ChatGPT’s capability to generate original content on any topic represents a concern for academic institutions, since students can plagiarise the AI-created content with a low probability of being caught. While prior research on academic misconduct emphasized the combined effects of perceived costs and benefits in explaining students’ decisions to cheat, the low detectability of ChatGPT-based plagiarism significantly reduces the cost of being caught, making rational choice theory less powerful in explaining academic misconduct in the AI era. Considering the effects of both rational decision-making and moral considerations, this study shows that individuals experiencing moral disengagement are more likely to engage in ChatGPT-based plagiarism. Other significant predictors include perceived benefits, punishment severity, and informal sanctions. Furthermore, moral disengagement enhances the effects of formal sanctions on ChatGPT-based plagiarism. The results have implications for professors, academic administrators, and plagiarism detection software designers to enforce academic integrity in the ChatGPT era.

Visually impaired individuals encounter significant challenges when walking and acting in unfamiliar environments, particularly in outdoor scenarios. The complexity of outdoor environments, characterized by diverse obstacles, traffic signals, and societal norms, poses substantial barriers to mobility of visually impaired individuals and makes long-distance walking especially arduous. Although GPS-based navigation systems can facilitate long-distance travel, they often suffer from location inaccuracies in urban areas and even completely fail indoors. Moreover, these systems lack the capability to provide detailed information about walkways and immediate surroundings, which are crucial for safe and efficient walking. To address these limitations, we introduce a proof-of-concept wearable navigation system named eLabrador, designed to assist visually impaired individuals in long-distance walking in unfamiliar outdoor environments. The eLabrador integrates public maps (e.g. Amap or Google Maps) and GPS for global route planning, while leveraging computational visual perception to provide precise and safe local guidance. This hybrid approach enables accurate and safe navigation for visually impaired individuals in outdoor scenarios. Specifically, the eLabrador utilizes a head-mounted RGB-D camera to capture environmental geometric terrain and objects in outdoor urban environments. These inputs are processed into a 3D semantic map, offering a detailed representation of the surrounding environment. The planning module then integrates this 3D semantic map with route information from the global map (i.e. Amap) to generate an optimized walking path. Finally, the interaction module utilizes the audio-haptic dual-channel to relay navigation instructions to visually impaired user. Together, these three modules work seamlessly to facilitate long-distance navigation for visually impaired individuals in outdoor environments. The eLabrador is evaluated with two real-world outdoor scenarios, involving 10 visually impaired and visually masked participants. The experiments show that eLabrador successfully guides visually impaired participants to their destinations in outdoor environments. Additionally, the eLabrador provides descriptive information about landmarks and other navigation cues, helping visually impaired users better understand their surroundings. Subjective evaluations further indicate that most participants felt a sense of safety and reported an acceptable cognitive load during navigation, indicating its usability and effectiveness. Note to Practitioners—Visually impaired individuals almost cannot walk long distance in unfamiliar outdoor environments. Without proper assistance, their mobility and quality of life can be severely impacted. To address this issue, this article presents a wearable navigation system eLabrador to assist visually impaired individuals in walking outdoors, such as traveling from a residential entrance to a nearby park. Experimental results from real-world scenarios involving 10 participants demonstrate that eLabrador safely guides visually impaired users to their destination, significantly enhancing their mobility and independence.

To meet the demands in terms of energy-efficient and fast production and delivery of goods, robotic fleets began to populate warehouses and industrial environments. To maximize the profitability of the operations, multi-robot systems are required to coordinate agents and avoid downtime efficiently. In this paper, agent coordination is formulated as a multi-robot task allocation (MRTA) problem with time and precedence constraints. The method capitalizes on a graph method to build a measure graph reflecting the sparsity of tasks and a precedence graph, which includes the task constraints, to group the tasks into batches. A batch solver is provided to obtain the final solutions to the MRTA. In this way, the sustainability and environmental impact of logistics operations can be improved by reducing the number of robots needed to complete tasks and also by assigning tasks closest to the robot location, reducing the amount of time and the total energy required for the robots to complete the job. Extensive experiments on both uniformly distributed and sparse data sets prove the effectiveness of the proposed algorithm compared to state-of-the-art algorithms such as MIP and TePSSI.Note to Practitioners—This paper was motivated by the problem of minimizing the energy consumption of multi-robot systems in the execution of complex tasks, which requires, in the most general case, the motion of the robot to a target location and further on-site operations. This scenario is particularly relevant in smart, automated warehouses, where mobile robots are repeatedly demanded to store or dispatch goods in a structured environment, where operation duration and future requests are known a priori. The paper formulates this problem by means of a batched multi-robot task allocation (BMRTA) optimization, which can include time windows and precedence constraints jointly. First, the task constraints are encoded into two graphs and then combined to group subtasks together in batches. Then, each batch is solved separately, minimizing the overall energy required to achieve the tasks in the batch. Although the optimality of the solution is ensured only locally, i.e., within the same batch, the task clustering improves the computational efficiency with respect to global approaches, especially in large-sized problems. Experimental results demonstrated that when comparing BMRTA with literature approaches such as MIP and TePSSI, not only the energy consumption but also the total travel distance can be minimized while the total duration of the tasks remains comparable.

Algorithms are increasingly used by human resource departments to evaluate employee performance. While the algorithms are perceived to be objective and neutral by removing human biases, they are often perceived to be less fair than human managers. This research proposes dignity as an important construct in explaining the discrepancy in perceived fairness and investigates remedial steps for improving dignity and fairness for algorithm-based employee evaluations. Three experiments’ results show that those evaluated by algorithms perceive lower levels of dignity, leading them to believe the process is less fair. In addition, we find that providing justifications for algorithm usage in employee evaluations improves perceived dignity. However, human-algorithm collaboration does not enhance perceived dignity.

Femtocells can extend the last-mile accessibility for the current cellular networks so as to serve more users indoors and improve the spectrum utility. However, since the indoor femtocell establishment can be ad hoc, HeNBs may also be inevitably vulnerable to the rogue-HeNB (uncoordinated or illegitimate HeNB) attack in practice. For detecting rogue-HeNBs, a two-dimensional received signal-to-interference-plus-noise ratio (SINR) map is exploited to construct a spatial radio-activity feature map (RAFM). Mathematical proof is derived to justify that the existence of any femtocell interference corresponds to a flat-bottom convex region in the RAFM. Advanced image processing techniques are designed here to localize the radio interference (rogue-HeNB) over the RAFM. Thus, a novel positioning scheme is proposed in this paper to undertake the accurate rogue-HeNB localization for the femtocell networks. Different indoor scenarios are evaluated through the real experiments for our proposed method. The average accuracy can be within one meter for a 6.75 meter by 8.1 meter laboratory.

This paper focuses on the roles of personality and social support in affecting the extent of emotional disclosure in social media (SM) and compares them to those in face-to-face encounters. Specifically, we consider the effects of the Big Five personality traits and perceptions of social support from friends, significant others, and family on the extent of sharing positive and negative emotions on Facebook (FB) vs. real life (RL). The data are collected via an online survey of a broad demographic range of FB users. Our findings suggest that certain personality traits (extroversion, agreeableness, and conscientiousness), as well as perceived social support from friends, are significantly related to the disclosure of positive emotions on FB. We also report and discuss the differences between drivers of emotional disclosure in SM and RL, as well as offer suggestions for future research.