Reviewerly/exHarmony huggingface dataset:
A comparative study on outlier detection techniques for noisy production data from unconventional shale reservoirs

Decline curve analyses (DCA) and rate transient analyses (RTA) are widely used to characterize the fluid flow through porous media and forecast future production. Oil and gas production data are routinely analyzed for history matching and optimizing the well stimulation methods in hydrocarbon exploration and production lifecycle. However, outliers add significant uncertainty and non-uniqueness to results from production data analysis. This study provides a structured and comprehensive overview of five widely used outlier detection (OD) techniques for identifying and removing outliers in production data. Each OD technique measures deviation differently and, therefore, has a different outcome even when applied to the same dataset, creating the need to test several methods and find the optimal technique for identifying and removing outliers from production data. First, we generated production data from a typical multi-fractured horizontal well using a numerical reservoir simulator and added random noise to the data. Then, we used five different OD techniques to identify the prelabeled outliers from the synthetic production data. Finally, we identified the best-performing OD algorithm by comparing the various evaluation metrics such as the mean absolute error (MAE), precision, sensitivity, and F1 score. Results showed that the angle-based OD (ABOD) had the best MAE, precision, sensitivity, and F1 score of 8%, 85%, 98%, and 0.90, respectively. The next best-performing OD technique was distance-based OD (DBOD), with MAE, precision, sensitivity, and F1 score of 16%, 71%, 100%, and 0.83, respectively. We tested the ABOD method on several field production datasets by assuming different outlier thresholds (fraction of the data points likely to be outliers). Visual inspection of the processed data showed that the ABOD method effectively identified and removed the outliers from a relatively clean dataset (outlier threshold of 20%) and a highly noisy dataset (outlier threshold of 80%). This algorithm is intuitive and can effectively identify and remove outliers from the field production data to improve production forecasting, reserves estimation, and rate transient analysis for multi-fractured horizontal oil and gas reservoirs.
CombiCSP: A python routine for dynamic modeling of concentrating solar power plants

CombiCSP is an open source software for dynamic modeling of concentrating solar energy power plants. CombiCSP utilizes solar resource, system engineering inputs as well as financial tools to provide dynamic simulations and annual yields of concentrating solar power plants. It readily provides modeling of plants based on solar power tower and parabolic trough collectors and it can be extended to novel solar energy modeling approaches and analyses as needed.
Fully Secure PSI via MPC-in-the-Head

We design several new protocols for private set intersection (PSI) with active security: one for the two party setting, and two protocols for the multi-party setting. In recent years, the state-of-the-art protocols for two party PSI have all been built from OT-extension. This has led to extremely efficient protocols that provide correct output to one party; seemingly inherent to the approach, however, is that there is no efficient way to relay the result to the other party with a provable correctness guarantee. Furthermore, there is no natural way to extend this line of works to more parties. We consider a new instantiation of an older approach. Using the MPC-in-the-head paradigm of Ishai et al. [IPS08], we construct a polynomial with roots that encode the intersection, without revealing the inputs. Our reliance on this paradigm allows us to base our protocol on passively secure Oblivious Linear Evaluation (OLE) (requiring 4 such amortized calls per input element). Unlike state-ofthe-art prior work, our protocols provide correct output to all parties. We have implemented our protocols, providing the first benchmarks for PSI that provides correct output to all parties. Additionally, we present a variant of our multi-party protocol that provides output only to a central server.
Recursive least mean dual p-power solution to the generalization of evolving fuzzy system under multiple noises

During the last two decades, evolving fuzzy systems (EFSs) have attracted more and more attention owing to their capacity to self-adapt both system structures and parameters dynamically. Conventional EFSs optimize the consequent parameters using the recursive least square (RLS) alg orithm based on mean square error (MSE) criterion. It only works well in Gaussian noise or noise-free situations. In this paper, we further propose an alternative approach to update consequent parameters based on the novel least mean dual p-power (LMDP) error criterion. By contrast with the MSE criterion, the LMDP criterion has a stronger noise rejection ability on non-Gaussian noise environments. Moreover, the LMDP criterion extends the range of p value in conventional LMP criterion from positive integer to floating point for more fine-grained optimum solutions. Details in this work, the recursive solution of LMDP criterion is respectively derived from global learning and local learning to update consequent parameter and the theoretical stability analyses for both learning cases are proven in that the stability of our considered EFS is guaranteed. The experiments on various benchmark datasets and a real-world typhoon path prediction validate that the proposed method is more accurate and owns better generalization performance under multiple noise conditions.
Clinical evaluation of super-resolution for brain MRI images based on generative adversarial networks

In magnetic resonance imaging (MRI), reducing long scan times is an urgent issue that could be addressed with super-resolution (SR) techniques. Most of the SR networks using deep neural networks (DNNs) have been evaluated only based on numeric metrics, and the image restoration quality for individual lesions is not evaluated sufficiently. Here, we evaluated the performances of different types of SR networks using DNNs for brain MRI, in terms of diagnostic performance and image quality. We focused on comparing the performance between generative adversarial networks (GANs) and non-GAN networks. There was a trade-off in such restoration quality between GAN- and non-GAN-based SRs, with the GANs being more accurate in restoring images of anatomical structures but less accurate in restoring those of lesions; non-GANs showed the opposite tendency. The non-GAN SRs were preferable in terms of diagnostic performance and image quality. This result suggested that the evaluation of DNN performance for lesions might be changed drastically by adding a clinical evaluation perspective. The dependence of network architecture on network performance obtained in this study will provide guidance for future development of SR DNN for medical images.
Handling negative samples problems in span-based nested named entity recognition

Named Entity Recognition (NER) is generally regarded as a sequence labeling task, and faces a serious problem when the named entities are nested. Span-based model, which enumerates all possible spans as potential entity mentions in a sentence and classifies them, is straightforward for nested NER but faces negative samples problems. In this paper, we propose a span-based nested NER model with BERT and try to solve the negative samples problems. In view of the phenomenon that there are too many negative samples in all spans, we employ a multi-task learning method, which divides NER task into entity identification and entity classification task. In addition, we propose the entity IoU loss function to focus our model on the hard negative samples. Our model is evaluated on three nested NER datasets: GENIA, ACE2004 and ACE2005, and the results show that our model outperforms other state-of-the-art models with the same pretrained language model, achieving 79.46%, 87.30% and 85.24% respectively in terms of F1 score.
Design of a modular neural network based on an improved soft subspace clustering algorithm

Being a commonly used way for task decomposition in modular neural network (MNN), clustering analysis is employed to decompose the complex task into several simple subtasks for learning. Recent studies mainly focus on hard clustering, but the clusters might be not sufficiently represented when the cluster boundary is ambiguous, which may degenerate the learning performance of subnetworks in MNN. To solve this problem, we design a modular neural network based on an improved soft subspace clustering (IESSC-MNN) algorithm in this study. Firstly, we propose an improved soft subspace clustering algorithm for task decomposition in MNN, which divides the original space into several interactive feature subspaces and allocates a weight item to each subspace to describe the contribution of the subtasks at the same time. Secondly, each RBF subnetwork is adaptively constructed using a structure growing strategy, and all subnetworks learning the corresponding subtask in parallel. Finally, all subnetworks' outputs are integrated by weighted summation using the contribution weight of subnetworks. The simulation results of the proposed model on five benchmark data and a practical dataset indicate that IESSC-MNN improves the modeling accuracy and generalization performance with a simple structure when compared with other MNNs.
Students’ knowledge on emotion expression and recognition in computer-mediated communication: A comparative case study

In this study, we explore processes of emotion expression and recognition in computer-mediated civic discussions in small groups. We ask what generates processes of emotion expression and recognition in digital discussions, what constitutes the emotion expression and recognition practices used in such contexts, and in what ways these practices are affected by medium. Ninety-eight participants took part in both written and video discussions and filled out questionnaires concerning their emotional experiences. Some of these participants were interviewed. A two-tiered thematic and linguistic analysis was applied to the data collected from the discussions and the interviews (totaling 7139 utterances). Findings reveal four generators giving rise to discussants' emotion expression and recognition processes, including firmness of the discussants' opinions and their cultural and religious background. Practices of emotion expression and recognition appear to vary substantially between written and face-to-face settings. For example, in the latter, participants rely heavily on tone of voice and facial expressions when recognizing others' emotions, while in the former, because of the lack of non-verbal cues, discussants assign more weight to their own emotions, experiences, and background. Moreover, participants choose their words more carefully, and are more likely to delay their response or to remain silent when expressing their emotions in writing.
Heatmap-based Explanation of YOLOv5 Object Detection with Layer-wise Relevance Propagation

Deep Neural Networks (DNNs) have been effective in providing solutions to most prevailing problems in the field Computer Vision. Modern Object Detector architecture families such as Single Shot Detector, You Only Look Once (YOLO), Region Based Convolutional Neural Networks (RCNN) and others are powerful models with strong capabilities of distinguishing many common object classes in images with unprecedented speed and accuracy. However, in most of their applications the use of deep networks conceals the inference of object class and location from the input data by introducing many layers of complex black-box functionality. This leads to sometimes bizarre and inexplicable detection outputs. A potential use of these networks in risk-prone applications motivates the development of techniques for visually interpreting their outputs, which could lead to more controlled training and deployment. Layer-wise Relevance Prop-agation (LRP) is a popular “eXplainable AI” (XAI) technique for constructing such explanations, frequently used in investigating the output of DNNs. For object detection, LRP could provide us with a useful heatmap of the input's relevance to the output class, localized within an estimate of a bounding box. In this work, we propose a new and efficient method for conducting relevance propagation in object detector DNNs that leads to visually informative results. In our experiments, we make use of the YOLOv5 model showing meaningful visual explanations of its output. Our implementation philosophy exemplifies how relevance propagation can be regulated for complex modular architectures.
Ultrasound Image Segmentation for Deep Vein Thrombosis using Unet-CNN based on Denoising Filter

Deep vein thrombosis (DVT) is caused by an abnormal blood clot condition in the network of blood vessels. Several risk factors that often cause DVT are advanced age, post-surgery, hospitalization, pregnant women, and obesity. In general, Diagnosis of DVT uses ultrasound images. However, diagnosis using ultrasound manually takes a long time, and the accuracy of image reading depends on medical personnel. It requires a system that can detect DVT automatically. Also, it can be obtained quickly and has good accuracy. This study proposes a segmentation model for ultrasound images of deep vein thrombosis using U-Net CNN based on a denoising filter. Furthermore, calculating the suspected area to be DVT predicted using U -Net CNN. The denoising filter consisted of eight filters. That model system was tested with an ultrasound image dataset. The dataset was obtained from four volunteers. The volunteers have been identified as having symptoms of deep vein thrombosis. The dataset was captured and recorded using an ultrasound device carried out by medical experts. Each DVT recorded dataset is extracted into frames. The full frames obtained are 317 frames. Then the ultrasound image data is manually labeled by medical personnel. The experimental results show that the Gaussian filter has the best results, with 99% of accuracy and 0.0252 scores of an average loss parameter. Meanwhile, the DVT prediction test using U-Net CNN segmentation based on the calculation of the mean IoU is 84.9% accurate. The measure of the mean Hausdorff distance is 4.17 of the score. We want to investigate the detection and classification of DVT for further research.
Log4jPot: Effective Log4Shell Vulnerability Detection System

The global digital landscape is changing rapidly with the advances in science and technology. A plethora of new breakthroughs are being made every day in several different fields, such as Internet infrastructure, Web 3.0, and AR/VR technologies. With these ever-increasing digital advancements, cybersecurity threats and vulnerabilities are also being exploited daily. In recent times, another critical vulnerability “Log4jshell” is identified in the logging tool Log4j. The ubiquity of this logging tool among many worldwide online services has exposed millions of devices to this vulnerability. In order to address this critical issue, the paper presents a framework for improving system security against Log4j attacks. The proposed framework deploys an in-house honeypot to detect and defend against various types of Log4j payloads. Experimental results prove the efficiency and accuracy of Log4j payload detection with an average execution time of 80.104 milliseconds for all utilized HTTP methods. In addition, the paper describes Log4shell vulnerabilities, webhooks, and provides a comparative assessment with previously proposed solutions.
On The Security of Advanced Encryption Standard (AES)

WhatsApp, Signal, VeraCrypt, 7-zip, and WinZip use AES (Advanced Encryption Standard) to encrypt data in unsafe communications or storage. Moreover, it is implemented in many processors such as Westmere and Jaguar. Therefore, it is essential to provide a security analysis of this encryption system. This paper presents cryptanalysis for AES in encrypting blocks and multimedia data. The study is for all AES modes, i.e., ECB mode abbreviation of Electronic Code Book, CBC mode abbreviation of Cipher Block Chaining, CFB mode abbreviation of Cipher Feedback, OFB mode abbreviation of Output Feedback, and CTR mode abbreviation of Counter. Several analyses are provided for only block encryption as passwords and multimedia data such as audio, videos, and images. Foremother, we study its runtime by comparing it with other encryption algorithms in the same hardware. Results show all types of attacks can break AES and attacks that AES can confront.
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection

The RGB complementary metal-oxide-semiconductor (CMOS) sensor works within the visible light spectrum. Therefore it is very sensitive to environmental light conditions. On the contrary, a long-wave infrared (LWIR) sensor operating in 8-14 μm spectral band, functions independent of visible light.In this paper, we exploit both visual and thermal perception units for robust object detection purposes. After delicate synchronization and (cross-) labeling of the FLIR [1] dataset, this multi-modal perception data passes through a convolutional neural network (CNN) to detect three critical objects on the road, namely pedestrians, bicycles, and cars. After evaluation of RGB and infrared (thermal and infrared are often used interchangeably) sensors separately, various network structures are compared to fuse the data at the feature level effectively. Our RGB-thermal (RGBT) fusion network, which takes advantage of a novel entropy-block attention module (EBAM), outperforms the state-of-the-art network [2] by 10% with 82.9% mAP.
Threat Modelling for 5G networks

The new fifth generation (5G) mobile cellular network brings enhanced mobile broadband, massive machine type communication (e.g. IoT), critical machine type communication and fixed wireless access and will accommodate new services and applications such as augmented reality, and seamless streaming to all. 5G will boost security with encrypted data, segmented networks (network slices), enhanced privacy, and user authentication, but the 5G success may also attract attackers to look for vulnerabilities, exploits or eavesdropping. The increase in connected devices creates more targets, and larger attack surfaces, hence attacks on vital connected systems could become more chaotic and consequential. The MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework attempts to provide a comprehensive overview of the methods (Techniques) by which an attacker can achieve various operational objectives (Tactics). However, some techniques may not be included in the MITRE ATT&CK matrices. This paper proposes to enhance the ATT & CK framework with Adversarial Tactics and Techniques catered for the mobile network infrastructure – CONCORDIA Mobile Threat Modeling Framework (CMTMF).
Toxicity in the Decentralized Web and the Potential for Model Sharing

The "Decentralised Web" (DW) is an evolving concept, which encompasses technologies aimed at providing greater transparency and openness on the web. The DW relies on independent servers (aka instances) that mesh together in a peer-to-peer fashion to deliver a range of services (e.g. micro-blogs, image sharing, video streaming). However, toxic content moderation in this decentralised context is challenging. This is because there is no central entity that can define toxicity, nor a large central pool of data that can be used to build universal classifiers. It is therefore unsurprising that there have been several high-profile cases of the DW being misused to coordinate and disseminate harmful material. Using a dataset of 9.9M posts from 117K users on Pleroma (a popular DW microblogging service), we quantify the presence of toxic content. We find that toxic content is prevalent and spreads rapidly between instances. We show that automating per-instance content moderation is challenging due to the lack of sufficient training data available and the effort required in labelling. We therefore propose and evaluate ModPair, a model sharing system that effectively detects toxic content, gaining an average per-instance macro-F1 score 0.89.
On Realizing Efficient Deep Learning Using Serverless Computing

Serverless computing is gaining rapid popularity as it enables quick application deployment and seamless application scaling without managing complex computing resources. Re-cently, it has been explored for running data-intensive, e.g., deep learning (DL), workloads for improving application performance and reducing execution cost. However, serverless computing imposes resource-level constraints, specifically fixed memory allocation and short task timeouts, that lead to job failures. In this paper, we address these constraints and develop an effective runtime framework, DiSDeL, that improves the performance of DL jobs by leveraging data splitting techniques, and ensuring that an appropriate amount of memory is allocated to containers for storing application data and a suitable timeout is selected for each job based on its complexity in serverless deployments. We implement our approach using Apache OpenWhisk and TensorFlow platforms and evaluate it using representative DL workloads to show that it eliminates DL job failures and reduces action memory consumption and total training time by up to 44% and 46%, respectively as compared to a default serverless computing framework. Our evaluation also shows that DiSDeL achieves a performance improvement of up to 29% as compared to bare-metal TensorFlow environment in a multi-tenant setting.
How Social Robots Can Facilitate Teaching Quality – Findings from an Explorative Interview Study

Social robots are at the beginning of their promising careers in various fields where social interactions take place. Whether they will be used in the classroom in the future, in what role and for what tasks, is still an open question. While pilot studies often emphasize the great potential in education, the social robots commercially available today cannot yet meet the high expectations. However, given the rapid pace of development, it is worth exploring the characteristics and usefulness of social robots for different learning and teaching purposes. We conducted semi-structured interviews with preservice teachers in business education as potential users of robot-assisted education to evaluate for what classroom teaching tasks a social robot would be suitable. The interviews were analyzed drawing from a framework for teaching quality comprising classroom management, student support, and cognitive activation. It became clear that, when considering ethical concerns, prospective teachers see the robot primarily in a supporting and assisting role to increase both student motivation and the competence experience. The interviewed preservice teachers have the intention to use social robots in the future. However, they stress the importance of more reliable technology and a highly interactive and ready-to-use learning scenario.