Reviewerly/exHarmony huggingface dataset:
Octahedron-shaped Convolution for Refining Aorta Semantic Segmentation

Refining 3D aorta segmentation is essential for clinical aorta analysis. The small tubular diameter of the aorta branches and the discontinuity of neighboring information make it difficult to get a continuous semantic segmentation map. In this paper, we propose a novel octahedron-shaped convolution (OSC) based on VNet and signed distance map (SDM). OSC aimes to aggregate more contextual information for each sample point in the aortic branches with small tubular diameters. SDM is introduced as auxiliary information to measure the similarity of neighboring points, and it provides weights for feature fusion. Furthermore, we embed the learned 3D offset field into OSC to avoid inaccurate segmentation on the region around the narrow tubular structures. The OSC module prolongs the predicted length of small aorta branches and then improves the tubular continuity of the aorta segmentation map. We evaluate the OSC module on our-collected dataset and MICCAI ASOCA2020 coronary artery dataset. Our method has achieved the state-of-the-art results in terms of Dice and Jaccard metrics.
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. The main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions. This extension allows the technique to be applied to the common scenario where the data is generated by requests coming in to a service, which is then aggregated at a fixed temporal frequency. We show the superior accuracy of our method on synthetic and public real-world data.
GalOP

In this paper, we present GalOP --- a GPU-accelerated main memory DBMS for OLTP. At the core GalOP is based on a novel deterministic concurrency scheme for GPUs which orders conflicting transactions before the execution on the GPU. In our initial evaluation, we show that GalOP can provide robust performance for high and low conflict scenarios and outperforms recent CPU-based schemes by up to 10x.
Spam Text Detection

Spam Detection is the process to classify text which contains irrelevant or unsolicited messages sent over the internet, typically to a large number of users, for the purposes of advertising, phishing, spreading malware, etc. Text summarization is the technique of converting long text to short. The intention is to make a coherent and fluent summary having only the most points outlined within the document. A USA based machine learning expert which had 13 years of experience and currently teaches people his skills, states his technique has proved to be critical in quickly and accurately summarizing voluminous texts, something which might be expensive and time-consuming if avoided machines. Machine learning models are usually trained to know documents and distil the useful information before outputting the specified summarized texts.
On Error Exponents in Quantum Hypothesis Testing

In the simple quantum hypothesis testing problem, upper bounds on the error probabilities are shown based on a key operator inequality between a density operator and its pinching.
An Automotive Unknown-Environment Explorer Robot Based on Braitenberg Vehicle Four

Abstract Environment exploration is an essential task in robotics, but it requires lots of high-tech sensors, which will make the robot very costly. To complete the exploration, a huge number of robots are required. Then, how to explore an unknown environment using simple robots with simple sensors is a significant problem to be studied. In this paper, robots are built based on Braitenberg Vehicle 4 and the robots are made to explore an unknown environment without a human. Braitenberg Vehicles are a series concept created by V. Braitenberg. Braitenberg Vehicle 4, particularly, is a robot that only has reflection level intelligence. The Robot’s logic is only four parts, going straight, turning left, turning right, and turning around. The simplicity of the design allows putting the Robot into mass production. MATLAB is used to simulate the working process of the Robot to test the Robot’s feasibility. Then, the map drawn by the Robot with the actual environment is compared. The result has shown that the map we got is almost the same as the real surroundings.
Aggregating Local Features of Convolutional Neural Network for Material Image Retrieval

Abstract Large-scale microscopic images in materials science need to be indexed and managed using practical management tools. Content-based Image Retrieval (CBIR), which indexes and searches images based on the image features, allows for long-term data management in large-scale image datasets. Considering the difference between material microscopy images and natural ones, we propose a novel CBIR method for material microscopic images. In the proposed method, convolutional neural networks (CNN) are used to extract local features from an image, and the scale-invariant feature transform (SIFT) model is used to generate a keypoint density map (KDM). Experiments on a material microscopic image dataset show that the proposed method achieves an approving retrieval performance.
YOLOv3 with Asymmetric Intersection over Union Based Loss Function for Human Detection

In order to improve the performance of the target detection model based on convolutional neural network, and make the bounding box representing the object location contain as complete original target information as possible, this paper improved the loss function of the bounding box regression part of the YOLOv3 network. A new metric, Asymmetric Intersection over Union (AIoU), is proposed to measure the similarity between the prediction box and the truth. Theoretical analysis shows that AIoU based loss function can better guide the direction of bounding box regression after introducing asymmetry. Experiments show that the improved YOLOv3 network achieves higher accuracy and maintains fast convergence in human target detection. The examples of bounding box regression on the test dataset show that the target information loss can be reduced by the proposed method. In addition, distinguishing between the prediction and the truth in calculation may have reference significance for the loss function design of network models for other tasks
An Approach to Social Media User Search Automation

The article presents an approach to searching for a user’s account on VK.com using data from that user’s account on Odnoklassniki, wich follows the research on the user identification across different social media. This issue is important as, to the author’s knowledge, there are no studies about user search automation for popular Russian social media. The proposed approach is based on the assumption that lists of friends on social media are a reflection of a user’s social environment and intersect across different accounts. Based on this intersection, it is possible to find accounts of a user on different social media and restore some hidden social ties of closed ones. This approach can be applied in the task of constructing and analyzing the social graph using information from different social media, and in studies on data security in social media. The accuracy of the proposed method is 0.753 on the test set of data.
Machine Learning-Based Speech Recognition of Chinese Dialects Method for Mobile Forensics

The software of QQ, WeChat and other voice chat tools become increasingly popular, people can use these methods to communicate expediently. In the meantime, criminals also employ these speech communication tools to engage in criminal activities. These voice messages will include in the smartphones, and the speech data are important for the law enforcement officers. China has large population, criminal gangs always have local characteristics and dialects are a common way of communication between criminals. So, lots of speech date will store in the mobile phones. In the work of mobile forensics, investigators should take much time and human effort to recognition and analysis the dialects voice data. For this reason, the law enforcement personnel need to use intelligent method to investigation local dialects voice data in the mobile phone forensics efficiently. In this paper, we propose a mobile forensics method that based on LSTM (Long Short-Term Memory) neural network technique to automatically recognize dialects voice data for investigation and forensics.
GATSBI: Generative Agent-centric Spatio-temporal Object Interaction

We present GATSBI, a generative model that can transform a sequence of raw observations into a structured latent representation that fully captures the spatiotemporal context of the agent’s actions. In vision-based decision making scenarios, an agent faces complex high-dimensional observations where multiple entities interact with each other. The agent requires a good scene representation of the visual observation that discerns essential components and consistently propagates along the time horizon. Our method, GATSBI, utilizes unsupervised object-centric scene representation learning to separate an active agent, static back-ground, and passive objects. GATSBI then models the interactions reflecting the causal relationships among decomposed entities and predicts physically plausible future states. Our model generalizes to a variety of environments where different types of robots and objects dynamically interact with each other. We show GATSBI achieves superior performance on scene decomposition and video prediction compared to its state-of-the-art counterparts.
A practical computational approach to stratigraphic analysis using conjoinable material culture

A recommendation of: Sébastien Plutniak The strength of parthood ties. Modelling spatial units and fragmented objects with the TSAR method – Topological Study of Archaeological Refitting https://osf.io/q2e69
Cross-Task and Cross-Model Active Learning with Meta Features

When the task model or data set changes, the active learning strategies based on heuristics are difficult to perform well all the time. In this paper, we propose a task-agnostic and model-agnostic active learning method based on meta-feature. This method draws on the idea of meta-learning. First, we designed the meta-features of an unlabeled sample at the current learning stage. These designed meta-features have nothing to do with the feature space of the data set or the form of the task model. So our method can be applied to any data set and any model. Second, we regard the active learning query selection procedure as a regression problem. We design a meta regressor that predicts the improvement of model performance for a candidate sample in a particular learning state. And we train the regressor on the experience from previous active learning outcomes. Experimental results show that our method is more stable and effective than the heuristic active learning method.
13‐3: Invited Paper: Video Frame Interpolation via Structure Motion based Iterative Feature Fusion

Video Frame Interpolation synthesizes non‐existent images between adjacent frames, with the aim of providing a smooth and consistent visual experience. Two approaches for solving this challenging task are optical flow based and kernel‐based methods. In existing works, optical flow based methods can provide accurate point‐to‐point motion description, however, they lack constraints on object structure. On the contrary, kernel‐based methods focus on structural alignment, which relies on semantic and apparent features, but tends to blur results. Based on these observations, we propose a structure‐motion based iterative fusion method. The framework is an end‐to‐end learnable structure with two stages. First, interpolated frames are synthesized by structure‐based and motion‐based learning branches respectively, then, an iterative refinement module is established via spatial and temporal feature integration. Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation. Experimental results on three typical benchmarks show that the proposed method achieves superior performance on all evaluation metrics over the state‐of‐the‐art methods, even when our models are trained with only one‐tenth of the data other methods use.
Effect or Program Constructs on Code Readability and Predicting Code Readability Using Statistical Modeling

Abstract In software, code is the only part that remains up to date, which shows how important code is. Code readability is the capability of the code that makes it readable and understandable for professionals. The readability of code has been a great concern for programmers and other technical people in development team because it can have a great influence on software maintenance. A lot of research has been done to measure the influence of program constructs on the code readability but none has placed the highly influential constructs together to predict the readability of a code snippet. In this article, we propose a novel framework using statistical modeling that extracts important features from the code that can help in estimating its readability. Besides that using multiple correlation analysis, our proposed approach can measure dependencies among di erent program constructs. In addition, a multiple regression equation is proposed to predict the code readability. We have automated the proposals in a tool that can do the aforementioned estimations on the input code. Using those tools we have conducted various experiments. The results show that the calculated estimations match with the original values that show the effectiveness of our proposed work. Finally, the results of the experiments are analyzed through statistical analysis in SPSS tool to show their significance.
Identifying malicious accounts in blockchains using domain names and associated temporal properties

The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars. Many machine learning algorithms are applied to detect such illegal behavior. These algorithms are often trained on the transaction behavior and, in some cases, trained on the vulnerabilities that exist in the system. In our approach, we study the feasibility of using the Domain Name (DN) associated with the account in the blockchain and identify whether an account should be tagged malicious or not. Here, we leverage the temporal aspects attached to the DN. Our approach achieves 89.53% balanced-accuracy in detecting malicious blockchain DNs. While our results identify 73769 blockchain DNs that show malicious behavior at least once, out of these, 34171 blockchain DNs show persistent malicious behavior, resulting in 2479 malicious blockchain DNs over time. Nonetheless, none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.
Partial zeta functions, partial exponential sums, and p-adic estimates

Partial zeta functions of algebraic varieties over finite fields generalize the classical zeta function by allowing each variable to be defined over a possibly different extension field of a fixed finite field. Due to this extra variation their rationality is surprising, and even simple examples are delicate to compute. For instance, we give a detailed description of the partial zeta function of an affine curve where the number of unit poles varies, a property different from classical zeta functions. On the other hand, they do retain some properties similar to the classical case. To this end, we give Chevalley-Warning type bounds for partial zeta functions and L-functions associated to partial exponential sums.
Security System Using Fingerprint Scanning

The Aim and moto of this project is that it is used to Access Internet Banking for Cash Related Tasks with more security and reliability. we use biometric system for highly secure transaction with hassle free processes. We use fingerprint Scanner and One Time Password (OTP) is send to the user registration mobile number. After that, user will be able to complete the transaction. This paper consists of conceptualized framework of design, specifications and model of the system.
Designing Visual Cards for Digital Mental Health Research with Ethnic Minorities

Gaining an understanding of people's diverse mental health needs is essential for informing the design of inclusive mental health technologies. However, conversations about mental health experiences can be challenging for both researchers and participants. We present the design of visual cards that illustrate an inclusive mental health concept to support researchers and participants in understanding and sharing mental health experiences. We illustrate the iterative design of the visual cards with our reflections and feedback from ethnically diverse participants. We found that designing the visual cards fostered insightful reflections within the design team regarding the roles of identity, gender, and ethnicity in designing culturally sensitive content and research. Participants from minority ethnic backgrounds valued the illustrative elements of the visual cards and highlighted the importance of supporting different languages and visual cultures. We discuss use cases for the visual cards and implications for designing culturally sensitive mental health technologies.
Fight Detection in Crowd Scenes Based on Deep Spatiotemporal Features

Over the last few decades, remarkable growths of distributing surveillance cameras have been noticed in privet and public facilities. Therefore, with a growing demand for security and to ensure public safety. An intelligent automated approach is essential for on-spot violence and fight detection as it would save the time and cost of manual fight detection from monitor screens. In this research, we propose a supervised deep learning-based approach to detect fight actions from crowd video scenes. By extracting the keyframes from video frames using the cosine similarity algorithm, using them to compute optical flow values of the magnitudes, orientation and velocity. Afterward, using these values to construct four 2D templates, which are supplied to the pre-trained network to extract deep spatiotemporal features. Finally, applying the Neighborhood Component Analysis (NCA) feature selection method, and the Vector Support Machine (SVM) classifier, to generate a model that able to identify fighting behavior from crowd video scenes. Three different public datasets, the Hockey dataset, Movies dataset and Violent-Flows dataset, were used to evaluate the proposed method. The efficiency of the proposed method outperformed other state-of-the-art methods in terms of accuracy and the required time for fight detection to be executed.
Energy and Exergy-Aware Workload Assignment for Air-Cooled Data Centers

The energy required to cool a data center (DC) depends on both thermal and workload management. Although existing energy- and temperature-aware workload assignment approaches reduce operational expenditure by minimizing cooling energy consumption, they do not address the loss of available cooling capacity (i.e., loss of thermodynamic exergy) due to unavoidable system inefficiencies. By quantifying the exergy efficiency, the actual cooling COP and maximum achievable COP during operation can be embedded in the DC control system. A multi-objective optimization problem is formulated that quantifies the tradeoff between energy and exergy efficiencies. By reducing the exergy loss, the available cooling capacity, and therefore the capital expenditure can be effectively utilized, potentially reducing the total cost of ownership. We identify scenarios when significant improvements in the utilization of cooling capacity are possible with only small increases in energy consumption.
Artificial Intelligence Used to Control Non-linear Systems

The aim of this paper is to establish the whether usage of artificial neural networks in robust robot arm control is a viable solution. The result of this study can be used later as a reference if such a solution would be used in a practical application. Controlled plant, which was a two arm robotic arm, was modelled using Simscape environment. Neural networks were utilized as a plant model, as trying to achieve a quality model of non-linear systems using a math equations is not only time consuming and very tough but sometimes even impossible.
FMEBA: A Fusion Multi-feature Model for Chinese Out of Vocabulary Word Embedding Generation

The word embedding model embeds each word into a low-dimensional space by using the distribution information of unlabeled words in the corpus, such that the generalization ability of lexical features can be improved. However, the performance of word embedding models is limited by Out of Vocabulary (OOV) words because the relevant information of OOV words can’t be fully used to generate accurate word embedding. To effectively process OOV words, morphological structure information and context information should be considered. In view of the characteristics of Chinese, we propose a Fusion Multi-feature Encoder Based on Attention (FMEBA) for processing Chinese OOV words, in which we use the radical feature of characters, as well as use character-level Transformer Encoder to process character sequence information and context information. To test our model, we conducted experiments on a Chinese power plan professional dataset. Experimental results on the dataset shows compared with other models, our model achieved the best results. We conclude that our method is suitable for processing Chinese OOV words.
Image Restoration Using Hybrid Technique Based on Noise Detection and Uncetainty

Image enhancement in the digital image processing area is one of the most important techniques, to restore images that have been corrupted with some types of noise. In this paper the restoration method includes suppression of impulse noise from grayscale images using a hybrid technique which is Morphological Opening and Modified Enhanced Recursive Median Filter based on noise detection and uncertainty. The first step of the proposed technique is detecting whether the pixel is corrupted or not, then a morphological opening is applied on corrupted pixels only. A second detection step and uncertainty process will be applied to the resulting image from the first step, then the only corrupted pixel will be manipulated using the modified enhanced recursive median filter to suppress a very high density of noise. The processing time is very short for the proposed technique compared to modified enhanced recursive median filter. The performance of these techniques have been measured using Mean Square Error and Peak signal to Noise Ratio.
Does task-irrelevant music affect gaze allocation during real-world scene viewing?

Gaze control manifests from a dynamic integration of visual and auditory information, with sound providing important cues for how a viewer should behave. Some past research suggests that music, even if entirely irrelevant to the current task demands, may also sway the timing and frequency of fixations. The current work sought to further assess this idea as well as investigate whether task-irrelevant music could also impact how gaze is spatially allocated. In preparation for a later memory test, participants studied pictures of urban scenes in silence or while simultaneously listening to one of two types of music. Eye tracking was recorded, and nine gaze behaviors were measured to characterize the temporal and spatial aspects of gaze control. Findings showed that while these gaze behaviors changed over the course of viewing, music had no impact. Participants in the music conditions, however, did show better memory performance than those who studied in silence. These findings are discussed within theories of multimodal gaze control.