Reviewerly/exHarmony huggingface dataset:
Snow Loss Prediction for Photovoltaic Farms Using Computational Intelligence Techniques

With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms.
PENGEMBANGAN E-MARKETING SEBAGAI SISTEM INFORMASI LAYANAN PELANGGAN PADA MEGA FLORIST BANDAR LAMPUNG

Mega Florist Bandar Lampung is a company engaged in the field of flower board rental and delivery services in the Bandar Lampung area, Mega Florist itself has been established since April 2015. The purpose of this research is to design an e-marketing system and implement an e-marketing system as a customer service at Mega Florist Bandar Lampung. The problem with Bandar Lampung's Mega Florist itself is that there is no system to market board flower rentals, and its marketing still uses brochures, print media, and advertisements so that it requires more costs to market board flower rental services to Mega Florist Bandar Lampung. The system development method uses the waterfall method and the system design uses the UML system design, and the system implementation uses an object-based programming language that is PHP, with Dreamwever and MySQL applications as a database. The result of this research is an online application based on e-marketing built by Mega Florist Bandar Lampung, focused on spreading information on package rental and flower delivery. This web board will present services, facilities, menus, promos and provide convenience for customers in finding information interest rates offered, as well as services in booking board interest rentals.
A New Deep Neural Architecture Search Pipeline for Face Recognition

With the widespread popularity of electronic devices, the emergence of biometric technology has brought significant convenience to user authentication compared with the traditional password and mode unlocking. Among many biological characteristics, the face is a universal and irreplaceable feature with simple detection methods and good recognition accuracy. Face recognition is one of the main functions of electronic equipment propaganda. The previous work in this field mainly focused on converting loss function in traditional deep convolution neural networks without changing the network structure. With the development of AutoML, neural architecture search (NAS) has shown remarkable performance in image classification tasks. In this paper, we first propose a new deep neural architecture search pipeline combined with NAS technology and reinforcement learning strategy into face recognition. We quote the framework of NAS, which trains the child and controller networks alternately. At the same time, we optimize NAS by incorporating evaluation latency into rewards of reinforcement learning and utilize the policy gradient algorithm to search the architecture automatically with the cross-entropy loss. The network architectures we searched out have achieved state-of-the-art accuracy in the large-scale face dataset, which achieved 98.77% top-1 in the MS-Celeb-1M dataset and 99.89% in LFW dataset with relatively small network size.
Analysis And Design of File Security System AES (Advanced Encryption Standard) Cryptography Based

The times has made human needs are increasing, including information needs. Therefore, sending and storing data through electronic media requires a process that is able to guarantee the security and integrity of the data that requires an encoding process. Encryption is the process of changing an original data into confidential data that cannot be read. Meanwhile, the decryption process is a process where the confidential data received will be converted back into the original data. In this case the Advanced Encryption Standard (AES) algorithm is used as the latest cryptographic algorithm standard. The previous algorithm was considered unable to answer the challenges of the development of communication technology very quickly. AES is a cryptographic algorithm using the Rijndael algorithm that can encrypt and decrypt blocks of data over 128 bits with a key length of 128 bits. In this study the application of AES as a file security system is carried out, where the encryption and decryption process is carried out on the file. In testing the system a trial is performed on all files with different file sizes and for the results of the encryption process (ciphertext) in the form of files with the file format with the *.encrypted extension.
An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.
Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years.Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian.Compared with spatial-based GCNNs, spectral-based GCNNs are capable of highly exploiting graph structure information, but always regard graphs undiredcted.Actually, there are many scenarios where the graph structures are directed, such as social networks, citation networks, etc. Treating graphs undirected may lose important information, which is helpful for graph learning tasks.This motivate us to construct a spectral-based GCNN for directed graphs.In this paper, we propose a scalable graph convolutional neural network with fast localized convolution operators derived from directed graph Laplacian, which is called fast directed graph convolutional network (FDGCN).FDGCN can directly work on directed graphs and can scale to large graphs as the convolution operation is linear with the number of edges.Furthermore, we find that FDGCN can unify the graph convolutional network (GCN), which is a classic spectral-based GCNN.The mechanism of FDGCN is thoroughly analyzed from spatial aggregation point of view.Since previous work has confirmed that considering uncertainty of graph could promote GCN a lot, the proposed FDGCN is further enhanced through extra training epochs on random graphs generated by mixed membership stochastic block model (MMSBM).Experiments are conducted for semi-supervised node classification tasks to evaluate the performance of FDGCN.Results show that our model can outperform or match state-of-the-art models in most cases.
Weakly Supervised Discriminative Feature Learning With State Information for Person Identification

Unsupervised learning of identity-discriminative visual feature is appealing in real-world tasks where manual labelling is costly. However, the images of an identity can be visually discrepant when images are taken under different states, e.g. different camera views and poses. This visual discrepancy leads to great difficulty in unsupervised discriminative learning. Fortunately, in real-world tasks we could often know the states without human annotation, e.g. we can easily have the camera view labels in person re-identification and facial pose labels in face recognition. In this work we propose utilizing the state information as weak supervision to address the visual discrepancy caused by different states. We formulate a simple pseudo label model and utilize the state information in an attempt to refine the assigned pseudo labels by the weakly supervised decision boundary rectification and weakly supervised feature drift regularization. We evaluate our model on unsupervised person re-identification and pose-invariant face recognition. Despite the simplicity of our method, it could outperform the state-of-the-art results on Duke-reID, MultiPIE and CFP datasets with a standard ResNet-50 backbone. We also find our model could perform comparably with the standard supervised fine-tuning results on the three datasets. Code is available at https://github.com/KovenYu/state-information}.
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning

Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks (Devlin et al., 2019).Most of the existing approaches rely on a randomly initialized classifier on top of such networks.We argue that this fine-tuning procedure is sub-optimal as the pre-trained model has no prior on the specific classifier labels, while it might have already learned an intrinsic textual representation of the task.In this paper, we introduce a new scoring method that casts a plausibility ranking task in a full-text format and leverages the masked language modeling head tuned during the pre-training phase.We study commonsense reasoning tasks where the model must rank a set of hypotheses given a premise, focusing on the COPA (Gordon et al., 2012), Swag (Zellers et al., 2018), HellaSwag (Zellers et al., 2019) and CommonsenseQA (Talmor et al., 2019) datasets.By exploiting our scoring method without fine-tuning, we are able to produce strong baselines (e.g.80% test accuracy on COPA) that are comparable to supervised approaches.Moreover, when fine-tuning directly on the proposed scoring function, we show that our method provides a much more stable training phase across random restarts (e.g ×10 standard deviation reduction on COPA test accuracy) and requires less annotated data than the standard classifier approach to reach equivalent performances.
Privacy-Preserving Linear Regression on Distributed Data by Homomorphic Encryption and Data Masking

Linear regression is a basic method that models the relationship between an outcome value and some explanatory values using a linear function. Traditionally, this method is conducted on a clear dataset provided by one data owner. However, in today's ever-increasingly digital world, the data for regression analysis are likely distributed among multiple parties and even contain sensitive information about the data owners. In this case, data owners are not willing to share their data unless data privacy is guaranteed. In this paper, we propose a novel protocol for conducting privacy-preserving linear regression (PPLR) on horizontally partitioned data. Our system architecture includes multiple clients and two noncolluding servers. In our protocol, each client submits its data in encrypted form to a server, and two servers collaboratively determine the regression model on pooled data without learning its contents. We construct our protocol with Paillier homomorphic encryption and a new data masking technique. This data masking technique can perturb data by multiplying a rational number while the data are encrypted. Due to the use of the data masking technique, the efficiency of our protocol is greatly improved. We provide an error bound of the protocol and prove it rigorously. We also provide security analysis of the protocol. Finally, we implement our system in C++ and Java, and then we evaluate our protocol using real datasets provided by UCI. The experiments show our protocol is one of the most effective approaches to date and has negligible errors compared with performing linear regression on clear data.
Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure

Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints’ locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints’ locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks

Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models.This work focuses on AMR-to-text generation -A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR).Existing graph-to-sequence approaches generally utilize graph neural networks as their encoders, which have two limitations: 1) The message propagation process in AMR graphs is only guided by the firstorder adjacency information.2) The relationships between labeled edges are not fully considered.In this work, we propose a novel graph encoding framework which can effectively explore the edge relations.We also adopt graph attention networks with higherorder neighborhood information to encode the rich structure in AMR graphs.Experiment results show that our approach obtains new state-of-the-art performance on English AMR benchmark datasets.The ablation analyses also demonstrate that both edge relations and higher-order information are beneficial to graph-to-sequence modeling.
Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping

Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.
Blockchain Technology into Gamification on Education

As we know, Indonesia has begun to enter the era of revolution 4.0 which in that era there were many changes in all fields including the presence of blockchain technology which began to be in demand. Including in the field of education, the changes that occur in the world of education today are so significant with the commencement of the abandonment of teaching methods that still use conventional methods. Keep track of tasks by book, face to face communication, which of course will cause many losses in a certain period of time, as many tasks that have long been buried become difficult to find when needed, and the possibility of manipulation of tasks is still great. The learning method is considered as a boring and insecure way, where students cannot explore learning because the collected files can still be manipulated by other parties. So from now on the application of learning methods is changed by the concept of gamification which relies on blockchain technology. The gamification learning method was created to compensate for the times when students preferred to play games rather than learning, therefore the gamification method could be applied to management education in higher education.
In vivo panoramic human skin shape and deformation measurement using mirror-assisted multi-view digital image correlation

Panoramic shape and deformation measurements of human skin in vivo may provide important information for biomechanical analysis, exercise guidance and medical diagnosis. This work proposes the application of an advanced mirror-assisted multi-view digital image correlation (DIC) method for dynamic measurements of 360-deg shape and deformation of human body parts in vivo. The main advantage of this method consists in its capabilities to perform full-panoramic non-contact measurements with a single pair of synchronized cameras and two planar mirrors thus representing a lean yet effective alternative to conventional multi-camera DIC systems in ‘surrounding’ configuration. We demonstrate the capabilities of this method by measuring the full-panoramic shape of a plastic human head, the deformation of a woman face and the principal strain distribution over the full-360-deg surface of a forearm during fist clenching. The applications of this method can be the most disparate but, given the possibility to determine the full-field strains and derived information (e.g. skin tension lines), we envisage a great potential for the study of skin biomechanics in vivo.
Automated Gleason Grading and Gleason Pattern Region Segmentation Based on Deep Learning for Pathological Images of Prostate Cancer

Prostate cancer is the second-deadliest cancer in men in the United States, seriously affecting people's life and health. The Gleason grading system is one of the most reliable methods to quantify the invasiveness of prostate cancer, which is of great significance for risk assessment and treatment planning for patients. However, the task of automating Gleason grading is difficult because of the complexity of pathological images of prostate cancer. This paper presents an automated Gleason grading and Gleason pattern region segmentation method based on deep learning for pathological images of prostate cancer. An architecture combining the atrous spatial pyramid pooling and the multiscale standard convolution is proposed for the segmentation of the Gleason pattern region to get accurate Gleason grading. In addition, the postprocessing procedure based on conditional random fields is applied to the prediction. The quantitative experiments on 1211 prostate cancer tissue microarrays demonstrate that our results have a high correlation with the manual segmentations. The mean intersection over union and the overall pixel accuracy for the Gleason pattern region are 77.29% and 89.51%, respectively. Furthermore, the results of the automatic Gleason grading were comparable to the results of experienced pathologists. The inter-annotator agreements between the model and the pathologists, quantified via Cohen's quadratic kappa statistic, was 0.77 on average. Our study shows that the method of combining different deep neural network architectures is suitable for more objective and reproducible Gleason grading of prostate cancer.