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Traffic context plays an important role in supporting automated driving and intelligent transportation systems. Smart vehicles explore surrounding environments by analyzing sensor data and periodically communicating with neighbors and road infrastructures. The context can be well learned in this way to support driving, but the vehicle trajectory can be also easily exposed under eavesdropping attacks. The pseudonym is proposed to hide the real identity of the vehicles. However, the effectiveness of anonymity, the safety of driving, the convenience of implementation and the utilization of resources in previous approaches have not been well-balanced. Therefore, focusing on efficiently replacing pseudonyms with the premise of ensuring driving safety, we propose a secure dynamic silent mix zone pseudonym changing scheme (TLAS) based on the real-time traffic context prediction for urban regions. It naturally takes the area in front of the red traffic light as a silent mix zone, which avoids the driving security issue caused by signal silence. Besides, the area length is dynamically configured according to the traffic context predicted in the last green light cycle, so the anonymous effect can be improved. In addition, considering the resource utilization and accuracy requirement, the adaptive prediction algorithm is applied. We conduct simulation experiments with real-world traffic history using SUMO and OMNET++, the results show that TLAS strategy can indeed achieve a better anonymous effect (reducing standardized traceability rate by 8.2%) with lower driving speed for safety concern. [ABSTRACT FROM AUTHOR]

Parasites can cause enormous damage to their hosts. Studies have shown that antiparasitic peptides can inhibit the growth and development of parasites and even kill them. Because traditional biological methods to determine the activity of antiparasitic peptides are time-consuming and costly, a method for large-scale prediction of antiparasitic peptides is urgently needed. We propose a computational approach called i2APP that can efficiently identify APPs using a two-step machine learning (ML) framework. First, in order to solve the imbalance of positive and negative samples in the training set, a random under sampling method is used to generate a balanced training data set. Then, the physical and chemical features and terminus-based features are extracted, and the first classification is performed by Light Gradient Boosting Machine (LGBM) and Support Vector Machine (SVM) to obtain 264-dimensional higher level features. These features are selected by Maximal Information Coefficient (MIC) and the features with the big MIC values are retained. Finally, the SVM algorithm is used for the second classification in the optimized feature space. Thus the prediction model i2APP is fully constructed. On independent datasets, the accuracy and AUC of i2APP are 0.913 and 0.935, respectively, which are better than the state-of-arts methods. The key idea of the proposed method is that multi-level features are extracted from peptide sequences and the higher-level features can distinguish well the APPs and non-APPs. [ABSTRACT FROM AUTHOR]

Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user. [ABSTRACT FROM AUTHOR]

The intelligent treatment of urban garbage is an important component of creating a smart city and also solves several problems associated with urban garbage. Many traditional garbage cans are widely distributed, leading to great consumption of human and material, untimely government. Therefore, in this paper, we propose an intelligent system based on edge computing and the narrow-band Internet of things (NB-IoT) for monitoring smart trash cans (STCs). The experimental results demonstrate that the proposed STC system improves the load capacity of garbage cans, requires few human and material resources, and allows for intelligent management of garbage in smart cities. [ABSTRACT FROM AUTHOR]

Edge computing brings data storage closer to the location where it is needed. Therefore, the edge devices, especially smart industrial edge devices, require higher storage systems. NAND flash memory has the advantages of small size, high speed, and strong shock resistance, which is widely used in various storage systems, providing a good choice for edge devices. NAND flash has unique physical characteristics, such as “out-of-place updates” and “prewrite erasure,” therefore, the traditional address mapping methods require improvement. This article presents a novel demand-based page-level address mapping algorithm called DPAM. The goal of DPAM is to provide efficient address translation by using a smaller address mapping table. Due to the high service cost of block-level address mapping and hybrid address mapping, a page-level address mapping scheme is proposed. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm provides improvements of 7.11% for the hit ratio and 7% for the number of block erasures compared with other approaches. [ABSTRACT FROM AUTHOR]

Health is one of the most important considerations for everyone. However, environmental pollution damages people's health seriously, especially those who live in industrial cities. Gas waste, water waste, and waste residue seriously endanger the health of people. Therefore, pollution regulation is one of the keys to improving people's health. In this article, we propose an intelligent pollution monitoring system (PMS) based on the Industrial Internet of Things to prevent direct emission of harmful substances and to build healthier cities. The PMS has three main parts: 1. Real-time pollutant monitoring equipment, which can monitor harmful substances in the gas and water in real time, and further cannot be damaged easily 2. A real-time alarm system, which can send alerts immediately after detecting harmful substances 3. An intelligent and fast response system, which can act to respond to an alarm The experimental results show that the proposed PMS can improve the efficiency of responses to pollution and increase people's health. [ABSTRACT FROM AUTHOR]

Both safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. Therefore, a street lamp is an indispensable part of the smart cities. However, current street lamps have lack of smart characteristics, which increases both danger and energy consumption. In order to address these problems, a smart street lamp (SSL) based on the fog computing for smarter cities is proposed in this paper. The advantages of the proposed SSL are as follows: 1) fine management, because every street lamp can be operated independently; 2) dynamic brightness adjustment, all street lamps can be adjusted dynamically; and 3) autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, stolen, and so on. The experimental results showed that the proposed SSL can improve the energy efficiency and reduce danger. [ABSTRACT FROM AUTHOR]

Fog computing requires a large main memory capacity to decrease latency and increase the Quality of Service (QoS). However, dynamic random access memory (DRAM), the commonly used random access memory, cannot be included into a fog computing system due to its high consumption of power. In recent years, non-volatile memories (NVM) such as Phase-Change Memory (PCM) and Spin-transfer torque RAM (STT-RAM) with their low power consumption have emerged to replace DRAM. Moreover, the currently proposed hybrid main memory, consisting of both DRAM and NVM, have shown promising advantages in terms of scalability and power consumption. However, the drawbacks of NVM, such as long read/write latency give rise to potential problems leading to asymmetric cache misses in the hybrid main memory. Current last level cache (LLC) policies are based on the unified miss cost, and result in poor performance in LLC and add to the cost of using NVM. In order to minimize the cache miss cost in the hybrid main memory, we propose a cost aware cache replacement policy (CACRP) that reduces the number of cache misses from NVM and improves the cache performance for a hybrid memory system. Experimental results show that our CACRP behaves better in LLC performance, improving performance up to 43.6% (15.5% on average) compared to LRU. [ABSTRACT FROM AUTHOR]

In a modern multicore system, memory is shared among more and more concurrently running multimedia applications. Therefore, memory contention and interference are more and more serious, inducing system performance degradation significantly, the performance degradation of each thread differently, unfairness in resource sharing, and priority inversion, even starvation. In this paper, we propose an approach of coordinating channel-aware page mapping policy and memory scheduling (CCPS) to reduce intermultimedia application interference in a memory system. The idea is to map the data of different threads to different channels, together with memory scheduling. The key principles of the policies of page mapping and memory scheduling are: 1) the memory address space, the thread priority, and the load balance; and 2) prioritizing a low-memory request thread, a row-buffer hit access, and an older request. We evaluate the CCPS on a variety of mixed single-thread and multithread benchmarks and system configurations, and we compare them with four previously proposed state-of-the-art interference-reducing policies. Experimental results demonstrate that the CCPS improves the performance while reducing the energy consumption significantly; moreover, the CCPS incurs a much lower hardware overhead than the current existing policies. [ABSTRACT FROM PUBLISHER]

The increasing demand on the main memory capacity is one of the main big data challenges. Dynamic random access memory (DRAM) does not represent the best choice for a main memory, due to high power consumption and low density. However, the nonvolatile memory, such as the phase-change memory (PCM), represents an additional choice because of the low power consumption and high-density characteristic. Nevertheless, the high access latency and limited write endurance have disabled the PCM to replace the DRAM currently. Therefore, a hybrid memory, which combines both the DRAM and the PCM, has become a good alternative to the traditional DRAM memory. Both DRAM and PCM disadvantages are challenges for the hybrid memory. In this paper, a dynamic adaptive replacement policy (DARP) in the shared last-level cache for the DRAM/PCM hybrid main memory is proposed. The DARP distinguishes the cache data into the PCM data and the DRAM data, then, the algorithm adopts different replacement policies for each data type. Specifically, for the PCM data, the least recently used (LRU) replacement policy is adopted, and for the DRAM data, the DARP is employed according to the process behavior. Experimental results have shown that the DARP improved the memory access efficiency by 25.4%. [ABSTRACT FROM PUBLISHER]

Abstract: With the development of multicore architectures, multicore real-time scheduling has become a hot topic. Cache warm-up overheads introduced by task migration between two cores lead to some tasks miss deadlines in time-critical systems. In this paper, we propose a scheduling algorithm based on push-assisted model, which is a hardware mechanism used to proactive migrate cache lines of tasks. Our scheduling algorithm provides software support for proactive migration. It is shown that our algorithm is effective in some examples. [Copyright &y& Elsevier]

Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and biased. Since the heavy manual annotation workload, most pathological whole slide image (WSI) datasets are only partially labeled (without region annotations), which cannot be directly used in supervised deep learning. For these reasons, it is of great practical significance to design a laborsaving and high data utilization diagnosis method. In this paper, a self-supervised learning (SSL) based framework for automatically detecting eyelid MM is proposed. The framework consists of a self-supervised model for detecting MM areas at the patch-level and a second model for classifying lesion types at the slide level. A squeeze-excitation (SE) attention structure and a feature-projection (FP) structure are integrated to boost learning on details of pathological images and improve model performance. In addition, this framework also provides visual heatmaps with high quality and reliability to highlight the likely areas of the lesion to assist the evaluation and diagnosis of the eyelid MM. Extensive experimental results on different datasets show that our proposed method outperforms other state-of-the-art SSL and fully supervised methods at both patch and slide levels when only a subset of WSIs are annotated. It should be noted that our method is even comparable to supervised methods when all WSIs are fully annotated. To the best of our knowledge, our work is the first SSL method for automatic diagnosis of MM at the eyelid and has a great potential impact on reducing the workload of human annotations in clinical practice.