Reviewerly/exHarmony huggingface dataset:
Building of a Variable Context Key Enhancing the Security of Steganographic Algorithms

This article presents a concept of a context key for steganographic algorithms. The proposed scheme of building the context key is designed to enhance the level of security of the applied algorithm. Reading a hidden message should be possible when certain requirements defined as the context are met. For example, such requirements include a specific level of lighting, location and a password protected access, while typing the password itself can be arranged to require appropriate context. This may be characterized by the speed of typing the characters etc. Therefore, the context key will be used in mobile devices such as a smartphone, tablet, etc. The context key building scheme has been proposed, allowing the key to be changed at any stage of the hiding algorithm. The proposed solution protects subsequent bits of a hidden message against retrieving or forward inference, which means that a key broken at a certain stage does not allow concluding what key will be used in the next algorithm step and what key was used previously. In addition, the presented key scheme allows reading a message while the context is not fully met.The authors aimed to propose the possibility of using the context key as a type of strengthening the security of steganographic algorithms by adding new elements blocking the possibility of reading messages by unauthorized persons. The possibilities offered by sensors in mobile devices were used for this purpose. The novelty in the proposed key is the use of threshold methods of sharing secrets as a mechanism enabling the user to read a message even if not all environmental conditions are satisfied during the reading of the secured message.
A Scalable VLSI Architecture for Illumination-Invariant Heterogeneous Face Recognition

This paper proposes a scalable VLSI architecture of the local gravity face angle(LGFA) feature extraction algorithm. The LGFA is a novel feature extraction algorithm used for generating LG-face for illumination-invariant and heterogeneous face recognition. The proposed approach uses fixed-point arithmetic representation and min-max normalization process is modified at algorithmic and architecture stage for faster average computation (convergence) of angles. The architecture presented in this paper is pipelined and parallel and have no preprocessing stage for zero padding. The design is validated in Xilinx Virtex-5 FPGA (XC5VLX110T). The proposed architecture is scalable for any image up to \(128\times 128\) pixels size and the limitation is only due to the size of the internal counter and memory resources (Block RAM) available in FPGA platform. The simulation of the design in Xilinx ISE 14.7 shows that the emulated architecture can compute the LGFA of \(128 \times 128\) image with variation of 0.27\(^\circ \) at 68.312 MHz consuming only 42mW of switching power. This paper is a first attempt in regard to FPGA implementation of LGFA feature extraction.
Mitigating the Costs of Spatial Transformations With a Situation Awareness Augmented Reality Display: Assistance for the Joint Terminal Attack Controller 3-17

Evaluate and model the advantage of a situation awareness (SA) supported by an augmented reality (AR) display for the ground-based joint terminal attack Controller (JTAC), in judging and describing the spatial relations between objects in a hostile zone.The accurate world-referenced description of relative locations of surface objects, when viewed from an oblique slant angle (aircraft, observation post) is hindered by (1) the compression of the visual scene, amplified at a lower slang angle, (2) the need for mental rotation, when viewed from a non-northerly orientation.Participants viewed a virtual reality (VR)-simulated four-object scene from either of two slant angles, at each of four compass orientations, either unaided, or aided by an AR head-mounted display (AR-HMD), depicting the scene from a top-down (avoiding compression) and north-up (avoiding mental rotation) perspective. They described the geographical layout of four objects within the display.Compared with the control condition, that condition supported by the north-up SA display shortened the description time, particularly on non-northerly orientations (9 s, 30% benefit), and improved the accuracy of description, particularly for the more compressed scene (lower slant angle), as fit by a simple computational model.The SA display provides large, significant benefits to this critical phase of ground-air communications in managing an attack-as predicted by the task analysis of the JTAC.Results impact the design of the AR-HMD to support combat ground-air communications and illustrate the magnitude by which basic cognitive principles "scale up" to realistically simulated real-world tasks such as search and rescue.
A Survey on Image Encoders and Language Models for Image Captioning

Abstract Generating a natural language explanation for a given image is known as image captioning. An image captioning method aims to determine the significant objects present in an image together with the relationship between these objects. Also, the model has the capability to describe an image by a syntactically and semantically correct sentence. For image encoding, convolutional neural network {CNN] is applied and for producing natural language descriptions for a given image, language models (RNN &amp; LSTM etc.) are employed. In this paper, the image encoders and language models used by the state-of-the-art image captioning models is discussed.
Dynamic Network Selection for the Object Detection Task: Why It Matters and What We (Didn’t) Achieve.

In this paper, we want to show the potential benefit of a dynamic auto-tuning approach for the inference process in the Deep Neural Network (DNN) context, tackling the object detection challenge. We benchmarked different neural networks to find the optimal detector for the well-known COCO 17 database [14], and we demonstrate that even if we only consider the quality of the prediction there is not a single optimal network. This is even more evident if we also consider the time to solution as a metric to evaluate, and then select, the most suitable network. This opens to the possibility for an adaptive methodology to switch among different object detection networks according to run-time requirements (e.g. maximum quality subject to a time-to-solution constraint). Moreover, we demonstrated by developing an ad hoc oracle, that an additional proactive methodology could provide even greater benefits, allowing us to select the best network among the available ones given some characteristics of the processed image. To exploit this method, we need to identify some image features that can be used to steer the decision on the most promising network. Despite the optimization opportunity that has been identified, we were not able to identify a predictor function that validates this attempt neither adopting classical image features nor by using a DNN classifier.
La commutazione di codice nel parlato dell’insegnante di inglese come lingua straniera Uno studio di caso

In the current study, code-switching is analysed in the context of an English as a Foreign Language class. Following a brief review of the literature on the relationship between code-switching and language teaching, an observational study involving forty-one students in two classes is presented. The analysis is based on class observations, duly recorded and transcribed, and on two questionnaires completed by students and their teacher. Results reveal some positive effects of code alternation in the English as a foreign language classroom.
Rainwater for Domestic Use in Urban Area: A Simulation of Rainwater Harvesting System for Surabaya, Indonesia

Many urban areas in Indonesia currently are taken for granted for the availability of clean water from Local State Drinking Water Enterprise (Perusahaan Air Minum Daerah or PDAM) of Indonesia. The number of costumers of PDAM, specifically in Surabaya City increases in the last decades. It raises an issue of maintaining the sustainability of good quality and enough quantity of water delivered to the costumers. On the other hand, flood has become annual events in Surabaya City (2015). Rainwater harvesting, which has been practised in several rural areas in Indonesia, is tried to be simulated for urban area, specifically in Surabaya City to overcome those problems. It is expected to replace some amount of tap water withdrawal as well as to reduce the surface runoff in urban area. An analysis of Analytic Hierarchy Process was done to observe the main aspect needs to be prioritized on installing a Rainwater Harvesting System (RWHS) in a house. A survey to 55 respondents in a real estate in West Surabaya results that the main aspect to be prioritized is environmental impact. This result indirectly is along with the purpose of designing RWHS to conserve water. Rainwater collected through the roof of 31.14 m2 results in a maximum inflow of 432.9 L/day rainfall. 370 L/day of rainwater is used for gardening and toilet flushing. The complete simulation shows that RWHS installed in a house enables to reduce 21.5% of runoff due to the construction of the selected model of house.
Gaze and Heart Rate Synchronization in Computer-Mediated Collaboration

Computer-mediated collaboration has become an integral part of our every day functioning. Despite decreased non-verbal communication and face-to-face contact with partners of collaboration, people learned how to remotely work together. The consequences of decreased non-verbal signals such as gaze communication in remote collaboration are however not fully investigated. In a series of three experiments, we propose solutions to enhance quality of remote collaboration. The present paper is focused on examining the relation between gaze and heart reaction during face-to-face and remote collaboration.
Detecting factorization effect in continuous-time quantum walks

Abstract We consider an continuous-time quantum walk on triple graphs with a potential well in one of the terminal sites on the main chain and study its dynamical properties in the open system by introducing a ‘spontaneous damping’ process between the terminal site and its nearest neighbour one. Calculating the stable probability of the terminal site and the time evolution of probabilities of the other sites on the main chain, we show that the system can manifest the factorization and coherence protection phenomena when the length and position of the side chain satisfy some concrete conditions. Furthermore we investigate the effect of disorder on the hopping strength and show that the existence of the disorder can destroy the coherence protection phenomena and increase the stable probability of the terminal site on the main chain.
Object Detection using GM based Clustering Algorithms

Object detection of a moving object under variable illumination condition is one of the inspirational tasks for the researchers. In order to do so, background subtraction plays a vital role. In this paper, we constructed the background model by computing the geometric mean (GM) of log-normal features of frames in temporal direction. This is followed by subtracting the background model with the input frame. Finally, for object detection, we have proposed three clustering algorithm, i.e. fuzzy C-mean (FCM), fast fuzzy C-mean (FFCM) and adaptive fuzzy C-mean (AFCM) algorithm. We have implemented the proposed algorithm on the standard PET data set. Partition coefficient (PC), partition entropy (PE) and Xie Beni index (XBI) are used to validate the performance. The simulation result of our proposed algorithm has been compared with the existing work, and by using different validity index, we concluded that GM-based AFCM is better than GM-based FFCM and GM-based FCM.
Workshop: Data Science with Julia

Julia is one of the most widely used languages by the scientific community today, intending to develop applications where exhaustive numerical computation is required. The main reason for such use lies in the combination of four aspects: the ease and dynamism of Python together with the speed and efficiency of C. For this reason, Julia has become a trend to provide solutions for large-scale applications in scientific computing, data science, and artificial intelligence. In this workshop, an introduction to its syntax will be presented and some of its features will be evaluated for the implementation of solutions within the aforementioned areas.
Segmentation of Satellite Images Using Contractive Autoencoder (CAE) Aided Deep Learning Approach

The reliable extraction of contents from satellite images is a challenging problem as these images are very much useful in many real-life applications. In this paper, a deep learning-based method to extract different regions of interest (ROIs) is presented. In the work, at first, the high-resolution satellite images are processed with the contractive autoencoder (CAE) to remove the noises which are inherently present in the images during acquisition. The output of the CAE is taken as input to train a segmentation network which is a deep neural network. Finally, the output of the segmentation network is used to train both supervised and unsupervised classifiers to extract different ROIs from the satellite images. Experimental results show that the method is reliable for real-time application.
Surrounding Cell Suppression For Unsupervised Representation Learning In Hematological Cell Classification

Analysis of hematopoietic cells in bone marrow images is a newly emerging field in computer vision. Deep neural networks provide promising approaches for detection and classification tasks in this field. However, labelling a sufficiently large amount of images by medical experts is infeasible in practice. This can be resolved by semi-supervised methods that use image reconstruction as a way to incorporate images without labelled cells. However, this inevitably leads to an inclusion of surrounding cells into the learned representation. We propose and analyze several techniques for reducing their influence and show that this improves classification results of unsupervisedly learned cell representations.
Strongly Supervised Mitosis Detection In Breast Histopathology Images Using Weak Labels

Mitosis detection plays an import role in tumor grading of breast cancer. Automatic mitosis detection liberates pathologists from the time-consuming manual counting work. However, mitosis detection datasets are often provided only with centroid labels, limiting the performance of most deep learning methods. In this paper, we propose a novel method for mitosis detection to address this problem. First, we generate pixel-level labels directly from origin centroid labels with a gradient changing threshold approach. Then we apply a ResNet-based FCN to detect mitotic nuclei. Low confidence and tiny areas are removed from the prediction map to produce the final detections. By transforming the weak labels into strong labels, our method achieves F1-scores of 0.692, 0.608 and 0.805 on three public datasets, AMIDA 2013, ICPR 2014 and TUPAC 2016, respectively, outperforming all other state-of-the-art methods and showing great potential for clinical deployment.
Intensity-Based Wasserstein Distance As A Loss Measure For Unsupervised Deformable Deep Registration

Traditional pairwise medical image registration techniques are based on computationally intensive frameworks due to numerical optimization procedures. While there is increasing adoption of deep neural networks to improve deformable image registration, achieving a clinically suitable solution remains scarce. One of the primary difficulties lies in the choice of tractable distance functions to assess image similarity. Recent works have explored the Wasserstein distance as a loss function in generative deep neural networks. In this work, we evaluate a fast approximation variant - the sliced Wasserstein distance - for deep image registration of brain MRI datasets. Based on a VoxelMorph backbone architecture, which includes a combination of UNet and spatial transformer networks (STN) for deformable registration, we propose three implementation variants to compare the model's performance: the standard sliced Wasserstein, the Radon transform performing a low dimensional embedding, and a novel patch-based method that allows fine-grained deformation comparison. Experiments performed on public datasets of brain images from the Learn2Reg open challenge demonstrate the Wasserstein methods converge faster than the baseline mean square error method, with the proposed patch-based method yielding similar performance to baseline methods, and improved overall accuracy compared with other implementations. This makes the sliced Wasserstein a valuable metric for deep mono-modal and multi-modal deformable medical image registration problems with our proposed implementation.
Error Detection Using Syntactic Analysis for Air Traffic Speech

Air traffic controllers and pilots communicate primarily through voice/speech to perform their day-to-day operations. Automatic speech recognition when applied in this domain can reduce the workload of both controller and pilot. The speech processing has several challenges in this domain like poor quality of radio channel, faster speaking rate and very strict vocabulary with infinite accent combinations. As errors have impact over the speech recognition process, the focus is on error detection in air traffic controllers speech when using automatic speech recognition. In this line, various error detection techniques available in normal English speech recognition are compared, and specific techniques which can help in air traffic controllers speech domain are discussed. Also, this paper emphasizes on syntactic analysis as a major component in the post-processing. Syntactic analysis along with phonetic string distance analysis helped to obtain close to 10% overall improvement in word error rate and 10–15% improvement in concept recognition rate for the experiments conducted over air traffic speech data considered for discussions.
From Bielefeld to the Unified Patent Court – A Never-Ending Story?

Abstract The authors have known Jochen Pagenberg from different perspectives: Tilman Müller-Stoy for almost 20 years as a partner in the law firm bearing Jochen’s name; Rudolf Teschemacher since the start of Jochen’s and his own career when both got in touch with IP at the Max Planck Institute in the early 1970s and later on for more than 15 years as a senior consultant at Bardehle Pagenberg. Thus, they paint a colourful picture, in particular throwing a glance at a leitmotiv of his work: European patent litigation.
Application of Image Processing in Improving the Precision of a 3D Model

To meet the needs of different aspects, original image data usually need to be processed. The Jiahu bone flute is a precious musical relic in China. In 2012, researchers used medical Computed Tomography to three-dimensionally scan the bone flute, but the accuracy of the data obtained at that time was not high. Due to the cultural relic protection policy, it is impossible to directly obtain image data on the bone flute today. In this paper, a Lagrange interpolation algorithm conducting grey interpolation is used to process the CT data of the original bone flute, and more sectional images are obtained. The mean squared error, peak signal-to-noise ratio and image information entropy are used to evaluate the algorithm. Then, the filtering effects of the three methods of mean filtering, median filtering and Wiener filtering on the bone flute CT data are compared. The results show that the median filtering effect is the best, and the processed bone flute CT image is more delicate and realistic. The image processing method used in this article is also applicable to general image data, which has referential significance for image processing in traditional printing and emerging 3D printing.
REMOTE TEACHING OF INFORMATICS: FEATURES, PROBLEMS, DIGITAL TOOLS

Today, in a pandemic crisis, there is a forced transition from the traditional system of education, designed for full-time «classroom», to a paradigm of learning, where face-to-face stages must be organically combined with remote, and their alternation must occur as required by conditions quarantine restrictions. The usual «quarantine vacations» of previous years have become impossible, when students were able to simply stay at home, and the lost time was further compensated by the intensification of education. Education now faces a situation where views on the basic organizational and methodological principles of learning, the format of the lesson, the use of digital tools, the involvement of students in online work, the use of new distance forms and methods of learning must change. During 2020, significant changes in the regulatory framework took place in education in Ukraine, in particular, the provisions on the distance form of full general secondary education and sanitary regulations for general secondary education institutions were approved, and electronic journals were introduced. However, the analysis of the definitions showed that different documents provide different definitions of the basic concepts of «distance education», «distance learning». Much attention is now being paid to teaching teachers the current way of teaching and the digital tools that can provide it. However, there are still many problems and issues regarding the effectiveness of distance learning, the choice of appropriate programs and services, the rational presentation of educational material. The article highlights a topical issue – providing remote teaching of certain school subjects, in particular the subject «Computer Science». Various methods and approaches to learning are given, digital learning tools tested by the authors of the article are proposed, which can be used as an alternative to local software during distance learning, relevant examples are given.
Towards Individualized Production with Flexible Shopfloor IT based on Distributed OPC UA Services

Abstract Nowadays, manufacturers face the problems to adjust their production according to the fast-changing global competition. Car manufacturers, for example, are in front of a new era of individual mobility. They need to switch from combustion engines to electric drives. In general, manufacturers have to develop production strategies to lower set up time and meet requirements faster. The classical production line is most effective if the product variety is low and the lot size is high but facing the rapid growing demand for individualized products and fast-changing requirements, manufacturers must discover new ways of manufacturing. One possibility is the flexible shopfloor. In this work, we will present our concept of a flexible shopfloor IT for controlling a robotic demonstrator. We use OPC UA over TSN as a platform for communication and CODESYS as Soft SPS for the control of sensors and actuators. In order to create valid data for ongoing discussions we use function point analysis and counting lines of sourcecode to measure software size. Our expectation is that the initial effort for implementing a flexible shopfloor IT is higher compared to the classic approach, but for all the following implementation it will take less time, because of the possibility of reuse already developed systems.