Reviewerly/exHarmony huggingface dataset:
Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum

Abstract This article sheds new light on the informational efficiency of the cryptocurrency market by analyzing investment strategies based on structural factors related to on-chain data. The study aims to verify whether investors in the cryptocurrency market can outperform passive investment strategies by applying active strategies based on selected fundamental factors. The research uses daily data from 2015 to 2022 for the two major cryptocurrencies: Bitcoin (BTC) and Ethereum (ETH). The study applies statistical tests for differences. The findings indicate informational inefficiency of the BTC and ETH markets. They seem consistent over time and are confirmed during the COVID-19 pandemic. The research shows that the net unrealized profit/loss and percent of addresses in profit indicators are useful in designing active investment strategies in the cryptocurrency market. The factor-based strategies perform consistently better in terms of mean/median returns and Sharpe ratio than the passive “buy-and-hold” strategy. Moreover, the rate of success is close to 100%.
Video Moment Retrieval via Comprehensive Relation-Aware Network

Video moment retrieval aims to retrieve a target moment from an untrimmed video that semantically corresponds to the given language query. Existing methods commonly treat it as a regression task or a ranking task from the perspective of computer vision. Most of these works neglect comprehensive relations between video content and language context at a multi-granularity level and fail to efficiently model temporal relations among different video moments. In this paper, we formulate video moment retrieval into video reading comprehension by treating the input video as a text passage and language query as a question. To tackle the above impediments, we propose a Comprehensive Relation-aware Network (CRNet) to perceive comprehensive relations from extensive aspects. Specifically, we unite visual and textual features simultaneously at both clip-level and moment-level to thoroughly exploit inter-modality information, leading to a coarse-and-fine cross-modal interaction. Moreover, a background suppression module is introduced to restrain irrelevant background clips, meanwhile, a novel IoU attention mechanism and graph attention layer are efficiently devised to focus on the dependencies among highly-correlated video moments for the best choice selection. In-depth experiments on three public datasets TACoS, ActivityNet Captions, and Charades-STA demonstrate the superiority of our solution.
Classification Matters More: Global Instance Contrast for Fine-Grained SAR Aircraft Detection

Since significant intra-class differences and inconspicuous inter-class variations, fine-grained aircraft detection in synthetic aperture radar (SAR) images is challenging. And the inherent lack of detailed features and severe noise interference in SAR images make it difficult to learn class-specific feature representations. Current detection approaches focus more on localization accuracy and ignore classification performance, which is more critical in fine-grained detection. To address the above challenges, we present GICNet: Global Instance Contrast for fine-grained SAR aircraft detection. A Global Instance-level Contrast (GIC) module is proposed to improve inter-class divergences and intra-class compactness. With a specially constructed global instance set, GICNet can contrast a large number of different aircraft targets while keeping a small batch size. Furthermore, we design a novel quality-aware focal loss (QAFL) to facilitate the accurate classification of well-localized aircraft targets. Meanwhile, to maintain localization performance, we develop a new edge-aware bounding-box refinement (EABR) module to refine predicted coarse bounding boxes. Experimental results show that our GICNet outperforms current advanced detectors and achieves a new state-of-the-art performance on the GaoFen-3 SAR aircraft detection dataset. Especially, GICNet also has advantages in reducing misclassification and recognizing well-located targets.
Generating A-Star Algorithm Admissible Heuristics Using a Dynamic Dataloader on Neural Networks, Enhanced With Genetic Algorithms, on a Distributed Architecture

Heuristic search algorithms are informed search strategies that use heuristics to estimate the minimal cost of the path from the current state to the goal. Using this additional knowledge, this type of algorithms can distinguish non-goal states, and then directs its search towards those that look more promising. This makes these algorithms faster than uninformed search algorithms. The A* algorithm is a well-known example of heuristic-based algorithms that is guaranteed to find the least-cost path to a goal state if the heuristic used is admissible, which means that it never overestimates the real cost from the current state to the goal. In this article, we are going to present a neural network architecture that makes it possible to predict admissible heuristics for a given graph and destination node, then we are going to refine our models using genetic algorithms in order to obtain better results.
Optimal Implicatıon Based Fuzzy Control System for a Steam Generator

The main advantage of a fuzzy set approach to control engineering is that it can be used to replace very complex and computationally demanding systems of partial differential equations. In this paper, we propose a new method for choosing implications. Proposed method allows to compare six fuzzy implications. The problem of identifying a control system with nonlinearity and operating in uncertain conditions is considered, and the synthesis of the system is carried out according to the operation of fuzzy logic. The development of a fuzzy control algorithm for a highly uncertain control object – a steam generator, the architecture of the fuzzy control system and the definition of its elements are considered. The fuzzy identification of a dynamic object, the analysis of the influence of various fuzzy implications on the process identification and the determination of the optimal implication are carried out using the example of a steam generator of thermal-power-plant (TPP) power units.
Design Thinking for Safety and Security: Support to Vancouver 2010 Olympics

The safety and security challenges that permeate our current threat and risk landscape transcend jurisdictional and organizational boundaries (Horton et al. in International Security Management: New Solutions to Complexity. Springer, Berlin, (2021)). Major events such as sporting events and entertainment concerts draw large crowds in a small area making it vulnerable to threat actors. Events such as the bombing in Manchester at the Ariana Grande concert in 2017, Las Vegas active shooter in 2017, Boston Marathon in 2013 represent the inherent vulnerabilities and security challenges that reside within major events (sporting, cultural, religious, musical). As described in (Masys in Science Informed Policing. Springer, Berlin, p. 237 2020), ‘…the post 9/11 security landscape has seen a fundamental shift in security planning, organization and management across the national and global security domain. This shift is particularly evident with regards to special events’. Security planning for such events cannot exist within a silo but must be part of an integrated event planning approach that brings together various stakeholders from the event planners, safety planners and security planners. This chapter presents how design thinking was employed to support the development of the Major Event Security Framework (MESF). The MESF was part of a larger research program: The Major Events Coordinated Security Solutions (MECSS) project, a multi-agency collaborative partnership, established to reduce the security risk associated with the Vancouver 2010 Winter Olympics and Paralympics, and the G8/G20 Summits.
A Benchmark Database for Animal Re-Identification and Tracking

While there are multiple sources of annotated images and videos for human and vehicle re-identification, databases for individual animal recognition are still in demand. We present a database containing five annotated video clips each containing between 9 and 27 identities. The overall number of individual animals is 20,490, and the total number of classes is 93. The database can be used for testing novel methods for animal re-identification, object detection and tracking. The main challenge of the database is that multiple animals are present in the same video frame, leading to problems with occlusion and noisy, cluttered bounding boxes. To set-up a benchmark on individual animal recognition, we trained and tested 26 classification methods for the five videos and three feature representations. We also report results with state-of-the-art deep learning methods for object detection (MMDet) and tracking (Uni-Track).
An Error-Correcting Code-Based Robust Watermarking Scheme for Stereolithographic Files

Stereolithographic (STL) files have been extensively used in rapid prototyping industries as well as many other fields as watermarking algorithms to secure intellectual property and protect three-dimensional models from theft.However, to the best of our knowledge, few studies have looked at how watermarking can resist attacks that involve vertex-reordering.Here, we present a lossless and robust watermarking scheme for STL files to protect against vertexreordering attacks.Specifically, we designed a novel error-correcting code (ECC) that can correct the error of any one-bit in a bitstream by inserting several check digits.In addition, ECC is designed to make use of redundant information according to the characteristics of STL files, which introduces further robustness for defense against attacks.No modifications are made to the geometric information of the three-dimensional model, which respects the requirements of a highprecision model.The experimental results show that the proposed watermarking scheme can survive numerous kinds of attack, including rotation, scaling and translation (RST), facet reordering, and vertex-reordering attacks.
The Complexity of the Shortest Vector Problem

Computational problems on point lattices play a central role in many areas of computer science including integer programming, coding theory, cryptanalysis, and especially the design of secure cryptosystems. In this survey, we present known results and open questions related to the complexity of the most important of these problems, the Shortest Vector Problem (SVP).
Beyond rankings: Learning (more) from algorithm validation

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.
Enhancing review-based user representation on learned social graph for recommendation

In recent years, review-based methods have been widely used to learn user representations because reviews contain abundant information. However, few users would like to write reviews for items. The data sparsity problem in reviews has become an emerging challenge in the field. Meanwhile, other approaches resort to social information based on graph neural networks (GNNs) to augment user representation. However, the efficacy of these approaches is always jeopardized because social graphs are not available in most real-world scenarios. Therefore, we propose a new Enhancing Review-based User Representation Model on Learned Social Graph for Recommendation, named ERUR. Specifically, we first introduce a review encoder to model review-based user/item representations. Second, we design a graph learning network to learn social relations between users according to the review-based user representation. Third, a graph neural network is developed to augment the final user representation under the supervision of a generative adversarial network. It integrates user reviews and social relations to enrich the final user representation for recommendation and further alleviate the data sparsity problem in reviews. Finally, we conduct experiments on seven datasets to demonstrate the effectiveness of the ERUR model in user representation learning compared to the SOTA recommendation models.
Synthetic Data and Its Evaluation Metrics for Machine Learning

Artificial Intelligence (AI) has become the key driving force in Industrial Automation. Machine learning (ML) and Deep Learning (DL) can be considered to be the components of AI which rely on data for model training. Data generation has increased due to the Internet, connected devices, mobile devices and social networking which in turn have also given rise to cybercrime and cyber thefts. To prevent those and preserve the identity of individuals in the public data, government and policymakers have put stringent privacy-preserving laws. The economy of data collection, quality of data in the public domain, and data bias have made data accessibility and its usage a challenge for AI/ML training for research work or industrial purposes. This has forced researchers to look into the alternative. Synthetic Data offers a promising solution to overcome the data challenges. The last few years have seen many studies conducted to verify the utility and privacy protection capability of synthetic data. However, all of these have been exploratory. This paper focuses on various methods of synthetic data generation and their validation metrics. It opens up a few questions that need further study before we conclude that synthetic data offers a universal solution for AI and ML.
Cascade Transformer Decoder Based Occluded Pedestrian Detection With Dynamic Deformable Convolution and Gaussian Projection Channel Attention Mechanism

Occluded pedestrian detection is very challenging in computer vision, because the pedestrians are frequently occluded by various obstacles or persons, especially in crowded scenarios. In this paper, an occluded pedestrian detection method is proposed under a basic DEtection TRansformer (DETR) framework. Firstly, Dynamic Deformable Convolution (DyDC) and Gaussian Projection Channel Attention (GPCA) mechanism are proposed and embedded into the low layer and high layer of ResNet50 respectively, to improve the representation capability of features. Secondly, Cascade Transformer Decoder (CTD) is proposed, which aims to generate high-score queries, avoiding the influence of low-score queries in the decoder stage, further improving the detection accuracy. The proposed method is verified on three challenging datasets, namely CrowdHuman, WiderPerson, and TJU-DHD-pedestrian. The experimental results show that, compared with the state-of-the-art methods, it can obtain a superior detection performance.
Spatial Context-Aware Time-Series Forecasting for QoS Prediction

With the explosive growth of Web services, the increasing sparse and dynamic QoS (quality of service) data pose a great challenge to QoS prediction in service recommendation. How to fully utilize the contextual information of service invocation and their potential relationships becomes the key to improving the accuracy of QoS prediction. In this paper, a spatial context-aware time series forecasting (SCATSF) framework is proposed for QoS prediction by considering both the temporal and the spatial context of users and services. SCATSF has three main parts: time-aware neighbors selection, spatial context-aware interaction learning and time series forecasting for QoS prediction. First, a novel time series similarity (TSS) is proposed to measure the similarity of users or services based on time-varying QoS fluctuation, in order to select appropriate neighbors for the target user or service. Furthermore, the contextual information of neighbors, as well as the contextual information of users and services, are both integrated to enrich the spatial context of service invocation, and a pairwise multi-layer deep network (PMLDN) is developed to consolidate multiple features and to learn feature interactions in pairs. With the help of spatial contextual information and time-aware QoS information, a spatial context-aware GRU model (SCA-GRU) is finally present to complete the time series forecasting for QoS prediction. Experimental results on the prediction of response time in a real-world dataset demonstrate that our SCATSF approach can effectively utilize spatio-temporal contextual information, so that it can achieve higher accuracy than many existing methods.
Self-Supervised Learning With Data-Efficient Supervised Fine-Tuning for Crowd Counting

Due to the expensive and laborious annotations of labeled data required by fully-supervised learning in the crowd counting task, it is desirable to explore a method to reduce the labeling burden. There exists a large number of unlabeled images in the wild that can be easily obtained compared to labeled datasets. Based on the characteristics of consistent spatial transformation with the annotations of heads and image, this paper proposes a self-supervised learning framework with unlabeled and limited labeled data for pre-training and fine-tuning crowd counting model (SSL-FT). It includes an online network and a target network that receive the same image but are randomly processed by two defined augmentation transformations. We leverage unlabeled data to pre-train the online network based on a self-supervised loss and small-scale labeled data to transfer the model to a specific domain based on a fully-supervised loss. We demonstrate the effectiveness of the SSL-FT on four public datasets including ShanghaiTech PartA, PartB, UCF-QNRF and WorldExpo'10 utilizing a classical counting model. Experimental results show that our approach performs better than state-of-art semi-supervised methods.
Blockchain-based Secure Land Registry System using Efficient Smart Contract

Given the current situation, A great number of reports about forged titles to real estate, fake land registries, undue transferring of delayed ownership, and government officials' involvement in deceptive practices are regularly being reported. On the other hand, this suggests that the current system for registering land deeds is inefficient and cannot reliably guarantee the safety of transactions between buyers and sellers or ensure that they are settled in a timely manner. To find a solution to this issue. In this paper, we suggested utilizing blockchain technology to create a land register system. The uniqueness and appeal of blockchain technology are its transparency and security. Persistence, immutability, and decentralization are qualities that blockchain is inculcating. its ascension to new opportunities for efficiency and cost savings. A decentralized application was suggested in this article. We used the Ethereum network specifically to build and deploy the smart contract. Through frontend web pages, interactions with the deployed contracts are possible. When creating websites, React is employed. Next.js is utilized for the server and routing. The analysis and findings demonstrate the viability and effectiveness of the suggested methodology.
An Overview of Navigation Algorithms for Unmanned Aerial Vehicle

In future, Unmanned Aerial Vehicles (UAV) would occupy most of the space in the air. The challenge in front of us is to minimize the air traffic and collision free air transportation. Finding the best route between the UAV's source location and destination location is also crucial. Algorithms for path planning guarantee the best, collision-free route. A sequence of waypoints between source location and destination location is being produced by the various algorithms. The decision making is critical, especially for the moving vehicle. The decision dependency of the moving vehicle is much more than a stationary system. In this paper, a survey is done from basic navigation algorithms to advanced ones for both static and dynamic environments.
Blockchain and Internet of Things (IoT) Enabled Smart E-Voting System

In the present situation, most people are not satisfied with the final result of the voting system. This is because the current system for voting is centralized and fully controlled by the election commission. So, there is a chance that the central body can be compromised or hacked and the final result can be tempered. In this direction, a decentralized, Blockchain and IoT based methodology for voting system is devised and presented in this paper. Blockchain is totally transparent, secured and immutable technique because it uses concept like encryption, decryption, hash function, consensus and Merkle tree etc. which make Blockchain Technology an appropriate platform for storing and sharing the data in a secured and anonymous manner. IoT makes use of biometric sensors using which people can cast their votes in not only physical mode but also in digital mode. As a response, a message is received to the owner for casting his vote to ensure the authentication. In this way, the present voting system is more secure and trust-worthy by using the properties of both Blockchain and IoT, and therefore, election process in the democratic countries is valued more. The proposed method ensures security as well as reduces the computational time as compared to the existing approaches.