Reviewerly/exHarmony huggingface dataset:
Modified SLIC segmentation for medical hyperspectral cell images

Simple linear iterative clustering (SLIC) is a fast and effective method for superpixel segmentation. However, the similarity measurement method of typical SLIC based on spatial and spectral features fails to get precise segmentation boundaries, especially for the images with complex and irregular shapes. To address this issue, a modified SLIC (MSLIC) method based on spectral, color, and texture information is proposed for medical hyperspectral cell images. The Gabor filter is used to exploit detailed texture features, which processes the image by using signal Fourier transform in the frequency domain. The MSLIC employs normalization, Gamma correction, and principal component analysis (PCA) to preprocess medical hyperspectral images, in which the texture features are integrated with spectral and spatial features to measure the distance. The under-segmentation error and boundary recall are used as the criterion of segmentation. Experiments for two medical datasets indicate that MSLIC achieves better segmentation performance than the typical SLIC method.
Cybercrime as an Essential Element in Transnational Counterfeiting Schemes

Transnational counterfeiting conspiracy cases often link the developing and the developed world in illicit ways. In many of these cases, a forged copy of name-brand merchandise is produced cheaply, and transported and shipped to a destination market where it is sold to consumers at a high mark-up, producing multiple harms. An overlooked element of these counterfeiting schemes is the essential role that cybercrime plays in their operation. Several transnational organized crime and cybercrime prosecutions are examined here to assess the role of counterfeiting in these criminal operations. The three conspiracies involve electronics, pills, and perfume—all illustrating the centrality of cybercrime in their commission. The implications for investigation, policy and practice are noted.
Real-Time Hand Motion Recognition: A Robust Low-Cost Approach

This study presents a robust, low-cost hand motion recognition approach designed to run on low-end computer systems. Our method detects and tracks hand region using real-time images obtained from a low-resolution camera (i.e. webcam) and is not depended on any training or calibration and is not required any special camera apparatus or selectors. The proposed system involves several image processing techniques such as background subtraction, face detection, skin colour detection and template matching. The experimental results show promising performance under various conditions. The method has a wide range of applications where more natural ways of interaction required, such as virtual reality applications, assistive technologies and simulation.
Introduction: Non-classical Logics—Between Semantics and Proof Theory (In Relation to Arnon Avron’s Work)

We recall some of the better known approaches to non-classical logics, with an emphasis on the contributions of Arnon Avron to the subject and in relation to the papers in this volume.
Geometry-based symbol spotting in born-digital architectural floor plans

We describe a method for symbol spotting in born-digital architectural floor plans, which are a particularly challenging application domain of graphics recognition. We propose a hybrid method that capitalizes on strengths of both vector-based and pixel-based symbol spotting techniques. In the description phase, the salient geometric constituents of a symbol are extracted by a variety of vectorization techniques, including a proposed voting-based algorithm for finding partial ellipses. This enables us to better handle local shape irregularities and boundary discontinuities, as well as partial occlusion and overlap. In the matching phase, the spatial relationship between the geometric primitives is encoded via a primitive-aware proximity graph. A statistical approach is then used to rapidly yield a coarse localization of symbols within the plan. Localization is further refined with a pixel-based step implementing a modified cross-correlation function. Experimental results on the public SESYD synthetic dataset demonstrate that our approach clearly outperforms other popular symbol spotting approaches. Moreover, our approach yields promising results on real-world images, which are significantly more challenging in terms of overlap, occlusion, and presence of non-symbolic graphical data.
A Bibliometric Analysis of Bitcoin Scientific Production

Blockchain technology, and more specifically Bitcoin (one of its foremost applications), have been receiving increasing attention in the scientific community. The first publications with Bitcoin as a topic, can be traced back to 2012. In spite of this short time span, the production magnitude (1162 papers) makes it necessary to make a bibliometric study in order to observe research clusters, emerging topics, and leading scholars. Our paper is aimed at studying the scientific production only around bitcoin, excluding other blockchain applications. Thus, we restricted our search to papers indexed in the Web of Science Core Collection, whose topic is bitcoin''. This database is suitable for such diverse disciplines such as economics, engineering, mathematics, and computer science. This bibliometric study draws the landscape of the current state and trends of Bitcoin-related research in different scientific disciplines.
Behaviour Analysis of Web Users by Mean Shift Clustering

Society in present days is heavily using different forms of electronic communication. The amount of transferred data is growing and the need of quick reaction to cyber incidents is needed. The paper is contribution to this effort. There is possibility to save time and sources by concentration only sub group of potential threats caused by specific group of users. For that reason in this paper the possibility of the user clustering of a selected network on the base of their browsing behaviour is analyzed. The main source of information about selected group of users is web access log file where all necessary data are stored. The contribution also presents the concept of pre-processing of data from the selected specific files. As a method of machine learning was chosen a mean shift clustering algorithm which was applied for division of users to the specific collections on the base of their behaviour in the web environment. A presented method has a potential use in different areas of the cyber defence and also in applications where intelligent classification is required.
Desperately Seeking Standards: Using Text Processing to Save Your Time

As liaison librarian to several departments at UNLV, she teaches information literacy for many students, provides reference assistance to the campus
Research on the Internet Teaching Mode Based on the Mobile Terminals

With the development of Internet technology, mobile intelligent terminals have gradually become an emerging force of the modernized teaching which is highly valued by the education circles. The mobile intelligent terminals have been gradually introduced into classrooms with their characteristics of mobility, real-time performance and multimedia functions and so on. This new teaching mode can not only make full use of the network resources and enrich the teaching contents, but also optimize the traditional teaching mode and enhance the improvement of management mechanism. This paper mainly takes online classrooms and other intelligent teaching tools as the research objects to analyze the application of mobile intelligent terminals in the teaching process and put forward corresponding countermeasures and suggestions.
MoSS: Modular Security Specifications Framework

Applied cryptographic protocols have to meet a rich set of security requirements under diverse environments and against diverse adversaries. However, currently used security specifications, based on either simulation [11, 27] (e.g., 'ideal functionality' in UC) or games [8, 29], are monolithic, combining together different aspects of protocol requirements, environment and assumptions. Such security specifications are complex, error-prone, and foil reusability, modular analysis and incremental design. We present the Modular Security Specifications (MoSS) framework, which cleanly separates the security requirements (goals) which a protocol should achieve, from the models (assumptions) under which each requirement should be ensured. This modularity allows us to reuse individual models and requirements across different protocols and tasks, and to compare protocols for the same task, either under different assumptions or satisfying different sets of requirements. MoSS is flexible and extendable, e.g., it can support both asymptotic and concrete definitions for security. So far, we confirmed the applicability of MoSS to two applications: secure broadcast protocols and PKI schemes.
Selective Information Control and Network Compression in Multi-layered Neural Networks

The present paper aims to propose a new type of information-theoretic method called “selective information control” to produce a variety of internal representations from among which we can choose appropriate ones for interpretation. The new method aims to improve our network compression method to produce more interpretable representations by changing the selective information. The selective information proposed here represents to what extent a components network responds selectively to inputs. When the component responds more selectively to the inputs, the selective information in the component becomes higher. We applied the method to a simplified bank marketing data set. By gradual increasing or decreasing selective information, we could produce connection weights to improve generalization as well as weights close to the correlation coefficients of the original data set. The better interpretation could be obtained by the gradual selective information decrease. This means that better interpretation can be obtained by increasing the selective information in the lower hidden layers, and then, the information should be filtered out in the higher hidden layers.
Convolutional Neural Network Using for Multi-Sensor 3D Object Detection

Abstract The purpose of this article is to detect 3D objects inside the independent vehicle with great accuracy. The method proposed a Multi-View 3D System (MV3D) framework which encodes the sparse 3d-point cloud with a compact multi-view image, using LIDAR satellite image and RGB pictures as inputs, and predicts 3D boundary boxes. The network comprises two sub-networks: one for creating 3D artifacts and one for multi-visual fusion functionality. Propose an autonomous 3D object tracking approach to manipulate sparse and dense knowledge about romanticizing and geometry in stereo images. The Stereo R-CNN strategy applies Faster R-CNNs to stereo inputs such that objects are simultaneously identified and linked in conservative and liberal images. Such charts were then combined and fed into a 3D proposal generator to generate accurate 3D proposals for vehicles. In the second step, the refining network extended the features of the proposal regions further and carried through the classification, regression of a 3D package box, and guidance estimates, to predict vehicle location and heading in 3D area and add additional branches after the stereo region Proposal Network (RPN).
Behind The Game: Implicit Spatio-Temporal Intervention in Inter-personal Remote Physical Interactions on Playing Air Hockey

When playing inter-personal sports games remotely, the time lag between user actions and feedback decreases the user’s performance and sense of agency. While computational assistance can improve performance, naive intervention independent of the context also compromises the user’s sense of agency. We propose a context-aware assistance method that retrieves both user performance and sense of agency, and we demonstrate the method using air hockey (a two-dimensional physical game) as a testbed. Our system includes a 2D plotter-like machine that controls the striker on half of the table surface, and a web application interface that enables manipulation of the striker from a remote location. Using our system, a remote player can play against a physical opponent from anywhere through a web browser. We designed the striker control assistance based on the context by computationally predicting the puck’s trajectory using a real-time captured video image. With this assistance, the remote player exhibits an improved performance without compromising their sense of agency, and both players can experience the excitement of the game.
An Optimized Approach for Mapping Solar Irradiance in a Mid-Low Latitude Region Based on a Site-Adaptation Technique Using Himawari-8 Satellite Imageries

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Demonstration of an Integrated Photonic Magic T – a Robust Beam Splitter and Novel Polarization Diversity Building Block

We demonstrate an adiabatic magic T in a 45 nm CMOS photonics foundry platform, with 3 ± 0.25 dB power splitting and &lt; 1.2 dB loss over 1530-1600 nm, with a 60 µm-long device. It functions as a 2 × 2 beam splitter with splitting ratio from both input ports fundamentally robust by symmetry, and supports novel polarization diversity and modulator designs.
Towards Information Theoretic Interpretation of Practical Ciphers

In spite of the wide spread of practical crypto- systems and ciphers nowadays, they still lack a unique metric to measure the secrecy level they provide. Their strength is measured in an ad-hoc way by exposing them to different kinds of attacks. In addition, their ability to hold secure against these attacks is evaluated in time and computations. In this paper, we introduce an approach for calculating the equivocation of the secret key used in these ciphers. In addition, we prove that it can be used as an indicator for the work required to break the cipher. This will help in unifying the metrics used in evaluating the strength of the ciphers and in comparing them with the classical information theoretic secreacy measures.
Priyantha Wijayatunga’s Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong <i>et al.</i>

In privacy preserving, for example using a differential privacy framework, when a statistical operation is done on a database, then it is done so that its results are not overly dependent on any data record in the database. In this sense, one may think that this is all about avoiding, for example outliers, when the desired statistical results are generated. But on the other hand, utility of such a result is questionable. When an average of a certain measure is requested, giving the median or an average calculated from implementation of a suitable resampling procedure may preserve the privacy of individual data. Such methods may be simple to implement and may have an appreciable utility. Recently in Santos-Lozada et al. (2020) using US census data, the authors show that implementation of the differential privacy will produce dramatic changes in population counts for racial/ethnic minorities in small areas and less urban settings, significantly altering knowledge about health disparities in mortality. It is also important to note that, according to the Fundamental Law of Information Recovery; ‘overly accurate answers to too many questions (on statistics) will destroy privacy (of individual data) in a spectacular way’. The paper tries to use frequentist statistical hypothesis testing framework for defining their differential privacy framework. However, as many of us are aware the hypothesis testing is undergoing immense criticism, especially within the applied statistical community, for example so-called p-value problems. It may be that such problems and oppositions may appear in any privacy framework that is based on the frequentist statistical hypothesis testing methodology. Therefore, ideally the authors should touch upon such problems, especially to attract applied researchers (in the sense of the discipline of statistics) such as computer scientists, social scientists, etc., to their approach. In fact, the authors emphasize the importance of the use of the Neyman–Pearson hypothesis testing framework for interpreting differential privacy over other methods. According to original thesis of R. A. Fisher (1890–1962), the meaning of, for example, p-value < 0.05 is that the respective experiment should be repeated a few times. Such a practice should be handled by authors' composition results. Apart from above issues, it seems that some mathematical expressions should accompany with some verbal expressions too, for example the inequality in the Definition 1 is valid for a given (fixed) (S, S') pair. Therefore, it is helpful to write ℙ{M(S) ∈ E | S} rather than writing ℙ{M(S) ∈ E} and indicate how strong the conditional (in)dependence of M(S) on S' given S. Any other confusions should be eliminated. The author replied later, in writing, as follows.
Tackling the Subset Sum Problem with Fixed Size using an Integer Representation Scheme

Addressing the subset sum problem is relevant to study resource management problems efficiently. In this paper, we study a new scheme to sample solutions for the subset sum problem based on swarm-based optimization algorithms with distinct forms of selection pressure, the balance of exploration-exploitation, the multimodality considerations, and a search space defined by numbers associated with subsets of fixed size. Our experiments show that it is feasible to find optimal subsets with few number of fitness evaluations, and that Particle Swarm Optimization with Fitness Euclidean Ratio converges faster to the global optima with zero variability over independent runs. Since the search space is one-dimensional and friendly to parallelization schemes, our work is potential to study further classes of combinatorial problems using swarm-based optimization algorithms and the representation based on numbers.
On the practical applications of objective quality metrics for stereoscopic 3D imaging

Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.
Edu-Game media based on Android to learn Least Common Multiplication (LCM) and Great Common Divisor (GCD) for the 4th graders

Abstract Learning mathematics for elementary school students is often considered a creepy thing. Some of the tricks that can be used to remove this paradigm is the use of learning media that are attractive to students, one of which is the edu-game media based on android along with current technological developments. This development research developed the edu-game media based on android which aims to: describe the process of developing the edu-game media based on android to learn LCM and GCD for the 4 th graders and (2) analyze the feasibility level of the edu-game media based on android to learn LCM and GCD for the 4 th graders. The trial subjects of this study were the 4 th graders of Laboratory Unesa Elementary School Surabaya and Geluran 1 Elementary School Taman Sidoarjo. The model used in this research is Plomp’s development model. From this research, the results obtained from the material expert validator with a percentage of 94.7% (valid), and from the media expert validator with a percentage of 93.75% (valid). Testing the test subjects got an average percentage of the entire program of 93.4% (very feasible).
Faster R-CNN based substation scene image detection method

In recent years, salient target detection has become one of the hot research issues in the field of computer vision. This paper designs a salient target detection method based on Faster R-CNN in a scene with relatively complex background information. First, perform multi-scale superpixel segmentation on the image, and use Faster R-CNN to perform target detection on the image, and perform saliency screening on superpixels according to the characteristics of similarity. After obtaining the initial target location feature, perform saliency detection and optimization, and finally use the primitive The cellular automata method fuses multi-scale superpixel saliency maps. By conducting experiments on specific types of data sets and comparing and analyzing existing typical saliency detection, it is verified that the method in this paper can improve the accuracy of saliency detection in images with complex background.
Complexity-Based Lambda Layer for Time Series Prediction

Time series analysis forms the basis for the temporal sequences that we observe in everyday natural phenomenon. Examining and characterizing time series' forms the basis for research in areas spanning classifying heart rate variability, temperature prediction and stock price prediction. In recent history combining powerful techniques such as Fourier Analysis with Machine Learning techniques has improved our ability to predict future time series based on historic data. Complexity is one of such tools that incorporates long-range dependency based on the inherent self-similarity that exists in many natural phenomena. Leveraging the prowess of recurrent neural networks with that of complexity measures combines two very powerful techniques to improve prediction accuracy. In this work a Complexity Lambda Layer was initialized in series with Artificial Neural Networks (ANN) architectures to improve prediction accuracy for synthesized Brownian Noise. Window size for recurrence and the stationary interval size was optimized for increased performance. For both non-temporal and temporal studies, a 2-4 fold improvement in root-mean-squared accuracy was obtained. This approach was implemented as a Lambda layer, meaning the improvement can be done on-the fly with minimal overhead.