Reviewerly/exHarmony huggingface dataset:
Scaling graph traversal to 281 trillion edges with 40 million cores

Graph processing, especially high-performance graph traversal, plays a more and more important role in data analytics. The successor of Sunway TaihuLight, New Sunway, is equipped with nearly 10 PB memory and over 40 million cores, which brings the opportunity to process hundreds of trillions of edges graphs. However, the graph with an unprecedented scale also brings severe performance challenges, including load imbalance, poor locality, and irregular access of graph traversal workload.
The Effect Of Audio Visual Media On The Ability To Count Of Kindergarten

The problem of this research is based on the low ability of children in numeracy when using the number symbol and matching numbers with the number symbol in kindergarten.This study aims to improve the ability to count children at the age of 5-6 years through audio visual media of kindergarten. This type of research is an experimental study nonequivlent control group design. The research population of all children aged 5-6 years while the selected research samples were grade B2 and B4. Sampling techniques using purposive sampling techniques, this study uses the t-test formula preceded by the analysis requirements test in the form of normality test and homogeneity test. Based on the analysis of data obtained from the test results of data from the sample class that is normal and homogeneous distribution, further hypothetical tests with t-test showed obtained . thitung &gt; ttabel is 3,186&gt;2,042 and sig. &gt; sig.a (0.003
Optimizing performance of Real-Time Big Data stateful streaming applications on Cloud

Exponential growth in the volume of data generated over the last decade has triggered massive research and adoption of distributed big data analytics platforms. In real-time streaming analytics, data is received as a continuous event stream. These events are iteratively correlated and analyzed in micro-batches till the processing reaches a logical conclusive stage. For every iteration, data received as part of the new batch is correlated with the results of the previous iteration and cached as interim results. This real-time stateful analysis of streaming data has been made possible by distributed data-intensive computing frameworks like Apache Spark. Though the spark state storage is highly optimized, its access is limited within the context of the Spark job. Since, these interim results may need to be consumed by applications running external to Spark context, saving these results in external memory involves a significant overhead due to the high volume of I/O. Similar challenges are observed when the spark job needs to consume from an external data store. These challenges get compounded in cloud-based deployment. This paper presents the optimized data storage and retrieval pattern from the spark job to/from the independent storage using the standard off the shelf tools. Our focus on standard off the shelf storage is to ensure that the design patterns presented as part of this paper could be adopted for production deployment.
Statistical analysis of software development models by six-pointed star framework

Software Development Process Model (SDPM) develops software according to the needs of the client within the defined budget and time. There are many software development models such as waterfall, Iterative, Rapid Application Development (RAD), Spiral, Agile, Z, and AZ model. Each development model follows a series of steps to develop a product. Each model has its strengths and weaknesses. In this study, we have investigated different software development process models using the six-pointed star framework. Six-point star is a framework of project management industry standards maintained by Project Management Body of Knowledge (PMBOK). A survey is designed to evaluate the performance of well-known software process models in the context of factors defined by the six-point star framework. The survey is conducted with experienced users of the software industry. The statistical analysis and comparison of results obtained by the survey are further used to examine the effectiveness of each model for the development of high-quality software concerning lightweight and heavyweight methodologies for small, medium and large scale projects. After exploring the results of all factors of the six-pointed star model, we conclude that lightweight methodology easily handles small-scale projects. The heavyweight methodology is suitable for medium and large-scale projects, whereas the AZ model, which is one of the latest models, works efficiently with both small-scale and large-scale categories of projects.
Controllable Image Synthesis With Attribute-Decomposed GAN

This paper proposes Attribute-Decomposed GAN (ADGAN) and its enhanced version (ADGAN++) for controllable image synthesis, which can produce realistic images with desired attributes provided in various source inputs. The core ideas of the proposed ADGAN and ADGAN++ are both to embed component attributes into the latent space as independent codes and thus achieve flexible and continuous control of attributes via mixing and interpolation operations in explicit style representations. The major difference between them is that ADGAN processes all component attributes simultaneously while ADGAN++ utilizes a serial encoding strategy. More specifically, ADGAN consists of two encoding pathways with style block connections and is capable of decomposing the original hard mapping into multiple more accessible subtasks. In the source pathway, component layouts are extracted via a semantic parser and the segmented components are fed into a shared global texture encoder to obtain decomposed latent codes. This strategy allows for the synthesis of more realistic output images and the automatic separation of un-annotated component attributes. Although the original ADGAN works in a delicate and efficient manner, intrinsically it fails to handle the semantic image synthesizing task when the number of attribute categories is huge. To address this problem, ADGAN++ employs the serial encoding of different component attributes to synthesize each part of the target real-world image, and adopts several residual blocks with segmentation guided instance normalization to assemble the synthesized component images and refine the original synthesis result. The two-stage ADGAN++ is designed to alleviate the massive computational costs required when synthesizing real-world images with numerous attributes while maintaining the disentanglement of different attributes to enable flexible control of arbitrary component attributes of the synthesized images. Experimental results demonstrate the proposed methods' superiority over the state of the art in pose transfer, face style transfer, and semantic image synthesis, as well as their effectiveness in the task of component attribute transfer. Our code and data are publicly available at https://github.com/menyifang/ADGAN.
Progress of Machine Vision in the Detection of Cancer Cells in Histopathology

In recent years, with the rapid development of artificial intelligence, machine vision technology has been widely used in various fields. Traditional cancer detection methods are time-consuming, labor-intensive, and highly dependent on the experience of pathologists; therefore, these methods cannot meet the needs of modern medical treatment. Machine vision overcomes the disadvantages of traditional detection methods in cancer detection and can help pathologists improve the detection accuracy. According to the requirements of medical detection, this review summarizes the applications of machine vision in the detection of cancer cells in histopathological images and analyzes the advantages and disadvantages of existing methods in image preprocessing, segmentation, feature extraction and recognition. Finally, research on the detection methods of histopathological cancer cells is reviewed and prospected, and future development trends are predicted to provide guidance for follow-up research.
Experimental demonstration of error-free key distribution without an external random source or device over a 300-km optical fiber

Based on angle rotation, we proposed an error-free key distribution scheme that does not require pre-shared information. The key consistency comes from the consistency of angular differences, and the randomness of the key comes from random initial angles and methods of key generation. The initial angle is randomly rotated in order to improve the immunity against eavesdroppers, and the scheme can resist common attacks. The error-free secure key is obtained with key post-processing techniques. The proposed scheme is validated in the physical layer by mapping angular changes to phase variations, which does not require an external random source or an additional device. Experimental results demonstrate that an error-free key can be obtained with the key generation rate of 127.12 Mbit/s over a 300-km standard single-mode fiber.
Behind the Curtains: Comparing Mozilla Hubs with Microsoft Teams in a Guided Virtual Theatre Experience

This paper examines the perceived social presence experienced in social virtual environments (Mozilla Hubs) in a theatre context compared to video-based meeting platforms (MS Teams). To achieve this, we carried out a comparative study, designed a concept for a guided tour, recreated a theatre stage in a virtual reality environment, and granted students access to this stage as a replacement for visiting the location in person. Results show only minor differences between the conditions, which can be attributed to the difficult navigation control scheme provided by the devices (e.g., tablets, smartphones) and the lack of compelling onboarding features. Nonetheless, the proposed submission identifies design potentials of virtual social environments and offers insights into how the examined efforts are perceived both by the guides and students.
You’re in for a Bumpy Ride! Uneven Terrain Increases Cybersickness While Navigating with Head Mounted Displays

Cybersickness (i.e., visually induced motion sickness) serves as a significant obstacle to the usage and broader adoption of virtual reality (VR) technologies. This collection of symptoms akin to motion sickness can be impacted by different characteristics of a virtual experience, such as visual realism and optical flow. However, relatively little is known regarding how cybersickness is influenced by traversing uneven virtual terrain. In this study, we aim to better understand the impacts of different virtual terrain types on cybersickness in VR. We recruited 38 participants to navigate a virtual forest environment with three terrain variants: flat surface, terrain with regular bumps, and irregular terrain generated from Perlin noise. We collected cybersickness data using the Fast Motion Sickness Scale (FMSS) and Simulator Sickness Questionnaire (SSQ) in addition to galvanic skin response data. Our results indicate that users felt greater levels of cybersickness in the presence of regular bumps and irregular terrain than they did when traversing flat geometry. We recommend that designers exercise caution when incorporating uneven terrain into their virtual experiences, and maintain awareness of the risks carried by these design decisions.
ShapeFindAR: Exploring In-Situ Spatial Search for Physical Artifact Retrieval using Mixed Reality

Personal fabrication is made more accessible through repositories like Thingiverse, as they replace modeling with retrieval. However, they require users to translate spatial requirements to keywords, which paints an incomplete picture of physical artifacts: proportions or morphology are non-trivially encoded through text only. We explore a vision of in-situ spatial search for (future) physical artifacts, and present ShapeFindAR, a mixed-reality tool to search for 3D models using in-situ sketches blended with textual queries. With ShapeFindAR, users search for geometry, and not necessarily precise labels, while coupling the search process to the physical environment (e.g., by sketching in-situ, extracting search terms from objects present, or tracing them). We developed ShapeFindAR for HoloLens 2, connected to a database of 3D-printable artifacts. We specify in-situ spatial search, describe its advantages, and present walkthroughs using ShapeFindAR, which highlight novel ways for users to articulate their wishes, without requiring complex modeling tools or profound domain knowledge.
Sketching Across the Senses: Exploring Sensory Translation as a Generative Practice for Designing Data Representations

This workshop engages the phenomenon of synesthesia to explore how translating between sensory modalities might uncover new ways to experience and represent data: What does it mean to taste a timeline, hear a network, or touch a categorical scale? We employ the method of sketching, which traditionally favors visual representations, and consider what it means to 'sketch' in other modalities like sound, taste, and touch. Through a series of rapid, playful activities ideating data representations across sensory modalities, we will explore how the affordances of sketching—like intentional ambiguity—might help designers creatively map data to experience. We will also discuss challenges for sensory sketching in remote, collaborative environments and brainstorm suggestions for digital tools. The outcome of this workshop will be a series of exercises and examples that serve as a toolkit for designers, researchers, and data practitioners to incorporate sketching across the senses into their work.
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks

A plethora of methods have been proposed to explain how deep neural networks reach their decisions but comparatively, little effort has been made to ensure that the explanations produced by these methods are objectively relevant. While several desirable properties for trustworthy explanations have been formulated, objective measures have been harder to derive. Here, we propose two new measures to evaluate explanations borrowed from the field of algorithmic stability: mean generalizability MeGe and relative consistency ReCo. We conduct extensive experiments on different network architectures, common explainability methods, and several image datasets to demonstrate the benefits of the proposed measures. In comparison to ours, popular fidelity measures are not sufficient to guarantee trustworthy explanations. Finally, we found that 1-Lipschitz networks produce explanations with higher MeGe and ReCo than common neural networks while reaching similar accuracy. This suggests that 1-Lipschitz networks are a relevant direction towards predictors that are more explainable and trustworthy.
AC-VC: Non-Parallel Low Latency Phonetic Posteriorgrams Based Voice Conversion

This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic posteriorgrams based voice conversion system that can perform any-to-many voice conversion while having only 57.5 ms future look-ahead. The complete system is composed of three neural networks trained separately with non-parallel data. While most of the current voice conversion systems focus primarily on quality irrespective of algorithmic latency, this work elaborates on designing a method using a minimal amount of future context thus allowing a future real-time implementation. According to a subjective listening test organized in this work, the proposed AC-VC system achieves parity with the non-causal ASR-TTS baseline of the Voice Conversion Challenge 2020 in naturalness with a MOS of 3.5. In contrast, the results indicate that missing future context impacts speaker similarity. Obtained similarity percentage of 65% is lower than the similarity of current best voice conversion systems.
Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment

We determine information theoretic conditions under which it is possible to partially recover the alignment used to generate a pair of sparse, correlated Erdős-Rényi graphs. To prove our achievability result, we introduce the k-core alignment estimator. This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of k. We prove a matching converse bound. As the number of vertices grows, recovery of the alignment for a fraction of the vertices tending to one is possible when the average degree of the intersection of the graph pair tends to infinity. It was previously known that exact alignment is possible when this average degree grows faster than the logarithm of the number of vertices.
Use of a sine cosine algorithm combined with Simpson method for numerical integration

The sine cosine algorithm (SCA) is one of the most recent nature-inspired meta-heuristic optimisation algorithm, which the mathematical model based on sine and cosine functions. SCA has validated excellent performance in solving continuous problems and engineering optimisation problems. In this paper, we propose a new algorithm that encompasses the features of sine cosine algorithm and Simpson method (SCA-SM). The proposed procedure consists of two phases: in the first phase, the of sine cosine algorithm are used to find the optimal segmentation points on the integral interval of an integrand. In the second phase, the approximate integral value of the integrand is then calculated by a Simpson method. Numerical simulation results show that the algorithm offers an effective way to calculate numerical value of definite integrals, and it has a high convergence rate, high accuracy and robustness.
Design and Implementation of Transformation and Non-Chaotic Substitution Based Image Cryptosystem

In this article, a Modified Pseudo Hadamard Transformation (MPHT) and non-Chaotic substitution based image encryption scheme has been proposed. Due to intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, images are more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using MPHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for secrete images. An average 99.6089% of Number of Pixel Changing Rate (NPCR) and 33.4328% of Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing techniques
Hardware-based cyber threats: attack vectors and defence techniques

There are certain vulnerabilities associated with computing hardware that attackers can exploit to launch destructive attacks which often go undetected by the existing hardware and software countermeasures. Side channel attacks (SCAs) and Rowhammer attacks (RHAs), the consequences of hardware vulnerabilities, pose significant security and privacy threats to self-contained computing components and their end-users respectively. Such attacks compromise the security of computational environments, even those with advanced protection mechanisms such as virtualisation, sandboxes or robust encryptions. In light of these security threats against modern computing hardware, we perform an analysis overview of the modi operandi of SCAs and RHAs in hardware implementation and techniques that can be used to extract sensitive data such as secret keys. We then propose various countermeasures to safeguard against these attacks.
Machine Learning Algorithms

You do not need a background in algebra and statistics to get started in machine learning. The previous chapter introduced key foundation principles. Be under no illusions; mathematics is a huge part of machine learning. Mathematics is key to understanding how the algorithm works and why coding a machine learning project from scratch is a great way to improve your mathematical and statistical skills.
Parallel big image data retrieval by conceptualised clustering and un-conceptualised clustering

Content-based image retrieval is a hot topic which has been studied for few decades. Although there have been a number of recent studies proposed on this topic, it is still hard to achieve a high retrieval performance for big image data. To aim at this issue, in this paper, we propose a parallel content-based image retrieval method that efficiently retrieves the relevant images by un-conceptualised clustering and conceptualised clustering. For un-conceptualised clustering, the un-conceptualised image data is automatically divided into a number of sets, while the conceptualised image data is divided into multiple sets by conceptualised clustering. Based on the clustering index, the depth-first-search strategy is performed to retrieve the relevant images by parallel comparisons. Through experimental evaluations on a large image dataset, the proposed approach is shown to improve the performance of content-based image retrieval substantially in terms of efficiency.
Cryptocurrencies

A cryptocurrency is a digital or virtual currency, which is secured by cryptography that makes it impossible to double-spend on a distributed network. There are more than 1600 cryptocurrencies present in today's world that can be used for making payment transactions. Some cryptocurrencies like bitcoin, bitcoin cash, LTC, LINK, tether, etc. are based on decentralized networks like blockchain technology, which is a distributed ledger imposed by distinct nodes of the network. A blockchain behind cryptocurrencies is a public ledger, which is used to store the history of every transaction that cannot be tampered or changed. This makes the transactions secure and safer than the existing systems. But some blockchains do not use any cryptocurrency or tokens. So, it varies significantly that depends on the type of blockchain. In this chapter, we have described the functionality of some cryptocurrencies like bitcoin, ETH, XRP, LTC, USDT, BCH, LIBRA, XMR, EOS, BSV, and many others.
Bayesian Optimization

In Chapters 2 and 3 we explored several hyperparameter tuning methods. Grid search and random search were quite straightforward, and we discussed how to distribute them to save memory and time. We also delved into some more-complex algorithms, such as HyperBand. But none of the algorithms that we reviewed learned from their previous history. Suppose an algorithm could keep a log of all the previous observations and learn from them. For example, suppose it could observe that our model is being optimized near certain values of hyperparameters and could exploit this valuable information and proceed to the hyperparameters nearest to those good-performing ones, hence learning from its history. By doing so, the algorithm would not waste time on bad-performing hyperparameters while reaching the best-performing hyperparameters. In this chapter we’ll explore algorithms that have that capability.
Research on application of artificial intelligence model in automobile machinery control system

To improve the kinematics and ride comfort of the vehicle suspension system, this paper combines the ADAMS software with the artificial intelligence optimisation algorithm to simulate and optimise the suspension system of a certain type of off-road vehicle. The front suspension of a 1/2 virtual prototype model is established in ADAMS/view, and the kinematics analysis of the prototype model is made. In order to improve the kinematics characteristics of the front suspension, the genetic algorithm and the immune algorithm are used to optimise the positioning parameters of the front suspension based on the kinematics analysis of the suspension. The dynamic model of the whole vehicle is established in ADAMS/view, and the vehicle ride comfort is simulated and calculated. The spring stiffness and shock absorber damping of the rear suspension are optimised by using the immune algorithm, and the purpose of improving the ride comfort of the vehicle is realised.