Reviewerly/exHarmony huggingface dataset:
Mastering Fighting Game Using Deep Reinforcement Learning With Self-play

One-on-one fighting game has played a role as a bridge between board game and real-time simulation game in terms of research on game AI because it needs middle-level computation power with medium-size complexity. In this paper, we propose a method to create fighting game AI agent using deep reinforcement learning with self-play and Monte Carlo Tree Search (MCTS). We also analyze various reinforcement learning configuration such as changes on state vector, reward shaping, and opponent compositions with novel performance metric. Agent trained by the proposed method was evaluated against other AIs. The evaluation result shows that mixing MCTS and self-play in a 1:3 ratio makes it possible to overwhelm other AIs in the game with 94.4% win rate. The fully-trained agent understands the game mechanism so that it waits until being close to enemy and performs actions at the optimal timing.
Scoping the ethical principles of cybersecurity fear appeals

Abstract Fear appeals are used in many domains. Cybersecurity researchers are also starting to experiment with fear appeals, many reporting positive outcomes. Yet there are ethical concerns related to the use of fear to motivate action. In this paper, we explore this aspect from the perspectives of cybersecurity fear appeal deployers and recipients . We commenced our investigation by considering fear appeals from three foundational ethical perspectives. We then consulted the two stakeholder groups to gain insights into the ethical concerns they consider to be pertinent. We first consulted deployers : (a) fear appeal researchers and (b) Chief Information Security Officers (CISOs), and then potential cybersecurity fear appeal recipients: members of a crowdsourcing platform. We used their responses to develop an effects-reasoning matrix , identifying the potential benefits and detriments of cybersecurity fear appeals for all stakeholders. Using these insights, we derived six ethical principles to guide cybersecurity fear appeal deployment. We then evaluated a snapshot of cybersecurity studies using the ethical principle lens. Our contribution is, first , a list of potential detriments that could result from the deployment of cybersecurity fear appeals and second , the set of six ethical principles to inform the deployment of such appeals. Both of these are intended to inform cybersecurity fear appeal design and deployment.
Offline Signature Recognition Using Deep Features

Offline Signature Verification plays a significant role in Forensic offices. In our research, we investigate the performance of signature recognition utilizing features based on AlexNet (pretrained Convolution Neural Network model). All the investigations are performed on scanned signatures of fourteen users each from three datasets, namely CEDAR, UTSig, and BHsig260. Two classifiers, i.e., Support Vector Machine (SVM) and Decision Tree are utilized. Utilizing features based on Deep Convolution neural network and SVM as machine learning algorithms show better outcomes. The best output is achieved for Bengali signature recognition utilizing SVM with 100% accuracy. For Persian signature, we obtained an accuracy of more than 80% for each user. Twelve users out of fourteen users for Hindi signature are 100% recognized using SVM.
Extraction and analysis of 3D kinematic parameters of Table Tennis ball from a single camera

Vision is the first indicator for coaches to assess the quality of a sport gesture. However, gesture analysis using computer vision is often restricted to laboratory experiments, far from the real conditions in which athletes train on a daily basis. In this perspective, we introduce 3D ball trajectories analysis using a single camera with very few acquisition constraints. A key point of the proposal is the estimation of the apparent ball size for obtaining ball to camera distance. For this purpose, a 2D CNN is trained using a generated dataset that enables a reliable ball size extraction, even in case of high motion blur. The final objective is not only to be able to determine ball trajectories, but most importantly to retrieve their relevant physical parameters. Those kinematics parameters could help coaches and players to better analyze strokes with strong rotation effects during training sessions or matches. With a precise estimation of those trajectories, it is indeed possible to extract the ball tangential and rotation speed, related to the so-called Magnus effect. Validation experiments for characterizing table tennis strokes are presented on both a synthetic dataset and on real video sequences.
Recurrent Graph Neural Networks for Text Classification

Text classification is an essential and classical problem in natural language processing. Traditional text classifiers often rely on many human-designed features. With the rise of deep learning, Recurrent Neural Networks and Convolutional Neural Networks have widely applied into text classification. Meanwhile, the success of Graph Neural Networks (GNN) on structural data has attracted many researchers to apply GNN to traditional NLP applications. However, when these methods use the GNN, they commonly ignore the word order information of the sentence. In this work, we propose a model that uses a recurrent structure to capture contextual information as far as possible when learning word representations, which keeps word orders information compared to GNN-based networks. Then, we use the idea of GNN's message passing to aggregate the contextual information and update the word hidden representation. Like GNN's readout operation, we employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the critical components in texts. We conduct experiments on four widely used datasets, and the experimental results show that our model achieves significant improvements against RNN-based model and GNN-based model.
FPoW: An ASIC-resistant Proof-of-Work for Blockchain Applications

Blockchain is a public ledger which is distributed in nature and has become highly popular. Bitcoin is the most successful application of it. The reason behind Bitcoin's success lies in its Consensus mechanism which ensures the security from any kind of attack. Because of this, no dishonest miner can affect the chain to manipulate it according to their wish. Also, the double spending problem does not occur and there is no need to trust a third party in this network. The most common consensus protocol in bitcoin technology is Proof-of-Work (PoW) which entirely depends on the computation power of miners. Because of this dependency, Application-specific Integrated Circuits (ASIC) is designed for bitcoin mining. Thereafter, it has become a threat to its decentralized nature and has been monopolizing the validation of new blocks. It is not possible to halt the production of ASIC-based devices even if it threatens the decentralized applications. So, different types of consensus protocols are proposed to nullify this threat whereas all of them have failed to fully nullify it. ASIC devices are costly, so only a few miners can afford it and monopolize over blockchain network. In our work, Filtered Proof-of-Work (FPoW) is proposed and its ASIC-resistivity has been evaluated to make it a future-proof ASIC-resistant consensus protocol.
A note on the concrete hardness of the shortest independent vector in lattices

Blömer and Seifert [1] showed that SIVP2 is NP-hard to approximate by giving a reduction from CVP2 to SIVP2 for constant approximation factors as long as the CVP instance has a certain property. In order to formally define this requirement on the CVP instance, we introduce a new computational problem called the Gap Closest Vector Problem with Bounded Minima. We adapt the proof of [1] to show a reduction from the Gap Closest Vector Problem with Bounded Minima to SIVP for any ℓp norm for some constant approximation factor greater than 1. In a recent result, Bennett, Golovnev and Stephens-Davidowitz [2] showed that under Gap-ETH, there is no 2o(n)-time algorithm for approximating CVPp up to some constant factor γ≥1 for any 1≤p≤∞. We observe that the reduction in [2] can be viewed as a reduction from Gap-3-SAT to the Gap Closest Vector Problem with Bounded Minima. This, together with the above mentioned reduction, implies that, under Gap-ETH, there is no randomised 2o(n)-time algorithm for approximating SIVPp up to some constant factor γ≥1 for any 1≤p≤∞.
Convolutional Autoencoder Application for Breast Cancer Classification

There are many related works for breast cancer detection using convolutional neural networks (CNN), and most of them rely only on feature extraction after the convolutions. However, because of the huge number of parameters in the models, the time of computation will be increased. In the current work, a convolutional autoencoder was proposed to reduce the complexity of the model, the number of parameters and as a result, prevent overfitting the model. In this p aper, there were trained several autoencoder models from scratch with different architectures and different hyperparameters. Moreover, 37% noise was applied on the input images and the model could reconstruct the original image with 84.72% accuracy. The sensitivity and precision of the proposed model were 86.87% and 80.23% respectively.
Conceptual Comparison of Port Commmunity System and Blockchain Scenario for Maritime Document Handling

Recent developments in science and technology along with massive digitalization encourage businesses to look into new possibilities for products and services. Certain upcoming innovations are being reinvented from previously established concepts and are now feasible due to technological advances. The purpose of the paper is to study and compare the existing innovative concepts of document handling in maritime shipping, in particular Port Community System and the blockchain scenario for document coordination. The present study builds a conceptual comparison based on mixed-method, incorporating theoretical framework and extensive data from qualitative interviews that were conducted with key actors of the maritime industry in Denmark. Having the overview of both concepts, the results showed that blockchain scenario for shipping document handling has been developed as an exact solution to issues that Port Community System was intended to solve: speed-up of communication, data security, elimination of the role of central gatekeeper, establishing point-to-point communication with transactions visibility and permissioned transparency. The study contributes to research by being first revealing a direct similarity between the two concepts and providing a critical overview of each. Moreover, the case could serve as a basis for further analysis of port network communication.
Optimal Design of Interval Type-2 Fuzzy Tracking Controllers of Mobile Robots Using a Metaheuristic Algorithm

This paper describes a modification of a biological inspired algorithm based on shark behavior, the Shark Smell Optimization (SSO), for the optimization of the membership functions parameters for the fuzzy controllers of autonomous mobile robots. SSO is a metaheuristic technique based on the behavior presented by sharks in nature, which can be used for solving optimization problems. First, SSO is used to optimize benchmark control problems. Second, the traditional SSO is tested with the optimization of the membership function’s parameters of type-1 fuzzy controllers. Third, tests are also performed with the Interval Type-2 Fuzzy Logic Controller. The comparison of results between the controller optimized with SSO and the controller optimized with WDO demonstrates that the proposed method shows better performance in the optimal design of fuzzy controllers.
Ensemble Learning for Skeleton-Based Body Mass Index Classification

In this study, we performed skeleton-based body mass index (BMI) classification by developing a unique ensemble learning method for human healthcare. Traditionally, anthropometric features, including the average length of each body part and average height, have been utilized for this kind of classification. Average values are generally calculated for all frames because the length of body parts and the subject height vary over time, as a result of the inaccuracy in pose estimation. Thus, traditionally, anthropometric features are measured over a long period. In contrast, we controlled the window used to measure anthropometric features over short/mid/long-term periods. This approach enables our proposed ensemble model to obtain robust and accurate BMI classification results. To produce final results, the proposed ensemble model utilizes multiple k-nearest neighbor classifiers trained using anthropometric features measured over several different time periods. To verify the effectiveness of the proposed model, we evaluated it using a public dataset. The simulation results demonstrate that the proposed model achieves state-of-the-art performance when compared with benchmark methods.
Inferring, Predicting, and Denoising Causal Wave Dynamics

The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally parameterizable laterally connected network modules. DISTANA is specifically designed to identify the causality behind spatially distributed, non-linear dynamical processes. We show that DISTANA is very well-suited to denoise data streams, given that re-occurring patterns are observed, significantly outperforming alternative approaches, such as temporal convolution networks and ConvLSTMs, on a complex spatial wave propagation benchmark. It produces stable and accurate closed-loop predictions even over hundreds of time steps. Moreover, it is able to effectively filter noise—an ability that can be improved further by applying denoising autoencoder principles or by actively tuning latent neural state activities retrospectively. Results confirm that DISTANA is ready to model real-world spatio-temporal dynamics such as brain imaging, supply networks, water flow, or soil and weather data patterns.
Parallel Implementations of ARX-Based Block Ciphers on Graphic Processing Units

With the development of information and communication technology, various types of Internet of Things (IoT) devices have widely been used for convenient services. Many users with their IoT devices request various services to servers. Thus, the amount of users’ personal information that servers need to protect has dramatically increased. To quickly and safely protect users’ personal information, it is necessary to optimize the speed of the encryption process. Since it is difficult to provide the basic services of the server while encrypting a large amount of data in the existing CPU, several parallel optimization methods using Graphics Processing Units (GPUs) have been considered. In this paper, we propose several optimization techniques using GPU for efficient implementation of lightweight block cipher algorithms on the server-side. As the target algorithm, we select high security and light weight (HIGHT), Lightweight Encryption Algorithm (LEA), and revised CHAM, which are Add-Rotate-Xor (ARX)-based block ciphers, because they are used widely on IoT devices. We utilize the features of the counter (CTR) operation mode to reduce unnecessary memory copying and operations in the GPU environment. Besides, we optimize the memory usage by making full use of GPU’s on-chip memory such as registers and shared memory and implement the core function of each target algorithm with inline PTX assembly codes for maximizing the performance. With the application of our optimization methods and handcrafted PTX codes, we achieve excellent encryption throughput of 468, 2593, and 3063 Gbps for HIGHT, LEA, and revised CHAM on RTX 2070 NVIDIA GPU, respectively. In addition, we present optimized implementations of Counter Mode Based Deterministic Random Bit Generator (CTR_DRBG), which is one of the widely used deterministic random bit generators to provide a large amount of random data to the connected IoT devices. We apply several optimization techniques for maximizing the performance of CTR_DRBG, and we achieve 52.2, 24.8, and 34.2 times of performance improvement compared with CTR_DRBG implementation on CPU-side when HIGHT-64/128, LEA-128/128, and CHAM-128/128 are used as underlying block cipher algorithm of CTR_DRBG, respectively.
Integrating Mobile Multimodal Interactions based on Programming By Demonstration​

Mobile Multimodal Interaction aims at exploiting complementary aspects of human communication capacities and new mobile sensors. Recently, most mobile applications are limited to a basic interaction modality, namely touchscreen, which is subject to restricted interaction under certain situations. In this paper, we present On-the-Fly Interaction Editor (OFIE), an application that allows mobile end-users to define and integrate sensor-based unimodal and multimodal input interactions in their already installed applications according to their contexts. OFIE is based on the Event-Condition-Action rules and Programming By Demonstration approach that allows end-users to demonstrate their expected action simply by performing it on the application’s interface. We evaluated OFIE through a controlled user study. Our evaluation involves 15 participants distributed on 4 groups based on their programming experience. Each participant was invited to integrate six input interactions (three multimodal inputs). The initial results show that end-users are able to successfully integrate sensor-based input interactions using OFIE.
A Novel BN Learning Algorithm Based on Block Learning Strategy

Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated blocks. Then, a pruned dynamic programming algorithm is performed sequentially for all possible graph structures to get possible BNs and find the best BN by scoring function. Experiments show that for high-dimensional and sparse data, the BLMKM algorithm can achieve the same accuracy in a reasonable time compared with non-blocking classical learning algorithms. Compared to the existing block learning algorithms, the BLMKM algorithm has a time advantage on the basis of ensuring accuracy. The analysis of the real radar effect mechanism dataset proves that BLMKM algorithm can quickly establish a global and accurate causality model to find the cause of interference, predict the detecting result, and guide the parameters optimization. BLMKM algorithm is efficient for BN learning and has practical application value.
Lightweight network and parallel computing for fast pedestrian detection

Summary In recent years, researchers have made great efforts in computer vision task (e.g., object detection) with the widely use of convolutional neural networks (CNNs). However, object detection algorithms based on CNNs suffer from high computation cost even on the high‐performance computers. In addition, with the development of high‐resolution videos, the deployment of object detection algorithms becomes more and more difficult because of the large amount of data, let alone the portable platforms, such as unmanned aerial vehicles (UAVs). In this paper, we research a lightweight network on portable platform for outdoor tiny pedestrian detection. Concretely, we first set up a training dataset manually for lack of tiny pedestrian samples in common datasets. We provide a lightweight network, and then, parallel computing is introduced to make the most of the advantage of GPU. Finally, our method can achieve real‐time performance on Jetson TX2. Experimental results verify that the proposed model has promising performance in tiny pedestrian detection designed for portable GPU platforms.
GPS Continues Wave Jamming Canceller using an ANF Combined with an Artificial Neural Network

Global Positioning System (GPS) navigation usage has increased in many important areas in recent years. So having better accuracy and efficiency are of particular importance. The signal transmitted by satellites goes a long way to reach receivers on the ground which leads to decrease in signal power. This weak signal can be easily affected by the intentional noise signals (or socalled jamming) that produce on the surface of the earth with high power or even unintentional noises. Therefore, jamming suppuration is one of the most important discussed topics in this field. In this paper, in order to cancel the effect of jamming on the received GPS signal, an Infinite Impulse Response (IIR) Adaptive Notch Filter (ANF) is proposed. As an adaption method in order to calculate the filter coefficient, we use a particular Neural Network (NN). Because of the small size of the NN that we used, the train time is very fast in compare to such traditional methods that take a long time to find optimized coefficients. The anti-jamming performance is evaluated by calculating the Root Mean Square (RMS) of prediction error, and also Satellite View (SV) observation number. The proposed algorithm provides the desired SV observation number, even for more than four vehicles. It also provides a good range of RMS of prediction error.
A Layered Blockchain Framework for Healthcare and Genomics

Scientific research in the area of human genomics is no longer restricted to scientists. With the introduction of companies such as 23andme and Ancestry, the interested people of the public have been getting involved in genetic studies. However, the nature of the data being collected and its link to confidential, personal information will need to be properly secured and complete to prevent the same issues faced by electronic medical records in healthcare: fragmentation and risk of disclosure. By integrating blockchains with genomics and healthcare, patients and research participants will be able to participate in their own healthcare and research, increasing patient and user centricity. In this paper, a layered architecture of how blockchains can be implemented in genomics and healthcare is proposed. The proposed architecture shows how communication between users, researchers, and medical professionals can be improved while encouraging genetic research while improving security and privacy.