Reviewerly/exHarmony huggingface dataset:
Analysis on Simplified Method of IoT-based HHL Algorithm Corresponding Quantum Circuit for Quantum Computer Application

Whether it is a traditional industry or a Frontier field, it has unveiled the trend of industrial IoT construction and application, which plays a vital role in building a strong manufacturing country and promoting high-quality economic development in China. HHL algorithm has become one of the important quantum algorithms, but there are few researches on the construction of quantum circuits and the application of quantum sequencing. In this paper, a model based on the quantum circuit corresponding to the HHL algorithm to deal with the quantum application problem is proposed. A quantum circuit based on HHL algorithm is used to solve the linear system, and the numerical solution of the target partial differential equation is obtained. Finally, the experimental analysis shows that, in the process of processing quantum computer application problems based on quantum circuit, it can reduce the computation amount of quantum circuit corresponding to HHL algorithm, improve the simulation efficiency of quantum circuit, and reduce the occupation of hardware resources, which has a certain effectiveness and superiority. This discussion brings new ideas for intelligent IoT technology and provides implications for the study of simplified methods of HHL algorithms corresponding to quantum circuits to deal with computer application problems.
Research on Credit Risk Prediction under Unbalanced Dataset Based on Ensemble Learning

Aiming at the problem of high-dimensional features and data imbalance in credit risk assessment that affect model prediction results, a credit risk prediction method based on integrated learning is proposed from three levels of features, data and algorithms. First, a hybrid filter and Random Forest feature selection method is used to select features. This method uses the improved Relief algorithm to initially select features, then combines the maximum information coefficient to eliminate redundant features, and uses the Random Forest algorithm to further reduce the feature dimension. Second, on the basis of the Borderline-SMOTE method, an adaptive idea is introduced to generate a different number of new samples for each minority sample at the boundary, and a new interpolation method is used to make the new sample distribution more reasonable, so as to reduce the sample imbalance. Finally, the Focal Loss is used to improve the loss function of LightGBM, and the sample weight is adjusted through the parameters α and γ in the Focal Loss function so that the model pays more attention to minority samples and difficult-to-classify samples, and improves the accuracy of model classification. And use the improved algorithm as the base classifier and then use AdaBoost and random subspace methods to integrate to establish a credit risk prediction model. Through comparative experiments with other methods, the results show that this method effectively improves the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>G</mi> </math> -mean value and the AUC value and has a better default prediction effect.
A Dynamic Pruning Method on Multiple Sparse Structures in Deep Neural Networks

It is well known that enormous computational power and a mass of memory are needed in deep neural networks.That makes them difficult to apply in resource-limited environments.Therefore, many network compression and acceleration technologies have come out, among which connection pruning is widely applied due to its effectiveness and convenience.A novel connection pruning method with full model capacity on multiple sparse structures is proposed in this paper.We design a simple and efficient function called Dynamic Processing Unit (DPU) to deal with the evaluated weights.Our method has the following features: (1) Instead of being pruned directly or set to 0, the weights are controlled by the DPU to determine whether they will work for subsequent forward of the network during the iteration of pruning training.(2) It supports the traditional multi-steps prune method as well as the end-to-end training mode which can get a compressed network in only one stage where training and pruning are fused.(3) It can learn multiple useful sparse structures, including but not limited in depth-wise, filter-wise, channel-wise, 2Dfilter-wise, row-wise, column-wise, connection-wise and mixed sparse structures.Our method is tested on various widely-used datasets and models, such as LeNet and ResNet on MNIST and CIFAR-10.Significantly, it achieves good performance in all these cases.Some details about our method can be found at this https://github.com/hujie369/DPU.
Vibration and Displacement Monitoring of Surface and Rock Strata in Geological Hazards Based on Mixed Technologies

With the rapid development through the advancement of technology, China geological hazard monitoring system has made great progress in engineering application. Advanced geological technology on hazardous geological monitoring is emerging, Internet of Things (IOTs), Geographic Information System (GIS), and Remote Sensing (RS), for example. However, there are still many problems existing subjected to monitoring system, such as sensor power storage, stability, compatibility, and weak analysis ability, which hinders the further development and popularization of the automatic monitoring system on geological hazards. In this paper, a new buried rock sensor and a fixed inclinometer are adopted, combined with the IOTs and the Beidou navigation system, to build an automatic monitoring system for geological disasters; it not only improves the accuracy and timeliness of monitoring data of surface and rock strata vibration and displacement of geological disasters but also provides guidance for future system design and development, especially in remote areas with steep terrain.
Two-Criteria Technique for the Resource-Saving Computing in the Fog and Edge Network Tiers

Introduction . At present, the concepts of fog and edge computing are used in a wide range of applications of various kinds. One of the key problems in the organization of computing in groups of mobile devices that make up the edge/fog layer is the mission assurance based on battery power availability. In this context, a lot of developments aimed at energy saving of device systems have been presented to date. However, one important aspect remains beyond the consideration of the problem of resource saving, namely, the issue of saving the residual resource of a computing device. The aim of this research is to formalize the workload distribution problem as two-criteria optimization problem, and to develop the basic solution technique. Materials and Methods . Within the framework of this article, an approach to resource saving is proposed. It is based on the evaluation of two device criteria: battery life and residual resource of a computing device. The residual resource of a computing device can be estimated using the probability of failure-free operation of the device, or as the reciprocal of the failure rate, taking into account that the exponential law of failure distribution is used in the simulation. From this, a model of the problem of two-criteria optimization is formulated, taking into account the dynamics of the network topology in the process of performing a user mission. The topology dynamics is reflected in the model as a sequence of topologies, each of which corresponds to a certain period of time of the system operation. Results . Based on the proposed model of the two-criteria optimization problem, a method was proposed for resource saving in the edge and foggy layers of the network. It reflected the specifics of the dynamic layers of the network, and also took into account the importance of the criteria for estimating the consumption of device resources. An experiment was conducted to evaluate the impact of the method of distributing tasks over a network cluster on the probability of failure-free operation of devices and on the average residual resource. Discussion and Conclusions . The conducted experiment has demonstrated the feasibility of using the developed method, since the distribution of tasks among executing devices had a significant impact (up to 25 % according to the results of the experiment) on the average residual resource of a computing device.
A Submodular Optimization Framework for Imbalanced Text Classification With Data Augmentation

In the domain of text classification, imbalanced datasets are a common occurrence. The skewed distribution of the labels of these datasets poses a great challenge to the performance of text classifiers. One popular way to mitigate this challenge is to augment underwhelmingly represented labels with synthesized items. The synthesized items are generated by data augmentation methods that can typically generate an unbounded number of items. To select the synthesized items that maximize the performance of text classifiers, we introduce a novel method that selects items that jointly maximize the likelihood of the items belonging to their respective labels and the diversity of the selected items. Our proposed method formulates the joint maximization as a monotone submodular objective function, whose solution can be approximated by a tractable and efficient greedy algorithm. We evaluated our method on multiple real-world datasets with different data augmentation techniques and text classifiers and compared results with several baselines. The experimental results demonstrate the effectiveness and efficiency of the proposed method.
Adaptive Partially Reliable Delivery of Immersive Media Over QUIC-HTTP/3

The increasing popularity of head-mounted displays (HMD) and depth cameras has encouraged content providers to offer interactive immersive media content over the internet. Traditionally, dynamic adaptive streaming over HTTP (DASH) is the go-to standard for video streaming. However, HTTP is built on top of protocols such as the transmission control protocol (TCP), which prioritize reliability over latency, thereby, inducing additional delay due to acknowledgments and retransmissions, especially on lossy networks. In addition, such reliable protocols suffer from head-of-line (HOL) blocking problem at various levels, leading to playout interruptions of the video streaming application. The third generation of HTTP, i.e., HTTP/3 was recently introduced to deal with the issues posed by TCP. As a major change, HTTP/3 replaces TCP with QUIC at the transport layer, which solves the HOL problem at the transport layer. Moreover, the datagram extension of QUIC allows for unreliable data delivery, just like UDP, which could substantially reduce the latency. Combining this feature of QUIC with the quality adaptation capabilities of DASH-based streaming could bring interactive immersive media delivery to the next level. This work proposes the integration of DASH with the concept of partial reliability of QUIC to reduce playout interruptions and increase the quality of the delivered immersive content on lossy networks. Here, the DASH scheme takes quality and prioritization decisions based on the changing network conditions and the user’s viewport, respectively. Then, the part of the content with the highest priority, i.e., within the viewport, is delivered reliably and the rest unreliably. To the best of our knowledge, this is the first work to combine adaptive streaming with partial reliability. Herein, we provide an implementation of a headless player which supports HTTP/3 over partially reliable QUIC as well as state-of-the-art protocols like HTTP/3 over reliable QUIC and HTTP/2 over TCP. We performed an extensive evaluation of our proposed solution using real-world 5G throughput traces and bursty packet loss conditions, using point cloud streaming as the use case. Firstly, our evaluation shows that HTTP/2 is highly intolerant to loss and not suitable for streaming immersive media. Furthermore, even at a loss as high as 5%, the partially reliable framework achieves 46% higher throughput and delivers the content with 33% fewer playout interruptions compared to the reliable counterpart. Since current point cloud decoders are sensitive to loss, we applied the forward error correction mechanism to the data sent unreliably to ensure that the client decodes the content at a probability of 99.9%. Applying this overhead to our solution provides a significant gain of 25% in the throughput compared to the state of the art.
An AI Based Automatic Translator for Ancient Hieroglyphic Language—From Scanned Images to English Text

Recent advancements in the fields of Machine Learning and Deep Learning made a huge transformation in other fields that are not related to Computer Science. In this work, a new framework is proposed to tackle the problem of translating the old Egyptian Hieroglyphic writings to English language through deploying both Image Processing and Natural Language Processing techniques combined with AI approaches. Our primary goal is to design an application that completely revolutionizes a tourist’s experience while navigating Egyptian Historical sites. This work utilize different AI techniques to automatically convert the scanned photos of hieroglyphic language to understandable and readable English language, through two main sub-tasks: The automatic detection and recognizing of the scanned glyphs images and the translation of them into English language. Different data sources of this low-resource language were explored and augmented to train and test our models. Results of different models and algorithms are assessed and analyzed to evaluate our work. State-of-the-art results are achieved compared to literature in both automatic glyphs recognition, and glyphs-to-English translation.
Enabling Five-Degree-of-Freedom From Low-Dimensional Light Fields With Extended Light Ray Acquisition Field-of-View

Conventional captured-image-based virtual reality (VR) systems hardly support free movement of the viewpoint, greatly reducing the user’s sense of reality. A light field (LF), which generates a novel view at a free viewpoint through a combination of light rays, is a suitable approach for increasing the degree of freedom (DoF) in captured-image-based VR. Previous LF-based VR systems have increased LF dimensions or used various acquisition layouts to obtain as many light rays as possible and thereby expand the renderable view range. However, these attempts are costly and time-consuming. This paper enables 5-DoF outcomes from a low-dimensional LF by increasing the field-of-view during light ray acquisition. The method starts with the simple idea that if a three-dimensional (3D) LF is constructed with 360° images that can capture all directions, the renderable range can be widened in a cost-efficient manner. However, using light rays with a large incident angle in a 3D LF can cause visual artifacts. To cope with this problem, this paper initially defines and analyzes the visual artifacts. While these errors were simply ignored in the past, they become particularly noticeable when using light rays with large incident angles. Thus, a careful analysis of the errors and their impacts is required. Additionally, this paper proposes two schemes to deal with visual artifacts based on the error analysis: a horizontal LF and vertical depth (HLF-VD) view generation scheme that complements the structural limitation of 3D LFs and can address all types of errors, and a hybrid 3D LF connection scheme that effectively reduces connection errors by controlling the connection position of neighboring 3D LFs. The proposed system is implemented and verified in various real-world scenarios, and the experimental results show that the user’s movement is naturally reflected at a level similar to that of high-dimensional LF, with minimal visual artifacts.
A Double Clustering Approach for Color Image Segmentation

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first method used the minimum distance, and the second method used the clustering algorithm called DBSCAN. Both methods were tested with and without reclustering using the self-organizing map (SOM). The result from comparing the images after segmenting them and comparing the time taken to implement the segmentation process shows the effectiveness of these methods when used with SOM.
A Robust Light-Weight Fused-Feature Encoder-Decoder Model for Monocular Facial Depth Estimation From Single Images Trained on Synthetic Data

Due to the real-time acquisition and reasonable cost of consumer cameras, monocular depth maps have been employed in a variety of visual applications.Regarding ongoing research in depth estimation, they continue to suffer from low accuracy and enormous sensor noise.To improve the prediction of depth maps, this paper proposed a lightweight neural facial depth estimation model based on single image frames.Following a basic encoder-decoder network design, the features are extracted by initializing the encoder with a high-performance pre-trained network and reconstructing high-quality facial depth maps with a simple decoder.The model can employ pixel representations and recover full details in terms of facial features and boundaries by employing a feature fusion module.When tested and evaluated across four public facial depth datasets, the suggested network provides more reliable and state-of-the-art results, with significantly less computational complexity and a reduced number of parameters.The training procedure is primarily based on the use of synthetic human facial images, which provide a consistent ground truth depth map, and the employment of an appropriate loss function leads to higher performance.Numerous experiments have been performed to validate and demonstrate the usefulness of the proposed approach.Finally, the model performs better than existing comparative facial depth networks in terms of generalization ability and robustness across different test datasets, setting a new baseline method for facial depth maps.
Entanglement meter: estimation of entanglement with single copy in interferometer

Abstract Efficient certification and quantification of high dimensional entanglement of composite systems are challenging both theoretically as well as experimentally. Here, we demonstrate how to measure the linear entropy, negativity and the Schmidt number of bipartite systems from the visibility of Mach–Zehnder interferometer using single copies of the quantum state. Our result shows that for any two qubit pure bipartite state, the interference visibility is a direct measure of entanglement. We also propose how to measure the mutual predictability experimentally from the intensity patterns of the interferometric set-up without having to resort to local measurements of mutually unbiased bases. Furthermore, we show that the entanglement witness operator can be measured in a interference setup and the phase shift is sensitive to the separable or entangled nature of the state. Our proposal bring out the power of Interferometric set-up in entanglement detection of pure and several mixed states which paves the way towards design of entanglement meter.
A Range Query Method for Data Access Pattern Protection Based on Uniform Access Frequency Distribution

Data encryption is necessary to keep user information secure and private on the cloud. However, adversaries can still learn valuable information about the encrypted data by observing data access patterns. To solve this issue, Oblivious RAMs (ORAMs) are proposed to hide access patterns. However, ORAMs are expensive and not suitable for deployment in a large database. In this work, we propose a range query algorithm while providing data access pattern protection based on uniform access frequency. In the preprocessing, multiple key-value pairs in the database are grouped and stored in each storage module, and we make copies for frequently accessed key-value pairs and also add some dummy key-value pairs on each storage module. In the online query processing, according to the range query length of the received query access request, we visit the specific storage module for the query and obtain the query result. Based on the techniques above, our method makes the uniform distribution of access frequency of data blocks in the database and achieves a security guarantee as strong as the state-of-the-art method. Compared with data queries that do not provide data access pattern protection, the ratio of network communication overhead is constant rather than logarithmic in ORAMs.
Chinese Herbal Medicine Recognition Using a VCSEL-Based Time-Delay Reservoir Computing System

We numerically investigate the performance of Chinese herbal medicine (CHM) recognition using a time delay reservoir computing (TDRC) system consist of a vertical-cavity surface-emitting laser (VCSEL) subjected to optical feedback. A database of 960 images from 12 CHM categories is used to evaluate the recognition performance, most of which contain many CHM samples and have different backgrounds. The CHM image feature vector is masked and optically injected into the VCSEL, and the VCSEL output in two polarization modes are sampled as the virtual node states for readout. After testing the CHM recognition performance of TDRC under injecting the same information into two polarization modes of the VCSEL, we further test the performance of parallel processing by injecting half of the image information into each of the two polarization modes. By concatenating the virtual node states collected from two modes for readout, the recognition speed is doubled. Meanwhile, the influence of key parameters on recognition performance are investigated and the relationship between system idle state and recognition performance is also examined. The results show that, under parallel processing and orthogonal optical feedback, the TDRC achieves the minimum recognition error of 1.7% at a reservoir processing rate of 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> images per second.