Reviewerly/exHarmony huggingface dataset:
Applying Deep Learning to Solve Alarm Flooding in Digital Nuclear Power Plant Control Rooms

As the nuclear industry starts to shift to more digital controls and systems more information is provided to the control room and displayed on computer monitor workstations. This combined with alarm panels reduced to one alarm display creates the problem called alarm flooding, a situation where an overload of information can be caused during a plant disturbance or other abnormal operating condition. This project focused on finding a workable solution to assist operators in handling and understanding alarms during emergency situations. A generic pressurized water reactor simulator was used to collect process and alarm signals in scenarios that introduced common incidents causing expected alarms, as well as malfunctions causing unexpected alarms. Deep neural networks were used to model the collected data. Results showed that the models were able to correctly filter many of the expected alarms, indicating that deep learning has potential to overcome the problem of alarm flooding.
Social network extraction based on Web: 4. A framework

Abstract Social network extraction from information sources, as a research, requires the right steps. However, it is not enough, it need accurate description that explains the steps in one framework so that the flow of the extraction implementation information is well, as well as the extraction occurs systematically. In this paper, a framework for social network extraction has been outlined by involving several formulations and measurements as an explanation.
Effectiveness Evaluation Method for Cyber Deception Based on Dynamic Bayesian Attack Graph

The theoretical study of cyber deception technology often focuses on the analysis of specific defense technologies, and most researches are limited to the single-level qualitative measurement, which cannot be utilized in the complex environment. In this paper, to quantify the effectiveness of cyber deception comprehensively and reasonably, a method is proposed based on a dynamic Bayesian attack graph. By analyzing the capability of attackers, the Bayesian attack graph is established based on the dependence of network resources and vulnerability factors. Then we verify the consistency of host nodes in the project based on network features, device fingerprints, and file attributes, and update the Bayesian attack graph with network attack-defense events. Finally, in combination with the common vulnerability scoring system (CVSS) metrics, the concealment and effectiveness indexes are given to evaluate the cyber deception's performance. Experiments show that our method is feasible for the attack-defense adversarial network environment, which is beneficial for the defender to formulate the optimal deception strategy according to the current situation of attack-defense events.
On Approximating the Non-negative Rank: Applications to Unsupervised Image Reduction

Unsupervised Learning (UL) methods are a class of machine learning which aims to disentangle the representations and reduce the dimensionality among the data without any predefined labels. Among all UL methods, the Non-negative Matrix Factorization (NMF) factorizes the data into two subspaces of non-negative components. Moreover, the NMF enforces the non-negativity, sparsity, and part-based analysis, thus the representations can be interpreted and explained for the Explainable Artificial Intelligence (XAI) applications. However, one of the main issues when using the NMF is to impose the factorization rank r to identify the dimensionality of the subspaces, where the rank is usually unknown in advance and known as the non-negative rank that is used as a prior to carrying out the factorization. Accordingly, we propose a novel method for the non-negative rank r approximation to help solving this problem, and we generalize our method among different image scales. Where, the results on different image data sets confirm the validity of our approach.
Mobile learning in forestry education

Abstract In the article the experience of using mobile learning in forest education is considered. It examines the phenomenon of mobile pedagogy, mobile learning, describes structure of mobile teaching. The application of mobile pedagogy in learning in forest education is described. The attitude to the using of mobile pedagogic in forest high education of teachers and students is shown. Different mobile applications that can be used in the educational process are described. The experience of implementation of methods of mobile pedagogic in forest higher education in Saint-Petersburg State Forest Technical University is shown. The importance of using mobile pedagogic in nowadays education is stressed, because such innovational approaches may allow raising the students’ motivation for learning. Possibilities for the development of mobile pedagogic in forest high education are shown.
The sparse and low-rank interpretation of SVD-based denoising for vibration signals

Vibration signal denoising is one of the most important steps in condition monitoring and fault diagnosis, and SVD-based methods are a vital part of advanced signal denoising due to their non-parametric and simple properties. The relation-ships between SVD-based denoising and other advanced signal processing methods are very significant and can help speed up the development of SVD-based denoising methods. There is limited prior work into the sparse and low-rank meaning of SVD-based denoising. In this paper, we build the relationships among SVD-based denoising, sparse l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -norm minimization, sparse weighted l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm minimization, and weighted low-rank models, when the dictionary is designed by left and right singular matrices in sparse minimization. Using the derived conclusion, we establish weighted soft singular value decomposition (WSSVD) for vibration signal denoising. Finally, we perform one experimental study to verify the effectiveness of WSSVD considering impulse interference and amplitude fidelity.
Survey on Advanced Equipment Fault Diagnosis and Warning Based on Big Data Technique

Abstract Faults are main problem of Mechanical equipment. In general, in order to acquire the massive fault information of equipment, a large number of monitoring points need to be established on the equipment, and the number of sensors for the equipment is large, since equipment fault diagnosis enter the era of big data. The successful application of big data technology in track circuit and power equipment system indicates that the big data of equipment contains important information that reveals the evolution and nature of faults. This paper briefly introduces the development of fault diagnosis technology from three aspects: data acquisition, intelligent fault diagnosis approach and remote fault diagnosis. In addition, This paper expounds the development history of fault diagnosis technology, analyzes the challenges of intelligent fault diagnosis in the era of big data, and discusses the development trend of equipment fault diagnosis according to the existing foundation and challenges. Finally, the development trend of equipment fault diagnosis and early warning are pointed based on existing research.
Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition

Human action recognition has gathered significant attention in recent years due to its high demand in various application domains. In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos. The proposed scheme develops a discriminative codebook and a hybrid feature vector by encoding the features extracted from CNNs (convolutional neural networks). We explore different CNN architectures for extracting spatio-temporal features. We employ an agglomerative clustering approach for codebook generation, which intends to combine the advantages of global and class-specific codebooks. We propose a Residual Vector of Locally Aggregated Descriptors (R-VLAD) and fuse it with locality-based coding to form a hybrid feature vector. It provides a compact representation along with high order statistics. We evaluated our work on two publicly available standard benchmark datasets HMDB-51 and UCF-101. The proposed method achieves 72.6% and 96.2% on HMDB51 and UCF101, respectively. We conclude that the proposed scheme is able to boost recognition accuracy for human action recognition.
PudgyTurtle: Using Keystream to Encode and Encrypt

Stream cipher encryption works by modulo-2 adding plaintext bits to keystream bits, which are in turn produced by successively updating a finite-state machine initialized to a secret starting state. PudgyTurtle is a way to encode the plaintext in a keystream-dependent manner before encryption. Since it can use keystream from any stream cipher, PudgyTurtle functions somewhat like an encryption mode. The process begins by generating successive 4-bit groups of keystream (‘nibbles’) until one of them matches the current plaintext nibble to within one bit. The number of keystream nibbles required, as well as the nearness of this match, is then encoded into a variable-length codeword. Finally, this codeword is encrypted by modulo-2 addition to an equal amount of keystream. Compared to normal binary-additive stream ciphers, this process is less efficient (i.e., more time is required to generate extra keystream nibbles, and more space is needed for the codewords than for the plaintext). However, with this cost comes a benefit: PudgyTurtle resists time-memory tradeoff attacks better than standard stream encryption.
Efficient Programmable Random Variate Generation Accelerator From Sensor Noise

We introduce a method for nonuniform random number generation based on sampling a physical process in a controlled environment. We demonstrate one proof-of-concept implementation of the method, that doubles the speed of Monte Carlo integration of a univariate Gaussian. We show that we must measure and compensate for the supply voltage and temperature of the physical process to prevent the mean and standard deviation from drifting. The method we present and our detailed empirical hardware measurements demonstrate the feasibility of programmable nonuniform random variate generation from low-power sensors and the effect of ADC quantization on the statistical qualities of the approach.
The blurred line between the professional and the personal: Regulation of teacher behaviour on social media

As demonstrated by recent media reports concerning the Australian Public Service Social Media Policy and the Australian Rugby Union’s dispute with Israel Folau, social media often blurs the line between our professional and personal lives, and means that employers want to control what we do and say on social media. This is an issue which directly affects Australian teachers, whose professional obligations have always extended beyond their immediate work environments. Does social media mean that teachers are never ‘off the clock’? This article examines the current law relating to control of teacher behaviour and comment on social media. It analyses the implied freedom of political communication discussed in the 2019 High Court decision of Comcare v Banerji, and its ramifications for teachers and their employers. Additionally, using case studies involving social media policies of authorities which control teacher professional conduct, it considers whether such policies constitute lawful and reasonable directions, or whether they could be challenged as infringing teachers’ rights to a personal life.
The Data Hungry Home

The Data Hungry Home is the initial result of a research project deploying a critical design and research through design-led approach to challenging accepted paradigms of the role that devices fulfil in human-computer interactions. Founded on questions related to how devices could help improve experiences associated with navigation through the external environment, the project creates the concept of devices that exhibit certain aspects of life. In particular, it postulates, designs, and creates devices that require sustenance from data specifically collected and delivered by the human partner. In return, the device utilises these data in unexpected ways in order to generate experiences that add value to the interaction. Together, these interactions propose a rebalancing of human-device relationships. First-generation devices have been manufactured and will shortly enter in situ studies to evaluate actual interactions. The work may have the potential to add to the field of study investigating "beyond human-centred design".
Reversible steganography techniques: A survey

Steganography is the technique of embedding secret data to another multimedia type. A reversible steganography scheme (RSS) uses image features to hide the secret data into the host image. In the extraction procedure the concealed information is extracted from the stego-image, which can subsequently be restored to its original status. This chapter presents a detailed survey of different types of RSS, including the difference expansion scheme (DE), the histogram shifting scheme (HS), the pixel-value ordering scheme (PVO), the dual-image-based scheme, and the interpolation-based scheme.
A Mobile Robot Path Planning Algorithm Based on Multi-objective Optimization

A mobile robot path planning algorithm based on multi-objective optimization is proposed. The algorithm aims to achieve three kinds of optimization objects for planned paths: length, smoothness and security the chaotic sequence and heuristic methods based on environmental knowledge to initialize population so as to improve the individuals' ergodicity and feasibility in the search space. Meanwhile, according to characteristics of path planning, several genetic operators based on domain-specific knowledge are proposed to improve the efficiency of the algorithm. Simulation results show that the robot can plan a set of optimized smooth paths which can avoid collisions from the start to the target point in the environment with many obstacles, and these optimal paths.
An Analysis of the Role and Structure of Information (Conceptual) Systems

The nature of information (conceptual) systems is studied using a system–object approach. The conclusion is made that the hierarchies of conceptual and material systems are an object-oriented system, whose conceptual systems are external systems (class systems) that determine the properties of specific objects, and objects–material or internal systems (systems–phenomena) that perform real interactions. The results of a formal description of the hierarchy of conceptual systems are considered, taking their interconnection into account by formalization using descriptive logic of some provisions of the system–object approach. The syntax and semantics of the ALCOIQ descriptive logic and its extension to the SHOIQ logic are described. The SHOIQ logic is introduced; it formally describes the concepts of the volume and content of a conceptual system that expand system theory based on a system–object approach.
3840x Reliability Enhanced Robust NAND flash Optimized to Store Weight Data for Object Detection and Semantic Segmentation of Self-driving Car at High Temperature

This paper proposes reliability enhancement techniques in harsh environment to store weight data of machine learning (ML)-based applications, object detection and semantic segmentation for self-driving cars. Proposed techniques consist of robust NAND and Optimized Huffman Coding Compression (OHCC). Proposed robust NAND drastically decreases bit-error rate (BER) in extremely high temperature such as 210degC. Therefore, proposed techniques reduce miss recognition caused by weight data error and contribute to safety self-driving cars. Besides, proposed OHCC modulates weight data of ML-based image recognition applications by utilizing weight data characteristics that concerned around ‘0'. Consequently, proposed techniques extend data-retention (D. R.) time by 3,840 times for object detection and 2,550 times for semantic segmentation, respectively compared with conventional 3D triple-level-cell (TLC) NAND flash. In addition, proposed techniques achieve to decrease read access time and data-overhead by 39% and 94%, respectively.
M-Absence : Analysis and Design using Unified Modelling Language (UML)

Abstract The development of cellular technology is very fast; this has led to technological advances in all fields where one of them is education. E-absent is an Android-based technology that serves to help student guardians report or notify student absence to the teacher and enter the necessary documents into the system. Then the teacher will send the subject matter and lesson assignments to students when students are absent, so students do not miss the lesson. This paper discusses system analysis and design using the Unified Modelling Language (UML), which makes it easier to describe the system as a whole. And the results of this study are an e-absence model that fits the needs of the school.
Analysis and Comparison of Hough Transform Algorithms and Feature Detection to Find Available Parking Spaces

Abstract Parking space is one of the most critical needs of people’s lives, especially in Indonesia. According to the Central Statistics Agency, vehicle growth in Indonesia in the last ten years is 9% per year. Meanwhile, parking needs are being eroded by settlements, shops, and public service buildings. Limited parking lots make it hard for drivers to find available parking spaces. When looking for a parking space, it was causing impacts such as traffic jams, air pollution, causing noise and panic. The intelligent parking system is the solution to this problem. This system can provide information on available parking slots. In this study, parking locations are marked with a circle. If a circle is visible, then a parking lot is available, and if not, then the parking location has been filled by the vehicle. Circle objects in images taken using the camera can be identified by the Hough transformation method or feature extraction. These two methods are compared to measure the accuracy and speed of the process. Experiments and observations on the performance of both methods show that both methods can recognize the location of the available parking slot. The feature extraction method has a better detection speed with an average processing time of 1.1 seconds. The Hough transformation algorithm has an average processing time of 4.1 seconds. Then it can be concluded that the feature extraction method is better applied to the smart parking system.
2DInpaint: A novel privacy-preserving scheme for image inpainting in an encrypted domain over the cloud

The low cost, agility, and mobility of cloud services for processing and storage data have attracted user’s attention today. Untrusted third parties support these services, and users are always concerned about utilizing them for personal data. Addressing these data-privacy issues for image inpainting over the cloud infrastructure(s), we propose a model, 2DInpaint, to perform image inpainting by protecting image information from an eavesdropping adversary. Inpainting is a technique for modifying an image in an undetectable manner with applications ranging from restoration of damaged photographs to object-removal and replacement of lost blocks in image coding and transmission. It can be accomplished by propagating the information in the isophotes direction of the desired region(s) from the neighborhood. Performing this propagation when the image is in the encrypted domain (ED) is a challenging dilemma. The challenge is addressed by employing a modified version of 2D-bicubic interpolation over the region to be inpainted in ED. The ramp secret sharing scheme is utilized to secure image information and to reduce storage overhead over the cloud server. 2DInpaint is proved to be information-theoretical secure in a probabilistic viewpoint and through various cryptographic attacks. The qualitative and quantitative results of 2DInpaint are analyzed under the scenarios of classical image inpainting, object-removal, and text-removal, and compared with the schemes in the plain domain. Moreover, no limitations related to the topology of the region to be inpainted are required using our approach. To the best of our knowledge, 2DInpaint is the first move towards image inpainting in the ED.
Tensor-Based Approach for Background-Foreground Separation in Maritime Sequences

The complexity of a scene in addition to the need for real-time processing are the main challenges that face any background/foreground separation approach for maritime environment. Recent studies on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Low-rank and Sparse Separation</i> (LSS) achieved good performance when compared to traditional background subtraction techniques in segregating the foreground from a complex background. However, the issue of maintaining this type of a separation via an updating mechanism is not well addressed by the majority of LSS approaches. The study presents a tensor based singular value decomposition approach for background/foreground separation. The approach is uniquely designed to deal with most challenges related to a maritime environment such as sea dynamics, boat wakes, variety of foreground objects, and camera jitter. Furthermore, the proposed approach operates incrementally via updating the separation components as opposed to reperforming the decomposition on the entire video sequence when a set of frames arrives. Additionally, a forgetting mechanism is employed in the proposed approach to efficiently handle challenges such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stationary Foreground Objects</i> (SFOs) and ghost effects. The performance of the proposed method with several state-of-the-art LSS and Non-LSS techniques on videos with complex maritime scenarios are evaluated. The results exhibit better performance over most of the tested challenges and also demonstrate the capability of the proposed method to perform the separation in less computational time.
From Business Modeling to Software Design

Graphical system sequence diagrams (SSDs), used in Requirements Engineering and Analysis, are hard to draw for larger use cases. However, we wanted to be able to express SSDs for such use cases as well. While we were designing a grammar for textual SSDs, we noted that we needed the same kind of structuring mechanisms as for modeling business processes and programming languages. Only the basic building block differs: e.g., an assignment statement for (imperative) programming languages and a (basic) interaction step for SSDs. Can we generalize this similarity idea, leading to (uniform) grammars for instruction languages in general, and exploit this? This paper presents a grammar for ‘structured’ Ws (where W stands for ‘Whatever’), leading to grammars for (textual) instruction languages. The general grammar contains constructs necessary for specifying all kinds of instructions. We illustrate our theory with examples from several different application areas. A basic building block might even be a picture or an icon, as we will point out. The main contribution of the paper is a powerful and uniform grammar for instruction languages in general, containing generic constructs for specifying various instructions conveniently. Exploiting the similarity between the structuring mechanisms for modeling business processes, (textual) SSDs, and programming languages will ease the translation towards an implementation in a software system. Usually the grammar rules for basic building blocks are domain specific and must be specified and added per application.