Today in world the road infrastructure is actively monitored, primarily for the safety of all traffic participants, but is also actively tracked for traffic organization, timely response to newly emerging situations that can endanger infrastructure users. Supervision and tracking of transport infrastructure is important from several aspects.By constantly tracking traffic infrastructure and turnover, it is easy to spot potential problems and dangers that occur at a particular location.For active supervision of the road infrastructure is necessary to use modern technology. The use of intelligent transport systems in road infrastructure supervision can be used for preventive action and elimination of potential dangers to infrastructure users. By managing from the supervising center, intelligent warning systems located on motorways, give the possibility to drivers to timely warn at certain risks and difficulties in traffic infrastructure such as certain infrastructure damage, driving time conditions, traffic accidents, black spots, warning about certain works on the infrastructure, and various other driver information, essential for traffic safety. Certain devices installed on the roads are intended to send information on the state of the infrastructure to the main control center. The supervisory center, with timely information, has the ability to actively manage traffic and preventive action. The control center has a special function in the case of traffic accidents through the transmission of information to the urgent services. Particular attention is paid to road infrastructure supervision in large cities where there is a high frequency of vehicles as well as on the highway. This paper presents an overview of the use of intelligent transport equipment for road infrastructure supervision and the advantages of surveillance. The paper shows the review of supervision of road infrastructure in Bosnia and Herzegovina with special emphasis on Corridor Vc, using intelligent transport systems.

The decision-making process requires the prior definition and fulfillment of certain factors, especially when it comes to complex areas such as supply chain management. One of the most important items in the initial phase of the supply chain, which strongly influences its further flow, is to decide on the most favorable supplier. In this paper a selection of suppliers in a company producing polyvinyl chloride (PVC) carpentry was made based on the new approach developed in the field of multi-criteria decision making (MCDM). The relative values of the weight coefficients of the criteria are calculated using the rough analytical hierarchical process (AHP) method. The evaluation and ranking of suppliers is carried out using the new rough weighted aggregated sum product assessment (WASPAS) method. In order to determine the stability of the model and the ability to apply the developed rough WASPAS approach, the paper analyzes its sensitivity, which involves changing the value of the coefficient λ in the first part. The second part of the sensitivity analysis relates to the application of different multi-criteria decision-making methods in combination with rough numbers that have been developed in the very recent past. The model presented in the paper is solved by using the following methods: rough Simple Additive Weighting (SAW), rough Evaluation based on Distancefrom Average Solution (EDAS), rough MultiAttributive Border Approximation area Comparison (MABAC), rough Visekriterijumsko kompromisno rangiranje (VIKOR), rough MultiAttributiveIdeal-Real Comparative Analysis (MAIRCA) and rough Multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA). In addition, in the third part of the sensitivity analysis, the Spearman correlation coefficient (SCC) of the ranks obtained was calculated which confirms the applicability of all the proposed approaches. The proposed rough model allows the evaluation of alternatives despite the imprecision and lack of quantitative information in the information-management process.

Welcome to the inaugural issue of the ACM Transactions on Human-Robot Interaction! It is an exciting time to be part of the HRI community. Across its publication venues, Human-Robot Interaction is producing a burgeoning and compelling body of intellectual activity. ACM THRI is truly honored to participate in these developments as the first robotics journal offered by ACM Publications. As Editors-in-Chief, Chad and I are privileged to work with an esteemed and thoughtful editorial board in our consideration of research comprising the leading thought in HRI. Our editorial board has been thrilled to see inspiring research flowing through the journal, across the Behavioral/Social, Computational, Design, and Mechanical sections, and remain excited for the new work to come. In complement to ACM THRI, this March witnessed the gathering of the largest-ever group of almost 650 Human-Robot Interaction scholars in Chicago for the annual ACM/IEEE Human-Robot Interaction Conference. As the community of HRI researchers grows, we must ever more carefully consider how to keep up the rigorous nature of HRI research, while working to include a greater diversity of perspectives into the conversation. Being the premier journal in the field of HumanRobot Interaction, ACM THRI considers broad representation of high quality work as central to its mission. We are therefore committed to enabling the inclusion and dissemination of HRI research across disciplinary and geographical audiences, and to broadening the field’s scientific and societal impact. To highlight our aims for HRI and ACM THRI, this inaugural issue has focused on the topic of “New Frontiers for Human-Robot Interaction” with a combination of opinion editorials and top-quality research articles. Among these research articles, Bowen and Alterovitz present new insights into motion planning for dynamic human environments through the estimation of cost functions learned from demonstration. Carrillo et al. describe the innovative process of designing a social robotic rehabilitation aid for children with cerebral palsy, performed in the context of clinical deployment and with sustained engagement of diverse stakeholders. Lee and Riek’s critical look at assistive robots being developed for older adults suggests we need to go beyond the deficit

In this work, we develop a framework that jointly decides on the optimal location of wireless extenders and the channel configuration of extenders and access points (APs) in a Wireless Mesh Network (WMN). Typically, the rule-based approaches in the literature result in limited exploration while reinforcement learning based approaches result in slow convergence. Therefore, Artificial Intelligence (AI) is adopted to support network autonomy and to capture insights on system and environment evolution. We propose a Self-X (self-optimizing and self-learning) framework that encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning and learning in a truly autonomous fashion. The agent derives adequate knowledge from previous actions improving the quality of future decisions. Domain experience was provided to guide the agent while exploring and exploiting the set of possible actions in the environment. Thus, it guarantees a low-cost learning and achieves a near-optimal network configuration addressing the non-deterministic polynomial-time hardness (NP-hard) problem of joint channel assignment and location optimization in WMNs. Extensive simulations are run to validate its fast convergence, high throughput and resilience to dynamic interference conditions. We deploy the framework on off-the-shelf wireless devices to enable autonomous self-optimization and self-deployment, using APs and wireless extenders.

In this paper, we propose a self-deployment approach for finding the optimal placement of extenders in which both the wireless back-haul and front-haul throughput of the extender are optimized. We present an artificial intelligence (AI) case based reasoning (CBR) framework that enables autonomous self-deployment in which the network can learn the environment by means of sensing and perception. New actions, i.e. extender positions, are created by problem-specific optimization and semi-supervised learning algorithms that balance exploration and exploitation of the search space. An IEEE 802.11 standard compliant simulations are performed to evaluate the framework on a large scale and compare its performance against existing conventional coverage maximization approaches. Experimental evaluation is also performed in an enterprise environment to demonstrate the competence of the proposed AI-framework in perceiving such a dense scenario and reason the extender deployment that achieves user quality of service (QoS). Throughput fairness and ubiquitous QoS satisfaction are achieved which provide a leap to apply AI-driven self-deployment in wireless networks.