Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Routing for WSN-Based Tactical-Level Intelligent Transportation Systems

Wireless sensor network (WSN) is gaining popularity in recent years due to the advantages of the WSN such as mobility, flexibility and low power consumption. Therefore, the usage of WSN in tactical-level Intelligent Transportation Systems (ITS) is expected to be able to overcome restrictions that the conventional ITS can only detect the vehicle in fixed position and high cost of construction and maintenance. Thanks to highly dynamic nodes to forward the network packets to the destinations, WSN is particularly vulnerable to attacks of interception and flooding, forging and tampering packets. Accordingly, reliable communication between nodes is dependent on the mechanism to verify the network traffic authenticity and communicating peers identity. In this paper, we propose a Zero Knowledge Key Exchange (ZKKE) scheme to setup a lightweight and adaptive hop to hop zero knowledge authentication chain (ZKAC). We adopt the GNY cryptographic protocol to prove the correctness of ZKKE and ZKAC. Based on computational cheap hash function and public-key scheme without trust third party (TTP), ZKAC enables hop-to-hop as well as end-to-end integrity protection for both routing and transformation informations in the WSN-based tactical-level ITS.
The Family in Medical Practice: A Family Systems Primer

1 The History of the Family in Medicine. - 2 A Systems View of the Clinical Relationship. - 3 Family Systems Theory in Medical Practice. - 4 The Family Life Cycle in Medical Practice. - 5 Families and Illness. - 6 Applying a Systems Approach to Common Medical Problems. - 7 Systems-oriented Counseling. - 8 Using the Genogram (Family Tree) Clinically. - 9 Working with One's Own Family Issues: A Path for Professional Development. - 10 Future Directions for Family-oriented Medical Care: Research, Education, and Practice. - Appendixes. - A Some Residency Training Programs Emphasizing Family Systems. - B Some Family Therapy Training Programs, Centers, and Resources. - C Family Systems Research Programs/Centers. - D Some Family Systems Journals. - E Suggested Further Reading About Family Systems and Health. - Glossary of Terms. - Index of Names.
Predicting Bank Insolvency with Random Forest Classification

The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default rates for banks in the United States. This study extends the findings of a Random Forest model first introduced by Petropoulos et al. (2017) by extending their model by evaluating a longer sample period and adding macroeconomic variables to analyze how current market conditions impact the prediction of default rates of U.S. Banks from 1994-2016. Petropoulous et al. (2017) evaluated multiple traditional and artificial intelligence models to find that Random Forest produced the best results utilizing quarterly data from the FDIC from 2008-2014. Numerous studies have suggested that the financial condition of banks is purely determined by bank specific variables. Our empirical results confirm that theory as a bank default prediction model utilizing Random Forest classification performed worse with the addition of macroeconomic variables when compared to a model based purely on bank specific variables. (Less)
Tanbih: Get To Know What You Are Reading

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what’s behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.
Leveraging a Supervised Machine Learning Toolkit For Better Seismic Processing Quality Control

Summary Geophysicists are facing great challenges to process increasingly large volumes of seismic data in a faster, better and more reliable way. In order to achieve this, one key element is to be able to perform quality control of processing steps in an effective and efficient manners. In this paper we present two cases where machine learning techniques are used to help speed up the quality control process. The first case describes an application of supervised learning with K-nearest neighbor to identify areas that may potentially cause cycle-skipping in full waveform inversion. The second case uses a logistic regression machine learning approach to detect and classify the presence of rig noise on shot points. Furthermore, we also tested an automated data reduction method using a long short-term memory auto-encoder, that could speed up the algorithm significantly without losing key information in the original data.
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss

We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to be definitive. In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss. Specifically, we propose a neural encoder-decoder model with multi-level copy mechanisms to generate such questions. Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers. Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question is able to express the given predicate and correspond to a definitive answer.
Facial Expression Recognition via Deep Action Units Graph Network Based on Psychological Mechanism

Facial expression recognition (FER) is currently a very attractive research field in cognitive psychology and artificial intelligence. In this paper, an innovative FER algorithm called deep action units graph network (DAUGN) is proposed based on psychological mechanism. First, a segmentation method is designed to divide the face into small key areas, which are then converted into corresponding AU-related facial expression regions. Second, the local appearance features of these critical regions are extracted for further action units (AUs) analysis. Then, an AUs facial graph is constructed to represent expressions by taking the AU-related regions as vertices and the distances between each two landmarks as edges. Finally, the adjacency matrices of facial graph are put into a graph-based convolutional neural network to combine the local-appearance and global-geometry information, which greatly improving the performance of FER. Experiments and comparisons on CK+, MMI, and SFEW data sets reveal that the DAUGN achieves more competitive results than several other popular approaches.
Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization‐based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV‐assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real‐time applications. So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity.
The Depressed Female Characters From Their Intimidated Surrounding as Seen in The Yellow Wallpaper By Charlotte Perkin Gilman’s, A Rose For Emily By William Faulkner’s, and The Story of an Hour By Kate Chopin

In writing this thesis, the writer discusses the depression of women because of patriarchal traditions, even though they already know about women's rights and freedoms. This patriarchal tradition is that men hold full power over anything and women must always obey the rules of men. The women are required not to do any activities, in terms of education and employment. Women are only allowed to do homework. This applies to all women, both single and married. This began in the 90s, especially in the United States. In writing the thesis, the writer uses psychological and feminist theories according to Sigmund Freud and Maggie Humm, who will explore the psychological side of women who are oppressed by the existence of this patriarchal custom. The purposes of this paper are: (1) To describe psychological-feminist cases in female characters (2) To analyze psychological-feminists in depressed female characters (3) To explain the psychological-feminist influence with female characters in the short story of The Yellow Wallpaper from Charlotte Perkins Gilman, A Rose For Emily from William Faulkner, The Story Of An Hour by Kate Chopin. The author uses descriptive qualitative methods in processing data. Through analysis of several existing sources and data. Based on available data, the writer discover how the psychology of depressed female characters from their environment is intimidated based on the short story. In fact women can become depressed because their freedom of expression is hampered and prohibited by tradition. With the writing of this thesis, it is hoped that the public can find out what exactly the meaning of women's emancipation is without having to put down women or men.
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling

Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL performance on exploiting syntactic knowledge, achieving significant results. Pipeline methods based on automatic syntactic trees and multi-task learning (MTL) approaches using standard syntactic trees are two common research orientations. In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline. Besides, we present a MTL framework that includes the basic SRL module and a dependency parser module. Different from the commonly used hard parameter sharing strategy in MTL, the main idea is to extract implicit syntactic representations from the dependency parser as external inputs for the basic SRL model. Experiments on the benchmarks of Chinese Proposition Bank 1.0 and CoNLL-2009 Chinese datasets show that our proposed framework can effectively improve the performance over the strong baselines. With the external BERT representations, our framework achieves new state-of-the-art 87.54 and 88.5 F1 scores on the two test data of the two benchmarks, respectively. In-depth analysis are conducted to gain more insights on the proposed framework and the effectiveness of syntax.
Developing Risk Assessment Model for Altering Conditions of Forest Reserves in an Oil-Production Region

The scientific problem, the solution of which is aimed at this work, is the implementation of a systematic method of assessing and predicting the influence of anthropogenic impact on the natural environment of the oilproducing region. The significance of the research is confirmed by the absence of developed and enacted criteria for determining risks and finding techniques of their assessment, especially those which consider the specificity of the oil-producing region. A special feature of the proposed approach is the use of hybrid methods of machine learning in conjugation with the spatial information analysis on the basis of the history of incidents occurred in the forest reserves of the region. Some statistical estimates of the influence of the assessed factors of risk formation on an emergence probability of an incident have been obtained. Within the framework of this study, a vectorbased description of signs of incidents was formulated and validated followed by a forecast based on methods of machine learning. Developed models have a wide range of application and may become a framework for creating assessments of risk factors of human-caused impacts on the natural environment, including oil spills, illegal forest felling, unauthorized dumps of household and construction waste, etc. Achievement of the established goal will allow to rank risks in the forest reserves of the region, develop a model for the assessment of these risks, visualize by means of geo-information technologies for monitoring loads imposed by planned inspections by controland-supervision authorities on the region’s enterprises, as well increase the efficiency of the detection of violations. Keywords–data analysis, machine learning, neural networks, spatial analysis, geographic information systems, risk-based approach
Optimization and Hardware Implementation of SYBA-An Efficient Feature Descriptor

Optimization and Hardware Implementation of SYBA— An Efficient Feature Descriptor Samuel Gaylin Fuller Department of Electrical and Computer Engineering, BYU Master of Science Feature detection, description and matching are crucial steps in many computer vision algorithms. These rely on feature descriptors to be able to match image features across sets of images. This paper discusses a hardware implementation and various optimizations of our lab’s previous work on the SYnthetic BAsis feature descriptor (SYBA). Previous work has shown that SYBA can offer superior performance to other binary descriptors, such as BRIEF. This hardware implementation on an FPGA is a high throughput and low latency solution, which is critical for applications such as: high speed object detection and tracking, stereo vision, visual odometry, structure from motion, and optical flow. Finally, we compare our solution to other hardware methods. We believe that our implementation of SYBA as a feature descriptor in hardware offers superior image feature matching performance and uses less resources than most binary feature descriptor implementations.
Selecting causal brain features with a single conditional independence test per feature

We propose a constraint-based causal feature selection method for identifying causes of a given target variable, selecting from a set of candidate variables, while there can also be hidden variables acting as common causes with the target. We prove that if we observe a cause for each candidate cause, then a single conditional independence test with one conditioning variable is sufficient to decide whether a candidate associated with the target is indeed causing it. We thus improve upon existing methods by significantly simplifying statistical testing and requiring a weaker version of causal faithfulness. Our main assumption is inspired by neuroscience paradigms where the activity of a single neuron is considered to be also caused by its own previous state. We demonstrate successful application of our method to simulated, as well as encephalographic data of twenty-one participants, recorded in Max Planck Institute for intelligent Systems. The detected causes of motor performance are in accordance with the latest consensus about the neurophysiological pathways, and can provide new insights into personalised brain stimulation.
Stein Variational Gradient Descent With Matrix-Valued Kernels

Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference. In this work, we enhance SVGD by leveraging preconditioning matrices, such as the Hessian and Fisher information matrix, to incorporate geometric information into SVGD updates. We achieve this by presenting a generalization of SVGD that replaces the scalar-valued kernels in vanilla SVGD with more general matrix-valued kernels. This yields a significant extension of SVGD, and more importantly, allows us to flexibly incorporate various preconditioning matrices to accelerate the exploration in the probability landscape. Empirical results show that our method outperforms vanilla SVGD and a variety of baseline approaches over a range of real-world Bayesian inference tasks.
A high-performance MPPT algorithm combining advanced three-point weight comparison and temporary stopped running strategy for PV systems

This paper proposes a new maximum power point tracking (MPPT) control strategy, including a boost converter temporary stopped running (TSR) strategy and an advanced three-point weight comparison method (ATPWC). The proposed ATPWC was used to detect three power points of a photovoltaic (PV) module output, and a microcontroller unit (MCU) was used to calculate the slope of the three power points and to perform MPPT so as to improve the system ’ s conversion e ﬃ ciency. The proposed method was successfully applied to an independent solar power generation system, in which the PV module was connected to a boost converter and then connected in series with an inverter to a single-phase 110VAC / 60Hz output and connected to the power grid. The measured results showed that, in terms of the TSR control strategy, when the system output met a load of 110Vrms, TSR could reduce switching loss and conduction loss and result in a 10% higher overall system conversion e ﬃ ciency than that of traditional control. In terms of the MPPT algorithm, actual measurements were carried out under an irradiance level of 100 W / m 2 ∼ 700W / m 2 . Versus traditional hill climbing (HC) algorithm, the e ﬃ ciency of the proposed ATPWC was better.
Dual Reverse Attention Networks for Person Re-Identification

In this paper, we enhance feature representation ability of person re-identification (Re-ID) by learning invariances to hard examples. Unlike previous works of hard examples mining and generating in image level, we propose a dual reverse attention networks (DRANet) to generate hard examples in the convolutional feature space. Specifically, we use a classification branch of attention mechanism to model that ‘what’ in channel and ‘where’ in spatial dimensions are informative in the feature maps. Meanwhile, we introduce two branches of reverse attention modules in parallel way, which convert informative feature maps into hard examples of uninformative ones. In the proposed framework, both classification and dual reverse attention branches are learned in a joint way. Experimental results on three mainstream datasets demonstrate the efficacy of the proposed method.
JStap: a static pre-filter for malicious JavaScript detection

Given the success of the Web platform, attackers have abused its main programming language, namely JavaScript, to mount different types of attacks on their victims. Due to the large volume of such malicious scripts, detection systems rely on static analyses to quickly process the vast majority of samples. These static approaches are not infallible though and lead to misclassifications. Also, they lack semantic information to go beyond purely syntactic approaches. In this paper, we propose JStap, a modular static JavaScript detection system, which extends the detection capability of existing lexical and AST-based pipelines by also leveraging control and data flow information. Our detector is composed of ten modules, including five different ways of abstracting code, with differing levels of context and semantic information, and two ways of extracting features. Based on the frequency of these specific patterns, we train a random forest classifier for each module. In practice, JStap outperforms existing systems, which we reimplemented and tested on our dataset totaling over 270,000 samples. To improve the detection, we also combine the predictions of several modules. A first layer of unanimous voting classifies 93% of our dataset with an accuracy of 99.73%, while a second layer-based on an alternative modules' combination-labels another 6.5% of our initial dataset with an accuracy over 99%. This way, JStap can be used as a precise pre-filter, meaning that it would only need to forward less than 1% of samples to additional analyses. For reproducibility and direct deployability of our modules, we make our system publicly available.1
An Improved Tracking Algorithm Based on MDNet

The MDNet algorithm works well on tracking problems of the video sequence, but the speed is very slow. We have made some improvements to accelerate the process of feature extraction. It enhances the expressive ability of the feature map by removing the max pooling layer and using the method of expanding convolution to increase the receptive field of each point on the feature map. In addition, MDNet is building on CNN, and there are problems that similar targets have a large interference to the results. To address this problem, We use RNN to capture the long-term dependence of the target before and after the target data in the sequence data, and introduce the RNN to model the structure information of the target object, and then fuse the RNN feature and CNN feature of the tracked target object. In addition, another new loss term is introduced to make the targets in different domains away from each other in the shared feature space, thereby improving the algorithm’s ability to discriminate similar interferers. Compared with MDNet, our improved algorithm is much faster and the accuracy is improved.
Classroom Shared Whiteboard System using Multicast Protocol

Multiple hosts wish to receive the same data from one or more senders. Multicast routing defines extensions to IP routers to support broadcasting data in IP networks. Multicast data is sent and received at a multicast address which defines a group. Data is sent and received in multicast groups via routing trees from sender(s) to receivers. Demonstrative lectures require to share the computer screen of the lecturer to the students as well as to make discussion with the students. The Multicast protocol is the most suitable method because of its capability in speed and better synchronized process. The word "multicast" is typically used to refer to IP multicast which is often employed for streaming media, and Internet television applications.