Reviewerly/exHarmony huggingface dataset:
An Analysis of Pattern Recognition and Machine Learning Approaches on Medical Images

This chapter focuses on detailed overview of medical images as well as microscopic images to diagnosis the disease based on pattern recognition model in digital image processing. Pattern recognition is leading role in machine learning model which is used to detect the object such as text or character recognition, fingerprint recognition, face recognition, and biometric recognition. This chapter used pap smear images of cervical cancer to diagnosis a tissues by applying various image processing techniques followed by four tasks which are image acquisition, preprocessing, feature extraction, and object recognition. Cervical cancer is one of the very dangerous kind of cancer which may occur on the cervix part of the women. Diagnosis of tissue cells is based on the pixel variations of the images that may predict the cell is normal or abnormal state. This chapter presents machine learning algorithm of principle component analysis (PCA), Singular value decomposition (SVD), and linear discriminant analysis (LDA) under dimensionality reduction to recognize the pattern of the pap smear microscopic images.
Space-Efficient Subgraph Search Over Streaming Graph With Timing Order Constraint

The growing popularity of dynamic applications such as social networks provides a promising way to detect valuable information in real time. These applications create high-speed data that can be easily modeled as streaming graph. Efficient analysis over these data is of great significance. In this paper, we study the subgraph (isomorphism) search over streaming graph data that obeys timing order constraints over the occurrence of edges in the stream. The sliding window model is employed to focus on the most recent data. We propose an efficient solution to answer subgraph search, introduce optimizations to greatly reduce the space cost, and design concurrency management to improve system throughput. Extensive experiments on real network traffic data and synthetic social streaming data shows that our solution outperforms comparative ones by one order of magnitude with less space cost.
Leveraging Textual and Non-Textual Features for Documentation Decluttering

This paper describes the participation of a team from the University of Bari in the Decluttering Challenge organized in the scope of the DocGen2 workshop. We propose a supervised approach relying on a minimal set of non-textual features (length, overlapping between the comment text and the source code, code block type, tags, comment type) and classical textual features (bag-of-words). Our system ranked 2nd in the documentation decluttering task.
Utilization of Geochemical Signatures for Unconventional Reservoir Characterization, Saudi Arabia

Abstract Paleozoic tight sandstone and shale reservoirs in the Kingdom of Saudi Arabia are considered potential sources of natural gas that will meet the growing domestic demand. In order to enhance their exploration and exploitation, research focusing on the quality of these potential gas reservoirs has tremendously increased in the past few decades. Most of the Paleozoic reservoirs have similar lithologies and are poorly constrained biostratigraphically, making facies analysis, stratigraphic correlation, and geosteering problematic. This paper describes how chemostratigraphic markers are used to identify and separate individual lithologies. In this study, 320 data points were acquired from continuous core samples obtained from three Paleozoic formations. Raw elemental spectral data were acquired using a high-resolution tabletop µXRF system. The spectral data were used as geochemical signatures to provide a better understanding of the geochemical composition of each sample. Multivariate statistical analysis including principal component analysis (PCA), pairwise correlation and hierarchical clustering of principal components analyses (HCPC) were used to characterize the acquired geochemical signatures. Eleven chemofacies within three lithofacies were identified based on PCA and HCPC analyses of the geochemical signatures with clearly defined boundaries. Stratigraphic distribution patterns of the geochemical signatures (e.g. Si, Ca, Mn, Al, K, and Fe), such as sharp truncation at the upper and lower bounds, clearly defined each lithofacies. Thus, key marker elements for each lithofacies and chemozones within it were identified. Formation intervals were clearly defined based on the chemozones, even where the lithologies appeared homogenous. The overall results indicate sediment deposition under varied depositional settings, from fluvial to shallow marine environments. The results demonstrate that elemental composition analysis can be used as a discriminating tool for resolving stratigraphic uncertainties due to its ability to document subtle variations among samples that appear homogenous with other techniques. The definition of key marker elements for each chemofacies will serve a useful tool to support geosteering operations for better well placement.
Multi-Cue and Temporal Attention for Person Recognition in Videos

Recognizing persons under unconstrained settings is challenging due to variation in pose and viewpoint, partial occlusion, and motion blur. Inference only by face-based recognition techniques would fail in these cases. Previous studies mainly focus on this problem on still images while they cannot handle the temporal variations in videos. In this work, we aim to tackle these challenges and propose a Multi-Cue and Temporal Attention (MCTA) framework to recognize persons in videos. For the spatial domain, we extract features from multiple visual cue regions and utilize a Multi-Cue Attention Module to integrate them. For the temporal domain, we adopt a Temporal Attention Module to combine the video frames, which is learned to assess the quality of different frames adaptively. By this means, MCTA can comprehensively explore the complementary information in spatial-temporal dimensions for person recognition in videos. Moreover, we introduce Character Recognition in Videos (CRV), a new video dataset for character recognition under challenging settings. Extensive experiments on CRV demonstrate the effectiveness of our proposed framework. Dataset with annotations and all codes used in this paper are publicly available at https://github.com/zhezheey/MCTA.
Multiclass Image Classification Explanation with the Complement Perturbation Images

The paper describes the investigation of a possibility to explain the result of the black box neural network image classifier with the pair of images we call complement. We generate them from the initial image being recognized with the hiding of some parts of it. The first image in the pair should be classified correctly, while another one should have another class label. Searching for the parts to hide may be based on the clustering of an image or just its geometrical division. The iterative division of an image was tested as a way to form parts and compared with well-known clustering methods. Different hiding methods that include the replacing with fixed black or white color or the blurring of part with various distortion levels were investigated. The chances to find the explanation in a form of explanation success rate were estimated for different methods and conditions. It was shown that the recursive division may be successful in this task.
Situation Knowledge Service Model Based on Ontology Technology

To tackle problems of low coupling between knowledge organization and business process, and difficulty in balancing the efficiency and accuracy of knowledge retrieval, this paper proposes a situation knowledge service model based on ontology technology. Firstly, the situation ontology is encapsulated into business process-oriented knowledge nodes to meet operational command business requirements; then, knowledge nodes are organized into knowledge trees according to business flow by using directed acyclic graph; finally, a knowledge retrieval strategy is designed to gradually lock the target knowledge resources from two dimensions of semantic relationship and business logic. Through the performance analysis, it is found that the model in this paper can quickly and accurately retrieve the required knowledge resources in the situation knowledge sharing scenario, with high practical values.
Multi-objective Quantum Moth Flame Optimization for Clustering

This paper defines a new moth flame optimization version with quantum-behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, New thyroid, Wine, Liver Disorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Forty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, New thyroid, Wine, Liver Disorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Forty and was able to provide the exact number of clusters for all datasets.
University teachers’ needs for the efficient use of ICT in the pedagogical process

The appropriate use of Information and Communication Technology (ICT) along with Modern Teaching Practices (MTPs), such as flipped learning and project-based learning, can increase the efficiency of teaching and learning in the higher education (HE) process. To ensure effective implementation of ICT and MTPs, the University of Maribor (UM) has established institutional support in the form of the Centre for Teaching Support of the University of Maribor (CTSUM), which is available to university teachers of all 17 UM faculties. The CTSUM provides support for the use of ICT and implementation of MTPs through individual consultations and workshops. This study aims to assess teachers' interests and needs in the field of ICT and MTPs along with the self-evaluation of their existing ICT skills. It was conducted as an online survey with responses from 154 teachers (approx. 26% response rate). The results show that a significant majority of teachers are interested in the provided support and need training on the use of ICT for preparing interactive assignments, voting and providing feedback, performing project-based learning, designing graphically rich study materials, and organising course materials online. The obtained information helps the CTSUM prepare customised workshops and consultations aimed at strengthening teachers' digital literacy and didactic skills.
Style Transfer Using Whitening and Coloring to Realize Feature Transformation

Abstract This paper proposes a general image style transfer method based on VGG network. This method does not need to learn and pre-train any kind of style in advance. The algorithm mainly implements the style transfer task by reconstructing the automatic decoder. In the process of feature extraction, the algorithm matches the content features and style features through whitening and coloring. We conducted a series of experiments with different photographic images. The experimental results show that our method has a simple training process, low computational complexity and high computational speed and excellent generalization ability.
A Multi-level Approach to the Formal Semantics of Agent Societies

Typically, an agent society is composed of at least three main components, namely, the set of its individual agents, the set of multi-agent organisations that help coordinate the actions of those agents, and the environment that those agents share. However, other levels have also been pointed out in the agent society literature, for example, the internal normative systems that regulate the structure and operation of the organisations themselves and, more generally, the overarching cultural system of the society. Many types of formal semantics for such systems, such as for example operational semantics, are not immediately suitable for systems that have ongoing operations at several architectural levels and requiring that those levels synchronise when particular events occur. This paper addresses this problem by proposing an approach to giving formal semantics to multi-level agent societies. The approach allows for the reuse of existing semantics for the separate levels, improves readability, and reduces the number of inference rules to be written.
Linux Forensic Triage: Overview of Process and Tools

Digital forensics dates back into the 1980s, but the importance of Linux forensics was not taken into place until recently. Linux forensics is a distinctive world compared to example Microsoft Windows forensics. Although it is commonly used as a name for the entire operating system, Linux is just the name of the kernel, a piece of software that handles interactions between the hardware and end-user applications. Its popularity has not reached the popularity of the Windows operating system, therefore, without many reliable tools on the market, it represents a bigger challenge for digital forensics investigators. Digital triage is the process in which an investigator collects, assembles, analyzes, and prioritizes digital evidence from a crime. Since there are not many available tools on the market for performing Linux triage, the most important part is to understand the tool and its capabilities in order to know which one to use for a certain situation. This paper will describe how the Linux system is structured, what its architecture contains, how should one correctly approach and acquire the system, and how to understand the tools and results they provide.
Study of Intelligent Question Answering System Based on Ontology

This paper designs Intelligent Question Answering System (IQAS) by a combined use of the concept of ontology and search engine. The system can help users to obtain more accurate answers. It raises the issue of user word segmentation and keyword extraction, and searches keyword-related information on the web, and then returns results and saves them to the database, which enables users to extract easily. It is found through the search that the results are more accurate than those before, and the users have more choices.
The Research of Point Cloud Stitching Technique Based on Kinematic Relationship

In this paper, a novel point cloud stitching technique is proposed to solve the multi-view point cloud stitching problem through calculating the kinematic relationship. A standard sphere is used to calibrate the rigid transformation between the retro-reflective markers coordinate system and the line structured-light coordinate system. The binocular vision system is used to track the position and orientation of the line structured-light sensor. Then the multi-view point cloud can be stitching into a unified coordinate system. The proposed technique consists of a closed-form solution. Through experiments, a good result has been obtained by the proposed technique. Compared with the classical techniques, it does not need to attach any marker to the tested object and the line structured-light sensor can be moved freely.
Forensic Analysis of Handwritten Electronic Signatures

Just as many forensic document examiners (FDEs) experienced a decline in the number of typewriter cases being submitted as the “computer age” was emerging in the late 1980s, technological advancements taking place today are certain to change the landscape of the field once again. Although there are several additional steps when conducting an analysis of an electronically-captured handwritten signature as compared to a traditional wet-ink signature, the methodology and principles established in the field over a century ago still apply. As with many technological expansions, the overall effectiveness of a product or system is often decided by its efficiency and use by the general public. Someone trying to imitate another’s genuine signature often uses the static trace of a signature as a model. When conducting dynamic signature analysis, knowing the exact make and model of the specific capture device can be invaluable for several reasons.
Multimodal Image Retrieval Based on Eyes Hints and Facial Description Properties

Eyes are the most prominent visual components on human face. Obtaining the corresponding face only by the visual hints of eyes is a long time expectation of people. However, since eyes only occupy a small part of the whole face, and they do not contain evident identity recognition features, this is an underdetermined task and hardly to be finished. To cope with the lack of query information, we enroll extra face description properties as a complementary information source, and propose a multimodal image retrieval method based on eyes hints and facial description properties. Furthermore, besides straightforward corresponding facial image retrieval, description properties also provide the capacity of customized retrieval, i.e., through altering description properties, we could obtain various faces with the same given eyes. Our approach is constructed based on deep neural network framework, and here we propose a novel image and property fusion mechanism named Product of Addition and Concatenation (PAC). Here the eyes image and description properties features, respectively acquired by CNN and LSTM, are fused by a carefully designed combination of addition, concatenation, and element-wise product. Through this fusion strategy, both information of distinct categories can be projected into a unified face feature space, and contribute to effective image retrieval. Our method has been experimented and validated on the publicly available CelebA face dataset.
Recognition Method of Aerial Insulator Defects Based on Deep Learning

Abstract Aiming at the problem of low accuracy of aerial photography insulator defect identification during UAV patrol, a method of insulator defect identification and positioning based on improved YOLOv3 target detection model is proposed. This method uses asymmetric convolutional block (ACB) as the structural block of the convolutional neural network model in the training phase. On the basis of not increasing the inference time, the depth of neural network learning is improved. At the same time, the feature map combination of the pyramid multi-scale detection model is improved, and the distance index parameters of the K-means ++ algorithm are optimized, which is used for target prior frame clustering. The experimental results show that the improved method proposed in this paper improves the recognition accuracy of small defective targets. Without reducing the speed of inference, the average accuracy is increased from 84.5% to 91.9%, which meets the requirements of intelligent inspection of UAVs for overhead transmission lines.
Lower Bounds on the Runtime of Crossover-Based Algorithms via Decoupling and Family Graphs

The runtime analysis of evolutionary algorithms using crossover as search operator has recently produced remarkable results indicating benefits and drawbacks of crossover and illustrating its working principles. Virtually all these results are restricted to upper bounds on the running time of the crossover-based algorithms. This work addresses this lack of lower bounds and rigorously bounds the optimization time of simple algorithms using uniform crossover on the search space $$\{0,1\}^n$$ from below via two novel techniques called decoupling and family graphs. First, a simple steady-state crossover-based evolutionary algorithm without selection pressure is analyzed and shown that after $$O(\mu \log \mu )$$ generations, bit positions are sampled almost independently with marginal probabilities corresponding to the fraction of one-bits at the corresponding position in the initial population. In the presence of weak selective pressure induced by the probabilistic application of tournament selection, it is demonstrated that the inheritance probability at an arbitrary locus quickly approaches a uniform distribution over the initial population up to additive factors that depend on the effect of selection. Afterwards, the algorithm is analyzed by a novel generalization of the family tree technique originally introduced for mutation-only EAs. Using these so-called family graphs, almost tight lower bounds on the optimization time on the OneMax benchmark function are shown.
Reflexive Approach in Academic Pracices in the Preparation of Pedagogy Students

The article considers the problem of the academic education of students as pedagogues - the use of reflexive approach in their professional practical training. In this context, the concepts of refection, professional refection and its specificity in the field of education are explained. The pedagogical activities’ characteristic of the specific types of reflexes in different combinations of intellectual, personal and dialogical reflexes are presented. The basis of the theoretical ideas of a number of authors is represented by a reflexive model for the formation and development of pedagogical reflexive skills - based on the practical competence of the student pedagogues.
RETRACTED: Research on the Cultivation and Improvement Strategies of University Teachers’ Innovative Consciousness and Ability Based on Network Technology

Abstract In the computer network technology age, teachers in institutions of higher learning are responsible for the important tasks of cultivating socialist builders and successors. The main body of teaching reform is teachers. To cultivate high-quality talents with innovative ability, it is necessary to improve the innovative consciousness and ability of college teachers to have a new educational concept, noble personality and perfect knowledge and ability structure of the profession. To this end, colleges and universities should establish a teacher training system, take the road of integration of teaching and research, and let students participate in the evaluation of teachers’ teaching quality, establish an effective incentive mechanism, and create a relaxed academic environment.
Technological and methodological aspects of using reference and search tools in maintaining the database of Dubna University faculty’s publications

The author discusses the experience of building, maintaining and using the bibliographical database of science publications by the faculty of the state Dubna University. She analyzes in detail MegaPro ALIS retrieval functionality and describes different types of the system’s dictionaries and guides, their advantages and drawbacks in terms of entering data into bibliographic entry. The algorithm of using outer tables for reference when entering repeated subfields in one-click or entering data into several subfields of the bibliographic records. The example is given how hierarchical data of organization structure is used as an outer dictionary. Feasibility of using the directories of faculty authors and journals/other sources of information in publication databases is substantiated; example is given how to use them when entering several subfields of MARC21 fields 100, 700 and 773. The methodological solutions for maintaining directories of faculty authors and journals (and other information sources) are discussed. User and program interface for retrieving publications by author’s affliation, academic degree or title, institution, information source specific data are discussed as a result of building database of publications. The author emphasizes that the dictionaries may be used as supplementary information resources to support faculty publication activities and to integrate them into other university systems.