Reviewerly/exHarmony huggingface dataset:
Performance Evaluation Experiments of Bitcoin SV Scaling Test Network

Bitcoin SV Scaling Test Network (STN) is an experimental network for testing Bitcoin scalability using on-chain technology. A large amount of transactions are always sent to the peer-to-peer network, and huge blocks are constantly generated. In this study, by running our STN node, occupancy rate of transaction processing and blockchain split probability are estimated. As a result, the estimated occupancy rate was about 1.04 and the estimated split probability was 8.5%. In addition, transaction processing performance was experimentally evaluated by sending transactions including OP_RETURN script as frequently as once per minute during one week. As a result, the estimated probability that a transaction was finally approved was 98%. We also confirmed that the distribution of latency time until a transaction is approved follows a power-law distribution at the high tail. From these results, we concluded that it is consistent to analyze STN using the theory of priority queueing.
Situation Awareness and Target Recognition of Marine Big Data Battlefield based on Deep Learning

This paper first introduces the concept of marine big data battlefield situational awareness deep learning, and then introduces the framework and learning model of battlefield situational awareness deep learning system based on big data. On this basis, it focuses on the application of big data deep learning technology in three aspects of battlefield situational awareness target entity type, category and attribute identification. Finally, the application research of big data deep learning technology in battlefield situation awareness is summarized.
Research on Multi-NPC Marine Game AI System based on Q-learning Algorithm

As a typical representative of human-machine intelligence, games have always pursued the fidelity of NPCs to humans. In the traditional game design process, the behavior of NPCs is usually solidified, and a fixed behavior tree is designed for each NPC so that NPCs can choose appropriate behaviors according to the actual game state during the game progressing process. However, the behavior tree of this fixed coding mode is rigid in behavior and needs to design a complex and huge behavior tree to describe the complex NPC logic. As a dynamically upgradeable behavior tree, Q-learning has strong online adaptability. The artificial intelligence AI behavior tree is designed through the Q-learning algorithm, which can evaluate the environment and action selection and reversely affect the action plan to make human-like behavior feedback. This paper optimizes the Q-learning algorithm and proposes an optimized NPC behavior tree starting from the idea of simulated annealing.
Reinforcement Learning Path Planning based on Step Batch Q-Learning Algorithm

With the rapid development of artificial intelligence, people have put forward higher requirements for robot path planning. As a more commonly used algorithm, reinforcement learning learns from experience by imitating the process of human learning skills and continuously iterates to do. In this paper, an improved Step Batch Q-Learning algorithm is proposed to solve the problem that the traditional Q-Learning algorithm has a slow convergence speed in discrete states. Prediction can detect obstacles and target points earlier, reduce the number of iterations, and significantly speed up the convergence time. Under the same experimental conditions, the method in this paper is compared with the traditional Q-Learning algorithm. The experimental results show that the improved Step Batch Q-Learning algorithm has significantly improved convergence speed and good adaptability.
Head Movement-aware MPEG-DASH SRD-based 360° Video VR Streaming System over Wireless Network

This paper presents an effective adaptive 360° video VR streaming system based on the Spatial Representation Description (SRD) feature of MPEG-DASH. The goal of the proposed system is to provide a seamless 360° video VR streaming service with high spatial quality over the time-varying wireless network. To achieve this goal, the proposed system adopts tile-based viewport-adaptive streaming that is a promising way to efficiently deliver 360° video. In accordance with the SRD information and user’s head movement, the proposed system is designed to enhance the quality of the user experience by intensively allocating a limited bit budget to specific segments of interest without noticeable blanks. Additionally, we propose a fast recovery algorithm to minimize the effect of blanks caused by unpredictable head movement. Moreover, the proposed system considers the binocular disparity of the human eye to improve the subjective quality of stereo video. The proposed system is fully implemented by using well-known open sources (e.g., OpenHMD, Kvazaar and GPAC), C/C++, Python and head-mounted display (HMD) equipment. Finally, the experimental results are provided to verify the performance of the proposed system in real wireless network environments.
Deep-Reinforcement-Learning-based User-Preference-Aware Rate Adaptation for Video Streaming

Online video is the most popular Internet application. As the throughput would frequently change under different network conditions, it is important to adaptively select the proper bitrate and improve user’s quality of experience. In this paper, we propose a new DRL-based rate adaption algorithm for video streaming, which holistically captures user’s preference of video contents, network throughput and buffer occupancy, and select the proper bitrate for video to improve the QoE. Specifically, we use 3D Convolutional neural (C3D) network to learn the spatio-temporal features, and implement the semantic analysis of videos. We also apply the Term Frequency-Inverse Document Frequency (TF-IDF) method to analyze the user’s preference of different scene types, according to its viewing history. The dynamic adaptive streaming is formulated as a Markov Decision Process (MDP) problem, and use the Actor-Critic (A3C) algorithm to dynamically choose the optimal bitrate. As corroborated by simulations, our algorithm can accurately obtain the user’s preference, keep the bitrate allocation consistent with the user’s preference, and maintain video quality. Compared with the state-of-the-art Pensieve algorithm, our algorithm improves the average QoE by at least 12.5%. It also has a significant improvement over other baseline methods.
A Tailored Particle Swarm and Egyptian Vulture Optimization-Based Synthetic Minority-Oversampling Technique for Class Imbalance Problem

Class imbalance is one of the significant challenges in classification problems. The uneven distribution of data samples in different classes may occur due to human error, improper/unguided collection of data samples, etc. The uneven distribution of class samples among classes may affect the classification accuracy of the developed model. The main motivation behind this study is the design and development of methodologies for handling class imbalance problems. In this study, a new variant of the synthetic minority oversampling technique (SMOTE) has been proposed with the hybridization of particle swarm optimization (PSO) and Egyptian vulture (EV). The proposed method has been termed SMOTE-PSOEV in this study. The proposed method generates an optimized set of synthetic samples from traditional SMOTE and augments the five datasets for verification and validation. The SMOTE-PSOEV is then compared with existing SMOTE variants, i.e., Tomek Link, Borderline SMOTE1, Borderline SMOTE2, Distance SMOTE, and ADASYN. After data augmentation to the minority classes, the performance of SMOTE-PSOEV has been evaluated using support vector machine (SVM), Naïve Bayes (NB), and k-nearest-neighbor (k-NN) classifiers. The results illustrate that the proposed models achieved higher accuracy than existing SMOTE variants.
A Network-based IoT Covert Channel

Information leaks are a top concern to industry and government leaders. The Internet of Things (IoT) is a rapidly growing technology capable of sensing real-world events. IoT devices lack a common security standard and typically use lightweight security solutions, exposing the sensitive real-world data they gather. Covert channels are a practical method of exfiltrating data from these devices.This research presents a novel IoT covert timing channel (CTC) that encodes data within preexisting network information, namely ports or addresses. This method eliminates the need for inter-packet delays (IPD) to encode data. Seven different encoding methods are implemented between two IoT protocols, TCP/IP and ZigBee. The TCP/IP covert channel is created by mimicking a Ring smart doorbell and implemented using Amazon Web Services (AWS) servers to generate traffic. The ZigBee channel is built by copying a Philips Hue lighting system and executed on an isolated local area network (LAN). Variants of the CTC focus either on Stealth or Bandwidth. Stealth methods mimic legitimate traffic captures to make them difficult to detect while the Bandwidth methods forgo this approach for maximum throughput. Detection results are presented using shape-based and regularity-based detection tests.The Stealth results have a throughput of 4.61 bits per second (bps) for TCP/IP and 3.90 bps for ZigBee. They also evade shape and regularity-based detection tests. The Bandwidth methods average 81.7 Kbps for TCP/IP and 9.76 bps for ZigBee but are evident in detection tests. The results show that CTC using address or port encoding can have superior throughput or detectability compared to IPD-based CTCs.
WiP: Applicability of ISO Standard Side-Channel Leakage Tests to NIST Post-Quantum Cryptography

FIPS 140–3 is the main standard defining security requirements for cryptographic modules in U.S. and Canada; commercially viable hardware modules generally need to be compliant with it. The scope of FIPS 140–3 will also expand to the new NIST Post-Quantum Cryptography (PQC) standards when migration from older RSA and Elliptic Curve cryptography begins. FIPS 140–3 mandates the testing of the effectiveness of “non-invasive attack mitigations”, or side-channel attack coun-termeasures. At higher security levels 3 and 4, the FIPS 140–3 side-channel testing methods and metrics are expected to be those of ISO 17825, which is based on the older Test Vector Leakage Assessment (TVLA) methodology. We discuss how to apply ISO 17825 to hardware modules that implement lattice-based PQC standards for public-key cryptography - Key Encapsulation Mechanisms (KEMs) and Digital Signatures. We find that simple “random key” vs. “fixed key” tests are unsatisfactory due to the close linkage between public and private components of PQC keypairs. While the general statistical testing approach and requirements can remain consistent with older public-key algorithms, a non-trivial challenge in creating ISO 17825 testing procedures for PQC is the careful design of test vector inputs so that only relevant Critical Security Parameter (CSP) leakage is captured in power, electromagnetic, and timing measurements.
Sign Language Recognition Based on Computer Vision

With the application of deep learning in the field of computer vision, target detection, recognition and target tracking technologies have been developed. Sign language recognition is currently a hot topic in the field of machine learning, and this project uses computer vision domain technologies to implement sign language recognition to solve the problems of deaf people in daily communication. In this project, two algorithm models, CNN+LSTM network structure and YOLOv5 target detection, are studied, and the functions of sign language recognition are implemented by the two algorithms respectively, and their implementation effects are compared, and the advantages and disadvantages of the two algorithms in sign language recognition are derived. Among them, YOLOv5 completes the task of sign language recognition by detecting hand movements, and its fast detection speed is more in line with the needs of life scenes and satisfies the demand for real-time sign language translation, which is more promising for application.
WIP: Impact of AI/ML Model Adaptation on RAN Control Loop Response Time

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models.
A Joint Semantic Segmentation Loss Function for Imbalanced Datasets

Semantic segmentation is one of the most important applications in remote sensing image analysis. Since remote sensing datasets are often highly imbalanced in terms of class distribution, specialized loss functions such as focal loss are required. In this paper, a loss function that combines weighted focal loss with Jaccard loss has been developed. This loss function has been used to train U-Net and DeepLabV3+ semantic segmentation models on the recently introduced Land-cover.ai dataset, which has a high level of class imbalance. It has been observed through our experiments that the combined loss function leads to a performance improvement.
A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach

Abstract Statistics in sports plays a key role in predicting winning strategies and providing objective performance indicators. Despite the growing interest in recent years in using statistical methodologies in this field, less emphasis has been given to the multivariate approach. This work aims at using the Bayesian networks to model the joint distribution of a set of indicators of players’ performances in basketball in order to discover the set of their probabilistic relationships as well as the main determinants affecting the player’s winning percentage. From a methodological point of view, the interest is to define a suitable model for non-Gaussian data, relaxing the strong assumption on normal distribution in favour of Gaussian copula. Through the estimated Bayesian network, we discovered many interesting dependence relationships, providing a scientific validation of some known results mainly based on experience. At last, some scenarios of interest have been simulated to understand the main determinants that contribute to rising in the number of won games by a player.
Photon-by-photon quantum light state engineering

The ability to manipulate light at the level of single photons, its elementary excitation quanta, has recently made it possible to produce a rich variety of tailor-made quantum states and arbitrary quantum operations, of high interest for fundamental science and applications. Here we present a concise review of the progress made over the last few decades in the engineering of quantum light states. Although far from exhaustive, this review aims at providing a sufficiently wide and updated introduction that may serve as the entry point to such a fascinating and rapidly evolving field.
The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis

Today, comments can be made on many topics on web platforms with the development of the internet. Analyzing the data of these comments is essential for companies and data scientists. There are many methods for analyzing data. Recently, language models have also been used in many studies for sentiment analysis or text classification. In this study, Turkish sentiment analysis is performed using language models on hotel and movie review datasets. The language models are chosen because they are rarely used in Turkish literature. The pre-trained BERT, ALBERT, ELECTRA, and DistilBERT models for the Turkish language are trained and tested with these datasets. In addition, a text filtering method, which removes the words that can provide the opposition sentiment in the positive or negative labeled text, is proposed for sentiment analysis. These datasets obtained by this method are also retrained with language models and the accuracy values of their models are measured. The results of this study are compared with previous studies using the same datasets. As a result of the analysis, the accuracy values obtain state-of-the-art results with language models compared to previous studies. The best performance has been achieved by training the ELECTRA language model using the proposed text filtering method.
Assessing evolutionary terrain generation methods for curriculum reinforcement learning

Curriculum learning allows complex tasks to be mastered via incremental progression over 'stepping stone' goals towards a final desired behaviour. Typical implementations learn locomotion policies for challenging environments through gradual complexification of a terrain mesh generated through a parameterised noise function. To date, researchers have predominantly generated terrains from a limited range of noise functions, and the effect of the generator on the learning process is underrepresented in the literature. We compare popular noise-based terrain generators to two indirect encodings, CPPN and GAN. To allow direct comparison between both direct and indirect representations, we assess the impact of a range of representation-agnostic MAP-Elites feature descriptors that compute metrics directly from the generated terrain meshes. Next, performance and coverage are assessed when training a humanoid robot in a physics simulator using the PPO algorithm. Results describe key differences between the generators that inform their use in curriculum learning, and present a range of useful feature descriptors for uptake by the community.
Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs

This paper focuses on the concept of "absumption" which restrains over-general rules by decomposing them into several concrete rules, proposes the novel "absumption" for continuous spaces by improving the conventional absumption to achieve high performance (e.g., the acquired rewards) in a noisy environment, and integrates it into the XCS for real-valued inputs (XCSR) to evaluate it through a comparison with the conventional absumption. Concretely, the proposed absumption mechanism based on Overgenerality and Condition-clustering based specialization (called Absumption-OC) manipulates the balance between the Overgenerality of the rules and the specialization of Condition of the rules. Through the intensive experiments of three different types of continuous space problems, the following implications have been revealed: (1) XCSR with Absumption-OC shows the statistically significant performance in the acquired reward, the system error, and the population size against XCSR with the conventional absumption; and (2) this effectiveness of Absumption-OC becomes to be clear in noisy reward environments in comparison within noiseless reward environments.
Measuring optimiser performance on a conical barrier tree benchmark

The common method for testing metaheuristic optimisation algorithms is to benchmark against problem test suites. However, existing benchmark problems limit the ability to analyse algorithm performance due to their inherent complexity. This paper proposes a novel benchmark, BTB, whose member functions have known geometric properties and critical point topologies. A given function in the benchmark is a realisation of a specified barrier tree in which funnel and basin geometries, and values and locations of all critical points are predetermined. We investigate the behaviour of two metaheuristics, PSO and DE, on the simplest manifestations of the framework, ONECONE and TWOCONES, and relate algorithm performance to a downhill walker reference algorithm. We study success rate, defined as the probability of optimal basin attainment, and inter-basin mobility. We find that local PSO is the slowest optimiser on the unimodal ONECONE but surpasses global PSO in all TWOCONES problems instances below 70 dimensions. DE is the best optimiser when basin difference depths are large but performance degrades as the differences become smaller. LPSO is the superior algorithm in the more difficult case where basins have similar depth. DE consistently finds the optimum basin when the basins have equal size and a large depth difference in all dimensions below 100D; the performance of LPSO falls away abruptly beyond 70D.
On funnel depths and acceptance criteria in stochastic local search

We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic performance. Three distinct iterated local search (ILS) acceptance strategies are considered: better-or-equal (standard), annealing-like, and restart. Funnel measurements are analysed for their connection to ILS performance on the underlying combinatorial problems. We communicate the findings through hierarchical clustering of LONs, network visualisations, subgroup analysis, correlation analysis, and Random Forest regression models. The results show that funnel depth is associated with search difficulty, and that there is an interplay between funnel structure and acceptance strategy. Standard and annealing acceptance work better than restart on both deep-funnel and shallow-funnel problems; standard acceptance is the best strategy when optimal funnel(s) are deep, while annealing acceptance is superior when they are shallow. Regression models including funnel depth measurements could explain up to 96% of ILS runtime variance (with annealing-like acceptance). The runtime of ILS with restarts was less explainable using funnel features.
Visual discomfort factor analysis and modeling for worldwide stereoscopic 3D maps

EarthView3D is the first worldwide orthographic stereoscopic 3D (S3D) map. It presents an accurate presentation of the Earth's surface and can provide an immersive geospatial infrastructure for the upcoming metaverse ecosystems. This study conducted the first comprehensive assessment of the worldwide S3D maps, investigated the factors of visual discomfort, predicted discomfort level, and provided design recommendations for improving visual experience. Participants rated the S3D map images and reported the reasons for their visual discomfort. General Eye Symptom Questionnaires (GESQs) and Simplified Simulator Sickness Questionnaires (SSSQs) were used to record the development of visual fatigue with time. Six categories of measurable variables presumably related to visual discomfort were proposed based on participants' reports, IEEE standards, and literature on visual experience. Factor analysis extracted four principal factors, disparity, terrain texture, luminance, and amplitude spectrum, whose contributions to variance were 38%, 23%, 16%, and 11%, respectively. The selected variables and subjective mean rating scores were used to construct a regression model for the prediction of visual discomfort. Performance evaluation Root Mean Square Error (RMSE) was lower than that reported in previous studies on different S3D databases. The results indicated that besides vergence-accommodation conflict and depth cue conflict, perception of Earth terrain's textures and luminance played an important role in visual discomfort of viewing orthographic S3D maps.