Reviewerly/exHarmony huggingface dataset:
An Approach of Automatically Selecting Seed Point Based on Region Growing for Liver Segmentation

Liver region extraction in abdominal CT images is a very important research field, a method of liver segmentation based on region growing for automatic selection of seed points is proposed in this paper. Firstly, the original image is binarized, and the initial area of the liver is extracted by the maximum area measurement method; After that, the improved region growth algorithm was used to segment the liver, and the location of seed points was automatically obtained by finding the center of the maximum inscribed circle locked in the initial liver area, which was used as the basis for the selection of seed points; Finally, the segmented liver region is treated by morphological methods. The experimental results show that the approach effectively solves the problem of manually selecting seed points for regional growth, and can improve the efficiency and accuracy of seed point selection, which avoids the selection of seed points at the wrong positions such as edges or noise due to subjective factors.
Design of intelligent camera-based security system with image enhancement support

Abstract Security is a desired condition in order to achieve stability in the life of society. Security issues are often related to efforts to prevent and detect criminal acts in the community and along with technological developments various methods and devices are created and implemented to support security procedures. This research focuses on designing intelligent camera-based security systems with the support of image enhancement feature. The developed security system is the type of active security system that has the advantage of intercepting or providing real-time information on potential criminal acts that occur and this is done through system automation without involving humans as system supervisors. The developed security system is supported with digital image processing software to process snapshot and recorded images from system cameras with the aim of improving quality of visual images for the analysis purpose and also as digital forensic evidence that can be used in law enforcement related to criminal activity. The output of this research is active security system devices with the support of image quality enhancing software that can be used in various security and surveillance needs.
Different shape and color targets detection using auto indexing images in computer vision system

Abstract One of the main challenges in computer vision is to determine the number of different types, shapes, locations and color targets within the image plane for use in computer control systems. In this study, an algorithm introduced to detect the number of targets (one or two), their shapes (square or circle) and colors (red, green or blue). A new technique presented as a digital indexing code table to present the studied color targets images. The indexing table technique depends on decimal and binary numbers. In this study, there were 42 different cases represents all the input images. There is a special case considered for the similarity of input images in case it has the same shape and color, but a change in rotation and space between two image targets. This solved using referencing to indicate the same target in each case. Thus, the classification results were 100% for the presented algorithm for all input cases.
High-Throughput Hardware for 3D-HEVC Depth-Map Intra Prediction

Editor's note: High Efficiency Video Coding (HEVC), also known as H.265 and MPEG-H Part 2, is a video compression standard. An efficient hardware for 3D-HEVC depth-map intraprediction is discussed in the article. The authors show an insight view to the hardware realization and the very promising results in terms of throughput and latency.--Michael Hüner, Brandenburg University of Technology.
An End-to-End Augmented Reality Solution to Support Aquaculture Farmers with Data Collection, Storage, and Analysis

Augmented reality (AR) is being rapidly adopted by industries, such as logistics, manufacturing and military. However, one of the extremely under-explored yet significant areas is the primary production industry, and as a major source of food and nutrition, seafood production has always been a priority for many countries. Aquaculture farming is a highly dynamic, unpredictable and labour-intensive process. In this paper, we discuss the challenges in aquaculture farm operation based on our field studies with leading Australian fisheries. We also propose an ”AR + Cloud” system design to tackle the delayed in-situ water quality data collection and query, as well as aquaculture pond stress monitoring and analysis.
5D Combined Chaotic System for Image Encryption with DNA Encoding and Scrambling

The objective of this paper was to propose a 5D combined chaotic system used for image encryption by scrambling and DNA encryption. The initial chaotic values were calculated with a set of equations. The chaotic sequences were used for pixel scrambling, bit scrambling, DNA encryption and DNA complementary function. The average of NPCR, UACI and entropy values of the 6 images used for testing were 99.61, 33.51 and 7.997 respectively. The correlation values obtained for the encrypted image were much lower than the corresponding original image. The histogram of the encrypted image was flat. Based on the theoretical results from the tests performed on the proposed system it can be concluded that the system is suited for practical applications, since it offers high security.
Semantic Search System using Word Embeddings for query expansion

In the present the amount of documents on the web has increased a lot, which has brought as a consequence that the terms that were previously necessary to find the information the user needs now end up being very vague and less precise. In this paper we focus on the use of word embeddings based on Word2Vec to implement another information retrieval system with semantical capabilities, what has been achieved in the present work is a new way of increasing the capabilities of a common search system(demonstrated by the increase in MAP and Precision).
Comparison of Agile Maturity Models

Context: Agile software development is widely used by small teams. Companies want to check their implementation of Agile for different reasons. Many Agile Maturity Models (AMM) exist that support practitioners in assessing and improving their agility. However, practitioners need to be able to make informed decisions on which one to use. Objective: The aim of this work is to enable the comparison of existing AMMs. Method: We identified 14 AMMs in a non-systematic literature review, considering non-scientific sources as well. We propose criteria for their comparison based on our experience and our understanding of practitioners’ needs. Results: We present twelve comparison criteria and show how the identified AMMs differ along those criteria. Conclusion: Practitioners get an overview of existing models and can select a suitable one with the help of the comparison criteria.
Drboxlight: A Light Object Detection Model for Remote Sensing Applications

DrBox (detector using rotatable bounding box) achieves superior performance on detection of arbitrary rotated objects by applying rotatable bounding box that enables the network to learn rotational invariant property of objects. However, the current DrBox algorithm uses deep convolution architecture, which is redundant in model storage and inference. In this paper, we present a light weight detection model for remote sensing images. The proposed method, DrBoxLight, uses depthwise separable convolution to decrease model size. Remote sensing applications need strong generalization abilities of the model, which is usually a weak point of small models. We adopt knowledge distillation framework for training to strengthen the generalization ability of DrBoxLight. In the knowledge distillation process, a well-trained teacher network (DrBox) will provide supervision and guidance to student network (DrBoxLight) by optimizing the hint loss of feature extraction outputs and focal loss of soft labels from teacher network. The experiment results on our datasets demonstrate that DrBoxLight trained by knowledge distillation can achieve acceptable precision compared with a large model.
Entry–Exit Video Surveillance: A Benchmark Dataset

Techniques to automate video surveillance around places where cameras are forbidden due to privacy concerns are yet under-addressed. This can be achieved by building conceptual models and algorithms to investigate the credibility of monitoring of events using the video frames captured by mounting the cameras so as to have the view of the entrances of such camera-forbidden areas. Evaluation of these models and algorithms require standard datasets. The proposal here is to introduce a new benchmark dataset—“EnEx dataset” as no traces specific to the problem were found in the literature. The dataset comprises of video frames captured in 5 different locations accounting 90 entry–exit event pairs based on 9 different sequences involving 36 participants. Ground statistics of the dataset is reported. This work ventures a new sub-domain for research in the area of automated video surveillance.
A Web-Based Remote Assistance System with Gravity-Aware 3D Hand Gesture Visualization

We present a remote assistance system that enables a remotely located expert to provide guidance using hand gestures to a customer who performs a physical task in a different location. The system is built on top of a web-based real-time media communication framework which allows the customer to use a commodity smartphone to send a live video feed to the expert, from which the expert can see the view of the customer's workspace and can show his/her hand gestures over the video in real-time. The expert's hand gesture is captured with a hand tracking device and visualized with a rigged 3D hand model on the live video feed. The system can be accessed via a web browser, and it does not require any app software to be installed on the customer's device. Our system supports various types of devices including smartphone, tablet, desktop PC, and smart glass. To improve the collaboration experience, the system provides a novel gravity-aware hand visualization technique.
Adding Social Comparison to Open Learner Modeling

Achieving multiplication table fluency is a major concern during all primary school years and for many pupils it is considered a challenge. `Rote memory' approaches that have been deployed for years in the traditional school curriculum have contributed to the prevailing assumption of pupils that Mathematics is unpleasant and uninviting. This paper presents a game-based approach to assess and gradually improve multiplication skills by combining an adaptive mechanism for identifying and resolving pupil weaknesses, while exposing parts of the learner model to the user through easily perceivable visualizations. Moreover, the game offers social comparison information and pupils can access progress data about their peers or their entire class a feature that is expected to improve self-reflection, allow for self-regulated learning and increase user motivation. This paper also presents the feedback received by the preliminary testing of the game with a representative sample of pupils and discusses the effect of allowing access to the progress of peers and summative class scores.
Evaluation of a Charged Coupled Device Camera for the Detection of Ultraviolet Emissions by Corona Discharges

The current state of the art is UV cameras that use photomultiplier tube technology to make the comparatively weak UV emissions visible. These camera systems have a fixed lens with a fixed focal length, making it impossible to zoom or vary the focal length. In the application scenario of an overhead line inspection flight by helicopter, the cameras, which are available on the market, are approaching their limits. The inspector normally uses the available camera(s) during an inspection flight in parallel to the visual inspection, but this is often not possible due to the fixed focal length and resolution of the camera system. Hence, an adopted flight route would be advantageous for such UV cameras. As a result separate flights are needed for a visual inspection and corona detection.In this paper a compact camera with a Charged Coupled Device Sensor (short: CCD sensor), which covers a spectral working range of 190 nm–1100 nm was investigated within the scope of a research project. The camera is able to detect the UV emissions of corona discharges in the range of 250 nm to 400 nm. Another advantage of the front side illuminated CCD sensor is the high resolution of 1.4 megapixels and the dynamic range of 14-bit. The camera core offers the possibility to mount any lens with the appropriate focal length for the respective application. This circumstance allows a flexible use in helicopters for an overhead inspection flight.Laboratory performance tests of the camera system are promising and prove the feasibility. The use of the camera requires a preselection of the spectral range in order to take full advantage of the dynamics of the camera and to avoid overloading the sensor. For this purpose, filters with different bandpass characteristics were investigated. Finally the UV sensitivity of the camera at different intensities of corona surface discharges and in tip-plate arrangements was determined.
Extending storage support for unikernel containers

In recent years, the rapid adoption of the serverless computing paradigm has led to the proliferation of Function-as-a-Service computing frameworks. The majority of these frameworks utilize containers, a lightweight operating system virtualization technique, to ensure isolated function execution. Unikernels, which package applications within a single-address space library operating system, have been proposed as an alternative function isolation mechanism, which offers stronger isolation guarantees without suffering the performance penalties of full hardware virtualization. However, due to different storage semantics between containers and unikernels, the state-of-the-art approaches for using unikernels in place of containers result in decreased performance, inefficient resource utilization and limited functionality.
A Practical Evaluation Method for EM Information Leakage by Using Audible Signal

A method for evaluating electromagnetic (EM) information leakage through EM emanation from displays is introduced. The basic idea is that the method determines the information leakage by detecting an audio signal without reconstructing the screen image. We propose an image which is called the controlled image. When the controlled image is displayed on a device, an audible frequency range signal is generated at information leakage frequency. By detecting the audio signal, we can confirm the information leakage. Several leakage frequencies of a laptop PC are obtained by the proposed method and we were able to reconstruct the screen image of the laptop at the frequencies we obtained to confirm the validity of our method.
Optimising Prediction in Overlapping and Non-Overlapping Regions

Researchers working on real world classification data have identified that a combination of class overlap with class imbalance and high dimensional data is a crucial problem and are important factors for degrading performance of the classifier. Hence, it has received significant attention in recent years. Misclassification often occurs in the overlapped region as there is no clear distinction between the class boundaries and the presence of high dimensional data with an imbalanced proportion poses an additional challenge. Only a few studies have ever been attempted to address all these issues simultaneously; therefore; a model is proposed which initially divides the data space into overlapped and non-overlapped regions using a K-means algorithm, then the classifier is allowed to learn from two data space regions separately and finally, the results are combined. The experiment is conducted using the Heart dataset selected from the Keel repository and results prove that the proposed model improves the efficiency of the classifier based on accuracy, kappa, precision, recall, f-measure, FNR, FPR, and time.
Woman Lingual Cultural Type Analysis Using Cognitive Modeling and Graph Theory

This paper observes an analysis of lingual cultural type of woman by using graphical modeling approach. The study has been carried out in terms of applied linguistics and illustrates the integration of natural language processing technologies. The main contribution is an attempt to implement Text Mining tools to analyze poetic discourse of the late 16th – early 17th centuries and provide cognitive model of lingual cultural type. The topicality of cognitive modeling application in linguistic studies is the ability to structure and systematize the existing information, identify the scenarios for information system, and predict the relationship dynamics between the components of this system. The paper also examines the possibility of using graph theory to study lexical variables that reflect the objects of human life. Overall, the research findings indicate the most frequent connections between the lingual cultural type of woman and the lexical variables used to depict physical, social or spiritual objects of the reality in English culture.
Merging Variables: One Technique of Search in Pseudo-Boolean Optimization

In the present paper we describe new heuristic technique, which can be applied to the optimization of pseudo-Boolean functions including Black-Box functions. This technique is based on a simple procedure which consists in transition from the optimization problem over Boolean hypercube to the optimization problem of auxiliary function in a specially constructed metric space. It is shown that there is a natural connection between the points of the original Boolean hypercube and points from new metric space. For a Boolean hypercube with fixed dimension it is possible to construct a number of such metric spaces. The proposed technique can be considered as a special case of Variable Neighborhood Search, which is focused on pseudo-Boolean optimization. Preliminary computational results show high efficiency of the proposed technique on some reasonably hard problems. Also it is shown that the described technique in combination with the well-known (1+1)-Evolutionary Algorithm allows to decrease the upper bound on the runtime of this algorithm for arbitrary pseudo-Boolean functions.
Surveillance System for Multiple Moving Objects

To prepare for terrorist and/or criminal attacks using unmanned aerial vehicles (UAVs), we developed a Galvano Camera System. This system, which detects suspicious UAVs, is based on the high-speed and precise positioning technology of galvanometer scanners. The proposed system consists of galvanometer scanners, a zoom lens, a CMOS camera, an imaging processor, and a monitor. It is used with other position measurement sensors such as RADAR. The system changes the deflection angles of the galvano-mirrors, which are attached to the tip of the swaying shaft based on the location of the target provided by other sensors. Three targets are observed using proposed imaging process and changing angular process, alternately, to acquire multiple images that include the targets. The imaging processor makes three movies of each target, the monitor divides the screen, and displays these three movies on the same screen. The proposed system can simultaneously observe multiple moving objects, using the high response feature of the galvanometer scanners, and display objects on a single monitor. In addition, we have proved the effectiveness of the system through field tests.
Balanced Word Clusters for Interpretable Document Representation

We present Bag-of-Balanced-Concepts (BOBC), a document representation method for fuzzy and interpretable similarity estimation based on word clusters. For this purpose, a k-medoid variant is proposed, which iteratively resamples small clusters to introduce a tendency towards balanced cluster sizes. The necessary inter-word similarities for clustering are computed using GloVe or word2vec word embeddings. In this way, words that often share contexts tend to appear in the same clusters. Those clusters are used to represent documents as normalized probability distributions. Various distance measures acting as document dissimilarity estimators have been evaluated on five datasets. The impact of clustering parameters, input word vectors, and inverse document frequency weighting has been examined in our experiments. Furthermore, a comparison with document similarity estimation baselines has been performed. We demonstrate that, on average, our approach outperforms cosine similarity of both weighted Bag-of-Words vectors (TF-IDF and BM25) and word embedding centroids (Word Centroid Distance).
First Results of the DESIS Imaging Spectrometer On Board the International Space Station

DESIS (DLR Earth Sensing Imaging Spectrometer) is a space-based hyperspectral sensor currently installed and operated in the International Space Station (ISS). The instrument is the result of the collaboration between the German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing, while TBE provides the Multi-User System for Earth Sensing (MUSES), where DESIS is installed, and the infrastructure for operation.
Classification of multiclass datasets using genetic programming

This paper1 proposes an approach which uses optimized genetic programming (GP) with a new fitness function for multiclass dataset classification. In place of defining static thresholds as boundaries to differentiate between multiple classes, our work presents a method of classification where a GP system learns the relationships among experiential data and models them mathematically during the evolutionary process. We propose an optimized GP classifier based on a combination of pruning subtrees and a new fitness function. An orthogonal least squares algorithm is also applied in the training phase to create a robust GP classifier. Our approach has been assessed on four multiclass datasets and compared against three existing methods. The analyzed results illustrate that the developed classifier produces a productive and rapid method for classification tasks that outperforms the previous methods for more challenging multiclass classification problems.
Towards a Typology for Playable Digital Interventions in Urban Public

This paper explores the junctional juncture between playable digital interventions and urban public spatial design. Ubiquitous mobile digital devices and their pervasiveness in daily life are creating a shift in the traditional uses of urban public spaces and redefining how play occurs in urban public spaces. Activating possibilities of play in public space encourages people to go on a spontaneous and amorphous way of discovering new things in daily life relying on awareness, taking one's time, reflecting, experimenting, exploring, etc. Utilising the framework of playable digital interaction and exploring urban spatial environment narrative literature, twenty-five existing and proposed playable digital interventions in urban public space are studied to develop an initial typology. This typology includes characteristics such as diverse interactive mechanism, experience, and spatial features. This classification of playful digital interventions in urban public spaces will enable urban planners, designers, decision-makers, game designers, and researchers to realise the great potential and conceptualize the design of future digital playable installations for public spaces.