Reviewerly/exHarmony huggingface dataset:
Mammographic Mass Retrieval Using Multi-view Information and Laplacian Score Feature Selection

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among women all over the world. Content based mammographic mass retrieval can assist radiologists to retrieve biopsy-proven masses content similar with the diagnostic ones, which can help radiologists to improve the diagnostic performance. However, existing mammographic mass retrieval methods are based on single-view information although one mass has two different views in mammograms. In this paper, we propose a new multi-view based mammographic mass retrieval approach integrated with feature selection method. In our retrieval paradigm, the query example is a multi-view mass pair different from a single view mass in previous studies. Accordingly, in order to extract significant characteristics from the mass, a total of 99 handcrafted features are computed, and an optimal feature set is determined by Laplacian Score (LS) feature selection method. Initial experimental results show that the retrieval performance based on our approach is better than that based on single-view method.
Optimization of Neural Network Architecture using Lions Optimization Algorithm

For several years, humans have observed nature to find patterns and similarities between their problems and natural occurrences. Due to this, there has been a surge of research and algorithms regarding our environment. It is usually a debilitating assignment to structure and manufacture an ANN for a selected issue such as nature. There is a pair of thorough venture rules that are accessible to characterize the ANN and, in any case, numerous parameters to switch. The productive techniques are an option in contrast to counterfeit neural systems planning; however, the computational cost required for specific applications is restrictive. Accordingly, the critical point here is to contemplate progressed developmental calculation strategies, which likewise advance accomplishing ANN structures that commit to the task efficiently, explicit to each issue using Lion's Optimization Algorithm (LOA). Pima Indians Diabetes Database used as a model for the optimization of ANN. The results show that subsequent generations of modeled lions provide better accuracy and efficiency to tend towards the highly optimized solution.
Multipath Adaptive A*: Factors That Influence Performance in Goal-Directed Navigation in Unknown Terrain

Incremental heuristic search algorithms are a class of heuristic search algorithms applicable to the problem of goal-directed navigation. D* and D*Lite are among the most well-known algorithms for this problem. Recently, two new algorithms have been shown to outperform D*Lite in relevant benchmarks: Multi-Path Adaptive A* (MPAA*) and D*ExtraLite. Existing empirical evaluations, unfortunately, do not allow to obtain meaningful conclusions regarding the strengths and weaknesses of these algorithms. Indeed, in the paper introducing D*ExtraLite, it is shown that D*Lite outperforms MPAA* in benchmarks in which the authors of MPAA* claim superiority over D*Lite. The existence of published contradictory data unfortunately does not allow practitioners to make decisions over which algorithm to use given a specific application. In this paper, we analyze two factors that significantly influence the performance of MPAA*, explaining why it is possible to obtain very different results depending on such factors. We identify a configuration of MPAA* which, in the majority of the benchmark problems we use, exhibits superior performance when compared to both D*Lite and D*ExtraLite. We conclude that MPAA* should be the algorithm of choice in goal-directed navigation scenarios in which the heuristic is accurate, whereas D*ExtraLite should be preferred when the heuristic is inaccurate.
Compensating for Perspective-based Distortion on Large Interactive Floor Displays: the SpaceHopper Field Experiment

Very large floor displays can promote engaging public experiences, but incur perspective-related warping, making it challenging to comprehend and interact with distal objects when standing on the display. We introduce a perspective compensated view technique that maintains the relative size and shape of objects as they move away from the viewer, and explore the technique in SpaceHopper, a large-scale, floor-projected version of the game Asteroids. Players bounce on a hopper ball to control their ship in one of two control modes: bounce to repel or bounce to shoot. We evaluated SpaceHopper in a field experiment with 59 participants, finding that perspective compensated view yielded longer playing times (and higher scores) in the bounce to fire modality. Bystanders were highly engaged with players and also seemed to be unaware of the perspective warping, suggesting that visually compensating for the interactor's perspective does not adversely impact the enjoyment of passive participants and audience members, at least under some circumstances.
A Robust and High-performance White Blood Cell Segmentation Algorithm

White blood cell classification and counting of a blood smear is critical during patient diagnosis, however it is time consuming and fallible to do it manually. People has long been expecting systems to acquire clear blood smear images, segment and classify these images automatically, in which accurate white blood cell segmentation is a fundamental and essential process. This paper proposes a robust and highperformance white blood cell segmentation algorithm, which segments nucleus area from scaled saturation image first, then segments the plasma area in snipped sub image from green image and blue image delta normalized by red image. The algorithm is tested with 1350 images captured from various patients in diverse hospitals, which shows the algorithm results are 98% in accordance with the manual inspection results.
Criminal Law Protection of Cybersecurity Considering AI-based Cybercrime

Abstract As the development of artificial intelligence (AI) is unfolding, cybersecurity faces the invasion of AI. AI-based cybercrime becomes a product of technological development, posing a threat to national security, public security, and the protection of citizens’ personal property rights and privacy rights. In particular, the derivation of cybercrime that follows the development of science and technology leads to a qualitative change in the patterns of cybercrimes, expanding the scope and depth of the harm caused by cybercrime, producing a series of impacts on the conviction rules of traditional criminal law in China. The defects such as the features of the principal offender being weakened, the definition of the responsibility for accessory to the principal offender not clear, the narrow scope applicable to one-sided accomplice are present. To this end, in the era where AI and cybercrime have deep integration, given the derivative trend of AI-based cybercrime, it is urgent to adjust the focus of criminal strategy against cybercrime. The horizontal docking of domestic substantive law and legal interpretation is performed to achieve a gradient balance between judicial interpretation and legislative amendment and adjust the criminal boundary in cybercrime evaluation, thereby changing the current high threshold of conviction in the cybercrime governance and late launch of punishment power.
Performance Analysis of Supervised Learning Algorithms based on Classification Approach

Machine learning algorithm integrates with various features and methods for learning. The performance metric has a major role in determining the capacity and ability of any machine learning algorithm. The supervised approach supports learning based on predefined parameters. The classification approach classifies the set of data with labels for training and evaluation purposes. This paper applies method of supervised learning algorithm based on classification approach to analyzed in terms of performance metrics. Some algorithms were applied in the experiment process to determine the clear outcome from them. The analysis gives an outcome based on certain parameters to obtain the results to understand the ability of these algorithm and their implementation in any potential use-cases.
Acquisition and evaluation of depth data from humans, in robotized industrial environments

Abstract Industry 4.0 places great importance on collaborative robotics in industrial production and, therefore, on the safe interaction between humans and robots. In previous decades, robots that were part of flexible manufacturing systems remained isolated from human operators through physical enclosures, but current human-robot collaboration activities require a simultaneous concurrent workspace for both actors. Collaborative robotics security systems have used (among others) artificial vision systems based on red, green and blue additive color model images, as proven solutions in detecting humans within the work area of robots. In recent work, the combination of red, green, and blue additive color model imaging with depth imaging systems has demonstrated low sensitivity to lighting changes and a high positional match between distance data and color pixels in both types of acquired images. However, a detailed review resulted in the absence of databases of industrial robotic environments in this type of images. In this paper, an extensive database is delivered in the format already explained that contains people in robotic industrial settings. Furthermore, evaluating the database using machine learning techniques enables computer vision researchers to gain a better understanding of human detection using a Kinect sensor and convolutional neural networks.
The Concept of System for Automated Scientific Literature Reviews Generation

We present a concept of system which is aimed to create a literature review of scientific articles having a small sketch of statements as the input. Key elements of the system include transformer-based BERT encoder, deep LSTM decoder and a loss function which combines auto-encoder loss and forces generated summaries to be in the input text domain. We propose to use PMC open access subset for model learning.
Mi Fink app, a participative research-creation experience for afro-caucan territory protection in Colombia

The Traditional Afro-Caucan farm - TAF is part of the northern Cauca culture in Colombia, but it is threatened. Mi Fink is a mobile phone app that vindicates the tradition of the farm as a form of community work an afro-descendant expression about their identity in this territory. This app is the result of a research-creation project with children from the municipality of Villa Rica in the northern region of the Department of Cauca in Colombia. Through collaborative design, a team of professionals from different areas and academic researchers investigated the memories of care and use of their territory. Despite expected limitations related to digital technologies access, this experience shows how easy children can get involved, collaborate and carry out innovative activities with digital devices such as stop motion animation.
Single Space Object Image Super Resolution Reconstructing Using Convolutional Networks in Wavelet Transform Domain

With the increasing importance of space exploration, the research of space object is becoming more and more important because high-quality space object images are meaning for space attack and defense confrontation. However, high-quality space object images are very difficult to obtain because of the large number of various rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging. Image super resolution reconstruction methods are the most cost-effective way to solve the problem. In this paper, we propose a deep convolutional neural network based method to improve the resolution of space object image. The implementation of our method is in wavelet transform domain rather than spatial domain because wavelet transformation could decompose different frequencies of the image very effectively and this could further more enhance the performance. The experiment result shows that our method could achieve a very good performance.
Applying Wavelet Decomposition and Reconstruction to Solve Conflict Between Macroscale and Microscale Simulation Step of Buck Converter

To accurately describe the operation of power electronic converters, the switching device must be depicted by a microscale model based on quantum mechanics, which makes the step selection in simulation analysis difficult. Choosing the step suitable for the switching device will result in long calculation time and nonconvergence, but the accessible step for the circuit will result in the switching device characteristics' noncoincidence with actual. In order to solve the conflict between macroscale and microscale simulation steps, wavelet decomposition and reconstruction are applied in the simulation model. First, the method of coarse-to-fine scale transformation is confirmed. According to the Nyquist-Shannon sampling theorem and the partition rule of dynamic sampling grid of discrete wavelet decomposition and reconstruction, the simulation step should be selected smaller than a quarter of the switching time. Second, the macroscale model of circuit and the microscale model of switching device are combined based on wavelet decomposition and reconstruction. Third, in the simulation of the combined model, the exact solution of the switching process is obtained from the imprecise solution by using discrete wavelet decomposition and reconstruction. The operation of the switching device is precisely reflected. Finally, the simulation result is verified to be in good agreement with the experiment. The solution proposed in this article can be expanded to the simulation analysis of other power electronic converters.
Stacked Attention Recurrent Relational Networks for Question Answering

Abstract Relational reasoning is ability to reason about entities and their interactions, which is not applicable for many deep neural networks. Recurrent relational networks, introduced by Palm et al. (2017), increase the complexity of the reasoning tasks they can address [1]. In this paper, we introduce Stacked Attention Recurrent Relational Networks (SARRN) to answer natural language questions from facts, which fundamentally hinge on multiple steps of relational reasoning and improve the ability of reasoning. Our model is a stacked attention model that use recurrent attention to focus on fine-grained parts of the documents. We apply our model to the BaBi tasks, which have a set of proxy tasks that evaluate reading comprehension via question answering. Our model solve 19/20 tasks and the experimental results on the test sets tasks show that our model yields great improvement. We also use qualitative analysis to show the result intuitively.
Approved algorithmic security enhancement of stream cipher for advanced mobile communications

Confidentiality and integrity processes are based on SNOW and ZUC algorithms. These standardized algorithms are designed by the 3rd Generation Partnership Project (3GPP) for advanced mobile communication systems. Each algorithm has two S-boxes in its nonlinear layer structure. This paper proposes three different approaches to enhance the security level of both algorithms. The first approach aims to select the best combination of two S-boxes in the nonlinear part of each algorithm. Results showed that the best randomness properties are achieved by combining the Feistel structure (S1-box) and Rijndael (S2-box) in the SNOW algorithm and New Rijndael (S1-box) and Rijndael (S2-box) in the ZUC algorithm. The second approach aims to increase the nonlinearity and complexity of these algorithms by selecting a strong S-box to cascade the best-existing two S-boxes for both algorithms. The third approach is an extension of the former, based on a dynamic reform of such S-boxes as a function of the “Authentication Token” to increase the computational complexity of the upgraded two algorithms. These proposed upgrades are simulated by C language and tested successfully for their statistical properties using the standard NIST SP 800-22 test suite.
Colour segmentation of Gram-Negative bacteria using graph Quadratic Form and Random Walker

Abstract Gram-negative bacteria are one of the bacteria that are often pathogenic to the human body. This bacterium causes resistance due to nosocomial with other Gram-negative bacteria. In the medical stage, the bacteria that cause nosocomial traits removed first before antibiotic therapy carried out on the main bacteria. To identify these bacteria, the clinical laboratory needs to make manual observations under a microscope. The approach taken in this research is using the image processing technique. There are four stages: pre-processing, segmentation, feature extraction, and identification. Segmentation is a stage to emphasize the object sought in an image. In this research, the approach used to capture objects is one of them using the Graph Quadratic Form algorithm. This algorithm chose because it can determine the shortest distance of the object from the nearest node so that the process of convergence of the object search becomes faster. The result is that this algorithm is better than the morphology-based algorithm and the contour-based algorithm, while the number of samples taken from 50 patients affected by Gram-negative bacteria. The image under research has a size of 512x512 pixels, a resolution of 72 dpi with a bit depth of 24. The segmentation process is carried out on Gram-negative bacterial images using two classes producing an average accuracy of 89% to Ground truth.
What happens when the design process meets community engagement?

Community-engaged design is often framed as a binary opposite to commercial design. Whereas commercial design is usually characterized as a market-led paradigm focused on profits, community-engaged design is seen as a socially useful design paradigm that is concerned with addressing societal needs. This study uses qualitative data from four design project case studies to explore whether this understanding is in fact consistent with current design practices within commercial design studios. The findings of this study demonstrate that both commercial and community-engaged design practices are more similar than different. In addition, it is also suggested that it is not only the clients’ instructions and the commissions that determine the designers’ processes and practices, but the design studios’ intent, practice and values. This study has a number of practical implications for designers and design studios in how they address commercial and community-engaged design work.
Explicitly Consider Server-Attached Fans for Thermal Modeling in Edge Data Centers

Edge data center has become an important data center type in recent years. Comparing to a conventional data center, an edge data center often lacks sophisticated cooling equipment and infrastructure support. In the resulting poor thermal environment, fans attached to individual servers have to work harder in an edge data center than those in a conventional data center. Research have shown that power generated by server-attached fans are quite significant to be ignored from thermal standpoint when fan speed is high. In this paper, as we consider power efficiency in edge data centers, we argue that power generated by fans attached to the servers should be explicitly considered for thermal modeling in the overall thermal optimization framework. We propose a design that incorporates fan power in a neural network to better predict a server's thermal state in an edge data center. We further propose a task scheduling algorithm that utilizes the improved neural network to enhance an edge data center's overall power efficiency. Based on the experimental results from a field edge data center, the improved neural network achieves better accuracy in predicting individual server's thermal state, outperforming other neural networks on precision. The proposed task scheduling algorithm, powered by the improved neural network, saves as much as 11% power consumption comparing to unoptimized algorithms.
Learning to See in Extremely Low-Light Environments with Small Data

Recent advances in deep learning have shown exciting promise in various artificial intelligence vision tasks, such as image classification, image noise reduction, object detection, semantic segmentation, and more. The restoration of the image captured in an extremely dark environment is one of the subtasks in computer vision. Some of the latest progress in this field depends on sophisticated algorithms and massive image pairs taken in low-light and normal-light conditions. However, it is difficult to capture pictures of the same size and the same location under two different light level environments. We propose a method named NL2LL to collect the underexposure images and the corresponding normal exposure images by adjusting camera settings in the “normal” level of light during the daytime. The normal light of the daytime provides better conditions for taking high-quality image pairs quickly and accurately. Additionally, we describe the regularized denoising autoencoder is effective for restoring a low-light image. Due to high-quality training data, the proposed restoration algorithm achieves superior results for images taken in an extremely low-light environment (about 100× underexposure). Our algorithm surpasses most contrasted methods solely relying on a small amount of training data, 20 image pairs. The experiment also shows the model adapts to different brightness environments.
Diverse Security Practices and Comparison on Key Stroke Dynamics

Due to upsurge use of internet and technology which has simplified our lives to a greater extent has also exposed the various hidden threats is a matter for huge concern. Spawning of these threats occurs due to negligence or concealed vulnerabilities. To safeguard the system and distinguish between genuine and imposter an infallible system needs to be implemented. It is a testing zone of security and practice. User name and password have certain drawbacks and by using different permutations and combinations they can be decoded. Biometrics is used as one of the most convenient modes of user authentication. The two methods that are widely used in the field of biometrics are physiological and behavioral biometrics. Physiological biometrics takes into account the biological aspects such as fingerprints, DNA fingerprinting or handprint. But these are many user authentication methods that are widely used but they are rife with holes and flaws. For better authentication behavioral biometrics [1] methods have been introduced in which an insight into the behavioral aspects such as keystroke dynamics, speech, gait, and lip movement. As these methods do not require any external hardware they are more convenient to use. By calculating dwell time that is the time when the key is pressed and flight time when the key is released various parameters are taken into account. Based on this problem statement in this research work we will compare the different solutions to solve the aforementioned problems using keystroke dynamics like face recognition, fingerprint, iris recognition, keystroke, etc on smartphones and touch mobile devices, as an authentication approach, and compare them with the traditional approach.