Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Resolution of Irradiance Samples in Machine Learning Approaches for Irradiance Forecasting

Improving short term solar irradiance forecasting is crucial to increase the market share of the solar energy production. This paper analyzes the impact of using spatially distributed irradiance sensors as inputs to four machine learning algorithms: ARX, NN, RRF and RT. We used data from two different sensor networks for our experiments, the NREL dataset that includes data from 17 sensors that cover a 1 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area and the InfoRiego dataset which includes data from 50 sensors that cover an area of 94Km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Several studies have been published that use these datasets individually, to the author knowledge this is the first work that evaluates the influence of the spatially distributed data across a range from 0.5 to 17 sensors per km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We show that all of algorithms evaluated are able to take advantage of the data from the surroundings, from the very short forecast horizons of 10s up to a few hours, and that the wind direction and intensity plays an important role in the optimal distribution of the network and its density. We show that these machine learning methods are more effective on the short horizons when data is obtained from a dense enough network to capture the cloud movements in the prediction interval, and that in those cases complex non-linear models give better results. On the other hand, if only a sparse network is available, the simpler linear models give better results. The skills obtained with the models under test range from 13% to 70%, depending on the sensor network density, time resolution and lead time.
An End to End Framework With Adaptive Spatio-Temporal Attention Module for Human Action Recognition

Human action recognition is a challenging task in computer vision. Modeling spatial-temporal information in videos effectively is crucial for the performance improvement of action recognition. In this paper, we introduce an end to end framework, Spatio-Temporal Attention ConvNet (STACNet), which combines two novel attention modules and convolutional neural networks for action recognition. Two novel attention modules, Spatial Attention Module (SAM) and Temporal Attention Module (TAM), are proposed respectively. Spatial Attention Module is established by fusing the value feature and the gradient feature of the feature map, making the representation of ConvNet for action recognition focus on the informative motion regions of actions. Temporal Attention Module is built by combining global average pooling and global max pooling to explore key frames in videos. With the two attention modules, STACNet can adaptively distinguish key frames in a sequence and selectively pay different levels of attention to different spatial motion regions of human actions, at virtually negligible increase in computation cost. We demonstrate the effectiveness of SAM and TAM for action recognition, respectively. The experimental results show that STACNet can obtain superior performance on the datasets of HMDB51 and UCF101.
Uncertainty and Resolution of Speckle Photography on Micro Samples

Abstract The efficient development of new materials with defined properties requires fast methods of testing low volumes of material, such as a high-throughput investigation of spherical metallic micro samples with varying compositions and structuring treatments. A classical material testing method for macro samples, the tensile test cannot be employed for analyzing the mechanical properties of spherical samples with diameters below 1 mm since there are currently no methods for holding and stretching them. A combination between the incremental electrohydraulic extrusion as stress actuation unit and the speckle photography as strain measuring method is presented for obtaining the required mechanical characteristics. Positive longitudinal strain is generated at stepwise extrusion through &lt; 1 mm wide forming channels using a liquid punch and the deformation is observed in situ after each forming step at the interface between the micro sample and a transparent window integrated into the forming die. The occurring local strain fields with a lateral extension down to 100 µm and high gradients require displacement measurements with high lateral resolution over a large range of local dislocations between 0.1 and &gt; 10 µm. It is unknown, whether the speckle strain measuring method is able to provide the necessary low uncertainty for the required resolution in the whole measuring range. Therefore, theoretical and experimental investigations of the deformation measurability using the speckle correlation method are presented, showing that local displacements up to 10 µm can be measured with a spatial resolution between 3 and 10 µm depending on the displacement size. The dominant effect influencing the measurement uncertainty for displacements at this high spatial resolution is the speckle noise, resulting into measurement uncertainties of less than 100 nm. Hence, speckle photography is shown to be applicable for tensile test on micro samples.
A Decoder-Free Approach for Unsupervised Clustering and Manifold Learning with Random Triplet Mining

Unsupervised clustering is a very relevant open area of research in machine learning with many applications in the real world. Learning the manifold in which images lie and measuring the proximity distance of the sample points to the clusters in their latent space is non-trivial. Recent deep learning methods have proposed the use of autoencoders for manifold learning and dimensionality reduction in an effort to better cluster image samples. However, offline training of autoencoders is cumbersome and rather tedious to update. Moreover, trained autoencoders tend to be biased towards the training set and are impractical for performing data augmentation. In this paper, we introduce a novel method that uses a triplet network architecture in order to replace autoencoders, thus avoiding the need to pre-train autoencoders offline. Because our framework can be trained online, we can train our network with data augmented pairs which allows us to build a more robust encoder and improve accuracy. In contrast to other clustering methods that require nearest neighbor comparisons at every step, our method introduces a novel and adaptive approach of choosing the samples to train which we call Random Triplet Mining. Our method remains competitive compared with other current methods while we obtain state of the art results on the Fashion-MNIST dataset.
Reconstruction of bandlimited graph signals from measurements

Graph signal reconstruction is a fundamental topic in graph signal processing. Recently, graph signal reconstruction in three typical sampling schemes has been studied in the literature. In this paper, we study the reconstruction of bandlimited graph signals in a general measurement scheme, which includes the three existing sampling schemes as its special cases. Theorems for perfect reconstruction are established, and reconstruction by random measurement matrices is studied. An iterative reconstruction algorithm based on convex projection is proposed, and numerical experiments are implemented to demonstrate the effectiveness of the algorithm.
Isogeny Classes of Abelian Varieties over Finite Fields in the LMFDB

This document is intended to summarize the theory and methods behind fq_isog collection inside the ab_var database in the LMFDB as well as some observations gleaned from these databases. This collection consists of tables of Weil q-polynomials, which by the Honda-Tate theorem are in bijection with isogeny classes of abelian varieties over finite fields.
Blockchain for Digital Rights Management of Design Works

A design work not only has a complex functional composition, but also has special trading needs. These all result in the difficulties in content protection, copyright protection and the trading of works. To solve these problems, this paper proposes a scheme for digital rights management of design works using blockchain. Unlike existing digital rights management methods, this scheme binds the off-chain design work and its copyright record in the blockchain. The display of design effects, the confidentiality of design details and the compatibility with relevant laws are all taken into account. A new proof-of-delivery method is also proposed to ensure the fairness of the trade. By using smart contracts and public key cryptography, it has no need for active operation from participants in the trade. The scheme is evaluated, analyzed, and compared with other methods from multiple respects to demonstrate its rationality and effectiveness. Finally, a proof-of-concept experiment is described by taking digital garment design as an example.
Traceable Method for Personal Information Registration Based on Blockchain

When a user registers a digital service, the service provider often asks for the user's personal information, such as name, phone number and so on.With the digital services gradually becoming an indispensable part of our lives, the abuse and misappropriation of personal information have caused people's attention to the protection of it.At present, The technical schemes for personal information protection mainly focus on preventing information leakage.We provide protection in a different way: propose a traceable method for personal information registration based on blockchain, which can distinguish the service provider obtained the information legally or illegally, by storing the personal information transaction records on the blockchain.The proposed method includes both direct and third-party personal information transaction scenarios.In different scenarios, the user will send the encrypted personal information data or authorization file to the service provider.After the transaction record is confirmed to be on the blockchain, the certification center will assist the service provider to decrypt the personal information.With this method, users can clearly understand which personal information has been delivered to which service providers.Moreover, the user's personal information data involving privacy are not stored on or transmitted through the blockchain.Hence, there is no additional risk of information disclosure, so as to achieve the purpose of personal information protection.Additionally, we analyze the proposed method using Kailar logic, and conduct a transaction performance simulation using NS-3.It shows that our method has properties of privacy, reliability, and accountability, and can meet the transaction performance requirements under the practical application scene.
Step-wise Recommendation for Complex Task Support

Digital assistants help people perform simple tasks, including scheduling, home automation, information look up, and question answering. Current assistants offer less support for accomplishing more complex tasks comprising multiple steps. These tasks sometimes require the ability to leverage the capabilities of multiple devices. Ideal smart assistants for such complex scenarios would track task progress and recommend useful and appropriate information at every step of the procedure. To this end, we introduce the novel notion of step-wise recommendation as a means of automatically providing guidance relevant to the current step in a complex task. We employ a common real-life scenario for this purpose: recipe preparation. We demonstrate how a smart assistant can be developed to offer support for complex tasks enabled by multi-modal inputs and outputs through multiple devices (i.e., smart speakers, tablets, or other smart devices available in kitchens). We develop step-wise recommendation models for this scenario and analyze their efficacy for: (1) different prediction tasks (e.g., resources, devices), and (2) different contextual information used to make the prediction (e.g., completed steps, current step, and importantly, future steps). Our recommendation model achieves a prediction accuracy of 83-96%, depending on the prediction task and context used. The findings have implications for the design of intelligent systems to help people accomplish complex tasks.
Safecracker: Leaking Secrets through Compressed Caches

The hardware security crisis brought on by recent speculative execution attacks has shown that it is crucial to adopt a security-conscious approach to architecture research, analyzing the security of promising architectural techniques before they are deployed in hardware. This paper offers the first security analysis of cache compression, one such promising technique that is likely to appear in future processors. We find that cache compression is insecure because the compressibility of a cache line reveals information about its contents. Compressed caches introduce a new side channel that is especially insidious, as simply storing data transmits information about it. We present two techniques that make attacks on compressed caches practical. Pack+Probe allows an attacker to learn the compressibility of victim cache lines, and Safecracker leaks secret data efficiently by strategically changing the values of nearby data. Our evaluation on a proof-of-concept application shows that, on a common compressed cache architecture, Safecracker lets an attacker compromise a secret key in under 10ms, and worse, leak large fractions of program memory when used in conjunction with latent memory safety vulnerabilities. We also discuss potential ways to close this new compression-induced side channel. We hope this work prevents insecure cache compression techniques from reaching mainstream processors.
Application of ELECTRE Algorithm in Skincare Product Selection

Abstract Skincare is currently growing very fast. The community, especially women, both teenagers and parents, now believes more in Skincare services and products that change consumers’ faces to be cleaner, and shine for their appearance. However, until now there are still obstacles that are often faced by consumers in choosing Skincare that is in accordance with the needs of their skin type. This study aims to recommend the selection of Skincare products based on consumers using the ELECTRE method. The data collection method was carried out by means of interviews and questionnaires on 250 samples of consumers who use Skincare products in the city of Pematangsiantar. Based on these results the criteria for evaluating prices (X1), ingredient content (X2), side effects (X3), availability of goods (X4), and packaging (X5). The alternatives used are 4 namely: A1 = Gamier, A2 = Nature Republic, A3 = Pond’s, A4 = Wardah. The results of the research using the ELECTRE method show that Skincare Garnier (A1) products are recommended to be the best Skincare based on consumers. It is expected that research results can provide understanding to consumers in choosing Skincare products.
Comparison of Vehicle Detection Techniques applied to IP Camera Video Feeds for use in Intelligent Transport Systems

Vehicle detection is an important area in Transport and Artificial Intelligence. Through vehicle detection techniques, vehicles can be located across different images. Some of these models are robust enough to identify parts of vehicles in images where the vehicle might be partially occluded. Recent advances in detection methods gave rise to a range of different techniques that can be used for recognition and detection of vehicles. Although each technique has its merits, it is not always the case that the adopted model works well for scenarios involving IP Cameras. The motivation for this study is to compare several state-of-the-art techniques, including deep learning models and computer vision approaches. A set of experiments are developed in order to test these models on a number of low quality IP camera footages set in the transport domain in order to measure detection and recognition accuracy. The final evaluation compares detection accuracy using mean average precision, the semantics of the recognised vehicle as well as recognition robustness when applied to a dataset that contains images with different light conditions. The study also looks at persistence in recognition across frames in video data and a detailed description of the dataset used to train the evaluated models. Finally, the paper also goes through some scenarios that applies the results obtained in this study to ITS systems that use IP camera feeds.
Updating high average-utility itemsets with pre-large concept

HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility Itemset Mining) that provides a reliable measure to reveal utility patterns in light of the length of the mined pattern. Several works have been studied to improve mining efficiency by designing multiple pruning strat egies and efficient frameworks, but fewer studies have centered on the sophisticated database maintenance algorithm. Existing works still have to rescan the databases multiple times when it is necessary. We first use the pre-large principle in this paper to efficiently update the newly discovered HAUIs. For further updates and maintenance on the basis of the two thresholds, the Pre-large Average Utility Itemset (PAUI) can be maintained to increase the mining performance. Experiments will then be performed to compare the batch model, the Fast-Updated (FUP)-based model, and the Apriori-like HAUIM (APHAUIM) model designed in respect of the number of maintenance patterns, scalability, runtime, and memory usage.
ProximiTEE

Intel SGX enables protected enclaves on untrusted computing platforms. An important part of SGX is its remote attestation mechanism that allows a remote verifier to check that the expected enclave was correctly initialized before provisioning secrets to it. However, SGX attestation is vulnerable to relay attacks where the attacker, using malicious software on the target platform, redirects the attestation and therefore the provisioning of confidential data to a platform that he physically controls. Although relay attacks have been known for a long time, their consequences have not been carefully examined. In this paper, we analyze relay attacks and show that redirection increases the adversary's abilities to compromise the enclave in several ways, enabling for instance physical and digital side-channel attacks that would not be otherwise possible.
A robust automated surface‐matching registration method for neuronavigation

Purpose The surface‐matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient‐to‐image registration. Methods A coarse‐to‐fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three‐dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4‐points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine‐registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method. Results Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient’s head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy. Conclusions The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.
A Conceptual Framework for Trustworthy and Incentivized Trading of Food Grains using Distributed Ledger and Smart Contracts

Blockchain has transformed business processes from centralized to decentralized. It can better help in making food grain supply chain fully decentralized with peer to peer (P2P) business by removing intermediaries, and thus reducing the overall cost at end user side with better returns to farmers. In this paper, we explore a blockchain technology and the required smart contracts for trustworthy and incentivized P2P trading of food grains. We propose different smart contracts such as food grain supply, bidding, trading and utilization, which are deployed on ethereum blockchain for the decentralized trading of food grains. We use Vickrey-Clarkegrove (Vickrey auction) method for achieving the incentivized trading for both farmers and end users. The proposed framework offers P2P trading, security of food grain data, data transparency, user's anonymity and incentives in the trading process. The performance of the proposed framework is evaluated and analyzed in terms of fulfilling the requirements of food grain supply management.
A Pragmatical Study on Blockchain Empowered Decentralized Application Development Platform

Blockchain has been a latest trend and many of its applications show promising results in terms of transactions, validations, finances etc. and recently it has become an apple of eye for the tech industry and investors. The concept of decentralized application (commonly known as dApp) and the blockchain platform itself can help to reform many unresolved solutions for the betterment of services and generate new opportunities for the developers to focus on creating applications in a decentralized environment with ease. The adaptation of blockchain technology through dApps is increasing drastically; thus opportunity for the developers and entrepreneurs are growing rapidly. Considering the impactful revolution of dApps, the developer communities are getting more interest to the dApps development tools, frameworks and platforms. This paper is presenting a comprehensive study on some popular blockchain powered decentralized or distributed dApp platforms. In this paper we made analysis of these platforms based on different factors and parameters. This analysis provides an impactful direction to the developer and enterprise community in adopting of the development platform.