Reviewerly/exHarmony huggingface dataset:
Simplicial Complex Based Point Correspondence Between Images Warped onto Manifolds

Recent increase in the availability of warped images projected onto a curved manifold, especially omnidirectional spherical ones, coupled with the success of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection. Although, currently, several existing methods "flatten" such 3D images to use planar graph/hypergraph matching methods, they still suffer from severe distortions and other undesired artifacts, which result in inaccurate matching. Alternatively, current planar methods cannot be trivially extended to effectively match points on images warped on curved manifold. Hence, matching on these warped images persists as a formidable challenge. In this paper, we pose the assignment problem as finding a bijective map between two graph induced simplicial complexes, which are higher-order analogues of graphs. We propose a constrained quadratic assignment problem (QAP) that matches each p-skeleton of the simplicial complexes, iterating from the highest to the lowest dimension. The accuracy and robustness of our approach are illustrated on both synthetic and real-world spherical/warped (projected) images with known ground-truth correspondences. We significantly outperform existing state-of-the-art spherical matching methods on a diverse set of datasets.
Stable Machine Learning Knowledge Map Domain Analysis

Stable patterns that are widely used in today’s software engineering in modelling and it plays an important role in reducing the cost and condensing the time of software product lifecycles. Nowadays, many existing traditional patterns fail to model the subtle changes in context of the implementation of the model. As a result, the reusability of the pattern will significantly decrease. The goal of this paper is to present a pattern language for building a core knowledge of stable patterns called knowledge map. This paper will also represent the first attempt towards a machine learning knowledge map representation via stable patterns as a mean to discover, organize, and utilize machine learning core knowledge. Each stable pattern focuses on a distinctive activity and provides a way by which this activity can be conducted efficiently. The presented stable analysis and design patterns will provide a core knowledge of stable machine learning domain that is easily extensible, stable through time, and focus on stable machine learning of Unified (1) Functional and non-Functional Requirements (2) Unified Design.
Heterogeneous Deniable Authenticated Encryption Protocol

Deniable authenticated encryption can achieve confidentiality and deniable authentication in a logical single step. Such a cryptographic primitive simplifies the design of cryptographic scheme and reduces the cost of computation and communication. In this paper, we propose a heterogeneous deniable authenticated encryption scheme called HDAE. The proposed scheme permits a sender in a public key infrastructure environment to transmit a message to a receiver in an identity-based environment. Our design utilizes tag-key encapsulation mechanism (tag-KEM) and data encapsulation mechanism (DEM) hybrid encryption methods, which is especially applicable in some privacy protection occasions. In addition, we give how to design an HDAE scheme utilizing a heterogeneous deniable authenticated tag-KEM (HDATK) and a DEM. We also construct an HDATK scheme and provide security proof in the random oracle model. Comprehensive analysis shows that our scheme is efficient and secure.
PON: Proposal Optimization Network for Temporal Action Proposal Generation

Temporal action localization is a challenging task in video understanding. Although great progress has been made in temporal action localization, the most advanced methods still have the problem of sharp performance degradation when an action proposal generated. Most methods use sliding windows method or simply group frames according to frame-level scores. These methods are not enough to provide accurate action boundary and maintain reasonable temporal structure. In order to solve these problems, we propose a novel proposal optimization network to generate start score, end score, action score and regression score, and then remove the redundancy by NMS algorithm. In the proposed method, we introduce a metric loss function to maintain the temporal structure of action proposal in the training process. To verify the effectiveness of the proposed method, we have made comparative experiments on ActivityNet-1.3 dataset respectively, and the proposed method has surpassed some of the state-of-the-art methods on the dataset.
A framework for information synthesis into sentiment indicators using text mining methods

A formal statistical framework is proposed for synthesis of text information into sentiment indicators. Each text document is treated as an exchangeable collection of stems of words (tokens), and used in conjunction with a multinomial inverse regression approach to efficiently synthesize the information content in text documents. The proposed methodology is illustrated through the buildup of sentiment indicators using Twitter news outlet text information. These synthesizing indicators, quantitative in nature, can be built across disciplines to capture changes in the economic, financial, and social conditions, and also serve to reveal heterogeneity across countries, sectors, or markets. The proposed approach is computationally fast and allows for time variation in the indexes.
Analysis of Footwork Training in College Football Teaching Based on Computer Modeling and Simulation Software

Abstract Football receiving and passing technique is the foundation of football, while football footwork is the training foundation of football receiving and passing technique. So, strengthening footwork training in college football teaching is of great importance to students. At present, there are some problems in the football teaching in colleges and universities, such as it is difficult to change the position with the football, the center of gravity is not stable, the batting foot and supporting foot are not coordinated, the passing movement is not coordinated and so on. Based on this, this paper first analysis the practical application of footwork, and then uses computer modeling ideas to analyze the training methods of footwork in college football teaching.
Speech Stress Recognition by Sound Analysis and Multilayer Artificial Neural Networks

The report presents the study of recognition of stress states through Multilayer Backpropagation Neural Networks (MBNN) based on extracted sound indicators at speech signal analysis. Experimental neural architectures with tangent-sigmoid and logarithmic-sigmoid functions in the two hidden layers with different initial activation were tested. The models with initial logarithmic-sigmoid type, where the obtained maximum accuracy is 75.00%, are defined as inapplicable. In MBNNs with linear and tangential-sigmoidal functions in the initial layers, significantly better adequacy was found as the highest levels of the indicator reached 100.00%.
A Comprehensive Study of MeanShift and CamShift Algorithms for Real-Time Face Tracking

Human face detection is an essential area of computer vision that harness the digital vision for detecting, recognizing, and tracking human faces. Face detection and tracking is widely being used in different applications like security monitoring, home video surveillance, etc. This paper demonstrates an effective human face tracking system for a real-time video stream. In particular, we investigate the two widely used algorithms, MeanShift and CamShift algorithms, for face tracking. However, face detection is performed only in the first video frame using basic Haar cascade features and AdaBoost classifiers. The advantages and drawbacks of MeanShift and CamShift algorithms are discussed in detail with the help of real-time video frames. Results show that the CamShift algorithm is more relevant in head and face tracking, which makes the algorithm highly robust and practically adaptive.
SiblingClassTestDetector: Finding Untested Sibling Functions

Methods which are not covered by a project's test suite are especially prone to exhibiting unexpected behaviours and being more challenging to maintain over time. Some methods are untested but have related implementations in sibling classes (sharing a common superclass) that are tested. Our goal is to help developers improve test suites by identifying such untested methods. We have implemented a prototype tool, SiblingClassTestDetector, which accepts programs along with their test suites, and outputs a set of Completable Candidates (CCs). We have applied our prototype tool to 17 open-source benchmarks and identified 107 CCs within these benchmarks. We have also manually produced two tests for Completable Candidates and submitted Pull Requests for these tests, one of which has been merged by developers.
The spectra of the unitary matrix of an N-tessellable staggered quantum walk on a graph

Recently, the staggered quantum walk (SQW) on a graph is discussed as a generalization of coined quantum walks on graphs and Szegedy walks. We present a formula for the time evolution matrix of a 2-tessellable SQW on a graph, and so directly give its spectra. Furthermore, we extend these results to a 3-tessellable SQW and an n(≥4)-tessellable SQW on a graph under some condition.
WaSSI-C Model Visualization System Based on Component GIS

Based on the secondary development method of component-based GIS and WaSSI-C model operation mechanism, this paper adopts C / S mode architecture, takes. Net and Visual Studio 2012 as the development environment, use Geodatabase spatial database and SQLite relational database to store and manage the model running data, and realize the WaSSI-C model visualization system by combining C# and ArcGIS Engine. The system has the advantages of good human-computer interaction, friendly interface, low cost and strong portability. It can not only realize the simulation process of model based on text data, but also realize the visual simulation of spatial data, which promotes the further popularization and application of the model to a certain extent.
Design of a fast transfer alignment matching algorithm

Abstract In order to estimate the misalignment angle of align system effectively, this paper constructs a traditional 14 state rapid transfer alignment model, considers the problem that traditional velocity error definition is not completely accord with the consistency of coordinate system, the improved differential equation of velocity error is applied to transfer alignment, constructs an improved 12 state rapid transfer alignment model. Finally, the performance of the two alignment algorithms is tested by vehicle-mounted experimental data under the conditions of no maneuver, C shape maneuver and S shape maneuver, and the experimental result of the two algorithms is compared and analyzed. The experimental result shows that the improved 12 state rapid transfer alignment algorithm not only has better alignment accuracy than the traditional 14 state rapid transfer alignment algorithm, but also reduces the model complexity, reduces the system calculation, and avoids the influence of flexural deflection and dynamic misalignment angle. The yaw angle alignment accuracy is improved by 45%, 65% and 53% under the conditions of no maneuver, C shape maneuver and S shape maneuver, respectively.
Computer-based question and exam evaluation in summative knowledge assessment

This paper describes a system for automatic multiple-choice pen-and-paper exam generation, which includes evaluation of students' answers and evaluation of the quality of exams and questions. During the design phase examiner defines the number of questions, area for each question, and the number of variations of the exam. In the evaluation phase, the system automatically evaluates the students' answers. In the verification phase, the system uses Classical test theory (CTT) concepts to provide feedback to the examiner highlighting all questions identified as too easy or too difficult for students. When using the KR-20 formula to verify more than 100 exams and 5,000 students, results show that the reliability coefficients are higher than 80% on average, which is considered very high.
Mapping the use of Online Health Information Sources using Webometrics

A number of health information sources exist on the web and the number is growing. Some studies have tried to comment on the outreach of the websites by calculating the impact factor of the websites. Some studies have also assessed the quality of the information being disseminated. In this study, popular health related information sources have been assessed by using different kinds of data.
Background Survey and Translation Experiments

The current chapter is an analysis of the background survey and the translation experiments of this study.
Formjacking attack: Are we safe?

Purpose Today, online shopping and online business has become a new norm especially in the current pandemic scenario. With more businesses running online, cyber criminals are coming up with different tactics to steal identity and sensitive information such as credit card and banking credentials either for personal monetary gain or to sell in the dark Web. One form of such attack that is seen in the recent times is formjacking attack. This paper aims to review the current scenario of formjacking attack and its modus operandi . The paper also provides certain counter measures that can be adopted by the users and website owners. Design/methodology/approach The paper mainly focuses on the modus operandi of formjacking attack to understand the severity of the problem. Based on the way the attack is carried out, some guidelines to be followed are provided. Later, a brief review of machine learning techniques is furnished to understand how it may help as secure defense mechanism. Findings Formjacking attacks are on a rise in the past two years, especially during the holiday season. Cyber criminals have been using smart tactics to carry out these attacks which are very difficult to detect. Machine learning techniques may prove to be effective in combating these attacks. Originality/value Formjacking attack is not just a concern of the customers who may lose their sensitive data, but the onus also lies on the companies itself to ensure they protect their customer’s data from theft. Not much research is found regarding formjacking attack, as it is relatively a new form of attack. The paper reviews this attack and provides some measure that can be followed. It also provides few guidelines which can be used for further research in devising a security tool to mitigate this problem.
A DSM-Based Co-Occurrence Matrix for Semantic Classification

Traditional 2D textures cannot reflect objects’ real textures in 3D world, since they only consider spectral distribution in a 2D region which is a projection of 3D objects at a certain angle of view. The existing researches of 3D textures can only process volumetric data (VD) like multi/hyper-spectral images which are not real 3D geometric data. In this letter, we proposed a digital surface model (DSM)-based co-occurrence matrix (DSMB-CM) which extended 2D co-occurrence matrix (2D-CM) to 3D space for multispectral images with DSM. DSMB-CM is the first 3D feature in remote sensing areas considering the spectral distribution over 3D surface to represent real textures of objects in 3D space. Besides, a dimension reduction method was proposed to avoid curse of dimensionality. Experiments compared classification accuracies of different feature combinations of two data sets from ISPRS Benchmark of Semantic Labeling Contest. The results proved that DSMB-CM had better performance than traditional textures in identification of all categories.
Named Entity Recognition for Rental Documents Using NLP

Information retrieval is the process of extracting a pertinent set of facts from a text or a document. The documents are of unstructured format, and thus, information retrieval techniques aim at organizing this data. Named Entity Recognition is one of the information retrieval techniques which classifies a particular word or a phrase in its appropriate class. NER can thus, also be used in extracting entities from legal documents, which would help in providing an effective way to represent these documents. This would reduce the task of a lawyer scrutinizing the document, multiple times, to look for the same set of information. NER systems can be developed with different approaches, one of which is utilizing an NLP library. However, these pretrained NLP libraries may or may not be suitable for a particular use case. Hence, in this paper, we depict an approach to analyze rental documents by custom training spaCy NLP library for tagging named entities such as a person, address, amount, date, etc. The system will provide an interface for the user to upload rent documents, and the result analysis will be stored for quick insights into the document.
Non-nuclei characterization in histopathological images: a processing step to improve nuclei segmentation methods

This study presents a novel strategy to characterize and remove non-nuclei signal (noise) in histopathological images stained with hematoxylin and eosin (H and E), a preprocessing step to improve traditional nuclei segmentation methods. Any non nuclei structure is mapped to a noiselet space at different resolution levels where a classic classifier is trained to recognize the noiselet coefficients of this projection. The proposed approach was evaluated with two multi-organ datasets manually annotated, comparing the nuclei segmentation obtained by a watershed algorithm plus the presented approach against the watershed method alone. An average Dice Score in these datasets (MICCAI Challenge and TCIA) of 70.2 and 59.6 was obtained by applying the herein introduced method, while the obtained Dice Score with only the watershed method was of 66.7 and 55.5.
Season of migration from the oil lake: the economy of hatred in the Gulf states

In 2020, hatred speech is one of the most frequently stated problems in relation to the spread of coronavirus (COVID-19) in the Gulf States. Hatred speech has emerged in the social, political and media realms and has provoked racism against foreigners. This hatred is associated with increased unemployment among citizens in light of the economic transformations in the region. Within the hatred context, foreign workers have been framed as the main cause of drained national resources during the pandemic and the economic crises facing the Gulf countries. The study uses surveys to gain insights into the impacts of economic factors on the growing hatred towards expatriate workers with the outbreak of COVID-19.
Optimized Diet Plan using Unbounded Knapsack Algorithm

Cholesterol, hypertension and diabetes are the three major chronic diseases from which most of the people suffers and these peoples often use search engines to acquire related information about these problems. But, almost every information related to diet on the internet isn't suitable for people to gather information about the diet suggestions. A system for diet suggestion which can advocate a prudent diet for such peoples is suggested in this paper. We designed a system that recommends a proper diet which has the adequate knowledge of three above mentioned highly chronic diseases. We propose a solution to the menu recommending problem using the optimization algorithm known as unbounded knapsack. We designed a model which satisfies the nutritional requirements of individuals while imposing the “Laws of Nutrition”, a set of hypothesis used by almost all Latin America's nutrition scientists. This prototype corresponds to a numerical optimization problem with constraints. We design a menu items generator application model to set up a convenient menu for a user with different properties.
Ethical Behavior and Legal Regulations in Artificial Intelligence (Part One)

Artificially intelligent systems should make users' lives easier and support them in complex decisions or even make these decisions completely autonomously. However, at the time of writing, the processes and decisions in an intelligent system are usually not transparent for users. They do not know which data are used, for which purpose, and with what consequences. There is simply a lack of transparency, which is important for trust in intelligent systems. Transparency and traceability of decisions is usually subordinated to performance and accuracy in AI development, or sometimes it plays no role at all. In this chapter, the authors describe what intelligent systems are and explain how users can be supported in specific situations using a context-based adaptive system. In this context, the authors describe the challenges and problems of intelligent systems in creating transparency for users and supporting their sovereignty. The authors then show which ethical and legal requirements intelligent systems have to meet and how existing approaches respond to them.
Simple Effective Methods for Decision-Level Fusion in Two-Stream Convolutional Neural Networks for Video Classification

Convolutional Neural Networks (CNNs) have recently been applied for video classification applications where various methods for combining the appearance (spatial) and motion (temporal) information from video clips are considered. The most common method for combining the spatial and temporal information for video classification is averaging prediction scores at softmax layer. Inspired by the Mycin uncertainty system for combining production rules in expert systems, this paper proposes using the Mycin formula for decision fusion in two-stream convolutional neural networks. Based on the intuition that spatial information is more useful than temporal information for video classification, this paper also proposes multiplication and asymmetrical multiplication for decision fusion, aiming to better combine the spatial and temporal information for video classification using two-stream convolutional neural networks. The experimental results show that (i) both spatial and temporal information are important, but the decision from the spatial stream should be dominating with the decision from temporal stream as complementary and (ii) the proposed asymmetrical multiplication method for decision fusion significantly outperforms the Mycin method and average method as well.
Fuzzy based de-noising underwater images by Hybrid filters

In recent years, the underwater image processing has significantly improved due to the technological development. The real challenge is that the poor contrast and noise because of absorption of light and scattering in the oceanic environment inherently disturb these underwater images. Hybrid filters depending on fuzzy for de-noising underwater images is proposed and analyzed clearly. The proposed algorithm is compared with various existing de- noising algorithms such as Gaussian median filter, TMED and median filter. The PSNR and NMSE of each algorithm has been measured and analyzed, which compares the ability to de-noising.