Constraint-based Diversification of JOP Gadgets

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks, such as Jump-Oriented Programming (JOP) attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality, such as the code size or speed, of the generated code variants. This paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. For larger problems, we propose a combination of LNS with a structural decomposition of the problem. To further improve the diversification efficiency of DivCon against JOP attacks, we propose an application-specific distance measure tailored to the characteristics of JOP attacks. We evaluate DivCon with 20 functions from a popular benchmark suite for embedded systems. These experiments show that DivCon's combination of LNS and our application-specific distance measure generates binary programs that are highly resilient against JOP attacks (they share between 0.15% to 8% of JOP gadgets) with an optimality gap of 10%. Our results confirm that there is a trade-off between the quality of each assembly code version and the diversity of the entire pool of versions. In particular, the experiments show that DivCon is able to generate binary programs that share a very small number of gadgets, while delivering near-optimal code. For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extends the scope of compiler-based diversification to performance-critical and resource-constrained applications.

Investigating cluster validation metrics for optimal number of clusters determination

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term “optimal” is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms.

Study of dynamic and ecological properties of automotive bi-fuel engine

Gaseous fuels are increasingly used to power internal combustion engines. Spark-ignition engines are fuelled with liquefied petroleum gas. Engines powered by gaseous fuels are characterized by good ecological properties due to the emission of pollutants. The paper presents the results of empirical tests of two passenger cars with spark-ignition engines powered alternatively: with gasoline and LPG fuel. The engines were equipped with fifth generation LPG fuelling systems. The tests were performed on a chassis dynamometer in tests used in approval procedures in Europe (NEDC test) and in the United States of America (FTP-75 test). These tests were the basis for determining the average specific distance emission of pollutants (carbon monoxide, hydrocarbons, nitrogen oxides and carbon dioxide) during the tests. The engines were also tested in the conditions of the external speed characteristics while accelerating the car in third gear. It was found that the type of fuelling the engines with both fuels has little influence on the dynamic properties of the engine due to the effective power. The tests clearly showed a decrease in specific distance emission of carbon monoxide and carbon dioxide. The relative reduction in specific distance emission of carbon monoxide was in the order of (45–65)%, and carbon dioxide—about 10%. For hydrocarbons, there was an increase in specific distance emission of hydrocarbons for the fuelling of engines with LPG, while for hydrocarbons, there was a large difference in the value of the relative specific distance emission difference for both tests. (The relative difference was from 25 to 175%.) Specific distance emission of nitrogen oxides turned out to be significantly higher when running engines with LPG. The reason for this is leaning of the fuel mixture at high rotational speed during acceleration of the car, which may result from insufficient conversion efficiency of engine control algorithms in the LPG fuel mode.

Design and Development of Ternary-Based Anomaly Detection in Semantic Graphs Using Metaheuristic Algorithm

At present, the field of homeland security faces many obstacles while determining abnormal or suspicious entities within the huge set of data. Several approaches have been adopted from social network analysis and data mining; however, it is challenging to identify the objective of abnormal instances within the huge complicated semantic graphs. The abnormal node is the one that takes an individual or abnormal semantic in the network. Hence, for defining this notion, a graph structure is implemented for generating the semantic profile of each node by numerous kinds of nodes and links that are associated to the node in a specific distance via edges. Once the graph structure is framed, the ternary list is formed on the basis of its adjacent nodes. The abnormalities in the nodes are detected by introducing a new optimization concept referred to as biogeography optimization with fitness sorted update (BO-FBU), which is the extended version of the standard biogeography optimization algorithm (BBO). The abnormal behavior in the network is identified by the similarities among the derived rule features. Further, the performance of the proposed model is compared to the other classical models in terms of certain performance measures. These techniques will be useful to detect digital crime and forensics.

Contribution of Segments to Overall Result in Elite Triathletes: Sprint Distance

As an alternative to analysing the contribution of performance in specific segments of a triathlon to the overall result as measured in terms of time or position, which has several limitations, previous studies have instead analysed the performance indicator in triathlon. Therefore, the purpose of the study was to analyse the relationship between performance in specific segments and overall performance in terms of sprint distance in elite triathletes through the triathlon performance indicator, instead of using time or position. The official sprint distance results from World Triathlon Series elite events from 2012 to 2019 were examined. In total, 2144 entries were considered, 1143 of which were men and 1001 were women. Performance in the cycling segment presents the best concordance with the overall performance for both elite men (ICCa = 0.871, IC95% = (0.711–0.927)) and elite women (ICCa = 0.907, IC95% = (0.875–0.929)). Although the performance in the running segment does not show the best concordance with the overall performance, the position in this segment does better explain the overall position, especially in elite men and in draft-legal races. These results can support coaches and athletes to identify a specific profile of the strengths and weaknesses of triathletes in competitions, in comparison to their rivals, over a specific distance.

ESTIMATION OF APPLICATION EFFICIENCY OF THE PR30-HVS LEVELER IN DIFFERENT MEASUREMENT CONDITIONS

The article is devoted to comparative analysis of accuracy of horizontal positioning measurement results for different measurement conditions. Performing work of this type refers to a number of applied tasks that require high accuracy of the horizontal arrangement of objects. For the purposes under consideration, laser levelers are often used. Currently, the market offers many models of this type of devices. As a rule, the documents accompanying provide metrological characteristics with an indication of one specific distance, for which the result indicated and, in most cases, the influence of measurement conditions on the error of results obtained is not considered. However, for long distances or difficult operating conditions (rain, dust), a significant increase in the error should be expected due to peculiarities of laser radiation and its transportation in the environment.

Concealed Wireless Warning Sensor Based on Triboelectrification and Human-Plant Interactive Induction

With the continuous development of artificial intelligence, the demand for sensors with simple preparation and strong concealment continues to increase. However, most of the high-sensitivity sensors have complex manufacturing methods, high costs, and single functions. In this paper, a sensitive motion sensor based on the triboelectric interaction between a living plant and the human body was designed to detect the real-time movements of human beings and provide danger warning. A certain relationship exists between the triboelectric signal and the distance between the plant and the human body, with effective signals being detected in the range of 1.8 m. In addition, the triboelectric signal generated by each person is unique like a fingerprint, which can be used for biometrics. On the basis of the triboelectric signal, a wireless character entry warning system is designed. This sensor can not only send out a wireless warning signal at a specific distance but also allow one to receive the warning information synchronously on a mobile phone in real time. The wireless movement sensor receives signals through a living plant, and it has the characteristics of convenient use, strong concealment, and shielding difficulty. This sensor has the potential to be widely used in person recognition, danger warning, and motion monitoring.

Temperature Estimation Adaptive to Variables over Distance Using Infrared–LiDAR

Measuring accurate surface temperature using a long-wave infrared camera and a non-contact thermometer, is very difficult due to variables such as atmospheric transmittance, emissivity, and influences from the environment such as atmosphere, sun, and dust. Conventional approaches use geometric correction or atmospheric transmittance modeling for temperature correction. However, these approaches have limitations in finding an accurate temperature because it is difficult to fully model a physical phenomenon. In this paper, a new temperature estimation method using distance information of LiDAR and digital count of long-wave infrared camera is proposed. The proposed method estimates the temperature by redefining the mapping function between radiation and digital count by distance. Using the proposed method, if the digital count is measured at a specific distance, accurate temperature can be estimated through the redefined Radiation-Digital count mapping function at a specific distance. The most important property of proposed method is that complex physical modeling is complemented by mapping function of specific distances. In addition, digital counts that change according to the distance at the same temperature required for the mapping function are obtained through linear interpolation using digital count of specific distances. Experimental results using a blackbody, long-wave infrared camera and LiDAR verify that the proposed method estimates the precise temperature. In addition, through experiments on humans, it shows the possibility of accurate body temperature measurement through fusion of long-wave infrared cameras and LiDAR in the future. However, as a limitation, a new calibration is required when the temperature and humidity of the atmosphere change.

Being Together at a Distance, Talking and Avoiding Talk: Making Sense of the Present in Victory Square, Tianjin

This paper explores a “public gathering” which took place every evening from 1991 to 2017 in Victory Square (Shengli guangchang 胜利广场), a public square in Tianjin. The essay opens with an analysis of the type of publicness that stems from the way participants “do things together.” It then describes how a specific public realm appears through the way participants “talk together.” It finally suggests that even if they are overrun with doubt, indeterminacy and anxiety, or embedded in a specific distance-based sociality, the conversations on Victory Square are not a minor, secondary activity. On the contrary, they take place on a common stage where participants interact with one another, reveal themselves as unique individuals and discuss their everyday affairs and common practices. Grasped as an “intermediary public sphere,” this type of gathering engenders and reinforces not only shared meanings and evaluations but also practical knowledge whose validity goes beyond this situated gathering.

Potential Analysis of E-Scooters for Commuting Paths

The mobility needs of society are constantly increasing, resulting in congested urban areas. New mobility concepts such as e-scooters can help to reduce traffic. In particular, commuting paths, which generally remain within a specific distance, are short and manageable via an intermodal travel chain. In combination with public transport, commuting paths could be beneficial. To evaluate the potential of e-scooters used with commuting paths, a literature research focusing on mobility behavior and characteristics was conducted. In addition, an end-user survey was used to identify the ecological and economical potential for typical work routes. The research results indicate that both the mobility preferences of the users, e.g., acceptance of intermodal travel, and the technical specification of e-scooters, e.g., speed and range, meet the needs of commuting. The assessment of typical work routes shows that the use of e-scooters for the first and last mile, in combination with public transport, is highly beneficial. Furthermore, e-scooters have the potential to provide individual advantages in the areas of travel time and costs. From an ecological perspective, CO2-equivalent emissions may also be reduced for some users depending on the substituted modes.