Reviewerly/exHarmony huggingface dataset:
Application of Blockchain Technology in Cryptocurrency

This review focuses on blockchain technology, and its application and common problem with reference solution. The blockchain technology is nascent and complex and involves many different fields, which leads to the development of cryptocurrency. However, the crptocurrency has high volatility that demands prompt solution. Deep learning technology is considered as a promising approach to address this issue. After research, this paper develops four models with high efficiency and accuracy, including NLANN, JNN. LSTM and GRN to realize prediction in crptocurrency.
S-UbiTap: Leveraging Acoustic Dispersion for Ubiquitous and Scalable Touch Interface on Solid Surfaces

As various computing devices, such as smartphones, IoT devices, smart speakers etc, becomes omnipresent in our daily lives, interest in ubiquitous computing interfaces is increasing. In response to this, various studies have introduced on-surface input techniques that leverage the surface of surrounding objects as touch interfaces. However, most of them struggle to support ubiquitous interaction due to their dependency on specific hardware or environments. In this work, we propose <inline-formula><tex-math notation="LaTeX">${{\sf S \text{-}UbiTap}}$</tex-math></inline-formula> , an input method that turns any flat solid surface into a touch input space by listening to sound (i.e., with microphones already present in the commodity devices). More specifically, we develop a novel touch localization technique that leverages the physical phenomenon, called <i>dispersion</i> , which is the characteristic of sound as it travels through solid surfaces, and address the challenges that limit existing acoustic-based solutions in terms of portability, accuracy, usability, robustness, scalability, and responsiveness. Our extensive experiments with a prototype of <inline-formula><tex-math notation="LaTeX">${{\sf S \text{-}UbiTap}}$</tex-math></inline-formula> show that we can support sub-centimeter accuracy on various types of surfaces with minor user calibration effort. In addition, the accuracy is maintained even when the size of the touch input space increases. In our experience with real-world users, <inline-formula><tex-math notation="LaTeX">${{\sf S \text{-}UbiTap}}$</tex-math></inline-formula> significantly improves usability and robustness, thus enabling the emergence of more exciting applications.
Enhance Data Security in Cloud Computing with Digital Signature &amp; Hybrid Cryptographic Algorithm

The proposed paper focuses on a hybrid cryptographic algorithm that aims to provide integrity &#x0026; confidentiality to our sensitive data. With the use of cryptographic algorithms and a blended key exchange algorithm along with an authentication technique, verification of data can be protected while uploading it to any cloud platform. The cloud computing boom is ultimately on the pinnacle because of the pandemic that forces corporations to operate virtually and people to stay indoors for their work from home. Cloud computing offers advantages like on-demand services, shared hardware and software, but also involves storing data at unknown place. So they are sometimes susceptible to leaks of data or other malicious internet attacks, making it a serious worry for enterprises all over the world. The objective of this paper is to explore the different algorithms utilised for security, performance and finally to identify the most adequate and efficient combination. The emphasis is on finding the practical consequences of the presented results and not merely on theoretical principles.
A Translator for Indian Sign Boards to English using Tesseract and SEQ2SEQ Model

Language translator for Indian language to English have been developed and it have proven to a challenging domain due to large combination of character in Indic scripts such as Tamil, Kannada and Hindi. In this paper we propose a system which captures Indian printed character and translates it into English, we have discussed the various method and machine learning model that was used to build this system with an accuracy of 87&#x0025;.
TENAS: Using Taylor Expansion and Channel-Level Skip Connection for Neural Architecture Search

There is growing interest in automating designing good neural network architectures. The NAS methods proposed recently have significantly reduced the architecture search cost by sharing parameters, but there is still a challenging problem in designing search space. The existing operation-level architecture search methods require a large amount of computing power or designing the search space of operations very carefully. In this paper, we investigate the possibility of achieving competitive performance with them only using a small amount of computing power and without designing search space carefully. We propose TENAS using Taylor expansion and only a fixed type of operation. The resulting architecture is sparse in terms of channel and has different topology at different cells. The experimental results for CIFAR-10 and ImageNet show that a fine-granular and sparse model searched by TENAS achieves very competitive performance with dense models searched by the existing methods.
On-Demand Fast Trading on Decentralized Exchanges

We build a model to show that decentralized exchanges (DEX) require less computing power on average than traditional exchanges to accommodate the demand for high-speed trading services. Centralized exchanges acquire excess processing capacity to accommodate activity surges: The idle capacity's opportunity cost is an externality of low-latency trading. On DEXs, HFTs bid on gas fees in real-time to acquire time priority from a network of miners. The price of speed surges as HFTs compete during activity bursts. HFT-driven demand for speed peaks higher on DEX, but spans a shorter time interval. On aggregate, DEX infrastructure is more cost-efficient.
Consolidating Industrial Small Files Using Robust Graph Clustering

Small file management is widely encountered in industrial areas. Consolidating small files can benefit the performance of the data management system. Many existing consolidation solutions fail to realize the importance of a proper consolidation schema. Therefore, they use very primitive and ineffective schemas. In this paper, we focus on proposing an effective and robust consolidation schema. Unlike most of the existing solutions that only focus on the historical workload, we consider the workload uncertainty issue and propose a graph-clustering-based solution that is more robust to workload uncertainty in the future. To do this, we introduce robust optimization, a mathematical model that provides theoretical support for solving uncertainty issues. Then, we demonstrate that the robustness of the consolidation schema can be achieved using a graph clustering algorithm with a duplication mechanism. Since duplication leads to data redundancy, we propose a parameter to control the redundancy and the robustness of the schema. We also propose two algorithms to estimate the parameter automatically. Experimental results on both the synthetic and real-life data sets show the effectiveness of our algorithm.
An Interface-Oriented Data-Driven Scheme Applying into Eutectic Patterns Evolution

Solid-liquid interface dynamics plays a crucial role in microstructure evolution during solidification. However, it remains a challenge to quantify this process in a quick and low-dimensionally visualized manner. In this study, we proposed an interface-oriented two-point statistics method to solve the unbiassed statistics of the special information in the irregular and non-periodic microstructures, and then developed a data-driven scheme based on image processing technologies, interface-oriented two-point statistics and principal component analysis to address this goal. The proposed scheme can realize automatic extraction, accurate quantification and convenient visualization of the dynamic information during solidification. Taking eutectic solidification patterns simulated by phase-field method as an example, we verified the reliability and efficiency of this scheme. In addition, through the dynamic analysis of eutectic patterns evolution obtained by this data-driven scheme, the effect of anisotropic interface energy on the eutectic pattern evolution was uncovered. The results revealed that, compared to the case of isotropic interface energy, interfacial energy anisotropy not only prolongs the time of patterns transformation, but also narrows the spacing range between elimination and creation of lamellae. Our proposed scheme is also able to be applied into more occasions about microstructure evolution in different materials systems.
Experimental Verification of the Modified PSA Algorithm

Being abundant and non-polluted, solar energy is an increasingly desirable energy source. Growing research is concerned about maximum solar energy acquisition from solar panels, especially in the automatic sun-tracking of the solar panels. This paper presents a modified algorithm based on the &#x201C;PSA Algorithm&#x201D; proposed in the 2001. And presents a new automatic solar tracking system with a two-axis is designed based on the algorithm. The PSA modified algorithm is used to calculate the sun&#x2019;s horizontal azimuth and zenith angle combined with time, longitude, latitude, and other information. We designed a prototype mainly composed of a microcontroller, a Global Positioning System (GPS) module, a digital compass, a tilt sensor, a 555 timer, and gates, optical couples, an H-bridge and two stepper motors. Then using the proposed prototype, a series of experiments are conducted in Tianjin City. Measured zenith angle values were collected and recorded based on the PSA modified algorithm and the original PSA algorithm, compared with the actual Zenith Angle of the Sun in Tianjin City. The comparison curves of Zenith Angle show that the measurement accuracy of the modified PSA algorithm is higher than that of the PSA original algorithm.
A Light-Weight Data Augmentation Method for Fault Localization

Context: Fault localization (FL) is essentially a search over the space of program statements to find suspicious entities that might have caused a program failure. However, the real-world programs are high dimensional and imbalanced, which limits the effectiveness and efficiency of existing FL methods. The state-of-the-art FL method (Aeneas) solves the imbalanced and high-dimensional problem but in a complex and time-consuming process.Objective: Due to the limited effectiveness of original FL methods and the low efficiency of the state-of-the-art data augmentation method Aeneas, this paper proposes Lamont , a L ight-weight d a ta aug m entati on me t hod to improve the effectiveness of original FL methods and the efficiency of Aeneas.Method: Lamont uses revised linear discriminant analysis (LDA) to reduce the dimensionality of the original coverage matrix and leverage synthetic minority over-sampling (SMOTE) to generate the synthesized failing tests. The balanced coverage matrix with reduced dimensionality is fed into FL algorithms to obtain the ranked suspicious list of statements. To evaluate the efficiency and effectiveness, we compare Lamont with six representative FL methods and Aeneas on 458 versions of 10 real-life programs.Results: It can be observed that Lamont outperforms in most cases for Top-K metric and reduces the number of statements that need to be checked from 17.45% to 79.81% compared with the original six FL methods. Furthermore, Lamont saves the time over the state-of-the-art data augmentation method Aeneas from 55.33% to 68.39% with comparable effectiveness.Conclusion: This work conducts a large-scale experimental study to investigate the effectiveness and efficiency of Lamont . Two conclusions can be obtained based on the experimental results. First, it shows that Lamont is more effective than the original FL methods. Second, it shows Lamont is more efficient than Aeneas with similar effectiveness in six FL methods.
A Pathway Model to Five Lines of Accountability in Cybersecurity Governance

In an in-depth field study, we investigate cybersecurity governance configurations vis-à-vis five lines of accountability (5 LoA) - that is, the Three Lines Model extended by the accountability of executive management and the board of directors. The aim is to explore the combined functioning of the 5 LoA by outlining the accountabilities organizations assign to various roles and how these roles interact to progress cybersecurity. We define the level of the 5 LoA adoption by: (i) the segregation of duties that spans from blended to segregated and (ii) the level of engagement in the line roles that ranges from reactive to proactive. We theorize how the level of the 5 LoA adoption is affected by the interplay of institutional forces and organizations’ need for efficiency and effectiveness and develop a pathway model towards the 5 LoA adoption over time. Organizations closest to full adoption are those under the prudential regulation (coercive forces), whereas efficiency motives and mimetic forces drive organizations to seek fluidity by ‘blending’ the segregated lines to ensure fast reactions to changing environment. Regardless of the number of lines and their blending, we found that all organizations see scope to improve engagement in the 5 LoA.
Political Sentiment and Innovation: Evidence from Patenters

We document political sentiment effects on inventors in the US. Democrat patenters are more likely to patent (relative to Republicans) after the election of Barack Obama but less likely to patent following the election of Donald Trump. These effects are 2-3 times as strong among active partisans (those that vote and donate), are present even within firms over time, and are detectable up to six years post election. We also find a large drop in patenting by immigrant inventors (relative to non-immigrants) following the election of Trump. Finally, we show partisan concentration by technology class and firm. For example, Republicans outnumber Democrats 3-to-1 in weapons patenting, but are outnumbered by Democrats 5-to-1 at Google.
Ethical Use of Algorithms in Times of Uncertainty and Crisis: The Case of High-Frequency Trading

The widespread implementation of algorithmic trading technologies in the financial market prompts questions about ethics. There is sparse literature analyzing the ethical implications of algorithms implemented in institutional trading. The current paper includes three main ethical concerns regarding algorithms: transparency and accountability, fairness, and what I refer to as "uncertainty management." This new perspective of ethical concern emphasizes that algorithms intensify existing crises and high uncertainty and create more uncertainty by generating bugs and errors. The proposed uncertainty management model includes five stages, including the essential step of creating possible scenarios of the algorithm’s involvement in an environment saturated with uncertainty. The model sketches an ethical map that considers the algorithm’s features and the different market situations in which the algorithm is developed and operated.
Object-Centric Quality Control and Aggregation of Image Segmentations in Crowdsourcing Setups

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Object-Centric Quality Control and Aggregation of Image Segmentations in Crowdsourcing Setups 20 Pages Posted: 4 Aug 2022 See all articles by Saber Mirzaee BaftiSaber Mirzaee BaftiUniversity of KentChee Siang Angaffiliation not provided to SSRNGianluca Marcelliaffiliation not provided to SSRNPanote SiriarayaKyoto Sangyo University Abstract
Host Load Prediction in Cloud Computing with Discrete Wavelet Transformation (Dwt) and Bidirectional Gated Recurrent Unit (Bigru) Network

Providing pay-as-you-go storage and computing services have contributed to the widespread adoption of cloud computing. Using virtualization technology, cloud service providers can execute several instances on a single physical server, maximizing resource utilization. A challenging issue in cloud data centers is that available resources are rarely fully utilized. The server utilization rate is poor and often below 30%. An accurate host workload prediction enhances resource allocation resulting in more efficient resource utilization. Recently, numerous methods based on deep learning for predicting cloud computing workload have been developed. An efficient strategy must predict long-term dependencies on nonstationary host workload data and be quick enough to respond to incoming requests. This study employs a Bidirectional Gated-Recurrent Unit (BiGRU), Discrete Wavelet Transformation (DWT), and an attention mechanism to improve the host load prediction accuracy. DWT is used to decompose input data into sub-bands with different frequencies and to extract patterns from nonlinear and nonstationary data in order to improve prediction accuracy. The extracted features are fed into BiGRu to predict future workload. The attention mechanism is used in order to extract the temporal correlation features. This hybrid model was evaluated with cluster data sets from Google and Alibaba. Experimental results reveal that our method improves prediction accuracy by 3% to 56% compared to a variety of state-of-the-art methods.
An Efficient Storage Structure and Management for Distributed Ledgers in Blockchain Systems: An Exploration Based on Purely Theoretical Approach

In this paper, an efficient data structure for storage, management, and security in blockchain systems is presented. This efficient structure is based on the AVL (Adelson-Velsky and Landis) tree and make an extension for the purpose of management and security. We first present a new storage structure for efficient data search based on the AVL tree. Next we expand this data structure and apply to the management and security of the blockchain ledger. The way of management for distributed account/data is divided into two operations, namely splitting and merging. This paper also makes a discussion about the significance of these two operations. We show a detailed description and make mathematical analysis with proof for the performance of all mechanisms proposed in this paper. In addition, a mechanism of dual authentication is also proposed and to provide a better tamper-proof.
An Improved Retinex Method for Low Light Image Enhancement

Enhancement of low-light image is difficult because it must account for not just brightness recovery but also more sophisticated concerns such as colour distortion and noise that are often hidden in dark. Simply increasing the brightness of a low light image can accentuate such artefacts. Image enhancement in low-light circumstance from the surveillance system plays a very important role for the security purpose. This is an active research topic, where many algorithms are proposed for magnifying the intensity of dark images. To overcome this issue a unique end-to-end attention-guided technique based on retinex is proposed. Here many effective image enhancement methods are ground on retinex theory. Subsequently diverse algorithms are used such as single scale retinex (SSR), multiscale retinex (MSR), multiscale retinex with color restoration (MSRCR) models. Â color space model is applied with retinex algorithm. To enhance the quality and brightness of image; gamma correction with multiple values are used before applying Improved Multi Scale Retinex with CIELAB color (IMSRLab) on image. Extensive tests on standard LOL datasets show that our technique can deliver high fidelity enhancement outcomes for lowlight images, and that it outperforms existing state-of-the-art methods both statistically and visually.
Integration of IMEI, RSA, and Signature to Secure Communication in Mobile Applications

The mobile instant messaging applications have become a famous wireless service in the world allowing a user to communicate with anyone in anywhere. Consequently, the manipulation of the user data creates a pressing need for securing the information and the associated technologies. The protection of these communications has become a paramount objective. The concept of cryptography has become a basic concept in communication and the exchange of messages within companies. This paper presents a hybrid security system allowing messages to be exchanged within a company in a secure manner. Our goal is to provide end-to-end encryption through the combination of asymmetric cryptography, signature, and integration of IMEI parameter. The confidentiality of the messages is ensured by the RSA algorithm, while the integrity of these messages and the sender authentication are ensured by the digital signature, that is carried out with the SHA-2 hash function.
Broad-Bandwidth Photonic Quantum Memory

Photonic quantum memories will play an essential role in synchronizing nodes of quantum networks. Here we present a system that can store polarization and time-bin qubits for up to 12.5 µ s over a THz bandwidth.
Quantum limits to polarization measurement of classical light

I derive ultimate lower bound on the precision of polarization estimation for classical coherent light. I show that it is attainable with conventional detection schemes and discuss role of the global phase in this problem.
Becoming Vincent: Using the “hero’s journey” to connect and design (digital) tourist experiences along Vincent van Gogh’s heritage locations

Abstract This case describes a research and design project, commissioned to Breda University of Applied Sciences (the Netherlands) by several regional tourism organizations in Brabant, a province in the south of the Netherlands. The aim of the project was to draft a narrative concept and storylines that would link and upgrade the various Vincent Van Gogh heritage sites in the area in order to eventually attract international tourists to the province, especially in view of the commemoration of Van Gogh’s 125 th death anniversary in 2015. In the creative phase, we used a 12 steps storytelling model ( Bouma, 2010 ). This is based on Campbell’s “monomyth” or “hero’s journey” (1945). It applies to many great tales and chronologically orders the (metaphorical) steps that drive the “hero” in his actions. We compared the 12 steps to Vincent’s life. The first 6 steps, which took place in Brabant, we placed under the overarching narrative concept of “Becoming Vincent”. At various locations in the province, tourists can experience how the events in Vincent’s early life led him to become the tormented yet brilliant artist so well-known from his time in France (which we defined as “Being Vincent”), thus making a full narrative circle. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © Licia Calvi &amp; Moniek Hover 2022
On Parallel or Distributed Asynchronous Iterations with Unbounded Delays and Possible Out of Order Messages or Flexible Communication for Convex Optimization Problems and Machine Learning

We describe several features of parallel or distributed asynchronous iterative algorithms such as unbounded delays, possible out of order messages or flexible communication. We concentrate on the concept of macro-iteration sequence which was introduced in order to study the convergence or termination of asynchronous iterations. A survey of asynchronous iterations for convex optimization problems is also presented. Finally, a new result of convergence for parallel or distributed asynchronous iterative algorithms with flexible communication for convex optimization problems and machine learning is proposed.