Reviewerly/exHarmony huggingface dataset:
Mathematical Basics as a Prerequisite to Artificial Intelligence in Forensic Analysis

The chapter examines a review and revisit to the current study of advancements in mathematics and statistical methods underlying the most sorted topic of artificial intelligence (AI). Inclusion of references is done for better and smooth discussion for more clarity to the underlying difficulties faced by readers. Mathematics motivates image processing and image processing improves methods involved with mathematics, which is to be explored. Mathematics stands as a back bone and with discussions of basics of neural network the path way to artificial neural network would be build. The struggle to recall the prerequisites faced by researchers is addressed in this chapter. The chapter will provide you through an ariel view by stating the definitions of prerequisites such as mathematics for image processing, mathematics for forensic image processing which includes basics of neural network and prerequisites of probability theory as a subsection. Forensic sciences utilize the concepts of probability density to a great extent. The topics briefed would provide the readers to have a quick recap of the concepts which though seem to be from different specializations but are deeply connected to one another. Section one is dedicated to mathematics for image processing and Section two connects mathematics, image processing with forensic sciences.
Enhanced Hybrid Privacy Preserving Data Mining Technique

At present, almost every domain is handling large volumes of data even as storage device capacities increase. Amidst humongous data volumes, Data mining applications help find useful patterns that can be used to drive business growth, improved services, better health care facilities etc. The accumulated data can be exploted for identity theft, fake credit/debit card transactions, etc. In such scenarios, data mining techniques that provide privacy are helpful. Though privacy-preserving data mining techniques like randomization, perturbation, anonymization etc., provide privacy, but when applied separately, they fail to be effective. Hence, this chapter suggests an Enhanced Hybrid Privacy Preserving Data Mining (EHPPDM) technique by combining them. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy as well as evidenced by our experimental results.
A novel approach to fuzzy c-Means clustering using kernel function

Clustering is an unsupervised procedure that divides a set of objects into homogeneous groups. Two types of clustering are possible, Hard clustering and Soft clustering/Fuzzy clustering. Hard clustering is not feasible for complex datasets that contain uncertainty and overlapping clusters, whereas fuzzy clustering efficiently handles it. FCM is sensitive to the initial values and challenging to cluster nonlinear data. A new approach is implemented here with the Fuzzy c-Means (FCM) clustering algorithm to improve the performance. The Kernel function ensures the linear separability of complex clusters by projecting the feature space into a higher dimension and not subject to the initial values. The Kernel-based FCM (KFCM) optimized the clustering. The relevant features are considered for clustering, and it improves the validity of clusters. The irrelevant features blur the clusters and reduce the quality. Silhouette index (SI) and Davies-Bouldin index (DBI) have been used as the evaluation function. The experiments are conducted on two benchmark datasets and one artificial dataset. The result justifies Kernel-based FCM, and the superiority of features reduced Kernel-based FCM clustering over other traditional fuzzy clustering techniques.
Research on unmanned driving path planning algorithm based on Robot Operating System

Abstract With the rapid development of communication and computer technology In recent years, automatic driving has attracted much attention. By using path planning technology, the AV can automatically find an optimal path to the destination while avoiding obstacles, so as to reduce transportation costs, improve driving efficiency, improve customer satisfaction and reduce traffic accidents, which is of important meaning to spurring the further development of intelligent transportation. Two algorithms of driverless path planning are studied, one is and the A* algorithm is for global path planning, another is the timed elastic band (TEB) algorithm for local path planning. Firstly, the direction of the exploration domain of the traditional A* algorithm is improved by adding an exploration domain to smooth the planned path.t. Secondly, some parameters of the TEB algorithm are adjusted to make the car stay away from obstacles and run more safely. Finally, on the mobile robot platform equipped with ROS operating system, the hybrid path planning is realized by combining these two algorithms, and the simulation experiment under a real environment is carried out. Experiment results show that the proposed method can autonomously plan path and navigation smoothly.
A Video Description Model with Improved Attention Mechanism

Abstract Video description generation refers to the automatic generation of text descriptions of videos by computers, which belongs to the intersection of computer vision and natural language processing. Aiming at the problem that the traditional attention mechanism has insufficient ability to extract video features, the model is complex and the description quality is not high, this paper proposes a video description model with an improved attention mechanism. The model is based on the encoder-decoder structure, uses inception-v4 as the encoder to extract features, and introduces a lightweight coordinate attention module (CA) into the attention mechanism, which improves the feature extraction effect and reduces the model complexity, and sends the extracted important feature information into the decoder long short-term memory network (LSTM) to generate the description sentence corresponding to the video. The model is validated on the MSVD dataset using various evaluation metrics (BLEU, ROUGEL, CIDEr, METEOR). The experimental results show that the improved attention mechanism of the video description model proposed in this paper has better accuracy in different performance metrics and can further improve the performance of video description.
Influence of Structural Parameters of Voltage Sensing Unit in Switchgear on Distribution Characteristics of Spatial Stray Capacitance

Among the medium and high voltage switchgear equipment, capacitive voltage transformer (CVT) is the most widely used power equipment in voltage measurement. The distribution characteristics of stray capacitance in the equipment space are directly related to the accuracy of equipment measurement. This paper analyzes the working principle of the voltage sensing unit, designs the structural model of the voltage sensing plate and the shielding plate, calculates the spatial capacitance distribution value when the axial length of the voltage sensing plate and the shielding plate changes, and reveals the influence of the structural change of voltage sensing unit in the switchgear on the distribution characteristics of spatial stray capacitance.
Introducing E4MT and LMBNC: Persian pre-processing utilities

In this paper, we introduce two utilities, extensively used in our services and products. A Persian pre-processor(E4MT) we use for both training and inference in our machine translation services and a corpora-level language model-based error corrector(LMBNC), which we apply to corpora before training. E4MT (Essential tools for MT) consists of character normalization, spell correction, entity tagging, and tokenization/detokenization modules. It handles the Persian large vocabulary size problem by approximately reducing the vocabulary size by a factor of 2. We show that applying E4MT on the English-Persian translation task, yields an improvement of at least 1.2 BLEU over other toolkits. We apply LMBNC on the training corpora, which uses a domain-specific language model to identify context-dependent misspellings. The results show, using this corrected training corpora improves the English-Persian translation quality by 0.6 BLEU over its baseline. Additionally, the manual evaluation shows 97.9% precision for E4MT and 98.1% precision for LMBNC.
A Novel Deep Learning Framework For Image KeyPoint Description

Many major computer vision tasks, such as image registration, data fusion, and change detection, begin with key-point detection and description. Recently, data-driven methods have challenged many classical approaches in many fields of computer vision. To this end, a variety of deep-learning-based techniques that particularly account for keypoint detection and description have been developed. Convolutional layers are an essential component of many deep-learning-based computer vision applications, but they are not totally rotation invariant. This restricts the capability of a CNN to extract suitable descriptors for keypoints. This paper provides a deep learning framework for image keypoint description to overcome this issue. The proposed method enriches the existing extracted descriptors by CNN. In this approach, a pre-trained network is employed for keypoint detection and descriptor extraction. To introduce a richer descriptor, the rotated versions of the input image are applied and a new descriptor is presented. The evaluation results show that the proposed method outperforms the original CNN framework in terms of the number of accurate correspondences, the proportion of correct correspondences, and the matching error.
NLED: Neighbor Linear-Embedding Denoising for Fluorescence Microscopy Images

As noise corruption is an inevitable issue for all imaging technologies, this problem causes serious difficulties in analyzing the biological fine-details of fluorescence microscopy images. While Gaussian only, Poisson only and mixture of Poisson- Gaussian can generally be observed, the mixed-noise is more prominent in fluorescence microscopy. In this paper, a novel patch-based denoiser-learning approach is proposed for the images captured by fluorescence microscopy. The developed algorithm mainly builds upon linear-embeddings of neighboring image patches, and it learns a linear transformation between noisy and clean intrinsic geometric properties of patch-spaces. Experimental results demonstrate that the proposed “Neighbor Linear- Embedding Denoising” (NLED) has competitive performance both visually and statistically when compared to other algorithms in literature, for noise corrupted fluorescence microscopy images.
A Novel Deformable Registration Method for Cerebral Magnetic Resonance Images

Image registration plays an important role in many cerebral image processing algorithms such as multi-modal image fusion, brain volumetry and parcellation, atlas-based morphometry, and so forth. The registration algorithms can be divided into the rigid and deformable categories. The former takes advantage of global rigid transformations (e.g. affine or rigid-body) while the later employ local deformable vector fields. In this paper, a novel deformable registration algorithm based on calculus of variations is proposed. Its energy functional consists of a second-order regulator term and an external energy functional based on the sum squared errors measure. The former makes the deformable vector field smooth and differentiable while the later increase the similarity between the wrapped moving image and fixed image by optimizing the vector field. Experimental results demonstrated superior performance of the proposed method for a large image-base including 150 cerebral MPRAGE magnetic resonance images, in three categories of cognitively normal, mild cognitive impairment, and Alzheimer disease. On average, our method improves the cross-correlation ratio for 8% compared to the rigid registration.
Span-prediction of Unknown Values for Long-sequence Dialogue State Tracking

Dialogue state tracking is one of the main components in task-oriented dialogue systems whose duty is to track the user’s goal during the conversation. Due to the diversity in natural languages and existing utterances, the user requests may include unknown values at different turns in these systems. However, predicting the actual values of the user requests is necessary for completing the intended task. In existing studies, these values are determined using span-based methods to predict a span in utterances or previous dialogues. However, the slots are not correctly filled when values are multi-word. In addition, in some scenarios, the slot values in a given turn may depend on previous dialogue states. However, due to the limitation of the input length of language models, it is impossible to access all the previous dialogue states. This study proposes a new approach that uses a span-tokenizer and adds the Bi-LSTM layer on top of the BERT model to predict the exact span of multi-word values. This approach uses parameters like user utterances, important dialogue histories, and all dialogue states as input to decrease the length of the sequences. The results show that this strategy has led to a 1.80% improvement in the joint-goal accuracy and 0.15% improvement in the slot accuracy metrics over the MultiWOZ 2.1 dataset compared to the SAVN model.
PeQA: A Massive Persian Question-Answering and Chatbot Dataset

TA question-answering (QA) system is an application able to communicate with humans using natural language processing. Modelling a dialogue between humans and machines is considered one of the most important tasks of Artificial Intelligence (AI). Creating a Chatbot with a good performance in modelling human-machine conversations is still one of the unsolved challenges in this field. Although Chatbots have many applications, in general, they should understand users' meaning through their words and provide them with relevant answers. In the past, Chatbot architectures mainly relied on rules or statistical methods. With the advent of deep learning methods, trainable neural networks soon replaced the traditional models. These sorts of deep models are highly affected by the dataset that would be fed into them, and there is no big enough one available in the Persian language! We present a huge dataset of 14 million Persian tweets from tweeter that is meticulously processed to create a rich collection of 420,000 pairs of question-answer data. We also present modelling results on Transformers, including Sensibleness and Specificity Average (SSA) and the BLEU metric. We will release our dataset, modelling code, and models publicly <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/mut-deep/PEQA
The Model of Mobile Edge Computing in the Wild West: Analysis and Evaluation

A hybrid model is proposed to study the characteristics of the quality of the edge computing network in the conditions of the location of network nodes and servers on rough terrain with multiple obstacles to the propagation of high-frequency radio signals. The model uses limited visibility graphs in combination with random task generation processes and queued service processes. Based on the model, the dependences of local and global characteristics of the quality of service in the network, such as the probability of timely solving the tasks of end users, the “lifetime” of the device, the half-life of the network and others, on the parameters of devices and servers and on the integral characteristics of the terrain are studied. Results are represented by dependency plots.
Framework for Network-Constrained Tracking of Cyclists and Pedestrians

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.
Gender screening on question-answering communities

Modern community Question Answering (cQA) platforms encourage their members to publish any sort of question, which later on can get numerous answers from other community peers. However, in this dynamic, there is an intrinsic delay from the moment questions are posted until the arrival of acceptable and/or diverse responses. Therefore, cQA platforms have the pressing need for promoting unresolved questions to potential answerers, while also reducing gender disparity across their topics, for example. Needless to say, demographic analysis occupies a crucial role in successfully responding to these challenges. Nonetheless, there are only a handful of studies dissecting automatic gender recognition across cQA fellows. As far as we know, this work is the first effort to tease out the contribution to this task of the different kinds of textual inputs contained in their profiles (i.e., question titles and bodies, answers and self-descriptions). With this goal, we compare three different types of machine learning approaches under several combinations of these four input signals: traditional neural networks (e.g., RCNN and CNN), fine-tuned pre-trained transformers (e.g., BERT and RoBERTa) and statistical methods enriched with hand-crafted linguistic features (e.g., Bayes and MaxEnt). In a nutshell, our results show that pre-trained transformers are superior when dealing with full questions, conventional neural networks when mixing diverse text signals, and statistical methods when the dataset encompasses mostly noisy user-generated content, namely answers. In addition, our in-depth analysis reveals that dependency parsing is instrumental in designing hand-crafted features capable of modelling topic information, and that both genders are conspicuously represented by some specific topic distributions.
Robust Bayesian attention belief network for radar work mode recognition

Understanding and analyzing radar work modes play a key role in electronic support measure system. Many classifiers, for example those based on convolutional neural network (CNN) and recurrent neural network (RNN), are available for recognizing radar work modes as well as emitter types from their waveform parameters. However, the performance of these methods may suffer significantly when confronting different types of signal degradation, e.g., measurement error, lost pulse and spurious pulse. To tackle this issue, we in this paper develop a Bayesian attention belief network (BABNet) based on Bayesian neural networks in which the probability distribution over weights can help to enhance the model robustness for corrupted data. In particular, we adopt pre-trained CNN as the Bayesian inference prior. This not only accelerates the convergence speed, but also avoids the training process getting stuck in bad local minima. Meanwhile, instead of using RNNs which are difficult to be implemented in parallel, the combination of padding operation and attention module in the proposed BABNet enables CNN, as the backbone, to process sequential data with variable length. Extensive experiments are conducted to demonstrate the recognition capability and robustness of the BABNet in different environments.
Immersive architectures for visual data literacy

Abstract The datafication process transforming society enables us to witness the pandemic from a global perspective. This article provides an example of immersive architecture in which coronavirus-related scientific literature was revealed during Ars Electronica 2021 . Like a starry sky, a network visualization representing more than 600,000 articles was showcased in the Deep Space 8K theater, where spectators were accompanied in reading insights. The case study of 3D Cartography of COVID-19 illustrates a novel way to present data in public spaces to foster conversations and reflects on how visual data literacy can be addressed in museums.
The meter of backscattering in aquatic environment measurement: methods and device for in situ monitoring

A new spectral backscattering meter is presented, the accuracy characteristics and the relationship between the medium parameters are estimated. It is shown, that backscattering spectral meter «BSS-K» application as a part of the «Kondor» biophysical complex, that includes the background water environment parameters (temperature, hydrostatic pressure and electrical conductivity (calculated salinity)); turbidity (the calculated TSM concentration parameter); the modules of TSM disperse composition and water environment dynamics (the currents’ speed and direction) as well as photosynthetically active radiation makes it possible to calculate the required corrections instrumentally, taking into account the changes in the influencing parameters of water ecosystem, as well as to obtain the complex interconnected informative data for a comprehensive analysis of water environment.
Saliency-based point cloud quality assessment method using aware features learning

This paper deals with a saliency-based no-reference (NR) method for 3D point cloud (PC) quality assessment. For this purpose, we firstly compute 3D visual saliency map for each distorted point cloud. Then, we use a threshold-based filter to select the most salient points. From these, we extract both geometrical a perceptual attributes. Estimates of their statistical properties (Entropy, Standard deviation, Skewness, Kurtosis, Median and Mean) form a features vector. In the end, the Support vector regressor (SVR) is utilized for the characteristics regression and the quality score prediction. To validate our method, a set of experiments are conducted on an open subjective colored point cloud dataset (SJTU-PCQA). Results show that the suggested method exceeds some competing methods accord-ina to correlation with average opinion score.