Reviewerly/exHarmony huggingface dataset:
The Vulnerable Nature of Decentralized Governance in DeFi

Decentralized Finance (DeFi) platforms are often governed by Decentralized Autonomous Organizations (DAOs) which are implemented via governance protocols. Governance tokens are distributed to users of the platform, granting them voting rights in the platform's governance protocol.Many DeFi platforms have already been subject to attacks resulting in the loss of millions of dollars in user funds.
CONVERSATIONS: Conversation with a Prominent Propagator: Carl Haynes-Magyar

interview Share on CONVERSATIONS: Conversation with a Prominent Propagator: Carl Haynes-Magyar Authors: David P. Bunde Knox College Knox CollegeSearch about this author , Zack Butler Rochester Institute of Technology Rochester Institute of TechnologySearch about this author , Christopher L. Hovey University of Colorado Boulder University of Colorado BoulderSearch about this author , Cynthia Taylor Oberlin College Oberlin CollegeSearch about this author Authors Info & Claims ACM InroadsVolume 14Issue 4December 2023pp 12–16https://doi.org/10.1145/3616016Published:27 November 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Can Blockchain Technology Bring any Value to Education?

research-article Share on Can Blockchain Technology Bring any Value to Education? Authors: Gary K. W. Wong The University of Hong Kong The University of Hong KongSearch about this author , John H. M. Huen Koding Kingdom (Hong Kong) Limited Koding Kingdom (Hong Kong) LimitedSearch about this author Authors Info & Claims ACM InroadsVolume 14Issue 4December 2023pp 73–77https://doi.org/10.1145/3623272Published:27 November 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Acknowledgment to reviewers

International Journal of Applied Psychoanalytic StudiesEarly View ACKNOWLEDGEMENT TO REVIEWERS Acknowledgment to reviewers First published: 27 November 2023 https://doi.org/10.1002/aps.1857Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Optimization of Deep CNN Techniques to Classify Breast Cancer and Predict Relapse

Breast cancer is a fatal disease that has a high rate of morbidity and mortality. Finding the right diagnosis is one of the most crucial steps in breast cancer treatment. Doctors can use machine learning (ML) and deep learning techniques to aid with diagnosis. This work makes an effort to devise a methodology for the classification of Breast cancer into its molecular subtypes and prediction of relapse. The objective is to compare the performance of Deep CNN, Tuned CNN and Hypercomplex-Valued CNN, and infer the results, thus automating the classification process. The traditional method used by doctors to detect is tedious and time consuming. It employs multiple methods, including MRI, CT scanning, aspiration, and blood tests as well as image testing. The proposed approach uses image processing techniques to detect irregular breast tissues in the MRI. The survivors of Breast Cancer are still at risk for relapse after remission, and once the disease relapses, the survival rate is much lower. A thorough analysis of data can potentially identify risk factors and reduce the risk of relapse in the first place. A SVM (Support Vector Machine) module with GridSearchCV for hyperparameter tuning is used to identify patterns in those patients who experience a relapse, so that these patterns can be used to predict the relapse before it occurs. The traditional deep learning CNN model achieved an accuracy of 27%, the tuned CNN model achieved an accuracy of 92% and the hypercomplex-valued CNN achieved an accuracy of 98%. The SVM model achieved an accuracy of 89% and on tuning the hyperparameters by using GridSearchCV it achieved and accuracy of 98%.
From 65 to 103, Older Adults Experience Virtual Reality Differently Depending on Their Age: Evidence from a Large-Scale Field Study in Nursing Homes and Assisted Living Facilities

There is growing interest in applications of virtual reality (VR) to improve the lives of older adults, but the limited research on older adults and VR largely treats older adults as a monolith, ignoring the substantial differences across 65 to 100+ year olds that may affect their experience of VR. There are also few existing studies examining the experiences and challenges facing those who facilitate VR for older adults (e.g., caregiving staff). We address these limitations through two studies. In study 1, we explore variation within older adults' experiences with VR through a field study of VR use among a large (N = 245) and age-diverse (Mage = 83.6 years, SDage = 7.9, range = 65–103 years) sample of nursing home and assisted living facility residents across 10 U.S. states. Age was negatively associated with the extent to which older adults enjoyed VR experiences. However, the negative relationship between age and older adults' attitudes toward VR was significantly less negative than the relationship between age and their attitudes toward other technologies (cell phones and voice assistants). In study 2, we surveyed caregiving staff (N = 39) who facilitated the VR experiences for older adult residents and found that the caregiving staff generally enjoyed the activity relative to other activities and felt it to be beneficial to their relationship with residents.
Adaptive Pricing and Online Scheduling for Distributed Machine Learning Jobs

Large-scale distributed machine learning (ML) systems involve extensive and costly computational resources. Pricing and scheduling, as two promising techniques for resource management, have garnered significant attention. However, existing job pricing and scheduling algorithms in cloud computing either charge fixed resource fees based on known job runtime or implement dynamic price setting with job preemption, unsuitable for distributed ML systems with high uncertainties and switching cost. First, whether the resources of a distributed ML job are placed together or not results in different job runtime. Second, various time-varying factors, including job arrival rates and competitors’ pricing, affect resource prices. Third, frequent price changes for the same resource can easily lead to system instability, ultimately jeopardizing user satisfaction. Addressing these uncertainties is challenging. This paper introduces APOS, an adaptive pricing and online scheduling framework, aiming at maximizing the operator’s overall revenue. APOS incorporates two innovations. 1) Intelligent Pricing: We represent each price using a feature vector that encapsulates relevant factors. Subsequently, based on the linear Upper Confidence Bound (UCB) techniques, we establish relationships between price features and two revenue-associated elements: job arrival rates and resource consumption rates. To ensure system stability, we introduce batch pricing to reduce the frequency of resource price updates. 2) Online Scheduling: We strive to compute a non-preemptive schedule that balances job utility with corresponding resource cost. We rigorously prove that APOS achieves truthfulness, individual rationality, system stability, and sublinear regret in polynomial time. Finally, extensive trace-driven simulations confirm that APOS outperforms four state-of-the-art baselines, yielding a minimum of 23.3% improvement in total operator revenue.
Continuous entanglement distribution from an AlGaAs-on-insulator microcomb for quantum communications

Using an aluminum gallium arsenide microring resonator, we demonstrate a bright quantum optical microcomb with &gt;300 nm (&gt;40 THz) bandwidth and more than 20 sets of time–energy entangled modes, enabling spectral demultiplexing with simple, off-the-shelf commercial telecom components. We report high-rate continuous entanglement distribution for two sets of entangled-photon pair frequency modes exhibiting up to 20 GHz/mW 2 pair generation rate. As an illustrative example of entanglement distribution, we perform a continuous-wave time-bin quantum key distribution protocol with 8 kbps sifted key rates while maintaining less than 10% error rate and sufficient two-photon visibility to ensure security of the channel. When the &gt;20 frequency modes are multiplexed, we estimate &gt;100 kbps entanglement-based key rates or the creation of a multi-user quantum communications network. The entire system requires less than 110 µW of on-chip optical power, demonstrating an efficient source of entangled frequency modes for quantum communications. As a proof of principle, a quantum key is distributed across 12 km of deployed fiber on the University of California Santa Barbara (UCSB) campus and used to encrypt a 21 kB image with &lt;9% error.
A Synthesized Voice Discrimination Method Using Characteristic Sounds Spoken by Humans

In this paper, to prevent victimization of crimes such as impersonation fraud using synthesized voice, we propose a method to determine whether the speaker is a human or a synthesized voice using characteristic sounds spoken by humans. In our proposed method, the input voice data is first segmented into regular intervals, and the segmented data is further converted into spectrogram images. The converted images are classified into the four characteristic sounds defined in this paper using a CNN. Using the relationship between breath sounds, saliva sounds, and speech duration in human voice and synthesized voice, which were investigated in advance, the speaker of the input voice data is discriminated as human or synthesized voice. We evaluate our proposed method using actual human voice and synthesized voice generated by Japanese speech synthesis tools, and show that the proposed method shows high discrimination results.
Design and Prototyping of a Low-Cost Autonomous Vehicle

This paper presents the prototyping of a low-cost autonomous vehicle using a children’s toy car as a platform and a GPS receiver for navigation. The prototype utilizes concepts such as Ackerman’s geometry and circle calculations to determine the optimal trajectory for autonomous navigation along a predefined path. The results demonstrate the feasibility of developing a cost-effective autonomous vehicle using minimal components. The findings underscore the potential of utilizing a simple GPS receiver for navigation purposes. This work serves as a foundation for future studies aiming to optimize the performance and reliability of low-cost autonomous vehicles, contributing to advancements in autonomous vehicle technology.
Enhancement of Dynamic Output Timing Control of Fragrance in Metaverse

In this paper, we enhance the dynamic output timing control of fragrance to extend its application to metaverse. This control dynamically adjusts the timing of fragrance output based on the video's playback speed (i.e., movement speed of user) and positions of fragrance sources. We also carry out QoE (Quality of Experiment) to clarify the parameters for the enhanced control.
The Effect of Solar Radiation on Solar Panels in Aeroponic Plant Systems

The sun is a large, continuous source of energy available to nature. Therefore, one of the uses of solar energy is to use a technology called solar panels or photovoltaic which can convert solar energy into electrical energy to meet human needs. The use of solar panels can encourage technological advances, one of which is in agriculture in order to produce an agricultural system that is environmentally friendly and has efficiency in the use of electrical energy. The model and further analyze the relationship between the effect of solar radiation intensity on the output power of solar panels as a source of electrical energy in aeroponic plants. The method used in this study uses a systematic observation method that is used to measure sunlight into solar radiation by making direct observations of the intensity of sunlight at a certain time using a light intensity meter which can then be converted to solar radiation intensity using a formula or conversion tool that has been determined. The results of the study are that solar radiation greatly influences the power generated in solar panels because solar radiation is a process of propagation of energy (heat) in the form of electromagnetic waves. So, when the photovoltaic process occurs, the higher the solar radiation value, the higher the output power generated by the solar panel. In the overall results of the research conducted for 7 days, when the solar radiation is high at 941.6 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the resulting output power is also high at 27.75 Watt. Conversely, when the solar radiation value is low, around 558.38 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the output power is also low at 18.83 Watt.
A Novel Approach to Reduce A Visual Gap Using Inpainting GAN

Image blending represents a technique employed in image composition, aimed at creating a composite image that appears as natural and realistic as possible. The objective of image blending is to ensure that the transitions between objects in the image appear seamless and without any color distortion. Recently, numerous studies investigated image blending methods adopting deep learning-based image processing algorithms and contributed to generating natural blended images. Although the previous studies show remarkable performance in many cases, they suffer from quality drop when blending incompletely cropped object. This paper introduces a novel approach that effectively reduces the unnatural edges and the color distortion in such cases. In this study, an algorithm which called edge checking (EC) is proposed to identify and manage incompletely cropped regions. To address missing or masked parts of the object image and integrate them into the image blending process, an inpainting generative adversarial model are employed.
A Two-stage Heterogeneous Ensemble Learning Algorithm Based on MLP-SVR-LGBM

A single machine learning approaches often encounter numerous challenges and limitations when addressing complex problems. In this study, we propose a novel stacking ensemble learning model for heterogeneous soft sensors, utilizing multi-layer perceptron (MLP), support vector regression (SVR), and light gradient boosting machine (LGBM) as base learners, with gradient boosting decision tree (GBDT) as the meta-learner. First, the proposed heterogeneous soft sensor model fully considers the diversity of the base learners and their learning performance to effectively exploit the advantages of various machine learning methods. Second, the meta-learner, GBDT, is an integrated learning algorithm that can better handle different data of the base learners and improve the performance of the overall model. Subsequently, we demonstrate the superiority of the proposed method is confirmed through an artificial dataset case study.
Machine Learning Model for Predicting Mortality in Heart Failure Patients Using Electronic Health Records and Exome Sequencing Data

Heart failure is a cardiovascular disorder in which the heart’s contraction and relaxation do not function properly, resulting in an inadequate supply of the blood to the body. Despite advancements in medical technology, predicting the mortality of heart failure patients remains challenging as it is influenced by various factors such as the patient’s lifestyle, medical history or genetic traits. Recently, machine learning based on Electronic Health Record (EHR) data to predict the mortality of heart failure patients has been arising [1]. However, even though genetic data holds the potential to influence patient mortality, it remains difficult to find cases where genetic data has been used to predict patient outcomes due to the complexity of data understanding and processing. In our study, we trained a machine learning model to predict the mortality of heart failure patients and improved its performance by incorporating Whole Exome Sequence (WES) data.
Empowering Partnerships in Business: a System for Real-Time Data Integration and Collective Analysis

In the dynamic and interconnected business landscape, the need for real-time access to consolidated data is paramount. This study aims to develop a software system that facilitates data integration and collaborative analysis among partners within a business ecosystem. The system not only enables instant data sharing but also empowers partners to collectively address crucial business process issues. Noteworthy is its feature of considering individual ownership shares, enhancing transparency, and fostering collaboration. By providing partners with relevant insights, the system ensures informed decision-making. The proposed software system stands as a progressive solution to the evolving demands of modern business, offering an agile and comprehensive approach to achieving shared success.
Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning

A UUV can perform tasks such as underwater surveillance, reconnaissance, surveillance, and tracking by being equipped with sensors and different task modules. Due to the complex underwater environment, the UUV must have good collision avoidance planning algorithms to avoid various underwater obstacles when performing tasks. The existing path planning algorithms take a long time to plan and have poor adaptability to the environment. Some collision-avoidance planning algorithms do not take into account the kinematic limitations of the UUV, thus placing high demands on the performance and control algorithms of UUV. This article proposes a PPO−DWA collision avoidance planning algorithm for the UUV under static unknown obstacles, which is based on the proximal policy optimization (PPO) algorithm and the dynamic window approach (DWA). This algorithm acquires the obstacle information from forward-looking sonar as input and outputs the corresponding continuous actions. The PPO−DWA collision avoidance planning algorithm consists of the PPO algorithm and the modified DWA. The PPO collision avoidance planning algorithm is only responsible for outputting the continuous angular velocity, aiming to reduce the difficulty of training neural networks. The modified DWA acquires obstacle information and the optimal angular velocity from the PPO algorithm as input, and outputs of the linear velocity. The collision avoidance actions output by this algorithm meet the kinematic constraints of UUV, and the algorithm execution time is relatively short. The experimental data demonstrates that the PPO−DWA algorithm can effectively plan smooth collision-free paths in complex obstacle environments, and the execution time of the algorithm is acceptable.
Fast calibration based photometric stereo for multiscale metallic surface measurement

Multiscale analysis and characterizations of metallic surface topographies is a crucial task in advanced manufacturing and precision engineering. In this paper, we present a multiscale measurement technique based on photometric stereo for accurately reconstructing surface topographies at different scales. Our system employs a zoom lens and LED illuminators, providing the flexibility to adjust the measurement scale. To ensure efficiency and accuracy during scale transition, a fast light source calibration method using a Lambertian sphere is proposed to obtain the position and orientation of the LED precisely and simultaneously. A surface reconstruction algorithm is designed through modeling the light propagation process, enabling the identification of surface features at different levels of detail. The system is calibrated using an Edmund USAF resolution target, which demonstrate the accuracy of our method with the minimum deviation as low as 4μm. The proposed approach is applied to investigate the multiscale surface topography produced by metal additive manufacturing (AM) technique, achieving an accurate representation from contour to roughness. The results are compared with those obtained from a commercial 3D scanner from Keyence, which validate the effectiveness of our method and show its great potential in quality control and optimization of manufacturing processes.
Spectral analysis algorithm of vibration effect in phase-shifting interferometry

In the phase-shifting interferometry, the surface phase diagram is corrected by spectral analysis of the ‘intensity-phase pattern’ (the relationship between the interference intensity and the measured phase) of each phase-shifting image to achieve the purpose of reducing the vibration effect. This spectrum analysis algorithm is different from the previous methods. It does not determine the phase diagram, but corrects the phase diagram obtained by PSI measurement. It has few restrictions on the surface shape, and unlike the spatial Fourier method, it does not require high-density spatial carrier fringes, although at least a fringe of phase departure is recommended. The error between the influence of the simulated vibration signal on the phase and the influence of the actual measurement signal on the phase is less than 5 %.
Discussion on the integrated design and innovation of enterprise information systems based on supply chain

The role of information systems in enterprise management research is very important, but there is a problem with large management errors. VB system management method cannot solve the integration problem in enterprise management research, and the information recognition rate is low. Therefore, this paper proposes a supply chain method for information system analysis. First, the management theory is used to study the information, which is deeply excavated to reduce the Irrelevant management factors. Then, the supply chain method divides the enterprise management information and forms the final information identification set. MATLAB simulation shows that the information identification accuracy and information of the supply chain law are known under the condition that the enterprise management requirements are known. The recognition time was better than the VB system management method.