Reviewerly/exHarmony huggingface dataset:
Cross-Modal Remote Sensing Image Retrieval Via Intra- and Inter-Modal Feature Matching

With the development of remote sensing (RS) acquisition technology, a mass of RS images have been produced, which brings challenges to the traditional manual retrieval methods and gives birth to the automatic RS image retrieval methods. Cross-modal RS image retrieval allows the usage of text and other modalities to retrieve RS images. For its flexible and convenient advantages, it has become a research hotspot. However, cross-modal RS image retrieval encounters the information asymmetry between modalities, i.e., RS images possess multi-scale, multi-objective properties and own rich information. At the same time, the query text is usually short and with less information. To solve the issues above, a cross-modal feature matching network is proposed to learn the feature fusion intra-modalities and the feature association inter-modalities to avoid the poor retrieval performance caused by the information asymmetry. Specifically, for the feature fusion intra-modalities, relying on the powerful feature representation ability of graph network, text and RS image graph modules are designed to fuse the intra-modal features. In terms of the feature correlation between modalities, RS image-text association module is created to attend the parts in text related to RS images and vice versa. Extended experiments on two public standard datasets verify the effectiveness of the proposed model.
The Affordances of Blockchain Platforms: Why Service Providers Use Blockchains

Contemporary blockchain platforms differ in their technological features and are under active development as open-source projects. Nonetheless, they have the potential to fuel substantial socio-economic changes. The scale of such changes, however, requires not only blockchain platforms but also organizations that develop and provide services on top of these platforms. Yet, little is known as to why service providers use specific blockchain platforms to offer their services or how they deal with the developmental state of blockchain platforms. This study, therefore, strives to identify the general affordances of blockchain platforms for service providers and aims to understand how service providers respond to different affordances on distinct platforms. Based on Affordance Theory and grounded in the analysis of 19 cases of blockchain service providers on the three most prominent blockchain platforms, we identify five types of salient affordances (i.e., validity affordances, analytical affordances, automation affordances, decentralization affordances, generative affordances). We explain how they result from specific features of blockchain platforms and from properties of the provided service. We lastly show that service providers’ use of a blockchain platform depends not only on the salient affordances of the platform but also on the values that are enacted by the open-source community behind it.
Evaluation of Re-Deployment for Environment-Adaptive Software

To use heterogeneous hardware, programmers needed sufficient technical skills such as OpenMP (Open Multi-Processing), CUDA (Compute Unified Device Architecture), and OpenCL (Open Computing Language). Therefore, I have proposed environment-adaptive software that enables high-performance operation by automatically converting and configuring the code once written, and have been working on automatic conversion and proper placement. However, until now, where to initially place the converted application has been considered, but the overall optimal placement has not been considered in consideration of the placement status of other users. In this paper, as a new element of environment-adaptive software, I study the relocation during operation, which improves the overall user satisfaction by considering the placement of other users, using a linear programming method. It was confirmed that it can be properly rearranged through simulation experiments.
3D Human Pose Estimation Based on Multi-feature Extraction

As an important computer vision task, 3D human pose estimation has received widespread attention and many applications have been derived from it. Most previous methods address this task by using a 3D pictorial structure model which is inefficient due to the huge state space. We propose a novel approach to solve this problem. Our key idea is to learn confidence weights of each joint from the input image through a simple neutral network. We also extract the confidence matrix of heatmaps which reflects its feature quality in order to enhance the feature quality in occluded views. Our approach is end-to-end differentiable which can improve the efficiency and robustness. We evaluate the approach on two public datasets including Human3.6M and Occlusion-Person which achieves significant performance gains compare with the state-of-the-art.
Wikipedia Infoboxes: The Big Data Source for Knowledge Bases behind Alexa and Siri Virtual Assistants

Knowledge bases such as Wikidata represent everything in the universe in a way computers can understand.
Deep Learning Based Identification Method for Signal-Level Wireless Protocol

Electromagnetic spectrum surveillance is the basis of space environment monitoring and management, while protocol identification is one of the most important methods.Protocol identification has been applied in many fields, such as target recognition, anomaly detection, security management and information countermeasure.In the past decade, a lot of deep learning based protocol identification methods have been proposed, based on the bit-level protocol.However, the data needs to be demodulated and decoded, depending on the prior information of the target system.In this paper, we focus on signal-level protocol, and introduce the deep learning based identification method.We first develop a multi-dimensional convolutional neural network based method for feature extraction of signal-level protocol.Then, a spatial pooling convolutional neural network is introduced for variable length protocol identification.At last, an unsupervised training network is proposed for feature extraction of unlabelled signal-level protocol.The proposed methods are evaluated by using the practical wireless protocols, and the effectiveness is verified by simulation results.
Mutual Support and Promotion: Learning Structure Compensation and Context Completion for Low-Light Vision

In recent years, visual perception tasks (e.g., object detection) in harsh scenes (e.g., low-light scenes) have received extensive attention due to their importance in various practical applications (e.g., autonomous driving). However, when addressing the above tasks, existing works are often too simplistic and straightforward to characterize different types of features, and often only focus on a single task while neglecting the performance of multiple tasks. To break the limitations of existing methods, this paper proposes a Structure Compensation and Context Completion Network (SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> N) for solving perception tasks in low-light scenes. Specifically, we first construct an object detection network and a self-supervised low-light image enhancement network to jointly extract various task-related features while meeting the task requirements. Furthermore, in order to reasonably utilize the effective information held by complex and different kinds of features, we design a structure compensation module and a context completion module to realize the information exchange between different tasks. Through the proposed bidirectional information interaction framework, the method could realize the mutual transfer and dynamic guidance of feature-level structural information and feature-level context information, thereby enabling the network to jointly improve low-light performance low-light images enhancement and detection. Finally, extensive comparative experiments fully verify the superiority of the proposed method compared with other state-of-the-art methods in both enhancement and detection. In addition, we also design a series of ablation analyses for each module to verify the effectiveness of the proposed method.
The Impact of the Sun on Trapped-Ion Quantum Computers

The sun causes fluctuations in the magnetic field on earth, both from slow, daily trends as well as from faster activity such as coronal mass ejections. These magnetic field fluctuations can lead to a miscalibration of the qubit frequency—which can induce memory error—in trapped-ion quantum computers. In fact, we find the magnetic field fluctuations from the sun are the dominant source of environmental drift of the magnetic field. The errors from these fluctuations can be mitigated to some extent by frequent calibration. Beyond this, we discuss additional methods both with and without hardware upgrades that can be implemented for further minimization of memory error in trapped-ion quantum computers.
Hidden Information General Game Playing with Deep Learning and Search

General Game Playing agents are capable of learning to play games they have never seen before, merely by looking at a formal description of the rules of a game. Recent developments in deep learning have influenced the way state-of-the-art AI systems can learn to play games with perfect information like Chess and Go. This development is popularised by the success of AlphaZero and was subsequently generalised to arbitrary games describable in the general Game Description Language, GDL. Many real-world problems, however, are non-deterministic and involve actors with concealed information, or events with probabilistic outcomes. We describe a framework and system for General Game Playing with self-play reinforcement learning and search for hidden-information games, which can be applied to any game describable in the extended Game Description Language for imperfect-information games, GDL-II.
LIREM: A Generic Framework for Effective Online Video Novelty Detection

Novelty detection in social video has drawn much attention of researchers and is applied to many tasks in real-world applications, such as e-commerce and e-learning. Existing methods cannot address this issue effectively, since most of them do not consider the quality of videos or the long-term information of online social videos. In this paper, we propose a general framework, Long-term Information REconstruction-based Model (LIREM), which cleans the video feature information and captures both short-term and long-term spatial-temporal information of video segments to detect novelty online. We first design a novel outlier detection method for feature cleaning to improve the learning performance. Then, an LSTM-Decoder model is constructed and applied to the cleaned video segments for predicting the reconstruction error of video features. Our experiments are conducted on three real datasets, and the experimental results demonstrate the performance of our model outperforms other novelty detection models.
Methods and Tools for Investigating Attacks - Memory Forensics

The memory of network attack and the reclusion of network crime make part of the key digital evidence only exist in physical memory or temporarily stored in the page exchange file, which makes the traditional file system-based computer forensics can not effectively deal with. Memory forensics as important supplement of traditional file system, is an important part of computer forensics science, through comprehensive access to memory data memory data, detailed analysis, based on the extraction and attack or network crime related to digital evidence, in recent years, sustained attention, memory forensics has won the security community obtained rapid development and wide application, in the network emergency response and network crime investigation play an irreplaceable role.
IOTA-Based Efficient and Reliable Scheme for Internet of Vehicles

Distributed Ledger Technology (DLT) has become popular with the creation of the Bitcoin Blockchain. However, it does not fully address issues such as scalability, transaction fees, and quantum security in application to the Internet of Vehicles (IoV). The IOTA Foundation has claimed to handle these issues: The Tangle. Directed Acyclic Graph (DAG)-based DLT named IoTA, encapsulating Tangles on the IoV environment. In this research, we demonstrate tip selection strategies to affect the IOTA tangle’s structure and behavior in IoV. Our proposed scheme describes Tangle’s implementation within the IoV use case scenario, providing a highly scalable and fee-less DLT mechanism. We use bundle constructing algorithms for making secured transactions among vehicles across the consensus process. This strategic platform strikes a good balance between the punishment of lazy or busy tips and managed to tackle the left-behind tips, reducing computational latency and approving higher inter-arrival data rates. Our findings show that the random data execution rate ( $$\alpha $$ ) and the transaction data arrival rate ( $$\lambda $$ ) are effective. Finally, our scheme shows an efficiency of 80% node execution and low latency-based reliable fee-less data delivery over the IoV network ends.
Formation of an analytical profile and strategies of an educational organization in the context of digitalization

In this study, the analysis of the educational organization of general education is carried out (using the example of secondary school No. 52 Sevastopol) in context of digitalization. At the first stage, an analytical profile of this organization is formed, that is, an analysis and forecast of the main indicators of its effectiveness is carried out. As a result of this stage, the key features, characteristics, problems and competitive advantages of the school in the educational services in context of digitalization market are identified. Thus, the authors introduce the concept of an analytical profile of an organization, which includes its general characteristics and features, competitive advantages and "problem points". At the second stage of the study, based on the results of the analysis and forecast, the strategies of the educational organization are formulated and their implementation is planned, while the developed strategies optimally correspond to the previously identified "problem points". The implementation of strategic alternatives formulated in this way contributes to improving the effectiveness of the organization under consideration with low educational results.
Breast Cancer Detection and Classification on Mammogram Images Using Morphological Approach

Breast cancer is one of the most common cancers affecting women worldwide. Mammography is the most well-known and effective method to detect early signs of breast cancer. The purpose of this paper is to detect breast cancer on the mammogram image to classify the disease through morphological techniques. Using conventional methods makes radiology difficult to detect cancer found in the patient’s breast. This proposal can be divided into several elements, which are input database, image preprocessing, image segmentation, morphological analysis, and object recognition. First, image preprocessing will be done using the Weiner and Median filters. Second, the thresholding method for image segmentation will be performed, and lastly, morphology will remove imperfections introduced during the image segmentation process. Finally, the image is classified into two classes: normal and cancerous images. A median filter and 0.95 thresholding achieve an accuracy of 93.71%, a sensitivity of 94.36%, and a specificity of 82.53% for the cancerous images.
Coordinating Multiple Cooperative Vehicle Trajectories on Shared Road Networks

This paper addresses the problem of planning trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle trajectories are coupled through interactions at shared areas, including intersections and road segments. A motion model is presented that includes the combined trajectories of all vehicles. The trajectories are optimized for travel time by solving a Mixed Integer Linear Program (MILP) that incorporates constraints to ensure collision avoidance. To improve the computation time of solving the MILP, two model modifications are introduced related to interactions: 1) The use of binary variables is reduced by grouping them at targeted travel times. 2) The relative travel order of interacting vehicles is constrained, guided by a heuristic solver. To further reduce computation time, an iterative method solves a sequence of smaller MILPs by targeting essential vehicle interactions with an algorithm that predicts interactions. Experiments are run on a simulated road network based on a real surface mine and haul truck trips. The proposed MILP methods are shown to significantly reduce solution costs compared to a reactive approach based on common driving practices. The MILP modifications significantly reduce computation time, trading off marginal solution quality. The reduction is extended by the iterative MILP scheme without degradation of solution quality.
Hyper-parameter Tuning for Progressive Learning and its Application to Network Cyber Security

The long-term deployment of data-driven AI technology using artificial neural networks (ANNs) should be scalable and maintainable when new data becomes available. To insure smooth adaptation, the learning must be cumulative so that the network consumes new data without compromising its inference performance based on past data. Such incremental accumulation of learning experience is known as progressive learning. In this paper, we address the open problem of tuning the hyperparameters of neural networks during progressive learning. A hyper-parameter optimization framework is proposed that selects the best hyper-parameter values on a task-by-task basis. The neural network model adapts to each progressive learning task by adjusting the hyper-parameters under which the neural architecture is incrementally grown. Several hyper-parameter search strategies are explored and compared in support of progressive learning. In contrast to the predominant practice of using imaging datasets in machine learning, we have used cybersecurity datasets to illustrate the advantages of the proposed hyper-parameter tuning algorithms.
Algorithmic Tacit Collusion: A Regulatory Approach

In light of the ongoing debate on algorithmic collusion, this paper intends to answer the following question: should algorithmic tacit collusion be prohibited under EU competition law? By algorithmic tacit collusion it is meant the capability of algorithmic pricing agents to unilaterally engage into tacitly collusive strategies without human intervention (we also call it ‘machine-to-machine cooperation’ or ‘algorithmic interdependent pricing’). Essentially, this practice raises the very same issues as the well-known oligopoly problem. Therefore, to make it prohibited one could envisage the traditional proposed solution of disentangling the categories of article 101 TFEU from the notions of an ‘act of reciprocal communication between firms’ or ‘meeting of minds’. The paper sets out to discuss this option and its implications. It argues that, from a competition law standpoint, although algorithmic tacit collusion remains undesirable, the notions of agreement and concerted practices should not be changed to encompass it. Rather, it embraces a regulatory perspective referred to as ‘algorithms by design’ which relies on introducing a legal obligation for firms to program algorithms in such a way as to prevent them from setting oligopolistic prices. In particular, while exploring this regulatory proposal, the paper discusses the peculiar case of algorithms that, though designed not to violate antitrust law, end up charging collusive prices. In this regard, the paper develops a second proposal: it introduces the idea of ‘outcome visibility’ to nail firms to their responsibility. This concept implies the idea that even if firms are not aware that their pricing algorithms are implementing a collusive strategy, they cannot ignore their visible market outcome.
An underwater image enhancement method based on color correction and transmission rate estimation

Aiming at the problems of color deviation and poor visibility of underwater images, an underwater image enhancement method based on color correction and transmission estimation (CCTE) is proposed. The method combines the global information of the attenuation channel with the pixel point information to adaptively correct the color deviation of the image so that the histogram distribution of the three channels is similar; the transmission is estimated based on the image blur to remove the blur effect of the underwater image; the multi-scale fusion process is combined Achieve artifact-free fusion of color-corrected images with sharp images. The qualitative and quantitative experimental results show that the enhanced images obtained by the CCTE method in this paper have good color perception and sharper details, and the UIQM and UCIQE scores on the RUIE dataset are 18.43% and 7.00% higher, respectively, compared to the optimal values in other methods, which are highly competitive.
Application of Content-Based Image Retrieval in Medical Image Acquisition

Content-based image retrieval is a promising technique to access visual data. With the huge development of computer storage, networking, and the transmission technology now it becomes possible to retrieve the image data beside the text. In the traditional way, we find the content of image by the tagged image with some indexed text. With the development of machine learning technique in the domain of artificial intelligence, the feature extraction techniques become easier for CBIR. The medical images are continuously increasing day by day where each image holds some specific and unique information about some specific disease. The objectives of using CBIR in medical diagnosis are to provide correct and effective information to the specialist for the quality and efficient diagnosis of the disease. Medical image content requires different types of CBIR technique for different medical image acquisition techniques such as MRI, CT, PET Scan, USG, MRS, etc. So, in this concern, each CBIR technique has its unique feature extraction algorithm for each acquisition technique.
Decentralized Machine Learning over the Internet

Decentralized machine learning has attracted extensive research interest in recent years. Compared to its centralized counterpart that relies on a server to collect and disseminate messages, decentralized machine learning relies on message passing among neighboring nodes, and thus avoids the communication bottleneck of the server. However, most of the existing decentralized machine learning algorithms operate within data centers that are equipped with multiple CPUs and/or GPUs, assuming that the underlying communication networks are error-free. In this paper, we implement decentralized parallel stochastic gradient descent (D-PSGD) over multiple computers connected with the Internet which is relatively unreliable. To cope with the inevitable packet losses caused by the Internet environment, we modify D-PSGD by allowing it to use stale messages. Numerical experiments confirm the performance gain of decentralized machine learning over its centralized counterpart in terms of runtime, as well as the robustness of the modified D-PSGD to packet losses.