Reviewerly/exHarmony huggingface dataset:
Self-Attentive Attributed Network Embedding Through Adversarial Learning

Network embedding aims to learn the low-dimensional representations/embeddings of vertices which preserve the structure and inherent properties of the networks. The resultant embeddings are beneficial to downstream tasks such as vertex classification and link prediction. A vast majority of real-world networks are coupled with a rich set of vertex attributes, which could be potentially complementary in learning better embeddings. Existing attributed network embedding models, with shallow or deep architectures, typically seek to match the representations in topology space and attribute space for each individual vertex by assuming that the samples from the two spaces are drawn uniformly. The assumption, however, can hardly be guaranteed in practice. Due to the intrinsic sparsity of sampled vertex sequences and incompleteness in vertex attributes, the discrepancy between the attribute space and the network topology space inevitably exists. Furthermore, the interactions among vertex attributes, a.k.a cross features, have been largely ignored by existing approaches. To address the above issues, in this paper, we propose Nettention, a self-attentive network embedding approach that can efficiently learn vertex embeddings on attributed network. Instead of sample-wise optimization, Nettention aggregates the two types of information through minimizing the difference between the representation distributions in the low-dimensional topology and attribute spaces. The joint inference is encapsulated in a generative adversarial training process, yielding better generalization performance and robustness. The learned distributions consider both locality-preserving and global reconstruction constraints which can be inferred from the learning of the adversarially regularized autoencoders. Additionally, a multi-head self-attention module is developed to explicitly model the attribute interactions. Extensive experiments on benchmark datasets have verified the effectiveness of the proposed Nettention model on a variety of tasks, including vertex classification and link prediction.
Decision Support System with Simple Additive Weighting for Recommending Best Employee

Human resources are significant assets in an organization. To increase work motivation for employees, various strategies are needed, such as giving rewards to employees who excel, giving sanctions to employees who break the rules and training employees. Rewarding for employees at Universitas Budi Luhur (UBL) is still based on the subjective assessment of the leadership. Determination of employees who perform well also has not been based on standard criteria or assessment. Therefore, in this study, a decision support system was developed to conduct the best employee assessment and selection. This study uses the Simple Additive Weighting (SAW) method. The SAW method was chosen because it was able to select the best alternative from several alternatives. Determination of the best employees using nine criteria, namely discipline, appearance, achievement, interpersonal skills, the ability to provide input, does not cause problems, ability to cooperate, coordination skills, and motivating abilities. The test results using the ISO-9126 model for web-based DSS applications developed in this study indicate that the quality of applications is 81%, which means that the criteria are excellent.
Multi Target Tracking from Drones by Learning from Generalized Graph Differences

Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. The weights of such network flow problem can be learnt efficiently from training data using a recently introduced concept called Generalized Graph Differences (GGD). This allows a general tracker implementation to be specialized to drone videos by training it on the VisDrone dataset. Two modifications to the original GGD is introduced in this paper and a result with an average precision of 23.09 on the test set of VisDrone 2019 was achieved.
Secure Communication:Using Synchronization On A Novel Fractional Order Chaotic System

In this article, the novel 3-D fractional order chaotic system has been synchronized in dislocated phase and anti-phase synchronization taking into account the uncertainties. Also the efficacy of the synchronization technique has been illustrated with the help of an example in the field of secure communication. Numerical Simulations have been performed to verify the theoretical results.
Estimation of rooftop solar energy generation using Satellite Image Segmentation

Rooftop solar energy potential has traditionally been estimated by surveying the number of large buildings in a given area. In this work, we propose a fast and low-cost method to estimate the rooftop photovoltaic solar energy generated in a particular area by utilizing satellite imagery - even though it may be of low resolution. We employ a deep learning based approach to carry out image segmentation on low resolution satellite images of Bangalore, India. Three different model architectures (U-Net, SegNet, FCN) were trained on manually hand-labelled data and tested for the task of semantic segmentation of satellite images. U-Net was found to yield the best results. By using a custom modified U-Net to segment the images, we arrive at the building rooftop area available for solar panels. To calculate the annual solar energy generated in gigawatt-hour (GWh), the incident solar insolation values from the U.S. National Renewable Energy Laboratory (NREL) based on observed weather patterns in Bangalore, and standard values from datasheets of photovoltaic manufacturers are used.
Social Networks, Exchange Rate Fluctuation, and Economic Growth: ARDL Approach

Abstract This study aimed to investigate the effects of social network propaganda on exchange rate and also exchange rate fluctuations on Iran economic growth. This study uses annual data to analyse the long-run and short-run relationship between variables for the period of 1993–2018. Data were collected from the Central Bank of Iran. The autoregressive distributive lag (ARDL) method proposed by Pesaran, Shin, and Smith (2001) was used. The results of long-run analysis show that a 1% increase in negative propaganda of social media about the exchange rate leads to a 3.8% decline in long-run economic growth. Also, a 1% increase in exchange rate fluctuations results in a 3.5% decrease in economic growth. Research findings also indicate negative short-run impacts of social networks on the excitement of the foreign exchange market and, ultimately, on economic growth.
Immersive Hand Gesture for Virtual Museum using Leap Motion Sensor Based on K-Nearest Neighbor

Virtual museum is a place where users can explore museum collection freely. In this study, we are discussing the 3D interactions presented in a virtual museum application using hand-sensing sensor named Leap Motion. In making 3D interaction, some hand gestures are needed to functions any interact in virtual world. To prevent miss-occurring in 3D interaction, it is necessary to do a hand pattern classification to improve accuracy and make it more precision so as not to reduce the quality of immersion in the virtual world. The classification method used in this study is K-Nearest Neighbor (KNN) classification methods. KNN is a method that is quite popular and simple. The first step is data acquisition processing that is used as training data using Leap Motion Controller which takes vector value data (x, y, z) from each fingertip. Then the data normalization process is carried out to facilitate the next process which is feature extraction process. Features are being extracted including angle value between fingers, angle value between fingertips, angle between fingertips and palms, distance vector between fingertips and palms, and elevation value between fingertips and palms. After that, extracted data are being trained and classified using K-Nearest Neighbor (KNN).
Advising reinforcement learning toward scaling agents in continuous control environments with sparse rewards

This paper adapts the success of the teacher–student framework for reinforcement learning to a continuous control environment with sparse rewards. Furthermore, the proposed advising framework is designed for the scaling agents problem, wherein the student policy is trained to control multiple agents while the teacher policy is well trained for a single agent. Existing research on teacher–student frameworks have been focused on discrete control domain. Moreover, they rely on similar target and source environments and as such they do not allow for scaling the agents. On the other hand, in this work the agents face a scaling agents problem where the value functions of the source and target task converge at different rates. Existing concepts from the teacher–student framework are adapted to meet new challenges including early advising, importance of advising, and mistake correction, but a modified heuristic was used to decide on when to teach. The performance of the proposed algorithm was evaluated using the case study of pushing, and picking and placing objects with a dual arm manipulation system. The teacher policy was trained using a simulated scenario consisting of a single arm. The student policy was trained to handle the dual arm manipulation system in simulation under the advice of the teacher agent. The trained student policy was then validated using two Quanser Mico arms for experimental demonstration. The effects of varying parameters on the student performance in the advising framework was also analyzed and discussed. The results showed that the proposed advising framework expedited the training process and achieved the desired scaling within a limited advising budget.
Forensic of Things: Revisiting Digital Forensic Investigations in Internet of Things

The emerging paradigm of Internet of Things (IoT) promises automation, real-time monitoring and management of critical infrastructure(s) allowing optimized productivity in consumer and commercial concerns. Diversity in IoT devices, communication protocols, and the extensive data generated by IoT-ware requires robust incident response primitives to counter IoT related cyber-attacks and deter cybercrimes. Although substantial work is underway in improving IoT security, there is a fundamental requirement to address the forensic requisites that may aid forensic investigators and law enforcement agencies (LEAs) in prosecuting IoT related cybercrimes. IoT presents substantial forensic challenges in terms of evidence source identification, artefact acquisition, insufficiency of IoT-specific forensic tools and techniques, and issues in multijurisdictional litigation. To this effect, the present papers comprehensively reviews the applicability of traditional, cloud and network forensics in the IoT domain. State-of-the-art IoT forensic models are critically discussed to highlight their practical applicability. Furthermore, employment of recent enabling technologies including software defined networking and blockchaining is examined in a forensic of things context. The study also discusses and makes recommendations towards the multi-faceted challenges faced by IoT forensic investigators due to non-validated methodologies, leading to deficiencies in operational readiness.
Weakly Supervised Learning of Recurrent Residual ConvNets for Pancreas Segmentation in CT Scans

Deep neural networks trained by medical images with dense annotations have revealed favourable performance on accurate organ segmentation. The current supervised methods demand voxel-level annotations which are not easily accessible due to the consuming of time and requirements of specialized knowledge and skills. In this paper, we propose a weakly supervised method based on a recurrent residual convolutional neural network trained only with image-level labels to generate voxel-level segmentation. The recurrent residual convolutional units take advantage of contextual information of successive slices and a spatial pooling layer is introduced after the last convolutional layer to aggregate local features and learn accurate localization. The final segmentation mask is computed by applying a conditional random field for spatial prediction. Our method shows competitive performance to fully supervised methods on the public NIH-CT-82 dataset for pancreas segmentation.
Prediction of Driver’s Attention Points Based on Attention Model

The current intelligent driving system does not consider the selective attention mechanism of drivers, and it cannot completely replace the drivers to extract effective road information. A Driver Visual Attention Network (DVAN), which is based on deep learning attention model, is proposed in our paper, in order to solve this problem. The DVAN is aimed at extracting the key information affecting the driver’s operation by predicting the driver’s attention points. It completes the fast localization and extraction of road information that is most interesting to drivers by merging local apparent features and contextual visual information. Meanwhile, a Cross Convolutional Neural Network (C-CNN) is proposed in order to ensure the integrity of the extracted information. Here, we verify the network on the KITTI dataset, which is the largest computer vision algorithm evaluation data set in the world’s largest autonomous driving scenario. Our results show that the DVAN can quickly locate and identify the target that the driver is most interested in a picture, and the average accuracy of prediction is 96.3%. This will provide useful theoretical basis and technical methods that are related to visual perception for intelligent driving vehicles, driving training and assisted driving systems in the future.
End-to-End Captcha Recognition Using Deep CNN-RNN Network

With the development of the Internet, the captcha technology has also been widely used. Captcha technology is used to distinguish between humans and machines, namely Completely Automated Public Turing test to tell Computers and Humans Apart. In this paper, an end-to-end deep CNN-RNN network model is constructed by studying the captcha recognition technology, which realizes the recognition of 4-character text captcha. The CNN-RNN model first constructs a deep residual convolutional neural network based on the residual network structure to accurately extract the input captcha picture features. Then, through the constructed variant RNN network, that is, the two-layer GRU network, the deep internal features of the captcha are extracted, and finally, the output sequence is the 4-character captcha. The experiments results show that the end-to-end deep CNN-RNN network model has a good performance on different captcha datasets, achieving 99% accuracy. And experiment on the few samples dataset which only has 4000 training samples also shows an accuracy of 72.9 % and a certain generalization ability.
Continuous-variable multiparty quantum key agreement based on third party

General quantum key agreement (QKA) protocols require each participant to make the same contribution to the generation of a shared key, but this situation cannot meet the needs of government or military which require a third party only to control key agreement process. This paper proposed a continuous-variable quantum key agreement (CVQKA) protocol with multi-party Greenberg–Horne–Zeilinger (GHZ) entangled states generated by a third party. The third party prepares quantum states by sending multi-photon beam with higher emission frequency and stronger signal. Taking continuous variable as carrier, the information carried by each carrier increases, which improves the security key rate. In this paper, the third party controls but does not contribute to the final key. After receiving the states, each participant encodes his or her own key, adds a decoy state and sends it to the next participant, until receiving the quantum state that was encoded by all the participants. Then, participants can measure and calculate to get the final shared key. It shows that our protocol is easy to implement and has high efficiency. Security analysis shows that our protocol can resist collusion attacks, beam splitter attack, etc.
Optimal development of location and technology independent machine learning photovoltaic performance predictive models

Photovoltaic (PV) power prediction is important for monitoring the performance of PV plants. The scope of this work is to develop a methodology for deriving an optimized location and technology independent machine learning (ML) model for power prediction. The prediction accuracy results demonstrated that the performance of the ML model was primarily affected by the dataset split method. In particular, for a 70:30 % train and test set approach, the ML model achieved a normalized root mean square error (nRMSE) of 0.88 % when using randomly selected samples compared to 0.94 % when using continuous samples. The accuracy of the developed model was also affected by the duration of the train set. For a random 70:30 % train and test set approach, the constructed ML topology achieved a nRMSE of 0.88 %, while when the dataset was split into a 30:30 % portion, the nRMSE was 0.95 %. Moreover, when low irradiance conditions were filtered out and 70 % of the entire dataset was randomly chosen for model training, a nRMSE of 1.41 % was obtained demonstrating that the model's accuracy was not improved. Finally, for a random 10:30 % train and test set approach, the FNNN achieved the lowest nRMSE of 1.10 % when the model was trained using the prevailing irradiance classes.
Unsupervised hashing based on the recovery of subspace structures

Unsupervised semantic hashing should in principle keep the semantics among samples consistent with the intrinsic geometric structures of the dataset. In this paper, we propose a novel multiple stage unsupervised hashing method, named “Unsupervised Hashing based on the Recovery of Subspace Structures” (RSSH) for image retrieval. Specifically, we firstly adapt the Low-rank Representation (LRR) model into a new variant which treats the real-world data as samples drawn from a union of several low-rank subspaces. Then, the pairwise similarities are represented in a space-and-time saving manner based on the learned low-rank correlation matrix of the modified LRR. Next, the challenging discrete graph hashing is employed for binary hashing codes. Notably, we convert the original graph hashing model into an optimization-friendly formalization, which is addressed with efficient closed-form solutions for its subproblems. Finally, the devised linear hash functions are fast achieved for out-of-samples. Retrieval experiments on four image datasets testify the superiority of RSSH to several state-of-the-art hashing models. Besides, it’s worth mentioning that RSSH, a shallow model, significantly outperforms two recently proposed unsupervised deep hashing methods, which further confirms its effectiveness.
Systematic encoders for generalized Gabidulin codes and the q-analogue of Cauchy matrices

We characterize the generator matrix in standard form of generalized Gabidulin codes. The parametrization we get for the non-systematic part of this matrix coincides with the q-analogue of generalized Cauchy matrices, leading to the definition of q-Cauchy matrices. These matrices can be represented very conveniently and their representation allows to define new interesting subfamilies of generalized Gabidulin codes whose generator matrix is a structured matrix. In particular, as an application, we construct Gabidulin codes whose generator matrix is the concatenation of an identity block and a Toeplitz/Hankel matrix. In addition, our results allow to give a new efficient criterion to verify whether a rank metric code of dimension k and length n is a generalized Gabidulin code. This criterion is only based on the computation of the rank of one matrix and on the verification of the linear independence of two sets of elements and it requires O(m⋅F(k,n)) field operations, where F(k,n) is the cost of computing the reduced row echelon form of a k×n matrix. Moreover, we also provide a characterization of the generator matrix in standard form of general MRD codes.
Efficient Auditing Scheme for Secure Data Storage in Fog-to-Cloud Computing

Fog-to-cloud computing has now become a new cutting-edge technique along with the rapid popularity of Internet of Things (IoT). Unlike traditional cloud computing, fog-to-cloud computing needs more entities to participate in, including mobile sinks and fog nodes except for cloud service provider (CSP). Hence, the integrity auditing in fog-to-cloud storage will also be different from that of traditional cloud storage. In the recent work of Tian et al., they took the first step to design public auditing system for fog-to-cloud computing. However, their scheme becomes very inefficient since they uses intricate public key cryptographic techniques, including bilinear mapping, proof of knowledge etc. In this paper, we design a general and more efficient auditing system based on MAC and HMAC, both of which are popular private key cryptographic techniques. By implementing MAC and HMAC, we give a concrete instantiation of our auditing system. Finally, the theoretical analysis and experiment results show that our proposed system has more efficiency in terms of communication and computational costs.
Evolutionary NetArchitecture Search for Deep Neural Networks Pruning

Network pruning is an architecture search process to determine the state (remove/remain) of neurons in the network. It is a com- binatorial optimization problem, and this combinatorial optimiza- tion problem is NP-hard. Most existing pruning methods prune channels/neurons based on the assumption that they are indepen- dent in network. However, there exists dependency among chan- nels/neurons. We try to solve the combinatorial optimization problem by evolutionary algorithm (EA). However, the traditional EA can't be used directly into deep neural networks (DNNs) because the problem dimension is too high. Attention mechanism (AM) can help us get parameter important score to reduce prob- lem difficulty, making the architecture search process more effective. Therefore, combining EA and AM, we propose an Evolutionary NetArchitecture Search (EvoNAS) method to solve network pruning problem. We demonstrate the effectiveness of our method on common datasets with ResNet, ResNeXt, and VGG. For example, for ResNet on CIFAR-10, EvoNAS reduces 73.40% computing operations and 73.95% parameters with 0.13% test accuracy increasement. Compared with the state-of-the-art methods, EvoNAS increases 30% reduction ratio at least.
Evaluation Infrastructures for Academic Shared Tasks

Abstract Academic search systems aid users in finding information covering specific topics of scientific interest and have evolved from early catalog-based library systems to modern web-scale systems. However, evaluating the performance of the underlying retrieval approaches remains a challenge. An increasing amount of requirements for producing accurate retrieval results have to be considered, e.g., close integration of the system’s users. Due to these requirements, small to mid-size academic search systems cannot evaluate their retrieval system in-house. Evaluation infrastructures for shared tasks alleviate this situation. They allow researchers to experiment with retrieval approaches in specific search and recommendation scenarios without building their own infrastructure. In this paper, we elaborate on the benefits and shortcomings of four state-of-the-art evaluation infrastructures on search and recommendation tasks concerning the following requirements: support for online and offline evaluations, domain specificity of shared tasks, and reproducibility of experiments and results. In addition, we introduce an evaluation infrastructure concept design aiming at reducing the shortcomings in shared tasks for search and recommender systems.