Reviewerly/exHarmony huggingface dataset:
Problems of forensic identification of handwriting in forensic examination

The handwritten signature as a characteristic of authentication has been legally and publicly recognised for centuries and has been used in the forensic field for many decades to identify an author. Approaches to biometric user authentication developed in recent years are also based in part on handwriting characteristics, such as automatic signature verification. This article systematically identifies handwriting features published in forensics and analyses which of these properties can be adapted as biometrics to verify the user. A user verification procedure based on a set of 14 forensic characteristics is presented, which is integrated into the biometric verification procedure. The results of the verification of these forensic features are compared in detailed tests with the features of non-forensic data, and it is shown that significant improvements in false detection rates can be achieved by including forensics. For hundreds of years, handwritten signatures have been legally and socially recognised as authentication. The reason for this is the uniqueness of human handwriting. Although a forger with some practice may visually imitate another person’s text or signature, the typical traces resulting from the (studied) behaviour of a victim of forgery are difficult to copy. To check the record, the pressure force of all overlay points with a pen at the beginning or inside the word, written letters on the weight are used. In addition, the way a writer unconsciously or even consciously connects words, letters, or parts of letters to each other is a specific trait for that person. In a forensic examination, experts examine these and other features to prove or disprove the authenticity of signatures or documents. In the field of forensic science, there are many procedures offered based on physical handwriting samples to determine a writer
PCAPFunnel: A Tool for Rapid Exploration of Packet Capture Files

Analyzing network traffic is one of the fundamental tasks in both network operations and security incident analysis. Despite the immense efforts in workflow automation, an ample portion of the work still relies on manual data exploration and analytical insights by domain specialists. Current state-of-the-art network analysis tools provide high flexibility at the expense of usability and have a steep learning curve. Recent—often web-based—analytical tools emphasize interactive visualizations and provide simple user interfaces but only limited analytical support. This paper describes the tool that supports the analytical work of network and security operators. We introduce typical user tasks and requirements. We also present the filtering funnel metaphor for exploring packet capture (PCAP) files through visualizations of linked filter steps. We have created PCAPFunnel, a novel tool that improves the user experience and speeds up packet capture data analysis. The tool provides an overview of the communication, intuitive data filtering, and details of individual network nodes and connections between them. The qualitative usability study with nine domain experts confirmed the usability and usefulness of our approach for the initial data exploration in a wide range of tasks and usage scenarios, from educational purposes to exploratory network data analysis.
A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes

Qualitative influence statements are often provided a priori to guide learning; we answer a challenging reverse task and automatically extract them from a learned probabilistic model. We apply our Qualitative Knowledge Extraction method toward early prediction of gestational diabetes on clinical study data. Our empirical results demonstrate that the extracted rules are both interpretable and valid.
Tracking Missing Objects in a Video Using YOLO3 in Cloudlet Network

In real time, people are using CCTV for monitoring activities continuously but sometimes theft is taking place. In this scenario, people need to roll back the video and need to identify when it happened. But in practice, it is a difficult and time-consuming process to identify a missing object in the video within the local machine because of the lack of computing resources. To solve this problem, we are presenting an algorithm in this paper to notify missing objects in a video offloaded from a mobile or CCTV using YOLO3 object detection in a cloudlet network. In the area of cloud computing, a cloudlet is a data center in the local network with a rich set of computing resources available for mobile users.
Review on Face Recognition Using Deep Learning Techniques and Research Challenges

In the research area of object recognition, last few decades many of the researches did research on face recognition and the research is still active because of its application and challenges present in the real world. In the constrained environment most of the recent face recognition techniques offer a better result but it fails in an unconstrained environment. An unconstrained environment such as the images were captured during various environments like different resolutions, in a different pose with various expression, illumination, and occlusions. In this article, various deep learning techniques used for face recognition have been discussed.
Multi-mode Big Data Mining and Analysis Based on Internet of Things on Power

With the development of big data mining and artificial intelligence technology, the big data analysis in power is more and more used. At present, the big data is composed of power multi-mode data mainly includes PMS system of power equipment management, Scada system of power data acquisition and monitoring control, online monitoring system of various equipment, patrol system, intelligent video monitoring system and so on. The scale and dimension of these data are huge. This paper is based on Internet of Things with Logistic Algorithms and Neural Network Algorithms. By analyzing and predicting the probability of power failure through real-time monitoring data, the state of the power equipment can be evaluated, the potential risks in power operation can be evaluated and predicted, and the active warning can be given to improve the security of the system.
Impact of Group Theory in Cryptosystem

This paper presents a new approach of group theory toward cryptosystem with its application of mathematical methods and functions for cryptographic regions. This new group theory in cryptographic paradigm is brought together with relation to existing functional groups, fields, rings, mathematical theorems, mathematical tools, etc. this study concentrated on semantic security solutions evaluation for cryptographic systems. Such cryptographic systems are mostly designed to use the group’s applications such as fields, ECC, functionality tests, cyclic groups, and Schnorr groups. In this paper, we discussed some possible applications of groups and group theory by introducing different forms of groups, fields, rings, theorems, etc. A brief description of some mathematical tools which include functionalities tests, methods, discrete logarithmic problems, ECC, factoring polynomials, and baby giant algorithm is also presented in this paper. Solutions for cryptographic problems inspired by group theory and its applications in this field are also described in the study.
VR-Dialogue - The AI-powered e-Participation Research Expedition

In our discussion panel, we are going to elaborate upon the novel research applying artificial intelligence and immersive virtual reality (VR) technologies to serious communication in the context of e-Participation behavioural research. We are going to present a research plan and preliminary results of specific EU-US research collaboration funded by the European Commission under The Next Generation Internet Explorers program on the topic of VR-Dialogue. The panel will gather researchers and experts directly involved in the research, approaching the problem from different perspectives.
Practical and high-quality partitioning algorithm for large-scale and time-evolving graphs

With the appearance of widespread large-scale graph data, distributed graph processing grows in popularity. In order to store and analyze a large-scale graph in a distributed manner, the graph should be properly partitioned in advance. Although the partitioning of static graphs has been sufficiently investigated, the emerging graphs nowadays are typically dynamic or time-evolving. State-of-the-art partitioning algorithms for such large-scale and time-evolving graphs either are impractical because of the complexity caused by the global optimization or sacrifice partitioning quality due to a huge number of cross partition edges. This paper investigates the problem and proposes a new practical and high-quality graph partitioning algorithm. First, graph updates accumulated in a time interval are expressed as a delta graph. Second, vertices in the delta graph are classified into several subsets based on a non-overlapping community detection method. Third, vertices in the same subset are assigned to a properly chosen partition as a whole. The chosen partition is lightly loaded as well as closely related with vertices in the subset. Experimental results show that compared with the widely used hash-based and heuristic-based partitioning algorithms, our proposed algorithm gains significant decrease in terms of the number of cross partition edges while maintaining a similar level of load balance.
Bibliometric analysis of scientific production on international trade and cryptocurrency

There has been a remarkable increase in the number of publications on international trade and cryptocurrency in recent years. This paper aims to analyze the literature on international trade and cryptocurrency in the Web of Science database. This study uses the bibliometric method and mapping analysis. The cluster analysis is conducted based on the keyword analysis. These publications are reviewed from different aspects such as type of publication, language, and book title. This study found that 767 articles which are related to cryptocurrency and international trade. Among the countries in which these studies are conducted, China ranks the first, followed by the USA and UK, respectively. Various organizations in different countries support studies on this topic. In conclusion, cryptocurrency technologies draw the attention of academia, and the use of cryptocurrency in international trade will determine the future trade structure. The innovative features of cryptocurrency can develop new business models, which may be the reason for the academic interest in this matter. It will be useful for businesses and governments to follow this potential carefully to benefit from the advantages of innovative business models.
Knowledge Distillation with Teacher Multi-task Model for Biomedical Named Entity Recognition

A Multi-task model (MTM) learns specific features using shared and task specific layers among different tasks, an approach that turned out to be effective in those tasks where limited data is available to train the model. In this research, we utilize this characteristic of MTM using knowledge distillation to enhance the performance of a single task model (STM). STMs have difficulties in learning complex feature representations from a limited amount of annotated data. Distilling knowledge from MTM will help STM to learn more complex feature representations during the training phase. We use feature representations from different layers of a MTM to teach the student model during its training. Our approach shows distinguishable improvements in terms of F1-score with respect to STM. We further performed a statistical analysis to investigate the effect of different teacher models on different student models. We found that a Softmax-based teacher model is more effective for token level knowledge distillation than a CRF-based teacher model.
Computer Vision for 3D Perception and Applications

Effective 3D perception of an observed scene greatly enriches the knowledge about the surrounding environment and is crucial to effectively develop high-level applications for various purposes [...]
Gauss-guided patch-based deep convolutional neural networks for no-reference image quality assessment

Patch-based deep convolutional neural network (DCNN) has been proved to have advanced performance in no-reference image quality assessment (NR-IQA). However, these methods generally take global quality score as the quality score of each patch mainly since local quality score is not provided. Unfortunately, the perceived quality of image patch is difficult to maintain a high degree of consistency. Thus, the use of the same global quality score in different patches of the same image may hinder training of DCNNs. In this paper, we propose a universal and nearly cost-free model called Gaussian Random Jitter (GRJ). According to the uncertainty of the perceived quality, GRJ divided the training images into high-confidence distorted images and low-confidence distorted images, and reasonably assigned different local quality scores to each patch through specific gaussian functions with the global quality score as the mean value and the undetermined hyperparameter as the standard deviation. We took one of the most advanced patch-based DCNNs models as backbone and tested the improved performance over three widely used image quality databases. We show that our model can further improve the performance of patch-based models and even help them comparable with those of state-of-the-art NR-IQA algorithms.
TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration

Despite rapid developments in the field of machine learning research, collecting high quality labels for supervised learning remains a bottleneck for many applications. This difficulty is exacerbated by the fact that state-of-the art models for NLP tasks are becoming deeper and more complex, often increasing the amount of training data required even for fine-tuning. Weak supervision methods, including data programming, address this problem and reduce the cost of label collection by using noisy label sources for supervision. However until recently, data programming was only accessible to users who knew how to program. In order to bridge this gap, the Data Programming by Demonstration framework was proposed to facilitate the automatic creation of labeling functions based on a few examples labeled by a domain expert. This framework has proven successful for generating high accuracy labeling models for document classification. In this work, we extend the DPBD framework to span-level annotation tasks, arguably one of the most time consuming NLP labeling tasks. We built a novel tool, TagRuler, that makes it easy for annotators to build span-level labeling functions without programming and encourages them to explore trade-offs between different labeling models and active learning strategies. We empirically demonstrated that an annotator could achieve a higher F1 score using the proposed tool compared to manual labeling for different span-level annotation tasks.
An Optimized Customers Sentiment Analysis Model Using Pastoralist Optimization Algorithm (POA) and Deep Learning

Users usually express their sentiment online which influences purchased products and services. The computational study of people's feelings and thoughts on entities is known as sentiment analysis. The Long Short-Term Memory (LSTM) model is one of the most common deep learning models for solving sentiment analysis problems. However, they possess some drawbacks such as longer training time, more memory for training, easily over fits, and sensitivity to randomly generated parameters. Hence, there is a need to optimize the LSTM parameters for enhanced sentiment analysis. This paper proposes an optimized LSTM approach using a newly developed novel Pastoralist Optimization Algorithm (POA) for enhanced sentiment analysis. The model was used to analyze sentiments of customers retrieved from Amazon product reviews. The performance of the developed POA-LSTM model shows an optimal accuracy, precision, recall and F1 measure of 77.36%, 85.06%, 76.29%, and 80.44% respectively, when compared with LSTM model with 71.62%, 78.26%, 74.23%, and 76.19% respectively. It was also observed that POA with 20 pastoralist population size performs better than other models with 10, 15, 25 and 30 population size.
The Concept of Transition from Smart University to Smart Business in Digital Economic Environment

The concepts of “Digital economy” and “Internet of Things” have now become common directioins of modern development where the “Industry 4.0” concept is considered as of prior importance. Higher education drives this aspect by means of practice-oriented approach in teaching where the required competences are studied and applied in practice for certain professional targets. The authors proposed three basic models reflecting the conceptual vision of the digital transformation of the smart environment management competencies required for effective activities of state unitary enterprise: Model of data collection and processing during the digital transformation; Model of audit by the IoT using, Model of three-stage implementation of the IoT System in the state unitary enterprise “Kupinskoye”.
Rectifying Pseudo Label By Mutual Disagreement Learning For Unsupervised Domain Adaptation Person Re-Identification

Unsupervised domain adaptation person re-identification(re-ID) aims to transfer knowledge from the labeled source domain to unlabeled target domain, which is still a challenging task due to the large domain discrepancy. The clustering-based methods maintain advanced performance, generating pseudo-labels for unlabeled target domain images by clustering. However, not making full use of all valuable images and label noise derived from imperfect clustering results dramatically impact further performance improvement. To alleviate the above two problems, we propose a novel mutual disagreement learning(MDL) framework. We attach some of outliers(unclustered samples) with small loss to training process in an adversarial strategy manner. To rectify label noise, we train two networks and their momentum-based moving average models, making them teach each other and using prediction disagreement samples to update networks. Extensive experiments on four unsupervised domain tasks, Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT, show that the advantages of our proposed MDL framework compared with other state-of-the-art methods.
The Effective Way to Develop Speaking Skills

Speaking is the skill of conveying messages through spoken language, by speaking so that the fulfillment of the need to communicate with the surrounding, environment based on initial observations with people around speaking skills is still lacking. The low ability to speak with the results of interviews with people around it’s caused by internal factors and external factors that come from their techniques in obtaining and applying theories regarding speaking skills. Interview techniques with certain topics are an attempt to improve speaking skills to describe something, give opinions and tell stories is the way to approach the object.  The benefits of this research include theoretical and practical benefits, theoretically, the results of this study are expected to contribute knowledge related to the use of appropriate techniques in learning to speak, practically the results of this study are expected to be useful for the author as the development of knowledge in the field of speaking skills. This study used a random design with people around, data collection was done by interviewing the non-test data collection tools used were interviews and observation.  The data analysis was done qualitatively. The conclusion of this research is to add the knowledge about what is the effective way to develop speaking skills and the author hopes all the readers can get the aim from this research.
Augmenting Topic Aware Knowledge-Grounded Conversations with Dynamic Built Knowledge Graphs

Dialog topic management and background knowledge selection are essential factors for the success of knowledge-grounded opendomain conversations.However, existing models are primarily performed with symmetric knowledge bases or stylized with pre-defined roles between conversational partners, while people usually have their own knowledge before a real chit-chat.To address this problem, we propose a dynamic knowledge graph-based topical conversation model (DKGT).Given a dialog history context, our model first builds knowledge graphs from the context as an imitation of human's ability to form logical relationships between known and unknown topics during a conversation.This logical information will be fed into a topic predictor to promote topic management, then facilitate background knowledge selection and response generation.To the best of our knowledge, this is the first attempt to dynamically form knowledge graphs between chatting topics to assist dialog topic management during a conversation.Experimental results manifest that our model can properly schedule conversational topics and pick suitable knowledge to generate informative responses comparing to several strong baselines.
Gated Knowledge Graph Neural Networks for Top-N Recommendation System

In recent years, the knowledge graph based recommendation system is a research hotspot and scholars propose a propagation-based method, which combines graph neural networks with knowledge graph. But the previous work faces two problems: The first is that exist propagation methods generate neighours of target entity through random sampling strategy which will bring noise to the system. The second problem is that exist models directly aggregate the neighbors information of the target entity at each step, while ignoring the fact that the propagation of high-order information also needs to be selective and memorable. To solve these problems, we propose a novel model: Gated Knowledge Graph Neural Networks for Top-N Recommendation System(GKGNN). This model uses pretrain technique to generate neighbor set with high priority of the target entity in the graph. At the same time, this model introduces the gated mechanism into the propagation process and the valuable information is remembered and unimportant information is forgotten during the high-order information propagation. Finally, we conduct comparative experiments of our model with other baseline algorithms on three real world datasets, and the experimental results prove the superiority of our model.
Use of Transforms in Biomedical Signal Processing and Analysis

Biomedical signals like electrocardiogram (ECG), photoplethysmographic (PPG) and blood pressure were very low frequency signals and need to be processed for further diagnosis and clinical monitoring. Transforms like Fourier transform (FT) and Wavelet transform (WT) were extensively used in literature for processing and analysis. In my research work, Fourier and wavelet transforms were utilized to reduce motion artifacts from PPG signals so as to produce correct blood oxygen saturation (SpO2) values. In an important contribution we utilized FT for generation of reference signal for adaptive filter based motion artifact reduction eliminating additional sensor for acquisition of reference signal. Similarly we utilized the transforms for other biomedical signals.