Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

Abstract. Implicit structural modeling using sparse and unevenly distributed data is essential for various scientific and societal purposes, ranging from natural source exploration to geological hazard forecasts. Most advanced implicit approaches formulate structural modeling as least squares minimization or spatial interpolation, using various mathematical methods to solve for a scalar field that optimally fits all the inputs under an assumption of smooth regularization. However, these approaches may not reasonably represent complex geometries and relationships of structures and may fail to fit a global structural trend when the known data are too sparse or unevenly distributed. Additionally, solving a large system of mathematical equations with iterative optimization solvers could be computationally expensive in 3-D. To deal with these issues, we propose an efficient deep learning method using a convolution neural network to create a full structural model from the sparse interpretations of stratigraphic interfaces and faults. The network is beneficial for the flexible incorporation of geological empirical knowledge when trained by numerous synthetic models with realistic structures that are automatically generated from a data simulation workflow. It also presents an impressive characteristic of integrating various types of geological constraints by optimally minimizing a hybrid loss function in training, thus opening new opportunities for further improving the structural modeling performance. Moreover, the deep neural network, after training, is highly efficient for the generation of structural models in many geological applications. The capacity of our approach for modeling complexly deformed structures is demonstrated by using both synthetic and field datasets in which the produced models can be geologically reasonable and structurally consistent with the inputs.
Detection of DDoS attack in SDN environment using KNN algorithm

- Software-Defined Networking (SDN) is a network architecture which is an optimization of traditional networks. SDN is used for programmatic management which is ideal for high-bandwidth applications. The centralized control being a primary benefit can also be a risk . If the unauthorized user succeeds in gaining access to the controller, he will have complete control of the system. The controller is extremely vulnerable to the Distributed Denial of Service (DDoS) assaults. SDN differs from traditional networks by decoupling the network control and forwarding functions. The control plane and data plane deals with network control and forwarding function respectively. SDN identifies malicious traffic and link failure present in the network. Apart from this flexibility, it is vulnerable to Distributed Denial of Service (DDoS) attacks which halts the whole network. To mitigate this problem, we propose to classify the traffic containing DDoS attacks by using machine learning techniques such as the KNN algorithm. In this project, we enter the features of DDoS attacks into a CSV file and train the algorithm. We use KNN for the classification process and hence we can able to detect the DDoS attack defined normal traffic from DDOS attack traffic using the machine learning technique. the novel features for DDoS attack detections. New features are saved in the CSV file to create the dataset and machine learning algorithms are trained on the created SDN dataset. Several previous works on DDoS detection have either used a non-SDN dataset or the research data has not been made public. Software Defined Networking (SDN) is the networking architecture defined by the software program. In SDN, the network traffic is managed by software, which leads the traffic between hosts. machine learning In proposed data, of attacks without discriminating the of traffic. sensitivity, the charge of the feature selection methods selected and the selected subset of features trained using classifiers. number of features selected by the algorithm itself or by the threshold value assigned to the threshold value, numbers of features are often selected and trained classifier. put into effect the proposed compare their and cope with the trade-off among the and capability of the safety The effects assault over 96. A light-weight and fast machine-learning algorithm back In order is They also propose a method to optimize this value based on system performance and traffic inputs. Experimental results from the test bed show that 97% of attack flows are detected and dropped. As for future research, refined methods for DDoS mitigation are being developed based on new data sets collected from ISPs to improve accuracy and the performance of the system.
The analysis of slip tendency of major tectonic faults in Germany

Abstract. Seismic hazard during subsurface operations is often related to the reactivation of pre-existing tectonic faults. The analysis of the slip tendency, i.e., the ratio of shear to normal stress acting on the fault plane, allows an assessment of the reactivation potential of faults. We use the total stresses that result from a large-scale 3D geomechanical–numerical model of Germany and adjacent areas to calculate the slip tendency for three 3D fault geometry sets with increasing complexity. This allows us to draw general conclusions about the influence of the fault geometry on the reactivation potential. In general, the fault reactivation potential is higher in Germany for faults that strike NW–SE and NNE–SSW. Due to the prevailing normal stress regime in the geomechanical–numerical model results, faults dipping at an angle of about 60∘ generally show higher slip tendencies in comparison to steeper or shallower dipping faults. Faults implemented with a straight geometry show higher slip tendencies than those represented with a more complex, uneven geometry. Pore pressure has been assumed to be hydrostatic and has been shown to have a major influence on the calculated slip tendencies. Compared to slip tendency values calculated without pore pressure, the consideration of pore pressure leads to an increase in slip tendency of up to 50 %. The qualitative comparison of the slip tendency with the occurrence of seismic events with moment magnitudes Mw>3.5 shows areas with an overall good spatial correlation between elevated slip tendencies and seismic activity but also highlights areas where more detailed and diverse fault sets would be beneficial.
C AUSAL -TGAN: M ODELING T ABULAR D ATA U SING C AUSALLY -A WARE GAN

Generative adversarial net (GAN)-based tabular data generation has recently received significant attention for its power for data augmentation when available data is limited. Most prior works have applied generic GAN frameworks for tabular data generation without explicitly considering inter-variable relationships, which is important for modeling tabular data distribution. In this work, we design Causal-TGAN, a causally-aware generator architecture that can capture the relationships among variables (continuous-type, discrete-type, and mixed-type) by explicitly modeling the pre-defined inter-variable causal relationships. The flexibility of Causal-TGAN is its capability to support different degrees of subject matter expert domain knowledge (e.g., complete or partial) about the inter-variable causal relations. Extensive experimental results on both simulated and real-world datasets demonstrate that exploiting causal relations in deep generative models could improve the generated tabular data quality compared to the state-of-the-art. Code is available at https://github.com/BiggyBing/Causal-TGAN-Public.
Design of compact filtering rat-race hybrid with λ/2-resonators

A novel compact filtering rat-race hybrid coupler with improved upper stopband is presented in this letter. Four coupled-resonator pairs are utilized to replace the λ/4- and 3λ/4-transmission lines in the conventional hybrid. Each coupled-resonator pair exhibits a bandpass filtering response and acts as a K-inverter. Eight resistors are placed at the center of the λ/2-resonators to suppress the 2nd-harmonic. For verification, a filtering rat-race hybrid is implemented with a center frequency of 1.27 GHz and 20-dB rejection band of 2.73 f0. Good in-phase and out-of-phase characteristics are obtained. The proposed device has a simple circuit topology, compact size and good performance.
IOT BASED SOCIAL DISTANCE MONITORING

- Social distancing plays a vital role in preventing the spread of viral diseases such as COVID-19. In this work, IoT based social distance monitoring system which can be integrated with Passive Infrared sensor (PIR sensor) is proposed. This smart device is handy for maintaining social distancing and thereby the public safety. This device will be useful for law enforcing agencies, authorities or managers of public premises where public crowds. It will also be useful for individuals in order to alert them if someone is close around. Use of Wi-Fi eradicates the need of wired connection and thus improves the utility of the system. In this case the Wi-Fi module (ESP 8266), acts as a gateway to connect the sensors to the Blynk server and those application can be monitored remotely through the Blynk mobile app. The method is reasonably accurate and can be very useful in maintaining social distance.
"It Feels Like Whack-a-mole": User Experiences of Data Removal from People Search Websites

People Search Websites aggregate and publicize users’ Personal Identifiable Information (PII), previously sourced from data brokers. This paper presents a qualitative study of the perceptions and experiences of 18 participants who sought information removal by hiring a removal service or requesting removal from the sites. The users we interviewed were highly motivated and had sophisticated risk perceptions. We found that they encountered obstacles during the removal process, resulting in a high cost of removal, whether they requested it themselves or hired a service. Participants perceived that the successful monetization of users PII motivates data aggregators to make the removal more difficult. Overall, self management of privacy by attempting to keep information off the internet is difficult and its’ success is hard to evaluate. We provide recommendations to users, third parties, removal services and researchers aiming to improve the removal process.
An Improved Algorithm for Simulating Surface Flow Dynamics based on the Flow-Path Network Model

This paper proposes an improved algorithm for simulating the surface flow dynamics based on the flow-path 10 network model. This algorithm utilizes the parallel-multi-point method to extract the critical points and the D8 algorithm to retrieve the drainage networks from the regular-grid digital elevation model (DEM) for constructing a drainage-constrained triangulated irregular network (TIN). Then, it combines the flow directions of triangular facets over TIN with resampled flow source points to track flow lines to generate the flow path network (FPN) based on the flow-path network model. On this basis, the proposed algorithm employs three terrain parameters (slope length factor, topographic wetness index and flow 15 path curvature) to improve the classical Manning equation based on the analytic hierarchy process (AHP) to enhance the accuracy of the flow velocity calculation. The topographic wetness index and flow path curvature are derived by the flow-path-network-triangular-facet-network (FPN_TFN) algorithm, a new flow-path-network-topographic-wetness-index (FPN_TWI) algorithm and the flow-path-network-flow-path-curvature (FPN_C) algorithm, respectively. Finally, the velocity estimation function and surface flow discharge simulation function are parallelized by the Compute Unified Device 20 Architecture (CUDA) to enhance its computational efficiency. The outcomes are compared with the algorithm before improvement (TIN_based algorithm) and the SWAT model. The results demonstrate that the speedup ratio reaches 15.7 compared to the TIN_based algorithm. The Nash coefficient increases by 6.49%, the correlation coefficient decreases slightly, and the balance coefficient increases by 19.08%. Compared with the SWAT model, the Nash coefficient and correlation coefficient increase by 97.56% and 4.60%, respectively. The balance coefficient is close to 1 and outperforms the 25 compared algorithms. function on the flow lines over the FPN. Combined with the preset travel time, the movement distance was predicted, and the surface flow discharge was simulated at any location to complete the dynamic simulation of surface water at any time. The proposed algorithm improved the Manning formula to enhance the accuracy and used CUDA to advance the real-time response efficiency. To verify the accuracy and computing efficiency of the improved algorithm, the simulation results and calculation time were compared with the TIN_based algorithm under different thresholds of retrieving the critical points and different scales of flow source points. In addition, the simulation results of the proposed algorithm were compared with those of the SWAT model at a flow source point scale of 30 m to further verify its accuracy. The experimental results show that, compared with the TIN_based algorithm, the Nash coefficient increases by 6.49%, the correlation coefficient decreases slightly and the
Making operating systems resilient to hardware

Device drivers are the the operating system’s interface to the outside world. Drivers have been a constant source of unreliability and security problems in modern operating systems due to (1) driver development process comprising of thousands of developers and (2) driver architecture which gives drivers privileged access to system resources to provide maximum functionality. Both these factors have led to signiﬁcant reliability research on drivers. This research has primarily looked at the driver-kernel interface and has applied coarse grained isolation techniques over device drivers. In my thesis proposal, I seek to improve the state of modern device drivers against the backdrop of reliability through three di ﬀ erent techniques. First, I improve the tolerance of modern drivers against unreliable hardware and make drivers more robust by automatically patching driver code. Second, I will study the current isolation and recovery techniques and wish to demonstrate the e ﬀ ectiveness of using a ﬁner grained driver isolation and low overhead recovery solutions. And ﬁnally, I will study the functional part of driver code, its workings, form and its abstraction architecture and propose some guidelines on how drivers can improve by organizing themselves better.
The composite development and structure of intense synoptic-scale Arctic cyclones

Abstract. Understanding the location and intensity of hazardous weather across the Arctic is important for assessing risks to infrastructure, shipping, and coastal communities. Key hazards driving these risks are extreme near-surface winds, high ocean waves, and heavy precipitation, which are dependent on the structure and development of intense synoptic-scale cyclones. This study aims to describe the typical lifetime, structure, and development of a large sample of past intense winter (DJF) and summer (JJA) synoptic-scale Arctic cyclones using a storm compositing methodology applied to the ERA5 reanalysis. Results show that the composite development and structure of intense summer Arctic cyclones are different from those of intense winter Arctic and North Atlantic Ocean extra-tropical cyclones and from those described in conceptual models of extra-tropical and Arctic cyclones. The composite structure of intense summer Arctic cyclones shows that they typically undergo a structural transition around the time of maximum intensity from having a baroclinic structure to an axi-symmetric cold-core structure throughout the troposphere, with a low-lying tropopause and large positive temperature anomaly in the lower stratosphere. Summer Arctic cyclones are also found to have longer lifetimes than winter Arctic and North Atlantic Ocean extra-tropical cyclones, potentially causing prolonged hazardous and disruptive weather conditions in the Arctic.
Zero-shot Active Learning with Topological Clustering for Multiclass Classiﬁcation

We present a novel approach for zero-shot active learning for multi-class classiﬁca-1 tion based on a clustering technique, called ToMATo , which is guided by topological 2 persistence. Our objective is to identify effective regions in the feature space for 3 label querying. The labeling of examples in these regions will allow the training of 4 efﬁcient multi-class classiﬁcation prediction functions. We have adapted ToMATo 5 with a density aware δ -Rips graph in order to obtain homogeneous simplicial trees. 6 From these trees, informative simplices are identiﬁed with respect to the annotation 7 effort, or the budget. Representative examples from each of them are labeled 8 by an oracle and these labels are then propagated through the trees. We adapt 9 ToMATo by computing our persistence diagram (PD) from a δ -Rips graph that is 10 estimated using a k -nearest neighbor distance matrix This allows the application 11 of the method to large scale scenarios. From this perspective we also propose a 12 local density estimator from the same distance matrix. Comparisons on different 13 benchmarks show that the proposed approach greatly improves performance with 14 respect to a random querying strategy for label assignment that has been found 15 outperforming state-of-the art approaches in previous works. 16
QUALITY MONITORING FOR FUSED FILAMENT FABRICATION PRODUCT: A REVIEW

-Additive Manufacturing (AM) is the process of creating products from three-dimensional (3D) model data by combining materials and building them up, frequently layer by layer. Material efficiency, resource efficiency, production flexibility, and part flexibility are four main environmental advantages of AM over traditional or subtractive manufacturing. There are a lot of different ways to manufacture products with additive manufacturing now but, the focus of this review is on Fused Filament Fabrication (FFF). FFF has a low production cost and the ability to construct complex geometries and shapes. However, this 3D printer needs to be manually stopped by the user if any of the defects or abnormalities are detected. Therefore, many studies have been done on monitoring the 3D printer to spot any defects in the products. This paper summarizes the monitoring based on the defects that are categorized into (1) geometrical defect, (2) layer defect, and (3) surface defect. Based on the reviews, the common sensor used in the monitoring system has been concluded, and some of the limitations are listed.
Design of a Balun Bandpass Filter with Asymmetrical Coupled Microstrip Lines

Abstract A new microstrip coupled-line balun topology and its application to the balun bandpass filter (BPF) with a triple mode response are proposed in this paper. The involved balun structure is composed of two back-to-back quarter-wavelength (λ/4) asymmetrical coupled-line sections. Detailed design formulas based on the asymmetrical coupled-line theory are given to validate the feasibility of the balun. Besides, to obtain filtering performance simultaneously, the balun is then effectively integrated with a pair of triple mode resonators. To demonstrate the design concept of the balun BPF, a prototype operating at 2.4 GHz with the fractional bandwidth (FBW) of about 19.2 % is designed, fabricated, and measured. Results indicate that between the two balanced outputs, the amplitude imbalance is less than 0.3 dB and the phase difference is within 180°±5° inside the whole passband. Both simulated and experimental results are in good agreement.
Zero-Shot Neural Machine Translation with Self-Learning Cycle

Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource-rich conditions. However, evaluations using real-world low-resource languages still result in unsatisfactory performance. This work proposes a novel zero-shot NMT modeling approach that learns without the now-standard assumption of a pivot language sharing parallel data with the zero-shot source and target languages. Our approach is based on three stages: initialization from any pre-trained NMT model observing at least the target language, augmentation of source sides leveraging target monolingual data, and learning to optimize the initial model to the zero-shot pair, where the latter two constitute a self-learning cycle. Empirical ﬁndings involving four diverse (in terms of a language family, script and relatedness) zero-shot pairs show the effectiveness of our approach with up to + 5 . 93 BLEU improvement against a supervised bilingual baseline. Compared to unsupervised NMT, consistent improvements are observed even in a domain-mismatch setting, attesting to the usability of our method.
Fine-tuned Language Models can be Continual Learners

Recent work on large language models relies 001 on the intuition that most natural language pro-002 cessing tasks can be described via natural lan-003 guage instructions.Language models trained on 004 these instructions show strong zero-shot per-005 formance on several standard datasets. How-006 ever, these models even though impressive can 007 still perform poorly on a wide range of tasks 008 outside of their respective training and evalu-009 ation sets and/or can be prohibitively large.A 010 natural solution to address this limitation is 011 Continual Learning: a model that could keep 012 extending its knowledge and abilities, without 013 forgetting previous skills. In spite of the limited 014 success of Continual Learning we show that 015 fine-tuned language models can be continual 016 learners . Our resulting model Continual-T0 017 (CT0) is able to learn 8 different and diverse 018 tasks, while still achieving similar zero-shot 019 performance on T0 evaluation tasks.As an ad-020 ditional finding, we notice that CT0 can gen-021 eralize to instruction composition, being able 022 to combine instructions in ways it was never 023 trained for. 1 024
Web LOG File Information Analysis and Visualization Inside Organizations

In the context of this work, organizations can be understood as organic systems composed of people, processes and procedures. The intranets are incorporated in organizations to support their mission and guarantee effective process delivery within an efficient framework. These technological platforms, with a web driven user front-end, are able to log several user interaction details. The main goal of this paper is to report how the analysis and visualization of interaction patterns inside an organization’s internal web based system can be achieved, as mentioned by [Eick01] in a more general context and by [Healey01] in a specific e-commerce scenario. In order to validate the research on conceptual models, information architecture and several visualization meth-ods, a prototype is being developed, tested and permanently upgraded. At this stage of iterative development, the prototype can be considered as an experimental research platform [Nunes03], capable of integrating and testing specific visualization schemes [Mealha04] and visual correlation procedures. Besides this, the prototype incorporates features that can visually support answers for questions, such as: