Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Tectonic Boundaries in Tikrit-Kirkuk Area Using Potential Data, North-Central Iraq

The gravity and magnetic data of Tikrit-Kirkuk area in central Iraq were considered to study the tectonic situation. The residual anomalies were separated from regional using space windows method with space of about 24, 12 and 10km to delineate the source level of the residual anomalies. The Total Horizontal Derivative (THD) is used to identify the fault trends in the basement and sedimentary rocks depending upon gravity and magnetic data. The identified faults in the study area show NW-SE, less common NE-SW and rare N-S trends. Some of these faults extending from the basement to the upper most layer of the sedimentary rocks. It was found that the depth of some gravity and magnetic source ranges between 1213Km, which confirm the basement depth that obtained by C.G. G. 1974. While the depth of shallow gravity source equals about 4Km, this depth may be lay with sedimentary cover in Triassic age. A comparison of the residual gravity and magnetic anomalies of the relatively shallow depths showed a general coincidence. The obtained tectonic boundaries divided the study area to many blocks. In general the tectonic boundaries control the river running in the area, at the same time the longitudinal faults control the main oil fields in the study area.
Nowhere to Leak: Forward and Backward Private Symmetric Searchable Encryption in the Multi-Client Setting (Extended Version)

Symmetric Searchable Encryption (SSE) allows users to out-source encrypted data to a possibly untrusted remote location while simultaneously being able to perform keyword search directly through the stored ciphertexts. An ideal SSE scheme should reveal no information about the content of the encrypted information nor about the searched keywords and their mapping to the stored (cid:28)les. However, most of the existing SSE schemes fail to ful(cid:28)l this property since in every search query, some information potentially valuable to a malicious adversary is leaked. The leakage becomes even bigger if the underlying SSE scheme is dynamic. In this paper, we minimize the leaked information by proposing a forward and backward private SSE scheme in a multi-client setting. Our construction achieves optimal search and update costs. In contrast to many recent works, each search query only requires one round of interaction between a user and the cloud service provider. In order to guarantee the security and privacy of the scheme and support the multi-client model (i.e. synchronization between users), we exploit the functionality o(cid:27)ered by AMD’s Secure Encrypted Virtualization (SEV).
There's No Place Like Home: How Residential Attributes Affect Family Functioning

There’s No Place Like Home: How Residential Attributes Affect Family Functioning Carly M. Thornock School of Family Life, BYU Master of Science The purpose of this study was to examine the ways in which actual (e.g., density and openness) and perceptual (e.g., crowding and distance) elements of the spatial home environment act as predictors of family functioning. Data were gathered from 126 families whose child was attending a university’s preschool/kindergarten facility in a mid-sized community in the Western United States. Structural equation modeling (SEM, AMOS 19.0) was employed to examine the strength of the relationships within the model. Results showed that though actual home items (specifically density and great room openness) affect family functioning outcomes, perceived crowding was especially influential as a mediational variable. Findings suggest that how one perceives his or her home environment has more of an effect on family functioning than actual home characteristics.
Much ado about nothing? Perceptions of the Air Berlin bankruptcy on airline competition in Germany, Austria and Switzerland

In Europe’s airline industry, the market exit of one of its largest industry players, the Air Berlin Group, has led to significant changes in the level of competition within the DACH-region (Germany, Austria and Switzerland). Existing research, however, is limited to provide insights into the shortand long-term im-plications of such market changes on the level of competition, particularly on price and quality. In response, this paper investigates how the bankruptcy of Air Berlin influences the level of price and quality airlines are offering to its passengers. Using a qualitative approach, airline-related professional and experts have been interviewed about their perception of the levels of quality, price and competition. The findings show that the Air Berlin bankruptcy resulted in a shortage of capacity on the market leading to increasing prices and short-term competition is likely to increase as various airlines try to replace Air Berlin on the market. However, in the long term, prices for airline tickets are expected to bounce back to a similar level like before the Air Berlin bankruptcy, as more competitors will penetrate the market, which leads to a more challenging market environment. In terms of quality, it was found that other Low-Cost Carriers (LCCs) offered similar products compared to Air Berlin, thus no or only minor changes in quality are expected.
Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration

Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density function. In this letter, a decentralized ergodic multi-agent trajectory planning algorithm featuring limited communication constraints is proposed. The agents’ trajectories are designed by optimizing a weighted cost encompassing ergodicity, control energy and close-distance operation objectives. To solve the underlying optimal control problem, a second-order descent iterative method coupled with a projection operator in the form of an optimal feedback controller is used. Exhaustive numerical analyses show that the multi-agent solution allows a much more efficient exploration in terms of completion task time and control energy distribution by leveraging collaboration among agents.
Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System

Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments earlier, an important step in improving sepsis outcomes. Increasing use of such systems means quantifying and understanding provider adoption is critical. Using real-time provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System) deployed at five hospitals over a two-year period (469,419 screened patient encounters, 9,805 (2.1%) of which were retrospectively identified as having sepsis), we found high adoption rates (89% of alerts were evaluated by a physician or advanced practice provider) and an association between use of the tool and earlier treatment of sepsis patients (1.85 (95% CI: 1.66 - 2.00) hour reduction in median time to first antibiotics order). Further, we found that provider-related factors had the strongest association with alert adoption and that case complexity and atypical presentation were associated with dismissal of alerts on sepsis patients. Beyond improving the performance of the system, efforts to improve adoption should focus on provider knowledge, experience, and perceptions of the system.
Clinical Evaluation of 6-Month Efficacy of 40% in-Office Whitening Treatment

Objective: To assess the 6-month efficacy of natural tooth color change in in-office bleaching treatment in terms of time by using a spectrophotometer. Material and Methods: A total of 20 participants were chairside treated with 40% hydrogen peroxide (HP), three applications each 20 minutes, in one appointment. Instrumental color measurement was performed on six anterior maxillary teeth before bleaching (baseline—t0), immediately after in-office bleaching and rehydration of the teeth (t1), 3 months (t2), and 6 months after bleaching treatment (t3). The spectrophotometer measured the tooth shades based on the CIE L*a*b* color notation system and Bleach index during the period of observation. CIE L*a*b* (ΔEab) color differences were calculated. Results: The color change at t1 was ΔEab = 3.2, at t2 was ΔEab = 1.8, at t3 was ΔEab = 1.2 and overall color change of in-office method was ΔEab = 3.6 (p<0.05). A significant effect for the mean CIEL*a*b* values was detected as within time b* values decreased significantly (p<0.05). Bleach index values significantly decreased during the time of observation, too (p<0.05). Conclusion: The in-office bleaching treatment using 40% hydrogen peroxide was effective, and the results showed a statistically significant decrease in color change during the period of 6-month observation.
The Political Visual Literacy App: Real-Time Symbol Recognition for Field Reporting

The Political Visual Literacy (PVL) app is a graphical-recognition system designed to support and inform the work of journal-ists as they encounter unfamiliar and evolving graphical imagery in the field. Using a combination of computer vision machine-learning techniques and user feedback, the mobile application allows users to select symbols within photographs on their mobile device and receive information about the meaning of those symbols in real-time. A web-based version of the tool allows newsroom editors and others the use the system on desktop devices. The goal is to provide journalists with more information about rapidly-evolving political symbols in a way that can be seamlessly integrated into their existing workflows.
Air Quality in the Italian Northwestern Alps during Year 2020: Assessment of the COVID-19 «Lockdown Effect» from Multi-Technique Observations and Models

The effect of COVID-19 confinement regulations on air quality in the northwestern Alps is here assessed based on measurements at five valley sites in different environmental contexts. Surface concentrations of nitrogen oxides, ozone, particle matter, together with size, chemical, and optical (light absorption) aerosol properties, complemented by observations along the vertical column are considered. The 2020 concentration anomalies relative to previous years’ average are compared with the output of a machine learning algorithm accounting for weather effects and a chemical transport model, their difference being within 10–20 %. Even in the relatively pristine environment of the Alps, the «lockdown effect» is well discernible, both in the early confinement phase and in late 2020, especially in NOx concentrations (NO decreasing by >80 % and NO2 by >50 %). While ozone shows little variation, secondary aerosols increase due to enhanced transport from the neighbouring Po basin and coarse particles decrease due to missing resuspension by traffic and, in the city, to the shutdown of a steel mill. The NO2 vertical column density decreases by >20 %, whereas the aerosol profile is mainly influenced by large-scale dynamics, except a shallow layer about 500 m thick possibly sensitive to curtailed surface emissions.
On Evidence Capture for Accountable AI Systems (cid:63)

This research explores evidence capture for accountable AI systems. First, diﬀerent scopes of AI accountability are set out by extending existing classiﬁcation. Based on these scopes, two important and fundamental questions in evidence capture are answered: what types of evidence need to be captured and how we can capture them to facilitate better AI accountability. We hope that this research can provide guidance on building better accountable AI systems with eﬀective evidence capture and initiate further research along this line
Using Blockchain for Tamper-Proof Broadcast Protocols

We present the tamper-resistant broadcast abstraction of the Bitcoin blockchain, and show how it can be used to implement tamper-resistant replicated state machines. The tamper-resistant broadcast abstraction provides functionality to: broadcast, deliver, and verify messages. The tamper-resistant property ensures: 1) the probabilistic protection against byzantine behaviour, and 2) the probabilistic verifiability that no tampering has occurred. In this work, we study various tamper-resistant broadcast protocols for: different environmental models (public/permissioned, bounded/unbounded, byzantine fault tolerant (BFT)/non-BFT, native/non-native); as well as different properties, such as ordering guarantees (FIFO-order, causal-order, total-order), and delivery guarantees (validity, agreement, uniform). This way, we can match the protocol to the required environment model and consistency model of the replicated state machine. We implemented the tamper-resistant broadcast abstraction as a proof of concept. The results show that the implemented tamper-resistant broadcast protocols can compete on throughput and latency with other state-of-the-art broadcast technologies. Use cases, such as a tamper-resistant file system, supply chain tracking, and a timestamp server highlight the expressiveness of the abstraction. In conclusion, the tamper-resistant broadcast protocols provide a powerful interface, with clear semantics and tunable settings, enabling the design of tamper-resistant applications.
A new data augmentation method of remote sensing dataset based on Class Activation Map

Remote sensing image scene classification is a significant direction in the field of remote sensing research. The method based on deep learning has become the most popular method in recent years because it can realize the automatic feature extraction and classification of remote sensing images. The deep learning requires a large number of samples for training and consumes large computing resources, and the data augmentation can alleviate the problem of insufficient samples. The image manipulation is one of the most commonly methods, but it may cause the loss of key information in the image. In this paper, we proposed an improved supervised data augmentation method based on Class Activation Map (CAM) and image manipulation, and then used this method to augment the high-resolution remote sensing images of NWPU dataset. We utilized three CNNs networks to count the classification accuracy of the remote sensing images. The experimental results show that the proposed method increases the accuracy of scene classification by more than 0.4%. The CAM-based methods provide a new technical support for the scene classification of remote sensing images based on deep learning.
Fault Structure Interpretation Based on Deep Learning—Risk Assessment of Agricultural Geological Disaster

China is a large agricultural country, shouldering the heavy responsibility of agricultural production, facing great pressure and challenges under the influence of geological disasters. Frequent agricultural geological disasters have a serious impact on people’s agricultural production and life. The complex geological structure of agricultural production areas, especially the wide distribution and large number of regional fault structures, especially the lack of popularization of geological disaster knowledge among rural residents, is also an important cause of geological disasters. Fault interpretation is the core of complex structure interpretation. The efficiency and degree of interpretation directly affect the progress of agroecological hazard risk assessment.
Human pose estimation based on Improved High Resolution Network

Human pose estimation has become a hot problem in human-computer interaction and other intelligent application technologies, but the results obtained by previous deep learning networks are not very satisfactory when facing small-scale human instances. Therefore, in order to solve the problem of scale variation in human pose estimation, especially to pinpoint the keypoints of small-scale human instances, an improved High-Resolution Network (Improved HRNet) is proposed in this paper. The main improvement work in this paper is as follows: In this paper, a double attention mechanism is added in the forward transmission of the parallel sub-network, with the aim of assigning weights to the propagated information without changing the number of channels, assigning the information with high weights as useful information and reducing the interference caused by irrelevant information. In this paper, the network structure is validated using COCO dataset, and the average accuracy (Average Precision, AP) of Improved HRNet is 66.4, which is 2.3 higher than the average accuracy of High-Resolution Network (HRNet) with only 0.4% increase in parameters.
Energy efficiency of autonomous vehicles on hilly roads

Energy efficiency is traditionally among the main development trends in the automobile industry. We suspected that intelligent speed control systems can provide economic and ecological benefits for implementing civil autonomous driving technologies. We introduced the approach for autonomous driving, which is based on the analysis of the precise Digital Terrain Model (DTM) and optimal vehicle control actions with respect to a requested transport task, which can be a task of fuel or energy economy in this particular case. We addressed the research question of fuel economy estimation with respect to the reference parameter that is applicable to compare different control algorithms. We demonstrated the use of roadway-related factors of DTM to affect fuel consumption. We contributed the algorithm for saving fuel while driving on a hilly road and examined the influence of vehicle load on possible economy values. We compared fuel and energy-saving values of gasoline and electric vehicles based on the same chassis with and without use of the proposed algorithm. Practical results of tests on public roads are presented.
Few-shot Object Detection with Camouflage Animals

Object detection is a hot issue in the field of computer vision, which is widely used in intelligent video surveillance, medical image analysis, and practice in the fields of military strategy. Previous object detection is based on a lot of training data, and for some special target, it is difficult to find enough annotation data to training model, so few-shot object detection was born. In this paper, a data set of animals with protective colors is constructed(CAD), which contains ten categories of different objects with high quality annotations. We combine the existing attention RPN and Feature Reweighting module to train our net, the experimental results prove that the Camouflage animals in nature can be well identified, and our results obtain 1.9% AP50, 1.2% AP75 improvement compared with before work.
Sentiment analysis based on food e-commerce reviews

Customer feedback is of great significance for food manufacturers such as wine to improve product quality. With the increase of consumer users, user feedback collection and classification methods in the traditional food industry have become very time-consuming. Aiming at this problem, this paper proposes a method of embedding TD-IDF weighted Word2vec word vector into a bidirectional long and short-term memory network based on the attention mechanism.
Research on Integrated Navigation Algorithm Based on Radial Basis Function Neural Network

The Qinghai-Tibet Railway is located on the Qinghai-Tibet Plateau in China. Due to its harsh climatic conditions and geographic environment, the line currently uses the Global Position System (GPS) to achieve train positioning. However, the navigation satellites are easily blocked by obstructions such as tunnels, resulting in a decrease in positioning accuracy. The conventional solution is to use multiple sensors such as an inertial navigation system (INS) for data fusion. But in the INS/GPS integrated navigation system, when the GPS signal is lost, the navigation accuracy will still decrease. In order to solve this problem, this paper proposes a radial basis function (RBF) neural network-assisted integrated navigation filtering algorithm. When the two systems are working normally, the measured raw information is used to train the RBF neural network. When the GPS fails, the measured and calculated values of the inertial navigation system are input to the trained network model to obtain the predicted error value, which is corrected to the inertial navigation system to obtain the final navigation information. The MATLAB simulation results show that when the short-term GPS information is missing, the integrated navigation system assisted by the RBF neural network has better estimation accuracy.
Interest Contour Region Fusion Method based on Gaussian Mixture Model Segmentation

This paper presents an edge feature light-shadow fusion algorithm based on image segmentation. A new edge light shadow feature fusion algorithm based on image segmentation, morphological operation and image filtering is presented to extract target objects from image and fuse them with new background, which is cumbersome, difficult to operate, and lack of fast and effective target acquisition and feature fusion methods. The algorithm first labels the input image through an interactive system, then divides the labeled image to get the image target information, then processes the target object, obtains the edge of the target object, expands the edge to form a mask, combines the target object with the background, and filters through the edge mask to achieve the effect of fusing the target object with the background. The experimental results show that this method can process images with different complexity, not only to generate a combination of portrait and monochrome background, but also to generate a combination of other objects and natural scenery, so that the image can produce transitional natural effects.
Mobile Robot Full Ergodic Path Planning Algorithm for Power Equipment Fault Detection

In order to reduce the abnormal operation of mobile robot caused by collision, a full ergodic path planning algorithm for power equipment fault detection is proposed. Based on the grid method, the workspace of the robot is divided, and the mathematical model of path planning is established. The bb-pso algorithm is introduced to calculate the path. On this basis, the mutation operator is used as the optimization iteration power of the bb-pso algorithm. Therefore, the collision avoidance calculation is carried out to improve the feasibility of the path. The simulation results show that the distance between the path and the obstacles of the proposed algorithm is basically stable between 30-40 cm, and the fluctuation range of the path running time is only 3 min, the average time is 40 min, which has high feasibility.
Trajectory time series classification algorithm based on conv-olutional self-attention mechanism

As a kind of time series data in the field of transportation, trajectory has complex multi-dimensional information. How to fully excavate the multi-dimensional information of the trajectory and analyse its complex dynamic pattern is an important problem to be solved in the classification of trajectory time series. In response to the above problems, this paper improves the existing self-attention mechanism, and proposes a trajectory time series classification algorithm based on convolutional self-attention mechanism. According to the characteristics of the trajectory data, the convolutional self-attention mechanism first extracts the characteristics of the trajectory fragments in the trajectory to obtain the flight status characteristics, and then performs the self-attention operation on these characteristics, so as to achieve a better feature extraction effect. The experimental results show that the trajectory time series classification algorithm based on the convolutional self-attention mechanism has better feature extraction ability and classification accuracy than other existing algorithms.