Search Results (11)

In ship navigation, determining a safe and economic path from start to destination under dynamic and complex environment is essential, but the traditional algorithms of current research are inefficient. Therefore, a novel differential evolution deep reinforcement learning algorithm (DEDRL) is proposed to address problems, which are composed of local path planning and global path planning. The Deep Q-Network is utilized to search the best path in target ship and multiple-obstacles scenarios. Furthermore, differential evolution and course-punishing reward mechanism are introduced to optimize and constrain the detected path length as short as possible. Quaternion ship domain and COLREGs are involved to construct a dynamic collision risk detection model. Compared with other traditional and reinforcement learning algorithms, the experimental results demonstrate that the DEDRL algorithm achieved the best global path length with 28.4539 n miles, and also performed the best results in all scenarios of local path planning. Overall, the DEDRL algorithm is a reliable and robust algorithm for ship navigation, and it also provides an efficient solution for ship collision avoidance. Full article

Prison sentences that exceed the natural lifespan present a puzzle because they have no more power to deter or incapacitate than a single life sentence. In three survey experiments, we tested the extent to which participants support these longer-than-life sentences under different decision contexts. In Experiment 1, 130 undergraduates made hypothetical prison sentence-length recommendations for a serious criminal offender, warranting two sentences to be served either concurrently or consecutively. Using a nationally representative sample (N = 182) and an undergraduate pilot sample (N = 260), participants in Experiments 2 and 3 voted on a hypothetical ballot measure to either allow or prohibit the use of consecutive life sentences. Results from all experiments revealed that, compared to concurrent life sentences participants supported the use of consecutive life sentences for serious offenders. In addition, they adjusted these posthumous years in response to mitigating factors in a manner that was indistinguishable from ordinary sentences (Experiment 1), and their support for consecutive life sentencing policies persisted, regardless of the default choice and whether the policy was costly to implement (Experiments 2 and 3). These judgment patterns were most consistent with retributive punishment heuristics and have implications for sentencing policy and for theories of punishment behavior. Full article

Food safety liability has required consideration around the world. It is realistic to establish a system of food safety liability insurance. However, the existing difficulties include members’ behavior decisions, influential factors, and the responsibility as well as obligations between the players. We build an evolutionary game model to discuss the behavior of food firms and the government in the context of food safety liability and identify evolutionary stability strategy of the participants in the game. Then, we simulate the stable evolution trend of the interaction between food firms and the government using MATLAB software. The results indicate that the initial probabilities of two-party game strategies affect the time length and convergence speed of system evolution but not the overall trend and final result of the evolution. Furthermore, decisions on purchasing food safety liability insurance be affected by the returns of food firms and government supervision. The more severe the punishment for food safety issues, the stronger the sense of crisis among food firms, and the higher probability they will purchase food safety liability insurance. The governance cost of food safety risks plays a role in affecting the strategic decision of the government. The ideal stable equilibrium state of the government and food firms cannot be achieved through spontaneous circulation. Suggestions are given from the perspective of government supervision and punishment to motivate food firms to insure actively. Full article

This article focuses on the structure of female and male crimes and gender disparities in sentencing in Lithuania, which present a significant gap in criminological research. Using Lithuanian court decisions on five types of offenses—murder, grievous bodily harm, actual bodily harm, drug distribution, and theft—we attempt to answer whether women are punished more leniently than men. Our research demonstrates that gender is a significant factor only in some sentences. Only the length of a prison sentence showed a statistically significant difference. When the importance of legal and extralegal factors in imposing prison length is compared, legal factors are found to be more significant predictors. The prison sentence length was mainly affected by the presence of a prior conviction, additional charges, and mitigating and aggravating circumstances. Although the average prison sentence for men in cases of grievous bodily harm and drug distribution was significantly longer than for women, the regression models developed for each offence type revealed that neither gender nor other extralegal factors appeared to be significant in determining the length of the prison sentence. The results allow us to argue that future research should focus more on analyzing extralegal factors and judges’ motives in discretionary sentencing decisions. Full article

Among the sources of legal considerations are judges’ previous decisions regarding similar cases that are archived in court decision documents. However, due to the increasing number of court decision documents, it is difficult to find relevant information, such as the category and the length of punishment for similar legal cases. This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian courts. Our results demonstrate that the decision document sections that strongly affected the accuracy of the prediction model were prosecution history, facts, legal facts, and legal considerations. The prediction of the punishment category shows that the CNN+attention model achieved better accuracy than other deep learning models, such as CNN, LSTM, BiLSTM, LSTM+attention, and BiLSTM+attention, by up to 28.18%. The superiority of the CNN+attention model is also shown to predict the punishment length, with the best result being achieved using the ‘year’ time unit. Full article

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied to approach the patient in a complex environment with deep reinforcement learning algorithms. The shape of the human body and bed is complex which may lead to the collision between the human and the robot. Because the sparse reward the robot obtains from the environment may not support the robot to accomplish the task, a neural network is trained to control the manipulators of the robot to prepare to hold the patient up by using a proximal policy optimization algorithm with a continuous reward function. Firstly, considering the realistic scene, the 3D simulation environment is built to conduct the research. Secondly, inspired by the idea of the artificial potential field, a new reward and punishment function was proposed to help the robot obtain enough rewards to explore the environment. The function is consisting of four parts which include the reward guidance function, collision detection, obstacle avoidance function, and time function. Where the reward guidance function is used to guide the robot to approach the targets to hold the patient, the collision detection and obstacle avoidance function are complementary to each other and are used to avoid obstacles, and the time function is used to reduce the number of training episode. Finally, after the robot is trained to reach the targets, the training results are analyzed. Compared with the DDPG algorithm, the PPO algorithm reduces about 4 million steps for training to converge. Moreover, compared with the other reward and punishment functions, the function used in this paper will obtain many more rewards at the same training time. Apart from that, it will take much less time to converge, and the episode length will be shorter; so, the advantage of the algorithm used in this paper is verified. Full article

Due to inherent and systemic biases, animal control policies in the US are over-enforced in low-income communities and communities of color, resulting in worse health outcomes for the pets in these communities. These outcomes are exemplified by higher confiscation, relinquishment, and euthanasia rates, lower return to owner rates, and extended lengths of stay in animal shelters. The Humane Communities framework operationalizes One Health and One Welfare concepts to comprehensively address issues of inequity at both the individual and structural levels to improve animal control policy and outcomes. Person-centered and culturally competent policies and programs that focus resources on addressing root causes of pet health and welfare issues as opposed to an emphasis on code enforcement can create more positive, scalable, and sustainable improvements in human, other animal, and environmental health and welfare outcomes. This shift from punishment-oriented approaches to support-based models of animal control aligns the animal welfare field with the modern human social justice movement. Full article

The authors of the paper deal with the philosophy of the privatization of elements of criminal justice, which in the last decades influenced the development of criminal justice in European countries, including the Slovak Republic. The philosophy of privatization in relation to criminal justice represents a wider acceptance of the individual interests of the subjects of criminal procedure. It is the strengthening of powers of the parties to the proceedings and at the same time the entrusting of criminal dispute solution to their own hands. Therefore, the aim of the paper is to examine the expanding philosophy of privatization of the Slovak criminal justice system (the so-called negotiating procedure, a.k.a. plea bargain agreement) which, with the aim of facilitating and simplifying the resolution of a criminal case, has brought the possibility of negotiating with the State the conditions for admitting the guilt of the accused, in exchange for imposing a less severe punishment. However, the aim of the paper is not only to examine the current legal regulation of this expanding phenomenon but also to show its seamy sides and to present comprehensible and reasonable legal opinion relating to its suitability. In the paper, the authors, therefore, deal with the question of whether the philosophy of privatizing the criminal justice is in compliance with the traditional values of the continental legal system. At the same time, they try to answer whether the philosophy of the so-called negotiated justice is not contrary to the fundamental principles of criminal justice. In the paper, the authors also ask questions like: Can the punishment be subject to negotiating? Is it justified on the ground of the society’s morality and fundamental values of the legal system if the State negotiates with the perpetrators of crime the conditions of their confession and the length of the punishment? Does the negotiated punishment fulfill its basic functions (preventative, repressive, protective, moral condemnation by society)? Full article

Generative conversational systems consisting of a neural network-based structural model and a linguistic model have always been considered to be an attractive area. However, conversational systems tend to generate single-turn responses with a lack of diversity and informativeness. For this reason, the conversational system method is further developed by modeling and analyzing the joint structural and linguistic model, as presented in the paper. Firstly, we establish a novel dual-encoder structural model based on the new Convolutional Neural Network architecture and strengthened attention with intention. It is able to effectively extract the features of variable-length sequences and then mine their deep semantic information. Secondly, a linguistic model combining the maximum mutual information with the foolish punishment mechanism is proposed. Thirdly, the conversational system for the joint structural and linguistic model is observed and discussed. Then, to validate the effectiveness of the proposed method, some different models are tested, evaluated and compared with respect to Response Coherence, Response Diversity, Length of Conversation and Human Evaluation. As these comparative results show, the proposed method is able to effectively improve the response quality of the generative conversational system. Full article

The proliferation of jails and prisons as places of institutionalization for persons with serious mental illness (SMI) has resulted in many of these patients receiving jail-based punishments, including solitary confinement. Starting in 2013, the New York City (NYC) jail system developed a new treatment unit for persons with SMI who were judged to have violated jail rules (and previously would have been punished with solitary confinement) called the Clinical Alternative to Punitive Segregation (CAPS) unit. CAPS is designed to offer a full range of therapeutic activities and interventions for these patients, including individual and group therapy, art therapy, medication counseling and community meetings. Each CAPS unit requires approximately $1.5 million more investment per year, largely in additional staff as compared to existing mental health units, and can house approximately 30 patients. Patients with less serious mental illness who received infractions were housed on units that combined solitary confinement with some clinical programming, called Restrictive Housing Units (RHU). Between 1 December 2013 and 31 March 2015, a total of 195 and 1433 patients passed through the CAPS and RHU units, respectively. A small cohort of patients experienced both CAPS and RHU (n = 90). For these patients, their rates of self-harm and injury were significantly lower while on the CAPS unit than when on the RHU units. Improvements in clinical outcomes are possible for incarcerated patients with mental illness with investment in new alternatives to solitary confinement. We have started to adapt the CAPS approach to existing mental health units as a means to promote better clinical outcomes and also help prevent jail-based infractions. The cost of these programs and the dramatic differences in length of stay for patients who earn these jail-based infractions highlight the need for alternatives to incarceration, some of which have recently been announced in NYC. Full article

“Life means life” is a mantra of elected state officials who would rather spend already-compromised state budgets on increasing the use of imprisonment as a punishing tool rather than being viewed by their constituents as “soft on crime”. As a result of tough-on-crime initiatives, approximately 160,000 out of 2.2 million inmates being held in jails and prisons in the United States are serving life sentences. While surviving imprisonment is a challenge for most individuals, prisoners who serve long sentences—including “life”—have different adaptation mechanisms, and for them, adaptation is a longer, more complex process. Further, while persons serving life sentences include those who present a serious threat to public safety, they also include those for whom the length of sentence is questionable. In particular, life without parole (LWOP) sentences often represent a misuse of limited correctional resources and discount the capacity for personal growth and rehabilitation that comes with the passage of time. The purpose of this article is to explore the “doing life” experiences of a man who has chosen to redirect the focus of his life by transforming himself and helping others. Full article