Infrared small target detection has significant applications in airspace surveillance, warning systems and so on. Detection of infrared small targets on a single image frame is a challenging work. First of all, compared to generic object detection, infrared small targets have the characteristics of low signal-to-clutter ratio, various sizes, and shapes. In addition, false alarm sources in the complex environment also impede precise detection. In the past years, scholars have performed many studies on infrared small target detection. The majority of traditional single-frame-based methods rely on the gray-scale discontinuity between the target and the neighborhood to detect infrared small targets. However, these traditional methods rely on prior assumptions. And when the real scenarios change dramatically, it is difficult to use fixed hyperparameters to handle such variations. As we all know, the deep convolutional neural network (CNN) can automatically learn the hierarchical features of images. However, CNN-based methods could not be directly applied for infrared small targets which are even small to several pixels because the pooling layer in the networks may result in the loss of targets.

Concurrency vulnerabilities caused by synchronization problems will occur in the execution of multi-threaded programs, and the emergence of concurrency vulnerabilities often cause great threats to the system. Once the concurrency vulnerabilities are exploited, the system will suffer various attacks, seriously affecting its availability, confidentiality and security. In this paper, we extract 839 concurrency vulnerabilities from Common Vulnerabilities and Exposures (CVE), and conduct a comprehensive analysis of the trend, classifications, causes, severity, and impact. Finally, we obtained some findings: 1) From 1999 to 2021, the number of concurrency vulnerabilities disclosures show an overall upward trend. 2) In the distribution of concurrency vulnerability, race condition accounts for the largest proportion. 3) The overall severity of concurrency vulnerabilities is medium risk. 4) The number of concurrency vulnerabilities that can be exploited for local access and network access is almost equal, and nearly half of the concurrency vulnerabilities (377/839) can be accessed remotely. 5) The access complexity of 571 concurrency vulnerabilities is medium, and the number of concurrency vulnerabilities with high or low access complexity is almost equal. The results obtained through the empirical study can provide more support and guidance for research in the field of concurrency vulnerabilities.

RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library, compared with an inventory list. The existing approaches for this purpose assume that the number of missing tags is small. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known as the inventory list, and hence many tags in the list are regarded as missing. The missing objects significantly increase the time required for stocktaking. In this paper, we propose an algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Moreover, we observe that the missing rate constantly changes during the identification because some of tags are verified present or absent, which affects time efficiency; accordingly, we propose a hybrid stocktaking algorithm called DLS (Dynamic inventory list based stocktaking) to adapt to such changes for the first time. According to the results of extensive simulations, when the inventory list is 20 times that of actually present tags, the execution time of our approach is 36.3 percent that of the best existing algorithm.

Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative support resulted in communication failures that accounted for 7% of the malpractice payments made from 2004 to 2008 in the United States. To address this problem, we have developed a novel machine learning method that can automatically and accurately identify cases that require prompt communication to referring physicians based on analyzing the associated radiology reports. This semi-supervised learning approach requires a minimal amount of manual annotations and was trained on a large multi-institutional radiology report repository from three major external healthcare organizations. To test our approach, we created a corpus of 480 radiology reports from our own institution and double-annotated cases that required prompt communication by two radiologists. Our evaluation on the test corpus achieved an F-score of 74.5% and recall of 90.0% in identifying cases for prompt communication. The implementation of the proposed approach as part of an online decision support system can assist radiologists in identifying radiological cases for prompt communication to referring physicians to avoid or minimize potential harm to patients.

RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.

Energy efficiency is an important design criterion for wireless communications. As parallel frequency channels are used for multiuser transmission, different channel bandwidth and power can be applied to transmit signals to different users. Channel bandwidth assignment and transmit power allocation are optimized to maximize the sum information rate with the total bandwidth budget, the total transmit power budget, and the user-specific rate requirements. Moreover, with variable total transmit power, the energy efficiency is measured as the maximum sum rate per unit of power used. In this paper, with fixed or flexible bandwidths of the user frequency channels, effective methods are developed to find the total transmit power along with the resource (bandwidth and power) allocation for maximum energy efficiency. This resource allocation ensures that, while each user's minimum rate requirement is satisfied, all of the excess resource of spectrum and transmit power is dedicated to the one user with the best channel quality. Simulation and experimental results validate the optimal solution of total transmit power along with resource allocation that supports the energy-efficient multiuser transmission.

Traveling ionospheric disturbances (TIDs) induced by acoustic-gravity waves (AGWs) in the neutral atmosphere are observable in trans-ionospheric radio signals such as Global Navigation Satellite System (GNSS) signals. In this research, we designed a wavelet-based method to enhance the detection and estimation for sounding the presence of atmosphere-wave-induced TIDs using measurements collected from GNSS networks near the epicenter of the March 11, 2011 Tohoku-Oki earthquake and the resulting tsunami. A comparison of GNSS/GPS based observations, global ionosphere-thermosphere model (GITM simulations, JPL's tsunami model simulations and ground motions are analyzed to understand the atmosphere-ionosphere responses in this tsunami/earthquake event. Through use of the wavelet coherence analysis, we are able to identify major wave trains presented in the data collected from networks (such as Japan GEONET and U.S. PBO), with different dominant frequency bands and characteristics. They are mostly due to the infrasound signals and gravity waves originated from the epicenter, Rayleigh waves from the ground surface motions, and tsunami propagation.

It is now widely recognized that appropriate measures are required for digital preservation to ensure that digital data can be accessed and used currently and in the future. Among all the risks of digital preservation, format obsolescence is one of the most important. There have been several projects or initiatives dealing with the measurement method of format obsolescence risk, but there has been no mechanism to quantify the preservation risk or durability of digital formats based on a self-improving assessment model, executed with the aid of computers. This paper deals with a methodology for measuring the preservation durability of digital formats, especially for their risk assessment. This method is based on a quantitative assessment model for format risk, and can shift the non-quantifiable knowledge or experiences of field experts to a machine identifiable and processible form, or ‘risk scores’. Results can be recognized and communicated by computers automatically and formally, which can assist in the automatic/semi-automatic risk management for digital preservation, sharing this quantified knowledge among communities. Because technologies are changing quickly, the quantitative assessment model for risks will not be a status quo situation. Thus, also presented is a method to fine tune the quantitative assessment model for risk of formats through a self-learning and self-improving style.