Reviewerly/exHarmony huggingface dataset:
Deep analytics for workplace risk and disaster management

Deep analytics for workplace risk and disaster managementWe discuss dynamic real-time analysis from multimodal data fusion for contextual risk identification to generate "Risk Maps" for the workplace, resulting in timely identification of hazards and associated risk mitigation.It includes new machine/deep learning, analytics, methods, and its applications that deal with the unconventional data collected from pictures, videos, documents, mobile apps, sensors/ Internet of Things, Occupational Safety and Health Administration (OSHA) rules, and Building Information Model (BIM) Models.Specifically, we describe a number of advances and challenges in this field with applications of computer vision, natural language processing, and sensor data analysis.Applications include automated cause identification, damage prevention, and disaster recovery using current and historical claims data and other public data.The methods developed can be applied to any given situation with different groups of people, including first responders.Finally, we discuss some of the important nontechnical challenges related to business practicality, privacy, and industry regulations.
FISHDBC: Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance

FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It is incremental and scalable: it avoids the $\mathcal O(n^2)$ performance of other approaches in non-metric spaces and requires only lightweight computation to update the clustering when few items are added. It is hierarchical: it produces a "flat" clustering which can be expanded to a tree structure, so that users can group and/or divide clusters in sub- or super-clusters when data exploration requires so. It is density-based and approximates HDBSCAN*, an evolution of DBSCAN.
Multifactor Authentication Methods: A Framework for Their Comparison and Selection

There are multiple techniques for users to authenticate themselves in software applications, such as text passwords, smart cards, and biometrics.Two or more of these techniques can be combined to increase security, which is known as multifactor authentication.Systems commonly utilize authentication as part of their access control with the objective of protecting the information stored within them.However, the decision of what authentication technique to implement in a system is often taken by the software development team in charge of it.A poor decision during this step could lead to a fatal mistake in relation to security, creating the necessity for a method that systematizes this task.Thus, this book chapter presents a theoretical decision framework that tackles this issue by providing guidelines based on the evaluated application's characteristics and target context.These guidelines were defined through the application of an extensive action-research methodology in collaboration with experts from a multinational software development company.
Solving logical tasks based on predicate models using generator of configurations

In this study, the method of solving logic tasks by declarative language Prolog based on the method of searching in the space of configurations is identified and analyzed. The essence of the method is to declare the generator of configurations, which forms the search space, and the selection procedure for sifting the constructed States according to a certain principle. The study proposes a classification of solutions by this method. Described a formal model of the decision of following problems: The generalized problem of the crossing, The task of the blood work and The problem of constructing code words.&nbsp; For the description of objects and their configurations it is offered to use bit chains, and for generation of the next state - bit operations. The described approaches to the construction of the desired options allow us to find all possible solutions of a given dimension.&nbsp; The perspectives of the generating rules of the knowledge base. The examples of declarative models obtained in the course of the study are used in teaching the course &nbsp;&lt;&lt;functional and logical programming&gt;&gt; at the Altai State University.
A Systematic Review of Literature on Learning Application for Dyslexia Children

Dyslexia is a language-based disability and irrespective of with a low level of intelligence.  People with dyslexia have difficulties in reading, writing and spelling.  Dyslexic will have trouble deciphering the sound made by a specific letter or blending the sounds of all the letters together to form a word and  they tend to omit or add letters in words. Learning application can be utilized as a gadget to help children with dyslexia in alleviating their difficulties. The purpose of this study is to provide an overview of learning applications, evaluation of usability application and effect of the application towards dyslexicâ€™s performance. The search parameters included articles from electronic databases on learning application for dyslexia children published between 2012 to 2018.  There were 20 articles selected and reviewed systematically. There were seventeen learning applications related to dyslexia had developed and only five of them were related to English Language. Evaluation of usability aspect showed that the children were eager to explore the learning application and enjoy using it.The meta-analysis results revealed that the mobile learning application is able to improve the dyslexicsâ€™ performance in reading, writing, spelling and Mathematics. It can be concluded that the evaluation aspect is useful and beneficial to researchers in developing effective learning application in future.
A Method for Determining the Affine Equivalence of Boolean Functions

Determining the affine equivalence of Boolean functions has significant applications in circuit and cryptography. Previous methods for determining this require a large amount of computation when Boolean functions are bent functions or when the truth table is sparse. This paper presents a new method to determine the affine equivalence based on matrix algebra. By transforming Boolean function to the corresponding matrix representation, we first propose and prove the congruent standard form of Boolean function. It lays the foundation for the determination of equivalence because affine Boolean functions must have the same standard form. Then we find the generators of orthogonal matrix group and symplectic matrix group, which greatly reduce the search space. The computation complexity of our method is o(2(r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2/2+n</sup> * <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n-r)</sup> )), where n is the number of bit operations, and r is the rank of the matrix, which is the product of Boolean-1 matrix of the test Boolean function and its transposition. The experimental results show that our method is useful when the test Boolean function is no more than 7 bits and r is greater than 2.
A Behavior Recognition Framework Based on Skeleton Spatio-Temporal Relation

Behavior recognition is a hot trending topic for smart cities in terms of the research and the public security. This paper focuses on the abnormal behavior that involves various people activities. In contrast to most of the existing works, which relies on image data as raw input and affected by environmental factors, this paper takes skeleton's depth and location information into consideration and proposes a behavior recognition framework based on skeleton spatio-temporal relationship to incorporate in-depth spatial information into each segment. Our experiment undertakes the real-world surveillance video data, and the results show that the framework achieves superior performance compares to the existing method.
Asynchronous Methods for Model-Based Reinforcement Learning

Significant progress has been made in the area of model-based reinforcement learning. State-of-the-art algorithms are now able to match the asymptotic performance of model-free methods while being significantly more data efficient. However, this success has come at a price: state-of-the-art model-based methods require significant computation interleaved with data collection, resulting in run times that take days, even if the amount of agent interaction might be just hours or even minutes. When considering the goal of learning in real-time on real robots, this means these state-of-the-art model-based algorithms still remain impractical. In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time. We evaluate our asynchronous framework on a range of standard MuJoCo benchmarks. We also evaluate our asynchronous framework on three real-world robotic manipulation tasks. We show how asynchronous learning not only speeds up learning w.r.t wall-clock time through parallelization, but also further reduces the sample complexity of model-based approaches by means of improving the exploration and by means of effectively avoiding the policy overfitting to the deficiencies of learned dynamics models.
Autonomous Entry Guidance based on 3-D Gliding Trajectory Analytical Solution

In this paper, an autonomous entry guidance is developed for hypersonic glide vehicle based on 3-D gliding trajectory analytical solutions. In the derivation of the solutions, a new dynamics of downrange, crossrange and heading angle with respect to energy is established based on the Auxiliary Geocentric Rotation (AGR) frame. After that, the new guidance uses the downrange formula to determine the longitudinal reference profile in real time, and regulates the first bank reversal by the crossrange formula to eliminate the terminal crossrange error. Subsequently, the second bank reversal is corrected by several trajectory simulations to guarantee terminal constraints. The simulations show that the new entry guidance has high accuracy and can handle various terminal conditions.
Combination of Federal State Educational Standards and Professional Standards, Portfolio of Students

The author examines how federal state educational standards of higher education (FSES-HE) and professional standards can be combined. Methods of assessing the effectiveness of students' research activities through the electronic portfolio of students are being developed. The information about the existing forms of organization of research activities of students of bachelor's, specialist's and master's degree programs at the university, the requirements for the effectiveness of research work at each educational level were analyzed. The main result of the work is the positioning of the electronic student portfolio as a modern effective means of monitoring his educational achievements which allow assessing the acquisition of professional competencies by the student, solving the problems of organization, planning, implementation and qualitative/quantitative evaluation of his research activities implemented within the educational process. Pedagogical monitoring is characterized as a modern means of control and evaluating the effectiveness of research activities, ensuring the efficiency and availability of information. The principles of purposefulness and prognostication, objectivity, integrity and continuity, development and self-organization, systematic and consistent implementation of pedagogical monitoring are revealed. The relevance of score-rating evaluation of the effectiveness of research activities of students as the most adequate and demonstrative system for assessing the success of each student in scientific work is substantiated. It is concluded about the stimulating impact on regular pedagogical monitoring of research activities performed through the electronic portfolio of students.
Memory Safety Preservation for WebAssembly

WebAssembly (Wasm) is a next-generation portable compilation target for deploying applications written in high-level languages on the web. In order to protect their memory from untrusted code, web browser engines confine the execution of compiled Wasm programs in a memory-safe sandbox. Unfortunately, classic memory-safety vulnerabilities (e.g., buffer overflows and use-after-free) can still corrupt the memory within the sandbox and allow Wasm code to mount severe attacks. To prevent these attacks, we study a class of secure compilers that eliminate (different kinds of) of memory safety violations. Following a rigorous approach, we discuss memory safety in terms of hypersafety properties, which let us identify suitable secure compilation criteria for memory-safety-preserving compilers. We conjecture that, barring some restrictions at module boundaries, the existing security mechanisms of Wasm may suffice to enforce memory-safety preservation, in the short term. In the long term, we observe that certain features proposed in the design of a memory-safe variant of Wasm could allow compilers to lift these restrictions and enforce relaxed forms of memory safety.
Detection of Medical Information Using LSTM

The growing number of medical consumers aiming for a better quality of life is boosting the Online to Offline (O2O) medical marketing industry, which saves time and money of clients by choosing reliable medical facilities and receiving high-quality medical services based on medical information from blogs distributed on the Web.Unstructured data generated from the Internet, mobile, SNS, etc., cannot guarantee the credibility of medical information because it directly or indirectly reflects the interest, preference, and expectation of the author in addition to professional medical knowledge.In this study, we propose a blog detection system that provides users with a higher quality medical information service by detecting medical blogs using big data and Long Short-Term Memory (LSTM) processing.We collect and analyze various medical blogs on the Internet based on the big data and machine learning technology, and develop a personalized health recommendation system for each disease.It is expected that the users will be able to maintain their health condition by continuously checking their health problems and taking the most appropriate measures.
Structure Matters: Towards Generating Transferable Adversarial Images

Recent works on adversarial examples for image classification focus on directly modifying pixels with minor perturbations. The small perturbation requirement is imposed to ensure the generated adversarial examples being natural and realistic to humans, which, however, puts a curb on the attack space thus limiting the attack ability and transferability especially for systems protected by a defense mechanism. In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural. The key idea of our approach is to allow perceptible deviation in adversarial examples while keeping structure patterns that are central to a human classifier. Built upon these concepts, we propose a \emph{structure-preserving attack (SPA)} for generating natural adversarial examples with extremely high transferability. Empirical results on the MNIST and the CIFAR10 datasets show that SPA exhibits strong attack ability in both the white-box and black-box setting even defenses are applied. Moreover, with the integration of PGD or CW attack, its attack ability escalates sharply under the white-box setting, without losing the outstanding transferability inherited from SPA.
Data Driven Conditional Optimal Transport

A data driven procedure is developed to compute the optimal map between two conditional probabilities $\rho(x|z_{1},...,z_{L})$ and $\mu(y|z_{1},...,z_{L})$ depending on a set of covariates $z_{i}$. The procedure is tested on synthetic data from the ACIC Data Analysis Challenge 2017 and it is applied to non uniform lightness transfer between images. Exactly solvable examples and simulations are performed to highlight the differences with ordinary optimal transport.
No-Reference Objective Video Quality Measure for Frame Freezing Degradation

In this paper we present a novel no-reference video quality measure, NR-FFM (no-reference frame-freezing measure), designed to estimate quality degradations caused by frame freezing of streamed video. The performance of the measure was evaluated using 40 degraded video sequences from the laboratory for image and video engineering (LIVE) mobile database. Proposed quality measure can be used in different scenarios such as mobile video transmission by itself or in combination with other quality measures. These two types of applications were presented and studied together with considerations on relevant normalization issues. The results showed promising correlation values between the user assigned quality and the estimated quality scores.
Behavioral Security in Covert Communication Systems

The purpose of the covert communication system is to implement the communication process without causing third party perception. In order to achieve complete covert communication, two aspects of security issues need to be considered. The first one is to cover up the existence of information, that is, to ensure the content security of information; the second one is to cover up the behavior of transmitting information, that is, to ensure the behavioral security of communication. However, most of the existing information hiding models are based on the "Prisoners' Model", which only considers the content security of carriers, while ignoring the behavioral security of the sender and receiver. We think that this is incomplete for the security of covert communication. In this paper, we propose a new covert communication framework, which considers both content security and behavioral security in the process of information transmission. In the experimental part, we analyzed a large amount of collected real Twitter data to illustrate the security risks that may be brought to covert communication if we only consider content security and neglect behavioral security. Finally, we designed a toy experiment, pointing out that in addition to most of the existing content steganography, under the proposed new framework of covert communication, we can also use user's behavior to implement behavioral steganography. We hope this new proposed framework will help researchers to design better covert communication systems.
Information Problems of Practical Training

The article discusses information, technological and organizational challenges in managing and financing the practical training of students. The main goal is to build on experience and good practices to uncover the information challenges of process efficiency. The tasks are related to the analysis of the characteristics of the processes of conducting and financing of the practical training, consideration of the technological features of the information provision and proposals for conceptual solution of the main information challenges. The results achieved are in the direction of presenting technological and conceptual solutions as well as formulating guidelines for future development.
LGP: A robust Linear Genetic Programming implementation on the JVM using Kotlin.

The desire for a system which can automatically craft computer programs has been known in the machine learning community for some time.Friedberg (1958) experimented with a system that solved problems by randomly changing instructions in a program and favouring those changes which most frequently achieved a positive result.Linear Genetic Programming (LGP) (Brameier & Banzhaf, 2007) is a paradigm of genetic programming that employs a representation of linearly sequenced instructions in automatically generated programs.
DIGITAL TRANSFORMATION OF CONTINUOUS EDUCATION: A NEW VOLUTION OF THE DEVELOPMENT OF NEUROPEDAGOGY

Transition to a digital civilization, which is the calling card of the third millennium, generates a new social reality and activities that require continuous learning. To analyze the ways of digital transformation of continuous education, the article considers the general picture of human life in a new digital space. A model of finding a person in a continuous multidimensional digital flow was described. The flow is not just a temporary immersion into digital reality. It is a new continuously changing environment in which, along with natural biological needs, the activity of the neural networks of brain and spinal cord becomes vital, as they process the sensory signals of a certain range and form a new reality based on digital perception, representation and even consciousness. The article defines the concept of “continuous digital flow of Internet space”. It is shown that inevitable changes occur in the human brain, which is in digital stream for a long time. The estimate of the residence time of the human brain in the digital stream is given. A huge, ever-increasing number of people around the world are in direct connection with digital space, loading their sensory channels with data stream with increasing speed. Digital transformations of lifelong education occur in a situation where a child, teenager, young person is immersed in the digital virtual Internet space of learning and socialization. Biological adaptation of a person in a continuous Internet stream can be of a positive and negative type. The features of the negative type of adaptation are presented: clip thinking, reducing the need and ability to memorize, Internet addiction, susceptibility to Internet manipulation, destructive physiological changes. The features of a positive type of adaptation on the Internet are: virtual cognitive and educational activities; virtual gaming activities; virtual communication and social activities; professional activities on the Internet; Internet business. Positive features often come hand in hand with negative adaptation processes. The presented analysis substantiates the need for a new round of the branch of pedagogical science – digital neuro-pedagogy, based on the methodological basis of neuropedagogy that emerged at the end of the 20th century. A foreign term often used is “educational neuroscience” or “cognitive neuroscience”. To study the processes of further immersion into the brain's behavior in the digital flow, the formation of a new neuro-pedagogical knowledge is necessary to understand how to make learning in this new environment and, more broadly, socialize the pedagogically controlled process.