, 12–14 y). The intra-individual agreement was examined. The t0–t1–(t2) agreement was poor to substantial; this was highest for smoking and worst for obstetric complications, followed by alcohol (Fleiss’ κ = 0.719 to −0.051). There were significant t0–t1–(t2) differences for all pregnancy variables (p < 0.017), except for 3rd trimester satisfaction (p = 0.256). For alcohol (t0 25.8%, t1 17.4%, t2 41.0%) and smoking (t0 11.9%, t1 16.4%, t2 22.6%), the highest self-reported rates were found during adolescence. During childhood, fewer obstetric complications (t0 84.9%, t1 42.2%) and worse partnerships were reported (t0 M = 8.86, t1 M = 7.89). Thought to be due to social stigmata and memory effects, pregnancy self-reports cannot be precisely reproduced. Creating a respectful and trusting atmosphere is essential for mothers to give honest self-reports that are in the best interest of their children. Full article

This work presents an efficient and effective system allowing hospitals to share patients’ private information while ensuring that each hospital database’s medical records will not be leaked; moreover, the privacy of patients who access the data will also be protected. We assume that the thread model of the hospital’s security is semi-honest (i.e., curious but honest), and each hospital hired a trusted medical records department administrator to manage patients’ private information from other hospitals. With the help of Homomorphic Encryption- and Private Set Intersection -related algorithms, our proposed system protects patient privacy, allows physicians to obtain patient information across hospitals, and prevents threats such as troublesome insider attacks and man-in-the-middle attacks. Full article