Reviewerly/exHarmony huggingface dataset:
Technology of Hiding and Protecting the Secret Image Based on Two-Channel Deep Hiding Network

The development of new media technology brings serious security problems to the transmission of secret remote sensing or military images.It is a new and challenging task to study the technology of protecting these secret images.In this paper, based on the powerful spatial feature extraction capability of the convolutional neural network, a novel two-channel deep hiding network (TDHN) is designed by introducing advanced ideas such as skip connection, feature fusion, etc., and the two channels are respectively used to input the cover image and the secret image simultaneously.This network consists of two parts: the hiding network and the extraction network.The sender uses the hiding network to hide a secret image in a common cover image and generates a hybrid image called the hidden image.The receiver uses the extraction network to extract and reconstruct the secret image from the hidden image.Meanwhile, an innovative loss function is constructed by introducing two metrics called MSE and SSIM.Experimental results show that the TDHN optimized by the loss function can generate the hidden image and extracted image in high quality.The SSIM value between the hidden image and the original cover image is up to around 0.99, and the SSIM value between the extracted image and the original secret image is up to around 0.98.Through testing on different datasets, it is verified that the designed and optimized TDHN has excellent generalization capability, and thus it has important theoretical significance and engineering value.
QR-code optical encryption in the scheme with spatially incoherent illumination based on two micromirror light modulators

Abstract An optical encryption scheme with spatially incoherent illumination based on two micromirror light modulators has been experimentally implemented for the first time. Currently, such modulators are the fastest tools for spatio-temporal light modulation; their high frame rates provide possibilities for developing optical encryption systems with a bandwidth of several gigabites per second.
Seq2Seq models for recommending short text conversations

The massive amounts of data on social media networks can be overwhelming for users; for this reason, recommending relevant content becomes an essential task to avoid information overload. In this paper, we propose a new task for recommending users that might be interested in join conversations on specific domains. To that end, we introduce a new corpus that contains conversations threads from popular users on Twitter on domains such as politics, sports, or humanitarian activism. Modeling short-text conversations on microblogs can be difficult because user-generated data is unstructured and noisy. Previous works focused on recommending content to users based on latent factors models and collaborative filtering methods. We propose a state-of-the-art recommendation model based on a sequence-to-sequence neural architecture that encodes the text of users' profiles and the conversations' context using several variants of recurrent neural networks. The experimental results show that our method provides as much as 20% higher recall compared to baseline methods. Moreover, we use an end-to-end learning framework that allows downstream applications to use recommender systems (RSs) that generalize better to new content by using pre-trained embeddings, thus being useful across domains or events.
A Symmetric Sine Cosine Algorithm With Adaptive Probability Selection

To improve the ability of the sine cosine algorithm (SCA) in the exploitation process, an improved symmetric SCA with adaptive probability selection (SSCA-APS), is proposed. The search process of this algorithm is divided into early and late stages. In the early stage, the operators of the traditional SCA algorithm continue to be used. In the late stage, three improvements were applied. Firstly, the symmetric sine and cosine operators are proposed. The adaptive probability selection strategy is adopted to integrate original sine and cosine operators and symmetric sine and cosine operators for dynamically adjusting the step size of the search range. Furthermore, to prevent the population from falling into local optimization, Gaussian perturbation is used to mutate the globally optimal individuals of the current generation. In addition, the information of two randomly selected individuals and the globally optimal individual is integrated by quadratic interpolation to maintain population diversity and produce a new individual. 23 test functions were used to verify the performance of the proposed algorithm. The simulation results indicate that the performance of the SSCA-APS algorithm has competitiveness when it is compared with classical SCA and some state-of-the-art SCA variants.
Adaptive Bitrate Video Streaming in Non-Orthogonal Multiple Access Networks

Facing the ever-increasing demand for high quality video streaming and the dramatic variations of wireless network environment, adaptive bitrate video streaming (ABS) emerges as a prominent video quality adaptation technique in improving users' quality of experience (QoE). Reinforcement learning (RL)-based ABS and network-assisted ABS can improve users' QoE through resource utilization efficiency enhancement. However, limited network capacity is still the bottleneck of QoE improvement. Considering the potential capacity gain of non-orthogonal multiple access (NOMA), we study network-assisted ABS in NOMA networks. Based on a synchronous video streaming model and the knowledge of channel state information for next segment, a joint optimization problem of resource allocation and bitrate adaptation is formulated to maximize users' QoE and minimize energy consumption. The original problem is NP-hard and intractable due to the interference coordination problem introduced by NOMA and the multi-slot sum rate constraint. We decompose the original problem into the subproblem of joint subchannel assignment and power allocation (JSAPA), and the subproblem of bitrate adaptation (BA) with Lagrange relaxation method. A joint resource allocation and bitrate adaptation (JRABA) algorithm is further proposed to solve the JSAPA and BA subproblems iteratively. Specifically, the JSAPA subproblem is solved by an extended Gale-Shapley matching algorithm and differ of convex (DC) programming method iteratively. For the BA subproblem, appropriate video bitrate is explored with subgradient method. Numerical results validate the effectiveness of the proposed algorithm and reveal that network-assisted ABS in NOMA networks benefits users in achieving higher QoE with less energy consumption, especially when radio resources are insufficient or with large segment size.
The Solar Forecast Arbiter: An Open Source Evaluation Framework for Solar Forecasting

We describe an open source evaluation framework for solar forecasting to support the DOE Solar Forecasting 2 program and the broader solar forecast community. The framework enables evaluations of solar irradiance, solar power, and net-load forecasts that are impartial, repeatable and auditable. First, we define the use cases of the framework. The use cases, developed from the project's initial stakeholder engagement sessions, include comparisons to reference data sets, private forecast trials, evaluation of probabilistic forecast skill, and examinations of forecast errors during critical periods. We discuss the framework's data validation toolkit, reference data sources, and data privacy protocols. We describe the framework's benchmark forecast capabilities for intra-hour and day ahead forecast horizons. Finally, we summarize the reports and metrics that communicate the relative merits of the test and benchmark forecasts. The reports are created from standardized templates and include graphics for quantitatively evaluating deterministic and probabilistic forecasts and standard metrics for quantitatively evaluating forecasts.
Game Theoretical Adversarial Deep Learning With Variational Adversaries

A critical challenge in machine learning is the vulnerability of learning models in defending attacks from malicious adversaries. In this research, we propose game theoretical learning between a variational adversary and a Convolutional Neural Network (CNN), participating in a variable-sum two-player sequential Stackelberg game. Our adversary manipulates the input data distribution to make the CNN misclassify the manipulated data. Our ideal adversarial manipulation is a minimum change to the data which yet is large enough to mislead the CNNs. We propose an optimization procedure to find optimal adversarial manipulations by solving for the Nash equilibrium of the Stackelberg game. Specifically, the adversary's payoff function depends on the data manipulation which is determined by a Variational Autoencoder, while the CNN classifier's payoff functions are evaluated by misclassification errors. The optimization of our adversarial manipulations is defined by Alternating Least Squares and Simulated Annealing. Experimental results demonstrate that our game-theoretic manipulations are able to mislead CNNs that are well trained on the original data as well as on data generated by other models. We then let the CNNs to incorporate our manipulated data which leads to secure classifiers that are empirically the most robust in defending various types of adversarial attacks.
BPMN Approach in Blockchain with Hyperledger Composer and Smart Contract: Reservation-Based Parking System

Technology always bring opportunities to business process to evolve. This paper aims to approach blockchain technology using Business Process Model and Notation (BPMN). Hyperledger composer is one of the tools to make blockchain. Our study includes three Hyperledger composer concepts (assets, participants and transactions) and smart contract concept. This paper elaborates our past work on microservices in reservation-based parking system as our case study and focus in mapping BPMN to the three Hyperledger composer concepts mentioned above. Our finding includes identification of assets, participants and transactions with smart contract incorporation in our case study. The results also show BPMN pools and tasks changes. We present these changes in comparison table and revised BPMN diagram.
Collaborative Augmented Reality for Chess Game in Handheld Devices

Augmented Reality (AR) merges virtual and real entities into single environment and thus, create a higher immersion and presence of virtual object in real environment. A collaborative interface allows AR entities to be shared across multiple devices. Handheld devices readily support network connectivity with high processing power can be used to enable collaborative Handheld Augmented Reality (HAR) interface for AR game. AR game is combining the complex rule in computerized game with real interaction and high immersion in physical game. This paper proposes an implementation of collaborative HAR interface in handheld devices for chess game. A design of chess game is developed, then enabled for AR display with image marker tracking by using Vuforia AR Software Development Kit. The implementation of Photon Unity Networking as the collaborative interface bridges the connection between multiple handheld devices to synchronize the AR chess game with multiuser interaction technique. The result from evaluation indicates that the chess game is readily acceptable by the users and is enhancing the gameplay experience of ordinary chess game. This paper depicts the significance in enabling a collaborative HAR interface in games to boost the gameplay experience while preserving the physical presence of the game.
Evaluating Apache OpenWhisk - FaaS

Function-as-a-Service (FaaS) platforms enable users to execute user-defined functions without worrying about operational issues such as the management of infrastructure resources. In order to improve performance, different FaaS platforms are implementing optimizations and improvements, but it's not clear how good these implementations are. In this work, Apache OpenWhisk platform is evaluated from an approach that allows to determinate and characterize the performance under different configuration options; it was found that under certain premises an improvement of the performance in cold-booting latencies up to 38% is obtain.
EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.
Modeling a Continuous Locomotion Behavior of an Intelligent Agent Using Deep Reinforcement Technique

In this current research work, we applied a Twin- Delayed DDPG (TD3) algorithm to solve the most challenging virtual Artificial Intelligence application by training a HalfCheetah robot as an Intelligent Agent to run across a field. Twin-Delayed DDPG (TD3) is a recent breakthrough smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art techniques in Artificial Intelligence, including continuous Double Deep Q-Learning, Policy Gradient and Actor-Critic. These Deep Reinforcement Learning approaches have the capabilities to train an Intelligent agent to interact with an environment with automatic feature engineering, that is, requiring minimal domain knowledge. Twin-Delayed Deep Deterministic Policy Gradient algorithm (TD3) was built on the Deep Deterministic Policy Gradient algorithm (DDPG). During the implementation of the TD3 model, we used a two- layer feedforward neural network of 400 and 300 hidden nodes respectively, with Rectified Linear Units (ReLU) as an activation function between each layer for both the Actor and Critics, and then a final tanh unit following the output of the Actor. Overall, we developed six (6) neural networks. The Critic received both the state and action as input to the first layer. Both the network parameters were updated using the Adam optimizer. The implementation of the TD3 algorithm was made possible by using the pybullet continuous control environment which was interfaced through the OpenAI Gym. The idea behind the Twin-Delayed DDPG (TD3) is to reduce overestimation bias in Deep Q-Learning with discrete actions which are ineffective in an Actor-Critic domain setting. After exposing the Agent to training for 500,000 iterations, the Agent then achieved a Maximum Average Reward over the evaluation time-step of approximately 1891. Twin-Delayed Deep Deterministic Policy Gradient (TD3) has prominently improved both the learning speed and performance of the DDPG in a challenging task in a continuous control setting.
Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms

This research presents a comparison study of Backpropagation and K-means clustering algorithms for egg fertility identification. Instead of candling the eggs manually, a smartphone camera is used for capturing an egg image, then we do the pre-processing step by performing image enhancement and gray scaling process. The feature extraction method applied in the pre-processed image is the Gray Level Co-occurrence Matrix (GLCM) with six parameters (Entropy, Angular Second Moment, Contrast, Inverse Different Moment, Correlation, and Variance). The result of GLCM's feature extraction image will be processed using two learning algorithms: Backpropagation and K-means Clustering. For evaluation, we use 100 data samples (each in training and testing). The results show that the Backpropagation algorithm (using 12 hidden layer neurons) provides a 93% accuracy rate, while the K-means clustering algorithm presents a 74% accuracy rate. Since the Backpropagation algorithm gives better results in detecting egg fertility, as a recommendation, egg fertility identification can be performed using this algorithm.
Building a Speech and Text Corpus of Turkish: Large Corpus Collection with Initial Speech Recognition Results

To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. Turkish is a language with limited resources for ASR. Therefore, development of a symmetric Turkish transcribed speech corpus according to the high resources languages corpora is crucial for improving and promoting Turkish speech recognition activities. In this study, we constructed a viable alternative to classical transcribed corpus preparation techniques for collecting Turkish speech data. In the presented approach, three different methods were used. In the first step, subtitles, which are mainly supplied for people with hearing difficulties, were used as transcriptions for the speech utterances obtained from movies. In the second step, data were collected via a mobile application. In the third step, a transfer learning approach to the Grand National Assembly of Turkey session records (videotext) was used. We also provide the initial speech recognition results of artificial neural network and Gaussian mixture-model-based acoustic models for Turkish. For training models, the newly collected corpus and other existing corpora published by the Linguistic Data Consortium were used. In light of the test results of the other existing corpora, the current study showed the relative contribution of corpus variability in a symmetric speech recognition task. The decrease in WER after including the new corpus was more evident with increased verified data size, compensating for the status of Turkish as a low resource language. For further studies, the importance of the corpus and language model in the success of the Turkish ASR system is shown.
Farsi handwritten phone number recognition using deep learning

An application of artificial intelligence in mobile phones which might be widely accepted by users of these phones is the intelligent system used to automatically detect, search and dial phone numbers using an image taken from a handwritten phone number. In this paper, a reliable method is presented for Farsi handwritten phone number recognition using deep neural networks. In order to recognize a Farsi handwritten digit string, the digit string is first converted to single digits using the proposed segmentation algorithm, and then each segment is classified using a single Farsi handwritten digit recognition algorithm. By classifying each segment, finally, the digit string of the Farsi handwritten phone number image is created. Since there is no database for Farsi handwritten phone numbers, this paper first collects a database of Farsi handwritten phone numbers. Accuracy of the proposed algorithm for Farsi handwritten phone number recognition is 94.6%. After recognizing digits of the phone number, the proposed algorithm is able to search in the phonebook.
On Birthing Dancing Stars

While computers causing chaos is a common social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order-and the systems we create with it-support highly-structured information tasks well, but provide poor support for less-structured tasks. We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offer what we term 'bounded chaos', and offers research directions that may support the creation of such interfaces
Blockchain Governance: What We Can Learn From the Economics of Corporate Governance

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Collaborative Creation and Training of Social Bots in Learning Communities

The interaction between instructors and students is one of the key concepts to improve the student's learning process. To personalize learning on a massive scale, social bots can be used as supporting technology. However, their development for virtual learning environments currently requires deep technical knowledge. This leaves learner communities relying on highly-skilled developers to generate and tailor these social bots. Participatory design, end-user development and model-driven principles bear the potential to close this technical gap. In this paper, we propose a model-driven approach for creating social bots. Using our framework, learners can create, train and utilize these for self-hosted virtual learning environments relying on OpenAPI specifications offered, e.g. by Blackboard. We support both retrieval-based bots that react to certain events in predefined ways, as well as generative bots by utilizing open source deep learning technologies. Our first evaluation shows the usefulness of model-driven generation and utilization of social bots. We see the potential of this approach to move the development closer to the actual learner.
Usability Evaluation on the SIPR Website Uses the System Usability Scale and Net Promoter Score

The population in Malang City increases every year, resulting in an increased need for shelter. Therefore, the City Government of Malang has developed a SIPR (Spatial Information System) which is a website-based or web GIS application system. The purpose of SIPR is to disclose information on good and responsible governance aimed at the people of Malang City. Because this website has just been released, there may be problems, such as user difficulties when using the application. Therefore, a comprehensive evaluation related to usability is needed to determine the usefulness (feasible / not) of SIPR and to find out user satisfaction in using SIPR. This study uses the System Usability Scale (SUS) questionnaire to measure aspects of usability in accordance with the user's subjective assessment. And use the Net Promoter Score (NPS) to measure user satisfaction. The results of the study using SUS are 52.50 which states that the SIPR is quite useful (Ok / Fair) for the citizens of Malang City and the NPS produces a value of 80 for user satisfaction with SIPR. This research is expected to be a developer evaluation to improve the quality of SIPR.