A dynamic yard space reservation algorithm based on reward-penalty mechanism

Traffic grooming in wavelength division multiplexing networks merges low-speed flows into large capacity pipes so that the bandwidth discrepancy between them will not lead to underutilization of resources. Dynamic grooming deals with requests for wavelength allocation based on a dynamic pattern of arrivals in contrast to the situation of static grooming in which the pattern of arrivals must be previously known. Solutions for dynamic grooming typically involve the construction of an auxiliary graph for deciding on the routing and wavelength assignment. An auxiliary graph can represent the network partially leading to scalable solutions, although blocking is not fairly distributed among calls in such solutions. A novel algorithm is thus proposed for achieving fairness in blocking among the calls. In an off-line step, it defines an appropriate set of OXCs for achieving fairness and efficient use of resources. Results reveal a higher degree of fairness obtained by the use of the novel algorithm than for previously proposed algorithms.

QoS-aware routing in ATM and IP-over-ATM

In multi-user container terminals, the berth schedule, in which, the berthing position, the berthing time and the number of the quay cranes are arranged for each ship, is made in advance based on the sailing schedule of the coming ships. However, in practice, the sailing schedule is often disrupted for many reasons, such as bad weather, or the decisions from the ship companies, etc. This kind of disruption will cause the original schedule in corresponding ports to be no longer optimal, simply unfeasible. Thus, the berth scheduling problem with sailing schedule disrupted was addressed in this paper. Initially, the disruptions from the sailing schedule were divided into the three following types: scheduled ships skipping over the port, ships changing their arrival time, and unscheduled ships arriving at the port. Subsequently, based on the theory of disruption management, a recovery model and deviation from the original schedule was established with the performance of the new schedule and the recovery cost considered. To solve the model, a heuristic algorithm based on neighborhood search was proposed; therefore, there were advantages utilized with the original information to formulate a fast solution. To conclude this information examples have been provided to show the efficiency of the proposed approach.

Colorization is a process of converting grayscale images into visually acceptable colorized images. It is a complex problem because no unique solution exists, i.e., the objects within the image cannot be associated with one color only. Colorized image quality assessment is also a complex problem because in real life the reference image for comparison with colorized results does not exist. The main purpose of colorization is convincing the observer in the credibility of the colorized image, disregarding color accuracy. For that reason, both subjective and objective evaluation of colorization results show imperfections. In this paper, the objective quality evaluation of colorized images has been conducted with five existing metrics and one new metric. The database of colorized images has been obtained by deep learning colorization methods. Based on the conducted subjective testing, a correlation between objective metrics and subjective grades has been determined. The results indicate that the improvement of objective metrics for the evaluation of colorized images is needed.

The author attempts to highlight the significance of the Iranian southern ports development strategy planning, which allows for the maximum container transit with minimum resources such as service capacities, human resources, and financial potential in order to expand the Iranian container transit. It should be noted that the strategic objectives, the business strategy and its implementation can be arranged only after the port vision and mission obviously delineated. For the purpose of improving the outcome of the ports operational management, it is recommended to concentrate on new strategies such as exploit of transit corridors for development of the Iranian South Ports. The main part of this paper is dedicated to evaluating the role of container transit from the Iranian south ports terminals from various transport corridors in order to improve Maritime Transport in Iran. The importance of defining the existing and potential competitors at the Persian Gulf and making a comparison between their and our own strengths and weaknesses is of utmost importance. Sometimes, the real business opportunities and threats are placed beyond one's own line of industry and business. It is therefore necessary to make a careful analysis of the port environment. After the port competition and environment have been analyzed, it would be possible to initiate the building of the SWOT analysis for the Iranian Southern Container Terminals such as Khoramshahr, Imam Khomani, Busher, Bandar Abbas and Chabahar port, which have suitable strategic position as transit base in the region. The SWOT analysis method is therefore applied, aiming at defining the weaknesses and strengths of the economic subjects as well as opportunities and threats coming from the environment. The next segment of this paper is dedicated to consider the role of dry ports and the International North-South Transit Corridor for the purpose of improving the Iranian container trade.

Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effective detection methods accordingly. In particular, data poisoning attacks can be realistic in an adversarial situation where we retrain a DL model with a new dataset obtained from an external source during transfer learning. By this motivation, we propose a novel defense method that partitions and inspects the new dataset and then removes malicious sub-datasets. Specifically, our proposed method first divides a new dataset into n sub-datasets either evenly or randomly, inspects them by using the clean DL model as a poisoned dataset detector, and finally removes malicious sub-datasets classified by the detector. For partition and inspection, we design two dynamic defensive algorithms: the Sequential Partitioning and Inspection Algorithm (SPIA) and the Randomized Partitioning and Inspection Algorithm (RPIA). With this approach, a resulting cleaned dataset can be used reliably for retraining a DL model. In addition, we conducted two experiments in the Python and DL environment to show that our proposed methods effectively defend against two data poisoning attack models (concentrated poisoning attacks and random poisoning attacks) in terms of various evaluation metrics such as removed poison rate (RPR), attack success rate (ASR), and classification accuracy (ACC). Specifically, the SPIA completely removed all poisoned data under concentrated poisoning attacks in both Python and DL environments. In addition, the RPIA removed up to 91.1% and 99.1% of poisoned data under random poisoning attacks in Python and DL environments, respectively.

The impact of digital gadgets is enormous in the current Internet world because of the easy accessibility, flexibility and time-saving benefits for the consumers. The number of computer users is increasing every year. Meanwhile, the time spent and the computers also increased. Computer users browse the internet for various information gathering and stay on the internet for a long time without control. Nowadays working people from home also spend time with the smart devices, computers, and laptops, for a longer duration to complete professional work, personal work etc. the proposed study focused on deriving the impact factors of Smartphones by analyzing the keystroke dynamics Based on the usage pattern of keystrokes the system evaluates the stress level detection using machine learning techniques. In the proposed study keyboard users are intended for testing purposes. Volunteers of 200 members are collectively involved in generating the test dataset. They are allowed to sit for a certain frame of time to use the laptop in the meanwhile the keystroke of the Mouse and keyboard are recorded. The system reads the dataset and trains the model using the Dynamic Cat-Boost algorithm (DCB), which acts as the classification model. The evaluation metrics are framed by calculating Euclidean distance (ED), Manhattan Distance (MahD), Mahalanobis distance (MD) etc. Quantitative measures of DCB are framed through Accuracy, precision and F1Score.

In Active and Assisted Living environments (AAL), one of the major tasks is to make sure that old people or disabled persons do feel well in their environment. Unfortunately, it is still a difficult task to design a learning system or build a machine learning model which can be trained on a group of subjects using physiological sensors and performs well when testing it on other subjects. This paper proposes a dynamic calibration algorithm which presents promising results for subject-independent human emotion recognition. The goal of the calibration module is to calibrate itself with respect to the features of a new subject by finding the most similar subject in the training data. In order to check the overall performance, this approach is tested using the well-known MAHNOB dataset. The results show a promising improvement based on different evaluation metrics, e.g., sensitivity and specificity.

Existing feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning. In label distribution learning, the associated labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms cannot be directly applied to label distribution learning. Discretizing label distribution data into multi-label data will cause part of the supervision information to be lost. In most practical applications of label distribution learning, the feature space is undefined, and the features are in the form of flow features. To solve this problem, this paper applies fuzzy rough set theory and applies the flow feature framework to propose a dynamic label distribution feature selection algorithm that handles flow features. Experimental results show that the proposed method is more effective than six state-of-the-art feature selection algorithms on 12 datasets with respect to six representative evaluation metrics.

The article analyzes the indicators of the quality of work of enterprises of Ukrzaliznitsa for 2000-2018. The paramount attention to the dynamics of quality indicators is due to the fact that they characterize the efficiency of resource use and the prospects of managerial decisions. Qualitative indicators determine the internal qualities, signs and characteristics of the phenomena studied. They are used to evaluate manufactured products in terms of their compliance with established requirements, to assess the economic efficiency of labor and material costs, as well as financial investments. The article considers such indicators as locomotive performance, train speed, average daily mileage of a locomotive, wagon productivity, average turnover period of a freight wagon, etc. It is noted that a decrease in locomotive performance indicators is associated with a critical level of their wear. It is emphasized that during the operation of rolling stock beyond the service life, the safety indicators of economic efficiency significantly worsen, and the resource and energy intensity of transportation increases. The decrease in locomotive performance leads to a lack of traction, which many railway company customers are already experiencing, primarily agricultural, metallurgical, as well as coal industry enterprises. Several factors influence the level of car productivity. First of all, this is a high level of wear, which does not allow the use of the entire fleet of cars. An analysis of the UZ carriage fleet shows that the specific gravity of both freight and passenger cars with an expired life is higher than the specific gravity of cars that are within the nominal life. Wagon productivity is closely correlated with the average mass of a freight train, the value of which also tends to decrease. An important indicator of the use of a car is its turnover, which has been increasing recently. This leads to an increase in the deficit of freight cars to meet the demand for transportation and inefficient use of the rolling stock. The main reasons for the increase in wagon turnover are deficit traction and a decrease in infrastructure capacity. An important factor is the wear of the track and the lack of high-quality network repair. A decrease in the quality indicators for the use of locomotives and wagons leads to an exacerbation of the problem of a shortage of the latter. Wagon shortage is experienced by many industries. The lack of wagons negatively affects the image of the railway company. The deterioration of the rolling stock does not allow to achieve an increase in the average local speed of freight trains, and this increases the period of cargo delivery, i.e. reduces the competitiveness of railway transport. The above-mentioned problems in the development of the railway industry indicate that the task of attracting large investments in the industry should be put first in the complex of tasks to be solved.

Humanity faces an enormous challenge: how to increase agricultural production to achieve food security in the 21st century and feed a population that is expected to grow to 10 billion people. This must be done while maintaining sustainable agricultural systems while facing challenges such as climate change, depletion of water resources and the potential for increased erosion and loss of productivity due to extreme weather events. The article deals with the problem of agricultural losses and food waste, which reaches a third of all food production. What affects the inefficient use of resources and what leads to economic, social and environmental problems. Reducing waste and improving the efficiency of resource use is one of the 17 Sustainable Development Goals (SDGs) of the UN General Assembly 2015. Results and discussions are presented on a new economic paradigm - a green economy. Highlighted the need for sustainable agricultural production, which includes: - increasing the efficiency of the use of labor and natural resources; – conservation and improvement of natural resources; - social development of rural areas and inclusive development of the agricultural sector; – increasing the resilience of human communities and ecosystems to external influences; – a sustainable system for managing the agro-food complex through the introduction of transparent and effective legislation, maintaining a balance of private initiative and public administration. Agricultural losses and food waste are climate change factors, accounting for about 8% of greenhouse gas emissions, for example. The main sources of greenhouse gases are: agriculture, animal husbandry, food systems that require energy costs, and recycling.

Priority setting of public spending in developing countries: do not try to do everything for everybody.

Next-generation digital broadcasting system transmits the DASH segment files. Delays and inefficient use of resources are the major challenges in the transmission of DASH segment files in broadcasting systems. Because the size of DASH segment files varies even though the files have the same quality, and broadcasting systems uses the fixed resource. Delay is an important problem in broadcasting systems because these are real-time systems. Therefore, next-generation digital broadcasting systems should minimize delays and use resource efficiently. This paper proposes a transmission model that can analyze delays as well as efficient use of resources.

Energy management algorithm development for smart car parks including charging stations, storage, and renewable energy sources

For the efficient use of limited frequency resources, the spectrum sharing allocation between mobile networks and different existing radio systems is promising. In this scheme, we focus on an allocation method that involves sharing spectrum bands with a mobile network operator (MNO). Recently, to improve the spectrum efficiency, dynamic spectrum allocation methods according to the MNO demand were proposed. However, in demand-based dynamic allocation, channel discontinuity in the frequency and time directions in the same MNO occurs when mapping the channel to MNOs. This channel discontinuity reduces the utility of shared frequencies. To address this problem, we propose a novel spectrum allocation method that maximizes frequency and time continuity for demand-based dynamic spectrum sharing. Our performance evaluation shows that compared with the existing methods, the proposed algorithm can improve the spectrum efficiency by 6%, channel continuity by 23%, and fairness by 5%.