Reviewerly/exHarmony huggingface dataset:
An object detection and classification method for underwater visual images based on the bag-of-words model

In this study, an autonomous underwater vehicle (AUV) detection technique was used as the background to investigate the target classification method for underwater visual images. The target classification method is based on the bag-of-words model, with reduced classification accuracy when the edges of underwater objects are blurred, and when there are many noise points. To solve this problem, an improved visual dictionary generation method was proposed based on the bag-of-words model. The proposed method includes a step on decomposing the complete contour of the target before extracting the features. The DCE (discrete contour evolution) algorithm was used to decompose the complete contour into segments of different lengths, and the SC (shape context) method was applied to extract the features of the contour segments. The FCM (Fuzzy C-Means) algorithm was then used to generate a visual dictionary. To address the classification accuracy problem that causes the underwater targets to decrease after adding spatial location information to the bag-of-words model instead of increasing, an improved spatial location information description method was proposed. The contour segments with the nearest-neighbor relationship were represented as visual phrases, constituting the spatial information of the bag-of-words model. Additionally, for the problem of the shape information of contour segments with different shapes, and the relative position information between the contour segments having different contributions to the target classification, a method was developed to balance the contributions of the two using the optimal weight selected from the experiments. The experimental results demonstrated that the proposed method can effectively improve the classification accuracy of underwater target images.
A GNN-Based Variable Partition Framework for DCOPs

Many problems of the Internet of Things (IoT), such as radio frequency allocation and sensor network, can be regarded as constraint optimal problems (COPs), which can be formulated as graphical representations. The scale of graph is large, which is hard to implement, and the information shared by all the variables is unsafe for all the variables running in an agent. On the other hand, supercomputers are playing a significant and growing role in various fields of large-scale processing tasks. When countering this scene, the supercomputers can accelerate to complete the task according to the distributed solution, where they divide the task into sub-tasks and each sub-task is running on an agent, such as a process or a computation node. However, finding an optimal distributed solution is difficult to minimize the completion time with the optimal computing resources. Putting the task on too many agents not only wastes resources but also increases the risk of attacks. Conversely, fewer agents may take too much time, which is unacceptable for users. Determining the number of agents needs to strike a balance between communication and computation. In this paper, we propose a new framework GVPNN for predicting the optimal numbers of agents for COPs and further provide the allocation from variable to agent. Experimental result shows the framework can learn the structure of the corresponding graphical representation well, and the 1-distant accuracy rate and the top 3 accuracy rate of GVPNN reach 74% and 70%, respectively.
Parallel bandit architecture based on laser chaos for reinforcement learning

Abstract Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a massive number of states, unlike previously demonstrated bandit problems where the number of states is only one. Q-learning is a well-known approach in reinforcement learning that can deal with many states. The architecture of Q-learning, however, does not fit well photonic implementations due to its separation of update rule and the action selection. In this study, we organize a new architecture for multi-state reinforcement learning as a parallel array of bandit problems in order to benefit from photonic decision-makers, which we call parallel bandit architecture for reinforcement learning or PBRL in short. Taking a cart-pole balancing problem as an instance, we demonstrate that PBRL adapts to the environment in fewer time steps than Q-learning. Furthermore, PBRL yields faster adaptation when operated with a chaotic laser time series than the case with uniformly distributed pseudorandom numbers where the autocorrelation inherent in the laser chaos provides a positive effect. We also find that the variety of states that the system undergoes during the learning phase exhibits completely different properties between PBRL and Q-learning. The insights obtained through the present study are also beneficial for existing computing platforms, not just photonic realizations, in accelerating performances by the PBRL algorithms and correlated random sequences.
ROLE OF BLOCKCHAIN

At present, with the rapid development of the Internet structure, the topic of information technology together with the concept of digitalization has become important for enterprises. Arguably, the technological advancements that make digitization possible have contributed to the digital transformation of business models. The concept of digital transformation has surfaced as an area where businesses have been working hard. Digital transformation refers to the overall transformation of a business based on mortals, business processes and technology to provide more efficient and effective services. Therefore, blockchain, known as a disruptive technology, is of great significance for icing on the cake for the digital transformation of enterprises. In this study, some decisions have been made to abandon blockchain technology. Many industries, such as finance, pharmaceuticals, manufacturing, and education, use blockchain operations to benefit from the unique properties of this technology. Blockchain technology (BT) promises benefits in accountability, collaboration, association, identification, trustworthiness and transparency. We also review the literature and promising blockchain-based open intelligence systems to describe the current state of exploration. To this end, we specifically examine these systems for their applicability and contribution to open intelligence, and categorize them according to their primary purpose. Keywords: blockchain technology; digital metamorphosis; blockchain advantages and challenges
Design and Analysis of Immersive Interaction Scenarios for Social Products

In the Internet era, with the continuous iterative updating of communication technology and the development of artificial intelligence, Internet of things, 5g, and other technologies, all kinds of social products have become one of the main ways of communication. For example, Facebook, WeChat, and ins are essential products for people in modern society. From personal communication to world interconnection, they are inseparable from the support of social products. At present, various social products in the market have different advantages or specific users and often cannot deal with all user groups. Because the needs of different customers are different and cannot fully meet the needs of different users, coupled with the development of technology, the communication media is becoming more and more innovative, the boundary between virtual and reality is broken, and the immersive scene breaks through the boundary of traditional communication, which is the way people want to communicate. In view of these shortcomings and needs, this paper introduces the methods of immersive scene diversification. First, sort out the user needs, then build different user models according to the specific characteristics of different scenarios, and realize the interaction of different scenarios according to different scenarios. The end-user model interacts with the corresponding design. After continuous testing, feedback, and improvement, the efficiency, quality, and specific methods of socialized products are improved. The experimental analysis results of this paper show the effectiveness and rationality of the immersive scene diversification method. The design can break the barriers and enhance the interaction efficiency and quality of both sides of information flow. The main body of information dissemination also presents a multilateral form and improves the inequality of information dissemination.
A rORAM scheme with logarithmic bandwidth and logarithmic locality

Oblivious Random Access Machine (ORAM) is a kind of cryptographic primitive that allows a client to access its private data from the server without disclosing the access pattern. To deal with consecutive requested blocks at a time efficiently, range ORAM (rORAM) is presented. In the previous rORAM scheme, the locality, namely, the number of discontinuous seeks to complete a request, is reduced to O(log2 N), nevertheless, the bandwidth cost is increased to the poly-logarithmic level. Hence, there exists an open question, that is, whether rORAM can be constructed with the same bandwidth efficiency as a regular ORAM, that is, O(log N)-block? In this paper, we propose a new rORAM scheme, called L2-rORAM. In our scheme, a compatible superblock technique is proposed, and it is combined together with an eviction technique for range blocks, so that it avoids duplication of multiple copies and extra dummy access. As a result, it obtains O(log N)-block bandwidth cost, which affirmatively answers the above open question. Meanwhile, the data locality is reduced to O(log N). In addition, the client storage is maintained at the small level of O(log N)-block, and the server storage is maintained at the unexpanded level of O(N)-block. Finally, experimental results show that the average response time of our L2-rORAM is reduced by one order of magnitude over the state-of-the-art rORAM scheme.
Feature Enhanced Graph Neural Network for Few-Shot Image Classification

There has been a rising trend of solving few-shot image classification problems utilizing graph neural networks (GNN) in recent years. However, most GNN-based approaches fail to fully exploit relationships among samples, resulting in poor image classification. To address this problem, we propose a feature-enhanced GNN model appropriate for few-shot image classification tasks. The suggested method first exploits an efficient convolutional block to generate accurate feature maps, enhancing the expressivity of the feature extraction module. Then, our technique employs flexible combinatorial distance metric functions to compute the exact relation score between samples and determine image relevance by minimizing the matching cost. Moreover, a multilayer perceptron based on the residual structure with an attention mechanism is developed to produce focused feature representations, allowing the model to obtain selective and relevant information among the samples. The proposed model is evaluated on a supervised few-shot image classification task utilizing four benchmark datasets, with the corresponding results demonstrating that our model achieves a higher accuracy performance than traditional few-shot image classification methods.
A Multiple Locking Group Scheme for Flexible Semantic Conflict Prevention in Real-Time Collaborative Programming

Real-time collaborative programming has attracted increasing attention and interest in recent years. To resolve the semantic conflict problem in real-time collaborative programming, a Dependency-based Automatic Locking (DAL) scheme was proposed in prior work. The DAL scheme prevents other collaborators from editing semantically related regions by automatically detecting depended regions and locking them. However, the DAL scheme lacks flexibility and is not well suited to the needs of programmers in a real-world development scenario. When the programmer switches to a new region, all previous locks are automatically released. For this reason, we propose the Multiple Locking Group (MLG) scheme, where each programmer can hold multiple locking groups and switch freely between multiple working regions. Accordingly, three release modes for releasing locking groups are proposed. Each programmer can customize the release modes in a fine-grained manner. In supporting the scheme, we have devised techniques and solutions, implemented a prototype system and conducted a preliminary user evaluation to validate the feasibility, effectiveness and usability of the MLG scheme.
Occurrence And Relationship Of The Aqra, Bekhme And Govanda Formations In The Soran (Rawanduz) Area, Kurdistan Region, Northeastern Iraq

The present study focuses on the presentation of field and laboratory evidences for the first record of the Aqra Formation (Maastrichtian) outcrop at the top of the Tanjero Formation in the Soran area, Erbil Governorate. The previous studies indicated its outcrops as Middle Miocene Govanda Formation between Merga Red Bed Series and Tanjero Formation. The present study discusses relations of this outcrop with Govanda and Bekhme formations in the Sulaimani and Duhok governorates in terms of environment and tectonics. In the Soran area, the formation has variable thickness ranging between 2-160 m and underlies either Red Bed Series or Govanda Formation (Middle Miocene). The study documented many stratigraphic and paleontological evidences to prove occurrence of the Aqra Formation in the area. Additionally, we discussed the significance of its occurrence in detail by which many tectonic and stratigraphic issues of the area are unlocked. One of the issues is occurrence of Tanjero Formation between the Aqra and Bekhme formations and it separates the two formations in two different age ranges and tectonic episodes. Another issue is the documentation of the occurrence of the Aqra Formation inside the Thrust Zone by which the distribution of the formation is extendable beyond Main Zagros Thrust Fault. The study includes a detailed stratigraphic column and tectonic model of the formation to show the coastal area, patchy reef, and shelf environment of the Aqra Formation.
Detection of Hardware-Assisted Virtualization Based on Low-Level Feature

Hardware-assisted virtualization is widely adopted in cloud computing and desktop platforms, significantly enhancing the security and efficiency of full virtualization systems by dividing multiple guest operating systems (OSes) and providing isolation between guest OSes and host OS. Thus, malware is usually analyzed in a guest OS to prevent the host OS from being compromised. However, an advanced malware will deliberately masquerade as a benign program when it detects that it is in a virtualized environment. The ideal goal of virtualization is to be utterly transparent to the guest processes, i.e., a guest process cannot be aware that it runs in a virtualized environment. Nowadays, although hardware-assisted virtualization detection is challenging, we present three novel approaches that can be leveraged to detect hardware-assisted virtualization. Our detection approaches are mainly based on the following features: i) The available CPU instruction sets are different in native and virtualized environments; ii) Modification to specific control register flag has different effects in native and virtualized environments; iii) The physical addresses in native and virtualized environments have different effects on the eviction of the level 2 CPU cache. We have conducted intensive experiments in both native and cloud environments. The experimental results show that our approaches can effectively detect the existence of hardware-assisted virtualization.
Adaptive Set-Based CLPSO for Path Planning of Multi-Target Surveillance with Moving Obstacles

Multi-target surveillance is an important application of path planning in the field of autonomous vehicles (AV). The existing researches are limited to either static or random obstacle environments. A model-based moving obstacle environment is proposed in this paper. By setting the start point of the AV, when evaluating the path, the moving process of AV is monitored. The positions of obstacles are updated according to the time, as well as the path threat. The cost of the route between any two targets depends not only on the distance, but also the path threat value weighted by a penalty factor. In this paper, a set-based comprehensive learning particle swarm optimization with local search (S-CLPSO+LS) is proposed to find the optimal path. The influence of the penalty factor on the obstacle avoidance ability of the algorithm is discussed. Four dynamic scenarios with different characteristics are designed and analyzed, and other path planning algorithms have been used for comparative experiments.
A Fuzzy Query Method Oriented to Knowledge Graph

Abstract With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the fuzzy query of the knowledge graph, this paper introduces the unclear expansion syntax of aggregate function based on summarizing and analyzing the existing SPARQL fuzzy expansion. Supplements the qualitative preference analysis method then proposes and SPARQL1. 1 Corresponding fuzzy SPARQL1.1 (f-SPARQL1.1) simultaneously provides strategies and techniques for rewriting ambiguous queries into classic SPARQL queries. In addition, this article also provides a visual interface for defining the membership functions of fuzzy terms and provides three interactive methods for setting fuzzy membership functions. Compared with the previous fuzzy query methods for RDF models, the method proposed in this paper has more vital fuzzy query ability and more flexible universality. The experimental results show the usability of the method proposed in this paper.
Multi-scale Deep Curve Estimation for Low-light Image Enhancement

Due to the limitation of the device, pictures taken in low-light environment usually consist of unpleasant deterioration, such as low contrast and color distortion. In this paper, we propose a Multi-scale Deep Curve Estimation network (MSDCE) for low-light image enhancement, which formulates the single low-light image enhancement task as a pixel-wise curve estimation by paired learning. To impose more priors of low-light regions, we propose an inverse illuminance map as part of the Curve Estimation network input. The curve estimation network backbone is composed of multi-scale modules which learns information from multi-scale feature streams and ensures the information exchange across different scales. Compared with several state-of-the-art methods, our method is significantly better. From the perspective of visual evaluation, our MSDCE can effectively improve the contrast and illumination of the image, and ensure the color fidelity of the image.
A Novel Smoke Concentration Detection Model Derived by Combining HSL Space and Improved Dark Channel

Traditional smoke sensors are limited by the measurement principle and generally can only measure the smoke concentration in a local area. With the development of image processing technology, spatial non-contact smoke concentration measurements have become possible. This research innovatively proposes an image-based spatial smoke concentration detection method that combines HSL color space and dark channel prior theory. First, we adjusted the brightness of a smoke image in the HSL color space, and then improved the calculation method of atmospheric light in the dark channel prior to obtain a more accurate transmission. We used the actual measured data as a sample to perform our experiments. Four image features, including transmission, saturation, Weber contrast of luminance, and colorfulness, were selected to train the support vector regression. The experimental results showed that the proposed model was effective and the image smoke concentration calculated based on the proposed model yielded a higher level of accuracy than did similar algorithms.
Research on Mine OFDM Intelligent Receiver Based on Deep Learning

Abstract coal is an important energy. Coal mining needs a series of equipment, among which communication equipment can ensure the safety of underground workers in the mining process and improve production efficiency. Aiming at the problems of multipath fading and anti-interference of signal transmission in coal mine, researchers propose to optimize the quality of signal transmission in coal mine with OFDM technology. Compared with traditional OFDM technology, this paper proposes a method of mine OFDM intelligent receiver based on deep learning, which aims to use deep learning technology to replace the signal recovery process of the receiver of traditional OFDM technology. The simulation results show that in terms of bit error rate, the signal recovery of the receiver based on deep learning technology is obviously better than that of the traditional OFDM receiver. This will improve the economic benefits of coal mining in the future.
Modified Pólya-Gamma data augmentation for Bayesian analysis of directional data

In this work, we develop new data augmentation algorithms for Bayesian analysis of directional data using the von Mises-Fisher distribution in arbitrary dimensions. The approach leads to a new class of distributions, called the Modified Pólya-Gamma distribution, which we construct in detail. The proposed data augmentation strategies circumvent the need for analytic approximations to integration, numerical integration, or Metropolis-Hastings for the corresponding posterior inference. Simulations and real data examples are presented to demonstrate the applicability and to apprise the performance of the proposed procedures.
Research on Longitudinal and Lateral Feedback Control on Autonomous Vehicle for Lane Changing Scene

In the real traffic environment, lane changing is a common behavior that causes accident frequently. In order to supplement the shortcomings of human behavior, make the vehicle achieve more reliable lane changing operation and improve the rationality and safety of vehicle lane changing, this paper presents a longitudinal and lateral feedback control method of automatic driving for lane changing scene. In this paper, the relationship model between time to collision and lane changing time is established, and the relationship between lane changing safety threshold is given, which improves the driving safety of lane changing scene. Then the trajectory planning model is established by polynomial curve method, and the vehicle control is achieved by linear quadratic regulator and proportional-integral-derivative control. Finally, simulation system is established based on Simulink and Prescan software. The experimental results verify the effectiveness of the control method.