Reviewerly/exHarmony huggingface dataset:
Solar Panel Tilt Angle Optimization Using Machine Learning Model: A Case Study of Daegu City, South Korea

Finding optimal panel tilt angle of photovoltaic system is an important matter as it would convert the amount of sunlight received into energy efficiently. Numbers of studies used various research methods to find tilt angle that maximizes the amount of radiation received by the solar panel. However, recent studies have found that conversion efficiency is not solely dependent on the amount of radiation received. In this study, we propose a solar panel tilt angle optimization model using machine learning algorithms. Rather than trying to maximize the received radiation, the objective is to find tilt angle that maximizes the converted energy of photovoltaic (PV) systems. Considering various factors such as weather, dust level, and aerosol level, five forecasting models were constructed using linear regression (LR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and gradient boosting (GB). Using the best forecasting model, our model showed increase in PV output compared with optimal angle models.
Stereo Vision-Based Object Recognition and Manipulation by Regions with Convolutional Neural Network

This paper develops a hybrid algorithm of adaptive network-based fuzzy inference system (ANFIS) and regions with convolutional neural network (R-CNN) for stereo vision-based object recognition and manipulation. The stereo camera at an eye-to-hand configuration firstly captures the image of the target object. Then, the shape, features, and centroid of the object are estimated. Similar pixels are segmented by the image segmentation method, and similar regions are merged through selective search. The eye-to-hand calibration is based on ANFIS to reduce computing burden. A six-degree-of-freedom (6-DOF) robot arm with a gripper will conduct experiments to demonstrate the effectiveness of the proposed system.
PULP-NN: A Computing Library for Quantized Neural Network inference at the edge on RISC-V Based Parallel Ultra Low Power Clusters

We present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly coupled cluster of RISC-V processors.The key innovation in PULP-NN is a set of kernels for Quantized Neural Network (QNN) inference, targeting byte and sub-byte data types, down to INT-1, tuned for the recent trend toward aggressive quantization in deep neural network inference.The proposed library exploits both the digital signal processing (DSP) extensions available in the PULP RISC-V processors and the cluster's parallelism, achieving up to 15.5 MACs/cycle on INT-8 and improving performance by up to 63× with respect to a sequential implementation on a single RISC-V core implementing the baseline RV32IMC ISA.Using PULP-NN, a CIFAR-10 network on an octa-core cluster runs in 30× and 19.6× less clock cycles than the current state-of-the-art ARM CMSIS-NN library, running on STM32L4 and STM32H7 MCUs, respectively.The proposed library, when running on GAP-8 processor, outperforms by 36.8× and by 7.45× the execution on energy efficient MCUs such as STM32L4 and high-end MCUs such as STM32H7 respectively, when operating at the maximum frequency.The energy efficiency on GAP-8 is 14.1× higher than STM32L4 and 39.5× higher than STM32H7, at the maximum efficiency operating point.
The Technological Obsolescence of Virtual Reality Learning Environments

The concept of technological obsolescence that affects computer programs is a readily observable phenomenon that has been widely studied over the past half century. The so-called virtual reality learning environments (VRLEs) which are used to support university classes are significantly affected by this technological obsolescence, decreasing their formative effectiveness as the obsolescence process advances. In this study, the technological obsolescence of two VRLEs is analyzed by means of an empirical research based on survey results (N = 135) after using the VRLEs in engineering classes. Several key performance indicators (KPIs) were analyzed during seven academic courses, including motivation, interactivity, ease of use and usefulness. Since both VRLEs were updated during this research work, the influence of these improvements is discussed in detail from a technological obsolescence point of view. Results suggest that the technological obsolescence negatively affects the students’ opinion regarding motivation and interactivity, but the other KPIs (ease of use and usefulness) are hardly affected. In contrast, results indicate that the technological obsolescence can be reversed if periodic updates of educational tools are carried out using modern development software.
Long video question answering: A Matching-guided Attention Model

Existing video question answering methods answer given questions based on short video snippets. The underlying assumption is that the visual content indicating the ground truth answer ubiquitously exists in the snippet. It might be problematic for long video applications, since involving large numbers of answer-irrelevant snippets will dramatically degenerate the performance. To deal with this issue, we focus on a rarely investigated but practically important problem, namely long video QA, by predicting answers directly from long videos rather than manually pre-extracted short video snippets. We accordingly propose a Matching-guided Attention Model (MAM) which jointly extracts question-related video snippets and predicts answers in a unified framework. To localize questions accurately and efficiently, we calculate corresponding matching scores and boundary regression results for candidate video snippet proposals generated by sliding windows of limited granularity. Guided by the matching scores, the model pays different attention to the extracted video snippet proposals for each question. Finally, we use the attended visual features along with the question to predict the answer in a classification manner. A key obstacle to training our model is that publicly available video QA datasets only contain short videos especially designed for short video QA. Thus, we generate two new datasets for this task on the top of TACoS Multi-level dataset and MSR-VTT dataset by generating QA pairs from the video captions, called TACoS-QA and MSR-VTT-QA. Experimental results show the effectiveness of our proposed method on both datasets by comparing with two short video QA methods and a baseline method.
DoT-Net: Document Layout Classification Using Texture-Based CNN

Document Layout Analysis (DLA) is a segmentation process that decomposes a scanned document image into its blocks of interest and classifies them. DLA is essential in a large number of applications, such as Information Retrieval, Machine Translation, Optical Character Recognition (OCR) systems, and structured data extraction from documents. However, identification of document blocks in DLA is challenging due to variations of block locations, inter-and intra-class variability, and background noises. In this paper, we propose a novel texture-based convolutional neural network for document layout analysis, called DoT-Net. DoT-Net is a multiclass classifier that can effectively identify document component blocks such as text, image, table, mathematical expression, and line-diagram, whereas most related methods have focused on the text vs. non-text block classification problem. DoT-Net can capture textural variations among the multiclass regions of documents. Our proposed method DoT-Net achieved promising results outperforming state-of-the-art document layout classifiers on accuracy, F1 score, and AUC. The open-source code of DoT-Net is available at https://github.com/datax-lab/DoTNet.
A Microscale Analysis of Hydrothermal Epidote: Implications for the Use of Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Mineral Chemistry in Complex Alteration Environments

Abstract High-resolution, quantitative imaging of epidote from the Ann Mason fault block, Yerington district, Nevada, using scanning electron microscopy (SEM), X-ray fluorescence (XRF), and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) has revealed at least two types of epidote (type 1 and type 2), each with different major and trace element chemistry. Type 1 epidote is coarser grained, typically greater than 50 μm in diameter, and forms euhedral crystals that display twins and sector zones enriched in Fe, Sr, and Mn. Type 2 epidote is finer grained, typically less than 30 μm in diameter, irregularly zoned with respect to Fe and Al, and forms polycrystalline aggregates that include void space. Two sources of intragranular compositional variability are defined in this study—one related to different generations of epidote only visible on the microscale, and the other related to crystallographic features such as sector zones and twins. Intragranular compositional variations within Yerington epidotes highlight the potential importance of detailed sample characterization in complex alteration environments prior to undertaking mineral chemistry studies in the context of resource exploration. Based on statistical analysis of LA-ICP-MS spot data from one sample, the optimal number of spot analyses to adequately represent the range in element concentrations within zoned or twinned crystals is determined to be at least 40.
Design, Modeling and Control of Fully Actuated 2D Transformable Aerial Robot with 1 DoF Thrust Vectorable Link Module

We present a novel transformable multilinked aerial robot which consists of link modules with 1 DoF thrust vectoring mechanism. Commonly used UAV is underactuated due to its simplicity and high flight duration, but can not control the position and orientation independently. To overcome this problem, fully actuated multirotor aerial robots have been developed. In our previous work we developed fully actuated multilinked aerial robot which can transform in the air. However, the transformation range was limited because of a singularity problem. In this paper we propose a new design of link module with a tilted rotor and 1 DoF thrust vectoring joint which enables to avoid singularity forms and keep the flight stable during transformation. We describe modeling and control for the fully actuated multilinked multirotor. Then we propose a transformation planning method utilizing the 1 DoF thrust vectoring angle with consideration of guaranteed minimum force/torque. Finally we perform an aerial transformation experiment with a real platform to demonstrate the feasibility of our proposed design and methods.
A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering

The keypoint-based copy-move forgery (CMF) detection is one of the most widely used CMF detection methods; however the keypoint-based CMF detection method cannot effectively and efficiently detect the small and extremely smooth tampered regions in the input image. A CMF detection method is proposed to tackle the above mention problem. In the proposed CMF detection method, the contrast of the input image is adjusted using the dynamic histogram equalization (DHE) method. A speeded-up robust feature (SURF) descriptor is used to extract features from the tampered image and matched using Euclidean distance. The novel modified density-based spatial clustering of application with noise (mDBSCAN) clustering technique is applied to the matched features to generate the binary mask followed by the detection of CMF regions. Three standard datasets, MICC-F220, MICC-F2000, and CoMoFoD, are used to evaluate the proposed CMF detection method performance. The experimental results indicate that the proposed CMF detection method outshines the state-of-the-art CMF detection method in terms of precision (P) and recall (R).
A Class of Quadrinomial Permutations With Boomerang Uniformity Four

In Eurocrypt'18, Cid et al. proposed a new cryptanalysis tool called Boomerang Connectivity Table (BCT), to evaluate S-boxes of block ciphers. Later, Boura and Canteaut further investigated the new parameter Boomerang uniformity for cryptographic S-boxes. It is of great interest to find new S-boxes with low Boomerang uniformity for even dimensions. In this paper, we prove that a class of permutation quadrinomials over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(2m)</sub> with m odd has Boomerang uniformity four, which gives the fifth class of such kind of permutation polynomials. Further, the occurrences of 0 and 4 in the BCTs of the investigated permutation polynomials are also completely determined.
A Secure End-to-End Verifiable Internet-Voting System Using Identity-Based Blind Signature

The end-to-end (E2E) verification enables a voter to check if his ballot is recorded as he intended and the public to check if the system has correctly counted all of the recorded ballots. The Internet voting systems based on the principle of E2E verifiability have many challenges; the most important is its security. Several E2E voting systems have been discussed in the last decade in terms of analyzing the e-voting system and formalizing its security requirements. This article presents an E2E verifiable internet voting system that provides mobility to a voter and allows him to cast his vote secretly in public computer with the benefit of early voting. The proposed system aims to support the election process universally by using the voter's unique identification and biometric features. We propose a new identity-based blind signature scheme that ensures the voter's anonymity. We adopt the Boneh-Lynn-Shacham short signature scheme that ensures the vote privacy with the least ballot size. The system provides a digital witness to a voter that enables him to check whether his vote is recorded as he meant and the public to check if all the recorded ballots are counted correctly. The privacy of the proposed system is achieved under the well-known elliptic curve discrete logarithm and gap Diffie-Hellman assumptions.
Authentication &amp; Encryption Based Security Services in Blockchain Technology

Without any involvement of third party, block chain has shown tremendous potential in establishing secure links with IoT.This technology can be widely used without using any centralized authority. By combining both the technologies throughput of the system can be enhanced. Due to its various applications it is being widely used in almost every field which makes it vulnerable to various attacks. In this paper, blockchain architecture along with its key properties has been discussed. Later part of paper comprises of various security principles such as confidentiality, integrity and availability, various attacks on network along with its countermeasures.
Practical Implementation of Privacy Preserving Clustering Methods Using a Partially Homomorphic Encryption Algorithm

The protection and processing of sensitive data in big data systems are common problems as the increase in data size increases the need for high processing power. Protection of the sensitive data on a system that contains multiple connections with different privacy policies, also brings the need to use proper cryptographic key exchange methods for each party, as extra work. Homomorphic encryption methods can perform similar arithmetic operations on encrypted data in the same way as a plain format of the data. Thus, these methods provide data privacy, as data are processed in the encrypted domain, without the need for a plain form and this allows outsourcing of the computations to cloud systems. This also brings simplicity on key exchange sessions for all sides. In this paper, we propose novel privacy preserving clustering methods, alongside homomorphic encryption schemes that can run on a common high performance computation platform, such as a cloud system. As a result, the parties of this system will not need to possess high processing power because the most power demanding tasks would be done on any cloud system provider. Our system offers a privacy preserving distance matrix calculation for several clustering algorithms. Considering both encrypted and plain forms of the same data for different key and data lengths, our privacy preserving training method’s performance results are obtained for four different data clustering algorithms, while considering six different evaluation metrics.
Quantum learning with noise and decoherence: a robust quantum neural network

Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.
QuickDedup: Efficient VM deduplication in cloud computing environments

Deduplication is one of the major storage optimisation techniques for Virtual Machines (VMs) in cloud environment. Usually, hashing of blocks helps in identifying duplicate data blocks. This paper proposes a novel deduplication approach, QuickDedup that reduces the overall deduplication time, metadata overhead and the number of hash computations, and subsequent comparisons for the VM disk images. In addition to minimising the deduplication related metadata, which is a necessary by-product useful in checking deduplication, QuickDedup, follows novel byte comparison scheme to prepare various block classes. This way, QuickDedup eliminates or minimises the need for hash calculation and subsequent comparisons. QuickDedup performs the calculation and comparisons of hashes within the respective categories only. QuickDedup saves the space required for hash storage during deduplication and makes deduplication of VM disk images much faster. We conducted a detailed evaluation of QuickDedup on various metrics with different kinds and sizes of VM images taken from publicly available datasets. The evaluation results show a substantial improvement of up to 96% in the overall deduplication time required to deduplicate VM images apart from significant savings in metadata and storage overhead.
Deep Dynamic Time Warping: End-to-End Local Representation Learning for Online Signature Verification

Siamese networks have been shown to be successful in learning deep representations for multivariate time series verification. However, most related studies optimize a global distance objective and suffer from a low discriminative power due to the loss of temporal information. To address this issue, we propose an end-to-end, neural network-based framework for learning local representations of time series, and demonstrate its effectiveness for online signature verification. This framework optimizes a Siamese network with a local embedding loss, and learns a feature space that preserves the temporal location-wise distances between time series. To achieve invariance to non-linear temporal distortion, we propose building a dynamic time warping block on top of the Siamese network, which will greatly improve the accuracy for local correspondences across intra-personal variability. Validation with respect to online signature verification demonstrates the advantage of our framework over existing techniques that use either handcrafted or learned feature representations.
Applying Dempster–Shafer theory for developing a flexible, accurate and interpretable classifier

Two approaches have traditionally been identified for developing artificial intelligence systems supporting decision-making: Machine Learning, which applies general techniques based on statistical analysis and optimization methods to extract information from a large amount of data looking for possible relations among them, and Expert Systems, which codify experts knowledge in rules, which are then applied to a specific situation. One of the main advantages of the first approach is its greater accuracy and wider generality for the application of the methods developed which can be used in various scenarios. By contrast, expert systems are usually more restricted and often applicable only to the domain for which they were originally developed. However, the machine learning approach requires the availability of large chunks of data, and it is much more complicated to interpret the results of the statistical methods to obtain some explanation of why the system decides, classifies, or evaluates a situation in a certain way. This issue may become very important in areas such as medicine, where it is relevant to know why the system recommends a certain treatment or diagnoses a certain illness. Likewise, in the financial sector, it might be legally required to explain that a decision to reject the granting of a mortgage loan to a person is not due to discriminatory causes such as gender or race. In order to be able to have interpretability and extract knowledge of available data we developed a classification method based on Dempster-Shafer’s Plausibility Theory. Mass assignment functions (MAF) must be established to apply this theory and they assign a weight or probability to all subsets of the possible outcomes, given the presence of a certain fact on a decision scenario. Thus MAF assignments encode expert knowledge. The method learns optimal values for the weights of each MAF using the Gradient Descent method. The presented method allows combination of MAF which have been generated by the method itself or defined by an expert with those that are derived from a set of available data. The developed method was first applied to controlled scenarios and traditional data sets to ensure that classifications and explanations are correct. Results show that the model can classify with an accuracy which is comparable to other statistical classification methods, being also able to extract the most important decision rules from the data.
Haplós: Vibrotactile Somaesthetic Technology for Body Awareness

Inspired by somatic methodologies and neurophysiology, Haplos is a low-cost, wearable technology that applies vibrotactile patterns to the skin, can be incorporated in existing clothing and implements, and can be programmed and activated remotely. We review existing vibrotactile technologies and known uses of vibrotactile stimuli; describe the hardware, textile, and software components of Haplos; describe results from a quasi-experimental workshop to evaluate Haplos; and discuss future research and development directions.