Reviewerly/exHarmony huggingface dataset:
A cyclostationary model for temporal forecasting and simulation of solar global horizontal irradiance

Abstract As part of Saudi Vision 2030 , a major strategic framework developed by the Council of Economic and Development Affairs of Saudi Arabia, the country aims to reduce its dependency on oil and promote renewable energy for domestic power generation. Among the sustainable energy resources, solar energy is one of the leading resources because of the endowment of Saudi Arabia with plentiful sunlight exposure and year‐round clear skies. This essentializes to forecast and simulate solar irradiance, in particular global horizontal irradiance (GHI), as accurately as possible, mainly to be utilized by the power system operators among many others. Motivated by a dataset of hourly solar GHIs, this article proposes a model for short‐term point forecast and simulation of GHIs. Two key points, that make our model competent, are: (1) the consideration of the strong dependency of GHIs on aerosol optical depths and (2) the identification of the periodic correlation structure or cyclostationarity of GHIs. The proposed model is shown to produce better forecasts and more realistic simulations than a classical model, which fails to recognize the GHI data as cyclostationary. Further, simulated samples from both the models as well as the original GHI data are used to calculate the corresponding photovoltaic power outputs to provide a comprehensive comparison among them.
A High-Performance Low-Power Barrett Modular Multiplier for Cryptosystems

This paper presents a fast architecture for Barrett modular multiplication. By replacing the integer multiplications in each iteration with carry-save compressions and using Booth coding plus operation rescheduling to increase parallelism, we eliminate costly multiplications while concurrently avoiding large-bitwidth additions. Our detailed error analysis proves that intermediate results are always less than twice the modulus. Experimental results show that the removal of multiplication eliminates the need for any DSPs. Even not accounting for this key benefit, compared to the best of prior art results, the proposed design results in 46.8% latency reduction with a similar area.
Electronic_Khipu_: Thinking in Experimental Sound from an Ancestral Andean Interface

Abstract A khipu is an artifact used in the ancient Inca Empire and previous Andean societies to process and transmit statistical and narrative information. It is known as one of the first textile computers, a tangible interface encrypted in knots and strings made of cotton and wool. This system was widely used until the Spanish colonization that banned and destroyed many of the existing khipus. This article presents the creation process of the Electronic_Khipu_, a new interface for musical expression. It takes the form of a MIDI controller, inspired by the original Incan device. The Khipu has been converted into an instrument for interaction and experimental sound generation by weaving knots with conductive rubber cords, thereby encoding musical compositions. The article goes on to document the implementation of the electronic instrument and evaluate its use in live performances. The research also explores the work of significant artists in this conceptual line who, from a decolonial perspective, have transformed and incorporated the khipu into different contemporary expressions of electronic sound art. These works, along with the practical example cited with the Electronic_Khipu_, suggest alternative practices of tangible live coding, computer music, and data sonification. Creative work with sound continues a legacy, almost lost in colonization, of the ancestral practice of weaving knots as code. In addition, some ideas will be presented to enhance the instrument's performance in the future.
Benchmark Keystroke Biometrics Accuracy From <scp>High‐Stakes</scp> Writing Tasks

Biometrics are physical or behavioral human characteristics that can be used to identify a person. It is widely known that keystroke or typing dynamics for short, fixed texts (e.g., passwords) could serve as a behavioral biometric. In this study, we investigate whether keystroke data from essay responses can lead to a reliable biometric measure, with implications for test security and the monitoring of writing fluency and style changes. Based on keystroke data collected from a high‐stakes writing testing setting, we established a preliminary biometric benchmark for detecting test‐taker identity by using features extracted from their writing process logs. We report a benchmark keystroke biometric accuracy of equal error rate of 4.7% for identifying same versus different individuals on an essay task. In particular, we show that the inclusion of writing process features (e.g., features designed to describe the writing process) in addition to the widely used typing‐timing features (e.g., features based on the time intervals between two‐letter key sequences) improves the accuracy of the keystroke biometrics. The proposed keystroke biometrics can have important implications for the writing assessments administered through the remotely proctored tests that have been widely adopted during the COVID pandemic.
NASA: Accelerating Neural Network Design with a NAS Processor

Neural network search (NAS) projects a promising direction to automate the design process of efficient and powerful neural network architectures. Nevertheless, the NAS techniques have to dynamically generate a large number of candidate neural networks, and iteratively train and evaluate these on-line generated network architectures, thus they are extremely time-consuming even when deployed on large GPU clusters, which dramatically hinders the adoption of NAS. Though recently there are many specialized architectures proposed to accelerate the training or inference of neural networks, we observe that existing neural network accelerators are typically targeted at static neural network architectures, and they are not suitable to accelerate the evaluation of the dynamical neural network candidates evolving during the NAS process, which cannot be deployed onto current accelerators via the off-line compilation.To enable rapid and energy-efficient NAS in compact single-chip solutions, we propose NASA, a specialized architecture for one-shot based NAS acceleration. It is able to generate, schedule, and evaluate the candidate neural network architectures for the target machine learning workload with high speed, significantly alleviating the processing bottleneck of one-shot NAS. Motivated by the observation that there are considerable computation sharing opportunities among the different neural network candidates generated in one-shot NAS, NASA is equipped with an on-chip network fusion unit to remove the redundant computation during the network mapping stage. In addition, the NASA accelerator can partition and re-schedule the candidate neural network architectures at fine-granularity to maximize the chance of data reuse and improve the utilization of the accelerator arrays integrated to accelerate network evaluation. According to our experiments on multiple one-shot NAS tasks, NASA achieves 33.52× performance speedup and 214.33× energy consumption reduction on average when compared to aCPU-GPU system.
Tracking the Heritage of Genes in Evolutionary Algorithms

This paper aims to introduce a methodology to trace the influence of the initial population of an evolutionary algorithm to the final population. The major challenge concerns tracking the heritage of multiple parent operators. In this paper, we propose a new encoding for tracking purposes. In addition, we propose modifications to the corresponding metrics for measuring the impact of individuals. With this approach, we provide several tools to not only track the influence of the initial population on the results but also to study the effects of different crossover and mutation operators. In our experiments, we evaluate the differences between two selected crossover and mutation operators and provide insight into the proposed approach.
Comparing Equity and Effectiveness of Different Algorithms in an Application for the Room Rental Market

Machine Learning (ML) techniques have been increasingly adopted by the real estate market in the last few years. Applications include, among many others, predicting the market value of a property or an area, advanced systems for managing marketing and ads campaigns, and recommendation systems based on user preferences. While these techniques can provide important benefits to the business owners and the users of the platforms, algorithmic biases can result in inequalities and loss of opportunities for groups of people who are already disadvantaged in their access to housing. In this work, we present a comprehensive and independent algorithmic evaluation of a recommender system for the real estate market, designed specifically for finding shared apartments in metropolitan areas. We were granted full access to the internals of the platform, including details on algorithms and usage data during a period of 2 years.
P-DIFF+: Improving learning classifier with noisy labels by Noisy Negative Learning loss

Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF+, which can train DNN classifiers but obviously alleviate the adverse impact of noisy labels. Our proposed probability difference distribution implicitly reflects the probability of a training sample to be clean, then this probability is employed to re-weight the corresponding sample during the training process. Moreover, Noisy Negative Learning(NNL) loss can be further employed to re-weight samples. P-DIFF+ can achieve good performance even without prior-knowledge on the noise rate of training samples. Experiments on benchmark datasets demonstrate that P-DIFF+ is superior to the state-of-the-art sample selection methods.
Transaction-based Static Indexing Method to Improve the Efficiency of Query on the Blockchain

In the context of advocating the reduction of greenhouse gases, green energy trade has become the focus of attention. At this stage, green energy is relatively dispersed (such as wind power, hydropower, solar power, etc.), and energy generation is real-time. At present, green energy trading based on blockchain has become a better solution. In the transaction itself, the blockchain has better security and traceability, but in terms of statistical information and interval query, because the basic data structure of the blockchain is a key-value data structure, performance problems are prone to occur. A transaction-based static indexing method is proposed, which can effectively improve the efficiency of statistical information and interval query on the chain.
Designing data center networks using bottleneck structures

This paper provides a mathematical model of data center performance based on the recently introduced Quantitative Theory of Bottleneck Structures (QTBS). Using the model, we prove that if the traffic pattern is \textit{interference-free}, there exists a unique optimal design that both minimizes maximum flow completion time and yields maximal system-wide throughput. We show that interference-free patterns correspond to the important set of patterns that display data locality properties and use these theoretical insights to study three widely used interconnects---fat-trees, folded-Clos and dragonfly topologies. We derive equations that describe the optimal design for each interconnect as a function of the traffic pattern. Our model predicts, for example, that a 3-level folded-Clos interconnect with radix 24 that routes 10\% of the traffic through the spine links can reduce the number of switches and cabling at the core layer by 25\% without any performance penalty. We present experiments using production TCP/IP code to empirically validate the results and provide tables for network designers to identify optimal designs as a function of the size of the interconnect and traffic pattern.
NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models

Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a default hyperparameter setting with a fixed pre-trained model, while both of them are not optimized for specific tasks and time constraints. Moreover, in cloud computing or GPU clusters where the tasks arrive sequentially in a stream, faster online fine-tuning is a more desired and realistic strategy for saving money, energy consumption, and CO2 emission. In this paper, we propose a joint Neural Architecture Search and Online Adaption framework named NASOA towards a faster task-oriented fine-tuning upon the request of users. Specifically, NASOA first adopts an offline NAS to identify a group of training-efficient networks to form a pretrained model zoo. We propose a novel joint block and macro level search space to enable a flexible and effi-cient search. Then, by estimating fine-tuning performance via an adaptive model by accumulating experience from the past tasks, an online schedule generator is proposed to pick up the most suitable model and generate a personalized training regime with respect to each desired task in a one-shot fashion. The resulting model zoo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is more training efficient than SOTA models, e.g. 6x faster than RegNetY-16GF, and 1.7x faster than EfficientNetB3. Experiments on multiple datasets also show that NASOA achieves much better fine-tuning results, i.e. improving around 2.1% accuracy than the best performance in RegNet series under various constraints and tasks; 40x faster compared to the BOHB.
Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM.
Development of Efficient E-Health Records Using IoT and Blockchain Technology

In the COVID-19 pandemic situation, the transmission of health records sharing and maintaining the integrity of data to receive the end-user is not consistent. Already, some countries decide to conduct lockdown using data models. In this scenario, the end user loss of integrity, loss of confidentiality and security on health records impacts the patients. The security of electronic health records (EHR) is a challenging task especially in COVID-19 like situations. It is a necessity to share secure EHR to physicians and patients which plays a vital role in COVID-19, and also data availability, scalability, and performance of data to receive the end-user is the loss of integrity. In this paper, we proposed an integrated approach of blockchain 4.0 technology used in IoT-based cloud environment that reduce the latency while transmission of health care records and introduce the blockchain-based electronic health records named as BEHR transmission between the physicians and patients. The performance of the proposed approach tested with the NodeJs software and ApacheJmeter open source JMeter Cloud Test environment and proved that the simulation-results of BEHR are an efficient approach in response time and file storage and transmission of EHR than the present conventional system.
Acceleration and Efficiency Warranty for Distributed Machine Learning Jobs over Data Center Network with Optical Circuit Switching

Based on a DCN with OCS, we propose a pattern-aware scheduling and fast convergence strategy for the distributed machine learning jobs. Experimental results show significant accelerations for completion time and convergence of the jobs.
Modeling Context-Sensitive Metacognitive Control of Focusing on a Mental Model During a Mental Process

Focusing on a proper mental model during mental processes is often crucial. Metacognition is used to control such focusing in a context-sensitive manner. In this paper, a second-order adaptive mental network model is introduced for this form of metacognitive control. The second-order adaptive network model obtained is illustrated by a case scenario concerning social interaction.
Brain Image Synthesis with Unsupervised Multivariate Canonical CSC ℓ<sub>4</sub>Net

Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition. However, obtaining full sets of different modalities is limited by various factors, such as long acquisition times, high examination costs and artifact suppression. In addition, the complexity, high dimensionality and heterogeneity of neuroimaging data remains another key challenge in leveraging existing randomized scans effectively, as data of the same modality is often measured differently by different machines. There is a clear need to go beyond the traditional imaging-dependent process and synthesize anatomically specific target-modality data from a source in-put. In this paper, we propose to learn dedicated features that cross both intre- and intra-modal variations using a novel CSCℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> Net. Through an initial unification of intra-modal data in the feature maps and multivariate canonical adaptation, CSC ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> Net facilitates feature-level mutual transformation. The positive definite Riemannian manifold-penalized data fidelity term further enables CSCℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> Net to re-construct missing measurements according to transformed features. Finally, the maximization ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> -norm boils down to a computationally efficient optimization problem. Extensive experiments validate the ability and robustness of our CSC ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> Net compared to the state-of-the-art methods on multiple datasets.
Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System

This paper proposes ELPSDeCS (Encoding, Learning, "Plausible" Sampling, and Decoding Classifier System) by extending ELSDeCS (Encoding, Learning, Sampling, and Decoding Classifier System) to increase both the accuracy and interpretability of the generated classifiers which matches the high-dimensional input such as images. The experimental results on the complex multi-class classification problem of the handwritten numerals show that both the accuracy and interpretability of ELPSDeCS are higher than that of ELSDeCS.
Comparison of VLP-16 and MRS-1000 LiDAR systems with absolute interferometer

Nowadays, LiDARs hold a relevant place in providing the environmental sensing required by most ADAS.Promoted by such increasing demand, many new manufacturers are emerging and, new LiDARs are continuously made available on the market.If, on the one hand, the availability of LiDARs with increasing performance and reducing cost has brought significant benefits also promoting the spread of such measuring systems in other areas such as industrial controls and agriculture, on the other, it has made it more difficult to extricate in the immense set of LiDARs present on the market today.In response to this growing need for standards and methods capable of comparing the various LiDARs, many international standards and scientific publications are being produced on the subject.In this paper, we continue our work on LiDARs characterization, focusing our attention on comparing the performances of two of the most popular systems -namely, the MRS 1000 by Sick and the VLP 16 by Velodyne.Starting from the analysis of the warm-up time and stability, such a comparison focused on analyzing the axial error of both systems.Such errors have been estimated by exploiting a custom rail system and an absolute interferometer.The obtained results revealed warm-up times of a few tens of minutes and maximum absolute axial errors of a few centimeters in the range [1.5, 21] m.
Collaborative Coded Computation Offloading: An All-pay Auction Approach

As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices, the cloud server is no longer able to handle the large-scale datasets individually. Given the improved computational capabilities of the edge devices, coded distributed computing has become a promising approach given that it allows computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. Specifically, by using polynomial codes, computed results from only a subset of devices are needed to reconstruct the final result. However, there is no incentive for the edge devices to complete the computation tasks. In this paper, we present an all-pay auction to incentivize the edge devices to participate in the coded computation tasks. In this auction, the bids of the edge devices are represented by the allocation of their Central Processing Unit (CPU) power to the computation tasks. All edge devices submit their bids regardless of whether they win or lose in the auction. The all-pay auction is designed to maximize the utility of the cloud server by determining the reward allocation to the winners. Simulation results show that the edge devices are incentivized to allocate more CPU power when multiple rewards are offered instead of a single reward.