Reviewerly/exHarmony huggingface dataset:
DScaler: A Horizontal Autoscaler of Microservice Based on Deep Reinforcement Learning

With the development of container technology, microservice architecture has become a powerful paradigm for cloud computing with efficient infrastructure management and large-scale service capabilities. Cloud providers require flexible resource management to meet dynamic workloads, such as autoscaling and provisioning. As one of the most popular open-source container orchestration systems, Kubernetes provides a built-in mechanism, Horizontal Pod Autoscaler (HPA), for dynamic resource autoscaling. However, the static rules of HPA are not adaptable to highly dynamic workloads. In this paper, we propose a deep reinforcement learning-based horizontal autoscaler(DScaler) for autoscaling of microservices deployed in Kubernetes. Under two workloads with different characteristics, our experiments show that the proposed approach reduces resource consumption by 19.90% and 10.80% while reducing SLA violations by 8.56% and 12.75% compared with HPA, respectively. In addition, our approach can significantly reduce resource consumption by about 60% compared to the existing reinforcement learning strategy while maintaining SLA within an acceptable level.
A Proposed Methodology for Enhancing User Experience in Mobile Applications Design for Cultural Promotion

User Experience (UX), as a theoretical and practical field of research, is based on the theory of Human Computer Interaction (HCI), studies systems, products, and services in the field of (mainly) Information Technology (IT), and aims at user satisfaction and better, more pleasant and more efficient interaction with systems. The popularity of Mobile Device Applications (MDAs) in the last decade has introduced new interaction environments and thus the need to formulate, develop and adapt to them, theories, and methods of UX. We present UX as a theoretical and practical framework and record its methodology - both the general one for various systems so far, and the specific one for Mobile Device Applications and more specifically for those that project cultural content in conventional ways and through augmentation. As part of the research, high-fidelity prototypes were designed and were tested during a user survey (usability test, questionnaire) for extracting conclusions to improve the design of the application.
Comparative Study of Classification Algorithms for Website Phishing Detection on Multiple Datasets

Phishing has become a prominent method of data theft among hackers, and it continues to develop. In recent years, many strategies have been developed to identify phishing website attempts using machine learning particularly. However, the algorithms and classification criteria that have been used are highly different from the real issues and need to be compared. This paper provides a detailed comparison and evaluation of the performance of several machine learning algorithms across multiple datasets. Two phishing website datasets were used for the experiments: the Phishing Websites Dataset from UCI (2016) and the Phishing Websites Dataset from Mendeley (2018). Because these datasets include different types of class labels, the comparison algorithms can be applied in a variety of situations. The tests showed that Random Forest was better than other classification methods, with an accuracy of 88.92% for the UCI dataset and 97.50% for the Mendeley dataset.
Path planning of improved DQN based on quantile regression

To solve the problems of slow convergence and overestimation of the value of quantile regression-deep reinforcement learning algorithm, a Dueling Double Depth Q algorithm based on quantile regression (QR-D3QN) was proposed. Based on QR-DQN, the calculation method of the target Q value is modified to reduce the influence of value overestimation. Combining the confrontation network and adding preferential experience sampling to improve the utilization efficiency of effective data. It is verified by the ROSGazebo simulation platform that the robot can effectively select actions, get a good strategy, and can quickly avoid obstacles and find the target point. Compared with D3QN, the route planned by the robot is shortened by 4.95%, and the obstacle avoidance path is reduced by 18.8%.
A Finite State Transducer Based Morphological Analyzer for The Kazakh Language

This paper presents a finite state transducer based morphological analyzer for Kazakh language which is able to decompose complex Kazakh words into consecutive morphemes including lemma, part-of-speech, and morphological tags. Due to the agglutinative nature of the language, the analyzer can produce more than one analysis for each word depending on word's complexity. We conducted several experiments to evaluate the performance of the analyzer. It achieved 92% coverage on large Wikipedia and 96% coverage on the news data, and the accuracy of analyzer was 98.40% on the test data.
Lossless Coding of Multimodal Image Pairs Based on Image-To-Image Translation

Multimodal image coding often uses standard encoding algorithms, which do not exploit multimodality characteristics. This paper proposes a new cross-modality prediction approach for lossless coding of multimodal images, based on a Generative Adversarial Network (GAN). The GAN is added to the prediction loop of the Versatile Video Coding (VVC) lossless encoder to perform cross-modality translation of an image to its counterpart modality. Then, such synthesized image is used as reference for inter prediction, followed by further optimization that includes rescaling and brightness adjustment. A publicly available dataset of Positron Emission Tomography (PET) and Computed Tomography (CT) image pairs is used to assess the performance of the proposed multimodal lossless image coding framework. In comparison with single modality coding using the VVC standard, average coding gains of 6.83% are achieved for the inter-coded PET images.
BİR YATIRIM ARACI OLARAK BİTCOİN: BİTCOİN İLE GLOBAL TEKNOLOJİ ENDEKSLERİ ARASINDA ASİMETRİK NEDENSELLİK İLİŞKİLERİ

The concept of blockchain and cryptocurrencies is one of the most popular concepts of recent years. Cryptocurrencies were first introduces with Bitcoin in 2008 and now they have an increasing variety and popularity. Recent developments in technology firms have brought into question whether there is a relationship between Bitcoin and technology indexes. To this end, this study investigates the causality relationship between Bitcoin and technology indexes using monthly data between the years 2016 and 2021 in G7 and E7 countries. To test the causality relationship between the variables, the Hatemi-J (2012) asymmetric causality test was used. Hatemi-J (2012) test reveals that the relationship between bitcoin and technology indexes becomes different for G7 and E7 countries. The results suggest that developed countries affect bitcoin prices while developing countries are affected by Bitcoin prices. The conclusion is that findings point out the existence of an asymmetric relationship between the series for G7 and E7 countries.
Explainable Hyperbolic Temporal Point Process for User-Item Interaction Sequence Generation

Recommender systems which captures dynamic user interest based on time-ordered user-item interactions plays a critical role in the real-world. Although existing deep learning-based recommendation systems show good performances, these methods have two main drawbacks. Firstly, user interest is the consequence of the coaction of many factors. However, existing methods do not fully explore potential influence factors and ignore the user-item interaction formation process. The coarse-grained modeling patterns cannot accurately reflect complex user interest and leads to suboptimal recommendation results. Furthermore, these methods are implicit and largely operate in a black-box fashion. It is difficult to interpret their modeling processes and recommendation results. Secondly, recommendation datasets usually exhibit scale-free distributions and some existing recommender systems take advantage of hyperbolic space to match the data distribution. But they ignore that the operations in hyperbolic space are more complex than that in Euclidean space which further increases the difficulty of model interpretation. To tackle the above shortcomings, we propose an E xplainable H yperbolic T emporal P oint P rocess for User-Item Interaction Sequence Generation (EHTPP) . Specifically, EHTPP regards each user-item interaction as an event in hyperbolic space and employs a temporal point process framework to model the probability of event occurrence. Considering that the complexity of user interest and the interpretability of the model,EHTPP explores four potential influence factors related to user interest and uses them to explicitly guide the probability calculation in the temporal point process. In order to validate the effectiveness of EHTPP, we carry out a comprehensive evaluation of EHTPP on three datasets compared with a few competitive baselines. Experimental results demonstrate the state-of-the-art performances of EHTPP.
Accelerating Random Forest on Memory-Constrained Devices Through Data Storage Optimization

Random forests is a widely used classification algorithm. It consists of a set of decision trees each of which is a classifier built on the basis of a random subset of the training data-set. In an environment where the memory work-space is low in comparison to the data-set size, when training a decision tree, a large proportion of the execution time is related to I/O operations. These are caused by data blocks transfers between the storage device and the memory work-space (in both directions). Our analysis of random forests training algorithms showed that there are two major issues : <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</i> Block Under-utilization: data blocks are poorly used when loaded into memory and have to be reloaded multiple times, meaning that the algorithm exhibits a poor spatial locality; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</i> Data Over-read: the data-set is supposed to be fully loaded in memory whereas a large proportion of data are not effectively useful when building a decision tree. Our proposed solution is structured to address these two issues. First, we propose to reorganize the data-set in such a way to enhance spatial locality and second, to remove the assumption that the data-set is entirely loaded into memory and access data only when effectively needed. Our experiments show that this method made it possible to reduce random forest building time by 51 to 95% in comparison to a state-of-the-art method.
Mobile Robot Combination Autonomous Behavior Strategy to Inspect Hazardous Gases in Relatively Narrow Man–Machine Environment

Abstract Selecting the optimal speed for dynamic obstacle avoidance in complex man–machine environments is a challenging problem for mobile robots inspecting hazardous gases. Consideration of personal space is important, especially in a relatively narrow man–machine dynamic environments such as warehouses and laboratories. In this study, human and robot behaviors in man–machine environments are analyzed, and a man–machine social force model is established to study the robot obstacle avoidance speed. Four typical man–machine behavior patterns are investigated to design the robot behavior strategy. Based on the social force model and man–machine behavior patterns, the fuzzy-PID trajectory tracking control method and the autonomous obstacle avoidance behavior strategy of the mobile robot in inspecting hazardous gases in a relatively narrow man–machine dynamic environment are proposed to determine the optimal robot speed for obstacle avoidance. The simulation analysis results show that compared with the traditional PID control method, the proposed controller has a position error of less than 0.098 m, an angle error of less than 0.088 rad, a smaller steady-state error, and a shorter convergence time. The crossing and encountering pattern experiment results show that the proposed behavior strategy ensures that the robot maintains a safe distance from humans while performing trajectory tracking. This research proposes a combination autonomous behavior strategy for mobile robots inspecting hazardous gases, ensuring that the robot maintains the optimal speed to achieve dynamic obstacle avoidance, reducing human anxiety and increasing comfort in a relatively narrow man–machine environment.
Two Channel Filter Banks on Arbitrary Graphs With Positive Semi Definite Variation Operators

We propose novel two-channel filter banks for signals on graphs. Our designs can be applied to arbitrary graphs, given a positive semi definite variation operator, while using arbitrary vertex partitions for downsampling. The proposed generalized filter banks (GFBs) also satisfy several desirable properties including perfect reconstruction and critical sampling, while having efficient implementations. Our results generalize previous approaches that were only valid for the normalized Laplacian of bipartite graphs. Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator. These GFTs are orthogonal in an alternative inner product space which depends on the downsampling and variation operators. Our key theoretical contribution is showing that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectral folding property</i> of the normalized Laplacian of bipartite graphs, at the core of bipartite filter bank theory, can be generalized for the proposed GFT if the inner product matrix is chosen properly. In addition, we study vertex domain and spectral domain properties of GFBs and illustrate their probabilistic interpretation using Gaussian graphical models. While GFBs can be defined given any choice of a vertex partition for downsampling, we propose an algorithm to optimize these partitions with a criterion that favors balanced partitions with large graph cuts, which are shown to lead to efficient and stable GFB implementations. Our numerical experiments show that partition-optimized GFBs can be implemented efficiently on 3D point clouds with hundreds of thousands of points (nodes), while also improving the color signal representation quality over competing state-of-the-art approaches.
An interpretable DIC risk prediction model based on convolutional neural networks with time series data

Abstract Disseminated intravascular coagulation (DIC) is a complex, life-threatening syndrome associated with the end-stage of different coagulation disorders. Early prediction of the risk of DIC development is an urgent clinical need to reduce adverse outcomes. However, effective approaches and models to identify early DIC are still lacking. In this study, a novel interpretable deep learning based time series is used to predict the risk of DIC. The study cohort included ICU patients from a 4300-bed academic hospital between January 1, 2019, and January 1, 2022. Experimental results show that our model achieves excellent performance (AUC: 0.986, Accuracy: 95.7%, and F1:0.935). Gradient-weighted Class Activation Mapping (Grad-CAM) was used to explain how predictive models identified patients with DIC. The decision basis of the model was displayed in the form of a heat map. The model can be used to identify high-risk patients with DIC early, which will help in the early intervention of DIC patients and improve the treatment effect.
Gecko-inspired hierarchically rough surface used as a reprintable paper

While paper continues to remain one of the most widely used materials for writing and printing, and while its demand continues to grow, the environmental cost of using paper is rather huge. Paper is made by extracting the cellulose fibres from wood, and to meet the demand for it, large numbers of trees are felled. Making paper requires huge quantity of water, energy, and also toxic chemicals. In fact, paper is considered the 3rd largest industrial polluter of air, water, and soil. Yet a viable alternative to paper doesn't exist. Inspired by the inherent ability of many natural surfaces to keep themselves clean, we have made a reprintable paper which can be used over 50 cycles. It is prepared by casting crosslinkable silicone on a porous template, leading to hierarchical random surface features having dimensions ranging from tens of nano-meter to hundreds of micrometers. On this surface, ink particles of wide-ranging sizes and surface chemical characteristics adhere via intermolecular interactions. However, unlike conventional papers, in which particles get mechanically trapped inside the porous network of the cellulose fibres, here the ink particles remain accessible for cleaning via gentle scrubbing with a rough surface, soaked with a suitable liquid. Thus, this paper continues to remain both cleanable and printable.
Color-Encoded Links Improve Homophily Perception in Node-Link Diagrams

Node-link diagrams enable visual assessment of homophily when viewers can identify and evaluate the relative number of intra-cluster and inter-cluster links. Our online experiment shows that a new design with link type encoded edge color leads to more accurate perception of homophily than a design with same-color edges.
Forecasting Solar Radiation by the Machine Learning Algorithm &amp; their Different Techniques

Abstract: The objective of this study is to give a summary of machine learning-based techniques for solar irradiation forecasting in this context. Despite the fact that numerous research describe methods like neural networks or support vector regression. Ranking the performance of such methods is difficult because of the diversity of the data collection, time step, forecasting horizon, setup, and performance indicators. The prediction inaccuracy is quite comparable overall. Others write. Global solar radiation recommended utilising ensemble forecasting or hybrid models to improve prediction accuracy. Forecasting the output power of solar systems is required for the smooth operation of the power grid or for the optimal control of the energy flows into the solar system. Prior to projecting the output of the solar system, it is essential to focus on solar irradiance. The two primary categories of methods for predicting the global solar radiation are machine learning algorithms and cloud pictures combined with physical models.
V2ReID: Vision-Outlooker-Based Vehicle Re-Identification

With the increase of large camera networks around us, it is becoming more difficult to manually identify vehicles. Computer vision enables us to automate this task. More specifically, vehicle re-identification (ReID) aims to identify cars in a camera network with non-overlapping views. Images captured of vehicles can undergo intense variations of appearance due to illumination, pose, or viewpoint. Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. These are, however, not always available or can be resource intensive for the model. Following the success of Transformer baselines in ReID, we propose for the first time an outlook-attention-based vehicle ReID framework using the Vision Outlooker as its backbone, which is able to encode finer-level features. We show that, without embedding any additional side information and using only the visual cues, we can achieve an 80.31% mAP and 97.13% R-1 on the VeRi-776 dataset. Besides documenting our research, this paper also aims to provide a comprehensive walkthrough of vehicle ReID. We aim to provide a starting point for individuals and organisations, as it is difficult to navigate through the myriad of complex research in this field.
Intensity self-compensation method against multi-factors for polarization-based Fabry–Perot interrogation system

Phase interrogation methods for fiber-optic Fabry-Perot (F-P) sensors may inevitably fail in the field due to the influences of irrelevant factors on signal intensity. To address this severe problem, this Letter proposes an intensity self-compensation method (ISCM) to eliminate the consecutive signal fluctuations of a polarization-based F-P interrogation system caused by multiple factors. By providing only the initial intensities of the reference signals, this attempt realizes the real-time intensity compensation of the output signals without affecting their quadrature relationship. Consecutive intensity fluctuations caused by variation of light source power, fiber loss, and polarization state are reduced to 2%-3% by the ISCM. Furthermore, the method performs ideally under dynamic modulation of the sensor. In addition, it can be applied against the inconsistent fluctuations between signals and is suitable for F-P sensors with single or multiple cavities. Owing to the high efficiency, real-time ability, and no moving parts advantage, the proposed method provides an excellent candidate for improving the accuracy and stability of F-P interrogation systems.
Reducing the Latency of Touch Tracking on Ad-hoc Surfaces

Touch sensing on ad-hoc surfaces has the potential to transform everyday surfaces in the environment - desks, tables and walls - into tactile, touch-interactive surfaces, creating large, comfortable interactive spaces without the cost of large touch sensors. Depth sensors are a promising way to provide touch sensing on arbitrary surfaces, but past systems have suffered from high latency and poor touch detection accuracy. We apply a novel state machine-based approach to analyzing touch events, combined with a machine-learning approach to predictively classify touch events from depth data with lower latency and higher touch accuracy than previous approaches. Our system can reduce end-to-end touch latency to under 70ms, comparable to conventional capacitive touchscreens. Additionally, we open-source our dataset of over 30,000 touch events recorded in depth, infrared and RGB for the benefit of future researchers.