Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Artificial Intelligence Detection System Based on Multi-sensor Data Fusion

<p class="0abstract"><span lang="EN-US">Aiming at <a name="_Hlk508710819"></a>solving </span><span lang="EN-US">the navigation and obstacle avoidance of the unmanned vehicle</span><span lang="EN-US">,</span><span lang="EN-US">the multi sensor data fusion technology</span><span lang="EN-US"> and</span><span lang="EN-US"> unmanned vehicle obstacle avoidance navigation algorithm </span><span lang="EN-US">were</span><span lang="EN-US"> studied</span><span lang="EN-US"> profoundly. A</span><span lang="EN-US">ccording to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multi</span><span lang="EN-US">sensor data fusion technology </span><span lang="EN-US">wa</span><span lang="EN-US">s applied to unmanned vehicle navigation and obstacle avoidance control system</span><span lang="EN-US">. In addition,</span><span lang="EN-US"> A*VFF navigation and obstacle avoidance algorithm </span><span lang="EN-US">based on</span><span lang="EN-US"> fuzzy neural network</span><span lang="EN-US"> was</span><span lang="EN-US"> improved</span><span lang="EN-US">. F</span><span lang="EN-US">inally</span><span lang="EN-US">,</span><span lang="EN-US"> through the construction of the simulation platform, simulation experiment</span><span lang="EN-US"> of</span><span lang="EN-US"> the unmanned vehicle obstacle avoidance navigation </span><span lang="EN-US">was </span><span lang="EN-US">completed, </span><span lang="EN-US">and</span><span lang="EN-US"> a better route</span><span lang="EN-US"> was planned for </span><span lang="EN-US">unmanned vehicl</span><span lang="EN-US">e</span><span lang="EN-US"> in a more complex environment</span><span lang="EN-US">. The results showed that it</span><span lang="EN-US"> realize</span><span lang="EN-US">d</span><span lang="EN-US"> the autonomous navigation of unmanned vehicle and obstacle avoidance function. </span><span lang="EN-US">Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance. </span></p>
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.
Improving Neural Abstractive Document Summarization with Structural Regularization

Recent neural sequence-to-sequence models have shown significant progress on short text summarization. However, for document summarization, they fail to capture the long-term structure of both documents and multi-sentence summaries, resulting in information loss and repetitions. In this paper, we propose to leverage the structural information of both documents and multi-sentence summaries to improve the document summarization performance. Specifically, we import both structural-compression and structural-coverage regularization into the summarization process in order to capture the information compression and information coverage properties, which are the two most important structural properties of document summarization. Experimental results demonstrate that the structural regularization improves the document summarization performance significantly, which enables our model to generate more informative and concise summaries, and thus significantly outperforms state-of-the-art neural abstractive methods.
Full Cycle Analysis of a Large-Scale Botnet Attack on Twitter

This work presents an in-depth forensic analysis of a large-scale spam attack launched by one of the largest Twitter botnets reported in academic literature. The Bursty botnet contains over 500,000; many of which have not been suspended. The bots have generated over 2.8 million spam tweets, with 2.2 million mentions directly targeting over 1.3 million distinct Twitter users. We reveal that the botnet used a network of URL shortening services and redirections to obfuscate the real landing pages. We show that users clicked on these URLs shortly after they were published and in large numbers. We even discovered the botmaster who was behind the whole operation, including creation of the Bursty botnet and registration of the several landing pages, which happen to be phishing websites. Furthermore, we found that this botmaster is still active selling Twitter bot related services. Our work reconstructs the complete course of the spam attacks, from planning to execution. This work provides in depth analysis and insight into the operation of cybercriminals on Twitter, and the cyberspace infrastructure and black-markets that they rely on. Finally, we address how the state-of-the-art bot classifiers are unable differentiate the Bursty bots from normal users, highlighting the need and importance of individual botnet analysis.
DE-Crawler: A Densification-Expansion Algorithm for Online Data Collection

Over the past two decades, online social networks have attracted a great deal of attention from researchers. However, before one can gain insight into the behavior or structure of a network, one must first collect appropriate data. Data collection poses several challenges, such as API or bandwidth limits, which require the data collector to carefully consider which queries to make. Many network crawling methods have been proposed; however, their performance depends on network structure. In particular, our previous work in [1] has shown that existing algorithms tend to either (1) Do well at exploring dense areas of a network, but have difficulty in transitioning to new areas of the network, or (2) Easily move between network regions, but fail to fully explore each region. In this work, we introduce DE-Crawler, a novel network crawler that attempts to capture the best of both worlds. DE-Crawler consists of two main stages: Densification, in which the crawler aims to find as many nodes as possible in the current dense region (or community), and Expansion, in which the crawler tries to escape from its current region and move to another dense region. We show that DE-Crawler performs well across networks with different structural properties, outperforming baseline algorithms by up to 28%.
A Teacher-Student Framework for Maintainable Dialog Manager

Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacher-student framework to extend RL-based dialog systems without retraining from scratch. Specifically, the “student” is an extended dialog manager based on a new ontology, and the “teacher” is existing resources used for guiding the learning process of the “student”. By specifying constraints held in the new dialog manager, we transfer knowledge of the “teacher” to the “student” without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.
Fine-Grained Object Detection Using Transfer Learning and Data Augmentation

Object detection plays a vital role in many real-world computer vision applications such as self-driving cars, human-less stores and general purpose robotic systems. Convolutional Neural Network(CNN) based Deep Learning has evolved to become the backbone of most computer vision algorithms, including object detection. Most of the research has focused on detecting objects that differ significantly e.g. a car, a person, and a bird. Achieving fine-grained object detection to detect different types within one class of objects can be crucial in tasks like automated retail checkout. This research has developed deep learning models to detect 200 types of similar birds. The models were trained and tested on CUB-200-2011 dataset. To the best of our knowledge, by attaining a mean Average Precision (mAP) of 71.5% we achieved an improvement of 5 percentage points over the previous best mAP of 66.2%.
A Novel Flexible Interconnection Scheme for Microgrid to Optimize the Capacity of Energy Storage System (ESS)

A novel flexible interconnection scheme for microgrids has been proposed to optimize the capacity of ESS, which is used to mitigate the power fluctuation of microgrid. Basic structure, function and operation principle of the novel scheme are illustrated, and the effectiveness of flexible interconnection in power fluctuation mitigation has been analyzed mathematically. Based on the existing sizing algorithm of ESS, two cases, including independent microgrid scheme and interconnected microgrid scheme, have been studied. The results demonstrate the benefits of flexible interconnection scheme for microgrid in optimizing the power and energy capacity of ESS.
Detecting symptoms of driver fatigue using video analysis

The article describes a system of fatigue symptoms detection of a driver, based on his behavior observed with a camera. The software was written in C++. Selected functions from OpenCV and Dlib libraries were used. We analyzed the following symptoms indicating driver fatigue: blinking, yawning, turning the head, falling head forward and to the side. Experiments were performed using YawDD database. Satisfactory effectiveness of fatigue symptoms detection was achieved. The effectiveness of blink detection was 61%. For the rest of symptoms the detection accuracy was about 86%.
Unsupervised Crisis Information Extraction from Twitter Data

While microblogging-based Online Social Networks have become an attractive data source in emergency situations, overcoming information overload is still not trivial. We propose a framework which integrates natural language processing and clustering techniques in order to produce a ranking of relevant tweets based on their informativeness. Experiments on four Twitter collections in two languages (English and French) proved the significance of our approach.
A GPU Accelerated Update Efficient Index for kNN Queries in Road Networks

The k nearest neighbor (kNN) query in road networks is a traditional query type in spatial databases. This query has found new applications in the fast-growing location-based services, e.g., finding the k nearest Uber cars of a user for ridesharing. KNN queries in these applications are non-trivial to process due to the frequent location updates of data objects (e.g., movements of the cars). This calls for novel spatial indexes with high efficiency in not only query processing but also update handling. To address this need, we propose an index structure that uses a "lazy update" strategy to reduce the costs of update handling without sacrificing query efficiency or answer accuracy. We cache the location updates of data objects and only update the corresponding entries in the index when they are queried. We further propose a kNN query algorithm based on this index. This algorithm takes advantage of the strengths of both the CPU and the GPU. It first identifies the queried region and updates the index over this region using the GPU. Then, it uses the GPU to query the index and produce a candidate result set, which is later refined by the CPU to obtain the final query answer. We conduct experiments on real data and compare the proposed algorithm with state-of-the-art kNN algorithms. The experimental results show that the proposed algorithm outperforms the baseline algorithms by orders of magnitude in query time.
Content-Preserving Image Stitching with Regular Boundary Constraints

This paper proposes an approach to content-preserving stitching of images with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with regular boundary. Existing methods treat image stitching and rectangling as two separate steps, which may result in suboptimal results as the stitching process is not aware of the further warping needs for rectangling. We address these limitations by formulating image stitching with regular boundaries in a unified optimization. Starting from the initial stitching results produced by traditional warping-based optimization, we obtain the irregular boundary from the warped meshes by polygon Boolean operations which robustly handle arbitrary mesh compositions, and by analyzing the irregular boundary construct a piecewise rectangular boundary. Based on this, we further incorporate straight line preserving and regular boundary constraints into the image stitching framework, and conduct iterative optimization to obtain an optimal piecewise rectangular boundary, thus can make the panoramic boundary as close as possible to a rectangle, while reducing unwanted distortions. We further extend our method to panoramic videos and selfie photography, by integrating the temporal coherence and portrait preservation into the optimization. Experiments show that our method efficiently produces visually pleasing panoramas with regular boundaries and unnoticeable distortions.
Neural Multitask Learning for Simile Recognition

Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.
Tensor Nuclear Norm LPV Subspace Identification

Linear parameter varying (LPV) subspace identification methods suffer from an exponential growth in number of parameters to estimate. This results in problems with ill-conditioning. In literature, attempts have been made to address the ill-conditioning by using regularization. Its effectiveness hinges on suitable a priori knowledge. In this paper, we propose using a novel, alternative regularization. That is, we first show that the LPV sub-Markov parameters can be organized into several tensors that are multilinear low rank by construction. Namely, their matricization along any mode is a low-rank matrix. Then, we propose a novel convex method with tensor nuclear norm regularization, which exploits this low-rank property. Simulation results show that the novel method can have higher performance than the regularized LPV-PBSID$_{\text{opt}}$ technique in terms of variance accounted for.
Lightning Wave Propagation Characteristics of an Overhead Line with Insulated Phase Conductors

Compact overhead distribution lines are composed of insulated phase conductors and bare grounded cables. The use of an insulating layer allows a greater proximity between phase conductors and modifies the per-unit-length matrix and propagation characteristics associated with the line. However, it is not clear whether it is possible to accurately calculate its per-unit-length capacitance matrix using simplified formulae available in the literature. Also, it is unknown to what extent the propagation characteristics of the line will be affected. This paper makes an attempt to clarify these aspects by using a rigorous formulation based on the Method of Moments for calculating the external parameters of a three-phase compact distribution line. The influence of considering covered phase conductors on the transient response of the line is also investigated by considering the injection of a typical lightning current at one of the line terminations. The analysis is performed using the Finite-Difference Time-Domain (FDTD) method. It concluded that considering insulated phase conductors reduces the associated phase velocity so that it approaches a value that is lower than the speed of light at high frequencies. Consequently, a distortion is observed on the tail of the calculated voltage waveforms due to a relative increase in the propagation time associated with the line, but with minimal effect on the calculated peak voltages. It is also shown that the proximity effect has minimal influence on the calculated per-unit-length capacitance matrix.
Syntax Encoding with Application in Authorship Attribution

We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is provably information-lossless. In specific, an embedding vector is constructed for each word in the sentence, encoding the path in the syntax tree corresponding to the word. The one-to-one correspondence between these “syntax-embedding” vectors and the words (hence their embedding vectors) in the sentence makes it easy to integrate such a representation with all word-level NLP models. We empirically show the benefits of the syntax embeddings on the Authorship Attribution domain, where our approach improves upon the prior art and achieves new performance records on five benchmarking data sets.
Phrase-level Self-Attention Networks for Universal Sentence Encoding

Universal sentence encoding is a hot topic in recent NLP research. Attention mechanism has been an integral part in many sentence encoding models, allowing the models to capture context dependencies regardless of the distance between the elements in the sequence. Fully attention-based models have recently attracted enormous interest due to their highly parallelizable computation and significantly less training time. However, the memory consumption of their models grows quadratically with the sentence length, and the syntactic information is neglected. To this end, we propose Phrase-level Self-Attention Networks (PSAN) that perform self-attention across words inside a phrase to capture context dependencies at the phrase level, and use the gated memory updating mechanism to refine each word’s representation hierarchically with longer-term context dependencies captured in a larger phrase. As a result, the memory consumption can be reduced because the self-attention is performed at the phrase level instead of the sentence level. At the same time, syntactic information can be easily integrated in the model. Experiment results show that PSAN can achieve the state-of-the-art performance across a plethora of NLP tasks including binary and multi-class classification, natural language inference and sentence similarity.
Behavioral and Neurophysiological Effects of Transcranial Direct Current Stimulation (tDCS) in Fronto-Temporal Dementia

Fronto-temporal dementia (FTD) is the clinical-diagnostic term that is now preferred to describe patients with a range of progressive dementia syndromes associated with focal atrophy of the frontal and anterior temporal cerebral regions. Currently available FTD medications have been used to control behavioral symptoms, even though they are ineffective in some patients, expensive and may induce adverse effects. Alternative therapeutic approaches are worth pursuing, such as non-invasive brain stimulation with transcranial direct current (tDCS). tDCS has been demonstrated to influence neuronal excitability and reported to enhance cognitive performance in dementia. The aim of this study was to investigate whether applying Anodal tDCS (2 mA intensity, 20 min) over the fronto-temporal cortex bilaterally in five consecutive daily sessions would improve cognitive performance and behavior symptoms in FTD patients, also considering the neuromodulatory effect of stimulation on cortical electrical activity measured through EEG. We recruited 13 patients with FTD and we tested the effect of Anodal and Sham (i.e., placebo) tDCS in two separate experimental sessions. In each session, at baseline (T0), after 5 consecutive days (T1), after 1 week (T2), and after 4 weeks (T3) from the end of the treatment, cognitive and behavioral functions were tested. EEG (21 electrodes, 10–20 international system) was recorded for 5 min with eyes closed at the same time points in nine patients. The present findings showed that Anodal tDCS applied bilaterally over the fronto-temporal cortex significantly improves (1) neuropsychiatric symptoms (as measured by the neuropsychiatric inventory, NPI) in FTD patients immediately after tDCS treatment, and (2) simple visual reaction times (sVRTs) up to 1 month after tDCS treatment. These cognitive improvements significantly correlate with the time course of the slow EEG oscillations (delta and theta bands) measured at the same time points. Even though further studies on larger samples are needed, these findings support the effectiveness of Anodal tDCS over the fronto-temporal regions in FTD on attentional processes that might be correlated to a normalized EEG low-frequency pattern.
Automatic recognition of the kidney in CT images

The article presents an original method of kidney recognition in CT images. Fast and precise visualization of a kidney is a very important task in medical diagnosis of kidney cancer. Calculation of a precise kidney's boundary allows to visualize any changes in the genitourinary system requiring specialist intervention. The method presented in this article uses shape analysis of objects' boundary and calculates geometrical coefficients for final detection. The results of the experiments show that the level of accuracy of automatic kidney boundary detection is 84 % comparing to expert's results.
Which generated test failures are fault revealing? prioritizing failures based on inferred precondition violations using PAF

Automated unit testing tools, such as Randoop, have been developed to produce failing tests as means of finding faults. However, these tools often produce false alarms, so are not widely used in practice. The main reason for a false alarm is that the generated failing test violates an implicit precondition of the method under test, such as a field should not be null at the entry of the method. This condition is not explicitly programmed or documented but implicitly assumed by developers. To address this limitation, we propose a technique called PAF to cluster generated test failures due to the same cause and reorder them based on their likelihood of violating an implicit precondition of the method under test. From various test executions, PAF observes their dataflows to the variables whose values are used when the program fails. Based on the dataflow similarity and where these values are originated, PAF clusters failures and determines their likelihood of being fault revealing. We integrated PAF into Randoop. Our empirical results on open-source projects show that PAF effectively clusters fault revealing tests arising from the same fault and successfully prioritizes the fault-revealing ones.
Application of Neural Networks in the Modelling of Dielectric Resonator Antenna Arrays

In this paper, a method to incorporate neural networks in the circuit modeling of a dielectric resonator antenna (DRA) array is presented. The neural networks are used to capture the coupling effect between elements of the DRA array at different element spacing to develop a fully adjustable circuit model. The result is a design environment that allows for accurate real-time simulations of DRA arrays that can be used to optimize DRA array.