Reviewerly/exHarmony huggingface dataset:
Real-time Shadow Detection and Removal by Illumination Drop Point Analysis

The existence of shadows in natural scenes cause challenges in computer vision applications such as object detection. In this paper, a proposed approach is introduced for shadow detection in single images. Unlike other approaches, our algorithm uses the variation in the RGB components in order to locate the drop in intensity and analyze it. The input image would be subjected to noise reduction using a Gaussian filter then vertical scanning is applied where the pixels at every column of the image are collected, grouped using a threshold to obtain smooth knee points through the variation of the intensity levels and analyzed. Shadow removal is done and then horizontal scanning is carried out following the same procedure. The analysis and optimization process was done on 300 images and the final testing was done on 4000 images from the SBU shadow images dataset. The results have shown the success of our algorithm to detect high accuracy of shadows than other approaches.
Exploration and practice of industrial control system information security incident emergency exercise

Industrial control system information security incident emergency exercise is a security management and technical activities, which is comprehensive, interdisciplinary and multi-link. Based on the global exercises and the existing exercise organization experience, this paper summarizes three characteristics, three forms and five procedures of industrial security emergency exercise. Besides, this paper proposes a detailed implementation idea and optimization method of PTDTE emergency exercise in five stages of preparation, test, design, training and exercise, and gives a specific explanation in combination with two examples of emergency exercise. By carrying out the procedural and systematic emergency exercise, the enterprises can test the emergency plan, improve the decision-making ability and safety skills, focus on investigation and traceability, coordinate the emergency disposal timely and effectively, and ensure the cyber security of industrial control system.
ORGANIZATIONAL AND METHODOLOGICAL SUPPORT OF THE APPLICATION OF H5P INTERNET SERVICE IN MATHEMATICS LESSONS IN CLASS 2

The question of "How to teach?" especially relevant now, when our society has already begun to be called informational. Psychologists and educators in their research show that low learning performance, deteriorating performance of many students is usually due to the use of ineffective tools and methods of learning. Today the teacher has the opportunity to freely focus on a creative approach to the organization of the educational process. The rather rapid development of modern education requires new forms, methods and means of organizing learning. Modernity dictates the need for such an education that could prepare students for a full life with the ability to quickly adapt to new conditions, think critically, be active and multifaceted personality. Accordingly, the modern teacher must also respond to changes in the world, must be able to apply innovative information and communication technologies and convey information to children. The Internet provides many opportunities to use available and free tools, services and resources that can restructure the learning process, make it interesting, creative and creative, meaningfully and instrumentally enrich learning. The article considers the concepts of "Internet service", "interactive multimedia educational content", the concept of organizational and methodological support for the use of Internet service H5P in mathematics lessons in primary school and its advantages over other forms of methodical work. Diagnosis of the advantages and possibilities of using the H5P service in mathematics lessons in primary school and methodical recommendations for the organization of work with the use of the H5P service in mathematics lessons in the 2nd grade. Criteria have been developed and the levels of readiness of the teacher to use the H5P Internet service in mathematics lessons have been described. The use of the H5P online service in math lessons provides a feedback process between teacher and students; to increase the level of visualization of the submitted educational material; provide an individual approach to each student; provide the ability to model the studied processes or phenomena; organize frontal, collective and group work; to carry out constant control and assessment of students' achievements.
Mathematical Optimization Modeling and Solution Approaches

In this chapter mathematical optimization modeling is reviewed and presented. The two broad classifications of mathematical modeling solution approaches, classical and meta-heuristic methods, are considered and various examples are given. The chapter also examines mathematical modeling approaches and gives practical steps on how to model mathematical problems. Furthermore, algebraic modeling languages (AMLs) and their solvers are considered and the advanced interactive multidimensional modeling system, which is a prominent AML, is introduced.
Resource Analysis for Lazy Evaluation with Polynomial Potential

Space and time requirements of lazy functional programs are hard to predict for both programmers and compilers. Previous work in compile-time amortised analyses for lazy functional languages by Simões, Jost et.al. was limited to bounds that are linear on the sizes of inputs. This paper presents an extension of these analyses with the method of polynomial potential (due to Hofmann and Hoffmann), allowing the system to derive univariate polynomial resource bounds. We present the analysis as a type system for tracking allocations in a simple functional lazy functional language with lists and pairs, an operational semantics (serving as cost model), and worked examples of deriving resource bounds. We also highlight some limitations and conclude with further research directions.
Augmenting Learning through VR Storytelling

This paper presents a narrative instructional model as an interaction framework in a Virtual Reality environment. Virtual Reality solutions provide the means to present a storytelling scenario through visual interactive models. The proposed implementation consists of a VR prototype developed to assist kindergarten and primary school pupils, in assimilating knowledge about different types of musical instruments, while developing a sense of practice-based interaction. The application is structured on three levels; each level is represented by an element of nature (water, earth and air), corresponding to the know-how complexity: water or level 0 which reflects an elementary level of intrinsic knowledge; towards the "air environment" that reflects the level of cumulative knowledge that will be reached by the end of the game. Knowledge is assimilated by interacting with elements in the virtual environment and by following the annotated guidelines.
Visual differential debugging in the domain-specific visual programming languages

As complex software evolves and becomes increasingly more feature complete, it introduces challenges when error detection is required. Finding errors is time consuming because of the complicated control flow graphs and data flows of the underlying complex systems. Identifying the cause of software faults is essential and well known process but in the context of complex systems it is not a trivial task. Developing features and improvements often results in introducing new faults known as software regressions. This means there is a prior version of the application running fine and recent version which is doing something unexpected. The developer could use a tool that loads and executes the two versions in parallel for comparing for differences. Some of the differences are not semantic and could be ignored. This paper provides a prototype for a visual differential debugger with a semantic layer capable of guiding the developer to the possible root of the fault.
Target-Oriented Transformation Networks for Document Retrieval

There are various techniques have been proposed for constructing neural ranking models, including representation-based models and interaction-based models, both employ shallow or deep neural networks to tackle the reranking problem in document retrieval, in which query is usually the first met object before we focus on documents. Thus query can be treated as target when modeling the matching patterns between it and documents, however, few works have attempted to do that. In this paper we propose a target-oriented transformation networks based neural ranking model TTRM, which encodes the target information into the document content and utilizing a context conserving transformation to encapsulate the contextualized features. By this way, can the model produce a new target-derived word level representations for the document, from which the salient abstract features can be extracted using a convolution network, and the final relevance score between the query-document pair can be obtained by stacking a feed forward network. The comparison results show that our proposed model can outperform the baseline models on two important evaluation metrics when conducting experiments on the benchmark collection.
Evaluating a programming topic using GitHub data: what we can learn about machine learning

Purpose The purpose of this paper is to define a methodology to analyze links between programming topics and libraries starting from GitHub data. Design/methodology/approach This paper developed an analysis over machine learning repositories on GitHub, finding communities of repositories and studying the anatomy of collaboration around a popular topic such as machine learning. Findings This analysis indicates the significant importance of programming languages and technologies such as Python and Jupyter Notebook. It also shows the rise of deep learning and of specific libraries such as Tensorflow from Google. Originality/value There exists no survey or analysis based on how developers influence each other for specific topics. Other researchers focused their analysis on the collaborative structure and social impact instead of topic impact. Using this methodology to analyze programming topics is important not just for machine learning but also for other topics.
Secure memory erasure in the presence of man-in-the-middle attackers

Memory erasure protocols serve to clean up a device’s memory before the installation of new software. Although this task can be accomplished by direct hardware manipulation, remote software-based memory erasure protocols have emerged as a more efficient and cost-effective alternative. Existing remote memory erasure protocols, however, still rely on non-standard adversarial models to operate correctly, thereby requiring additional hardware to restrict the adversary’s capabilities. In this work, we provide a formal definition of secure memory erasure within a symbolic security model that utilizes the standard Dolev–Yao adversary. Our main result consists of a restriction on the Dolev–Yao adversary that we prove necessary and sufficient to solve the problem of finding a protocol that satisfies secure memory erasure. We also provide a description of the resulting protocol using standard cryptographic notation, which we use to analyze the security and communication complexity trade-off commonly present in this type of protocols.
Analysis on Aigis‐Enc: Asymmetrical and symmetrical

Aigis-Enc is an encryption algorithm based on asymmetrical learning with errors (LWE). A thorough comparison between Aigis-Enc (with the recommended parameters) and a symmetrical LWE encryption scheme on the same scale (the sampling parameters are {η1, η2} = {2, 2} instead of {1, 4}) on Chosen-plaintext attack (CPA) security, computation complexity and decryption failure probability is made. In particular, the authors ascertain that the CPA security of Aigis-Enc is 160.895, and that of the symmetrical LWE encryption scheme on the same scale is 161.834. The ratio of computation complexity on the sampling amount of the former and the latter is 5:4 in the key generation phase and 19:14 in the encryption phase. The decryption failure probability of the former is 2−128.699 and that of the latter is 2−67.0582, then the authors show how to reduce the decryption failure probability of the latter significantly by increasing some traffic. Furthermore, those attacks presented by designers of Aigis-Enc, including primal attacks and dual attacks are generalised. Our attacks are more extensive, simpler, and clearer. With them, the optimal attacks and the ‘optimal-optimal attacks’ on Aigis-Enc and the symmetrical LWE scheme on the same scale are obtained.
An Evolutionary Algorithm for Quadcopter Trajectory Optimization in Aerial Challenges

Machine learning methods have been widely employed in robotics over the years, and recent developments in machine learning have completely re-shaped problem-solving in the area. Indeed, if we consider multi-objective planning, these models' optimization and learning capabilities can derive more robust strategies. Inspired by the species natural selection mechanism, Evolutionary Algorithms (EA) are among the best known computational approaches available for this purpose. In this scenario, this work proposed an EA model developed to find the best travel trajectory for a quadcopter in the “Desafio Petrobras” challenge. In the challenge, a set of landing platforms that the robot has to visit are displaced in the 3D-space. To find the best trajectory possible, we optimize an EA over a low-level control that can take the quadcopter from point A to B. We vary our fitness function to support more complex decisions. The software-in-the-loop technique was applied for a simulated quadrotor in the Coppelia simulated environment. The proposed approach has shown the capability to generate short trajectories while considering variables like UAV dynamics and energy consumption.
RETRACTED: Pentium microprocessor system structure and principal of political teaching and teaching material improvement

Designed a hybrid structural model of intelligent mobile learning platform based on the Android system. Designed a prototype of a front-end platform for mobile learning. The intelligent system shows the system test analysis to provide strong practicality and reference value and the new method under the mobile model to realize the ideology and political leaders in university education. The report on the election has been included in the government method of the four high school teachers. And the interest of these teachers to the election, by analyzing how that are trying to generate a student of politics to further the concept of, the author tries to understand the politics of the directives in better junior high school stage. Overall, the author's teachers believe that capture students' interest in everyday politics and elections. However, teachers often miss the opportunity to promote a subtle understanding of this interest in politics. The definition of political socialization problem is "the way people have their political skills, develop the direction and practices, how their experience is not shaping their own political base set". In this process is through a family relationship, or via the interaction at the school have been shown to occur in the main. In education, in the previous studies, in the elementary school of the lower grades, it indicates that are beginning the education of political socialization and students, the continued penetration of life. These ought to there is a great deal of analysis of political guidance that occurred in four classifications. In any case, these educators, a top pick of social investigations instructors in the United States, have gotten formal preparing on the most proficient method to instruct legislative issues. It must be emphasized that there is no. Educators perceive adulation and understudies; it should utilize an assortment of methodologies to cultivate this interest. Nonetheless, as brought up, don't accept that these measures are sufficient for the guidance. Subsequently, it has botched a remarkable chance to cultivate a political comprehension of the understudies.
Selected Design and Analysis Techniques for Contemporary Symmetric Encryption

Abstract In this chapter we provide an overview of selected methods for the design and analysis of symmetric encryption algorithms that have recently been published. We start by discussing the practical advantages, limitations and security of the keystream generators with keyed update functions which were proposed for reducing the area cost of stream ciphers. Then we present an approach to enhancing the security of certain encryption schemes by employing a universal homophonic coding and randomized encryption paradigm.
Didactical principles in creating a multimedial message

The process of creating multimedia message is a very specific creative act, a kind of art with universal characteristics, but with its unique tendecieses. In its ideal form, art usually targets the indefinite, while the message follows predictable targets in the short period of time. The art of creating a successful message is in decoding the meaning of that message. The essence of concept of multimedia message is hidden in its formula because the whole physionomy of that message and the volume of expected effect depend on the formula. It is the base for building up tipological, graphic and music concepts. Global development of informational and comunicological technologies catalyzes diversificiation of platforms for distribution of audiovisual contents. multimedial message allows more supstantional contents, and its main goal is to convince a reciptient. modern technologies transfer the pedagogical knowledge and form contemporary visual culture within the recipients. The use of contemporary technologies asks for particular principles. This paper is focused on didactical principles used in the process of creation of multimedia messages.
Visual Flight Rules-based Collision Avoidance System for VTOL UAV

The use of Unmanned Aerial Vehicles (UAVs) to perform complex tasks is very common today. The private transport sector will soon experience a significant change with the arrival of a new concept, the electric Vertical Take-Off and Landing (eVTOL) aircraft. These autonomous vehicles will, at first, be used in conjunction with other manned aircraft. That implies that the eVTOL aircraft will have to use the current civil airspace, where the aviation authorities require full compliance with the regulations that apply to General Aviation. This paper proposes a complete prototype of a Collision Avoidance System (CAS) for VTOL UAVs using civil airspace under the regulations of the Visual Flight Rules (VFR). The entire study corresponding to this CAS was implemented and tested in micro quadcopters that have the premise of bringing results with an experimental value that can grant proof of concept to the entire system.
Fuzzy Reasoning in Music

Music composition has been recently used by the many musicians and scientists on artificial intelligence. One of main problem here is how to evaluate synthesized music by a computer. This article presents concepts how fuzzy logic may be applied to music analysis and composition. Main idea is that ambiguity in the music is maintained not by classical set, with use of fuzzy set approach.
Privacy-Preserving Data Analysis: Providing Traceability without Big Brother

Collecting and analyzing personal data is important in modern information applications. Though the privacy of data providers should be protected, the need to track certain data providers often arises, such as tracing specific patients or adversarial users. Thus, tracking only specific persons without revealing normal users' identities is quite important for operating information systems using personal data. It is difficult to know in advance the rules for specifying the necessity of tracking since the rules are derived by the analysis of collected data. Thus, it would be useful to provide a general way that can employ any data analysis method regardless of the type of data and the nature of the rules. In this paper, we propose a privacy-preserving data analysis construction that allows an authority to detect specific users while other honest users are kept anonymous. By using the cryptographic techniques of group signatures with message-dependent opening (GS-MDO) and public key encryption with non-interactive opening (PKENO), we provide a correspondence table that links a user and data in a secure way, and we can employ any anonymization technique and data analysis method. It is particularly worth noting that no “big brother” exists, meaning that no single entity can identify users who do not provide anomaly data, while bad behaviors are always traceable. We show the result of implementing our construction. Briefly, the overhead of our construction is on the order of 10 ms for a single thread. We also confirm the efficiency of our construction by using a real-world dataset.
In Defense of Multi-Source Omni-Supervised Efficient ConvNet for Robust Semantic Segmentation in Heterogeneous Unseen Domains

Semantic segmentation renders a unified way of surrounding perception, where most of driving scene detection tasks can be covered by running a single efficient ConvNet through a forward pass. However, current frameworks posit the closed-world paradigm expressed as a single source of distribution over a predetermined set of visual classes, forgetting that a deep model must be deployed in the wild facing unseen domains and unforeseen hazards. In spite of being accurate in its comfort zone, the segmentation model may not generalize well to a new domain. In addition, a model trained with single dataset is heavily limited in terms of recognizable classes. In this paper, we propose an omni-supervised learning framework for semantic segmentation which is able to leverage heterogeneous data sources. Our omni-supervised training framework incorporates all available labeled and unlabeled data, meanwhile bridges multiple training sets to be capable of recognizing more classes that are needed for autonomous navigation application at hand in the new domain. A comprehensive variety of experiments shows that with the proposed multi-source omni-supervised learning solution, an efficient ConvNet like our ERF-PSPNet attains significant robustness gains in open domains that are of critical relevance to real deployment of vision algorithms. Our approach surpasses the state of the art on the highly unconstrained PASS and IDD20K datasets.
PBDE: An Effective Method for Filtering False Positive Boxes in Object Detection

An inevitable problem in the practice of object detection is the existence of false positive detection boxes. False detection boxes greatly reduce precision of object detection model and compromise the desired effect. In this paper, we propose a method named Prediction Box Density Evalution (PBDE). We summarize box density characteristics of true positive (TP) and false positive (FP) boxes to filter out a large number of FP boxes. After PBDE, we can obtain a significant improvement in precision with a low recall loss, and increase F1-score by up to 9 percentage when the confidence threshold is 0.1. The entire algorithm is carried out on the post-processing of object detection, and there is no need to change the original training method and network structure, which is of great practical importance.