Reviewerly/exHarmony huggingface dataset:
3D Gaze Estimation for Head-Mounted Devices based on Visual Saliency

Compared with the maturity of 2D gaze tracking technology, 3D gaze tracking has gradually become a research hotspot in recent years. The head-mounted gaze tracker has shown great potential for gaze estimation in 3D space due to its appealing flexibility and portability. The general challenge for 3D gaze tracking algorithms is that calibration is necessary before the usage, and calibration targets cannot be easily applied in some situations or might be blocked by moving human and objects. Besides, the accuracy on depth direction has always come to be a crucial problem. Regarding the issues mentioned above, a 3D gaze estimation with auto-calibration method is proposed in this study. We use an RGBD camera as the scene camera to acquire the accurate 3D structure of the environment. The automatic calibration is achieved by uniting gaze vectors with saliency maps of the scene which aligned depth information. Finally, we determine the 3D gaze point through a point cloud generated from the RGBD camera. The experiment result demonstrates that our proposed method achieves 4.34° of average angle error in the field from 0.5m to 3m and the average depth error is 23.22mm, which is sufficient for 3D gaze estimation in the real scene.
Zooming into chaos as a pathway for the creation of a fast, light and reliable cryptosystem

Abstract Motivated by today’s huge volume of data that needs to be handled in secrecy, there is a wish to develop not only fast and light but also reliably secure cryptosystems. Chaos allows for the creation of pseudo-random numbers (PRNs) by low-dimensional transformations that need to be applied only a small number of times. These two properties may translate into a chaos-based cryptosystem that is both fast (short running time) and light (little computational effort). What we propose here is an approach to generate PRNs—and consequently digital secret keys—that can serve as a seed for an enhanced chaos-based cryptosystem. We use low-dimensional chaotic maps to quickly generate PRNs that have little correlation, and then, we quickly (“fast”) enhance secrecy by several orders (“reliability”) with very little computational cost (“light”) by simply looking at the less significant digits of the initial chaotic trajectory. This paper demonstrates this idea with rigor, by showing that a transformation applied a small number of times to chaotic trajectories significantly increases its entropy and Lyapunov exponents, as a consequence of the smoothing out of the probability density towards a uniform distribution.
Rapid Class Augmentation For Continuous Deep Learning Applications

The amount of time required to train most deep neural networks makes them ill-suited for continuous learning applications where one would like to augment existing models with new classes on-the-edge and on-the-fly. This paper presents a new type of progressive learning approach called Rapid Class Augmentation (RCA) that can be used to rapidly augment an existing classifier with new classes. RCA has the distinguishing properties of optimizer feature memory and a class initialization vector that enable it to train on only the new class examples while optimally updating all the model’s classification weights. This paper will demonstrate that these properties allow RCA to augment existing models much faster and with reduced data storage requirements than conventional progressive learning techniques that rely on stochastic gradient descent optimization. This rapid class augmentation capability is expected to be especially advantageous for near real-time perception tasks that require continuous learning of new network classes on platforms with limited data storage and compute power.
Automatic Area-Based Registration of Optical and SAR Images Through Generative Adversarial Networks and a Correlation-Type Metric

The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural properties of the input data. In the present paper, this problem is addressed in the case of optical-SAR images by proposing a novel method based on deep learning and area-based registration concepts. The method integrates a conditional generative adversarial network (cGAN), an area-based cross-correlation-type l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> similarity metric, and the COBYLA constrained maximization algorithm. Whereas correlation-type metrics are typically ineffective in the application to multisensor registration, the proposed approach allows exploiting the image translation capabilities of cGAN architectures to enable the use of an l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> similarity metric, which favors high computational efficiency. Experiments with Sentinel-1 and Sentinel-2 data suggest the effectiveness of this strategy and the capability of the proposed method to achieve accurate registration.
Multiple extended stealth target tracking from image observation based on non‐linear sub‐random matrices approach

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi-Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB-filter-based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM-MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track-before-detect (TBD) approach, which is an efficient way to track low-observable ESTs, is proposed. In EST-RMM-TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST-sub-RMM-TBD composed of sub-ellipses is introduced, each representing an RMM. Based on such models, a sub-RMM-MB-TBD filter is used to estimate the kinematic states and extensions of sub-objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non-linear kinematic EST state is applied. The results indicate that the proposed SMC-sub-RMM-MB-TBD filter has more accurate cardinality estimation and smaller optimal sub-pattern assignment errors than the recent extended tracking filters.
<scp>Event‐driven</scp> data alteration detection using block‐chain

Abstract With the rapid development of Internet and Internet‐based applications, accessing of the file become easy from various locations at the same time around the globe, however, maintaining of data integrity turn into a challenging issue. In order to maintain a data integrity in a large database(s), database file is being kept to secure with the presence of control policies and firewalls, which basically limit the outside users with more strict control policies. Despite these control policies, there still lies a threat to the security of the data through an insider attack. An insider attack refers to someone who is a part of the organization and can have access to the resources (network, system, etc.) with inside knowledge of the system or knows the administrative rights, and thus interferes with the data, now become one of the most critical challenging issues for the organization as such attacks are hard to notice. An insider attacker is more crucial to the organization as they can perform espionage, embezzlement, theft of the intellectual property, disclosing of the research, and development being carried out in the organization, and sabotage. Therefore, to highlight this, an Event‐Driven Data Alteration Detection technique using the tamper resistance property of Block‐chain technology is being presented in this paper. The model has been tested and found that if any unauthorized alteration in the database is being made then it can be detected using the Block‐chain Database API. To make it robust to insider attacks we have implemented and tested the model on a web‐based application. A machine learning based technique will be considered for detection in the future scope of this paper. The results obtained after experiments show the performance of the proposed architecture and the comparison outcome shows that the proposed architecture is better than related models.
Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations

Abstract The importance of providing explanations for predictions made by black‐box models has led to the development of explainer model methods such as LIME (local interpretable model‐agnostic explanations). LIME uses a surrogate model to explain the relationship between predictor variables and predictions from a black‐box model in a local region around a prediction of interest. However, the quality of the resulting explanations relies on how well the explainer model captures the black‐box model in a specified local region. Here we introduce three visual diagnostics to assess the quality of LIME explanations: (1) explanation scatterplots, (2) assessment metric plots, and (3) feature heatmaps. We apply the visual diagnostics to a forensics bullet matching dataset to show examples where LIME explanations depend on the tuning parameter values and the explainer model oversimplifies the black‐box model. Our examples raise concerns about claims made of LIME that are similar to other criticisms in the literature.
Neural Networks Algorithm for Arabic Language Features-Based Text Mining

Abstract Text mining aims to understand texts correctly by utilising several phases to collect those features of Arabic words which are valuable and important to the applications mentioned above in making a correct decision. The technology then builds a strong system that relies on AI techniques, such as neural networks, to collect words in accordance with those features. An ANN is a collection of connected nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron is one that receives a signal then processes it and can signal to neurons connected to it. The current study is concerned with building a system for analysing words in the Arabic language. This system can be included in any application to address the Arabic language, becoming part of it. The system generates strings for all names and pronouns appearing in the entered text and depends mainly on the automatic assembly of a set features by using neural networks. We implemented the system, with its two phases, on the documents in succession. The results were encouraging, ranging between 83% and 96%.
3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds

Three-dimensional (3D) object detection is essential in autonomous driving. Three-dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles, cycles, pedestrians, and other objects on the road. Although Lidar can generate point clouds in 3D space, it still lacks the fine resolution of 2D information. Therefore, Lidar and camera fusion has gradually become a practical method for 3D object detection. Previous strategies focused on the extraction of voxel points and the fusion of feature maps. However, the biggest challenge is in extracting enough edge information to detect small objects. To solve this problem, we found that attention modules are beneficial in detecting small objects. In this work, we developed Frustum ConvNet and attention modules for the fusion of images from a camera and point clouds from a Lidar. Multilayer Perceptron (MLP) and tanh activation functions were used in the attention modules. Furthermore, the attention modules were designed on PointNet to perform multilayer edge detection for 3D object detection. Compared with a previous well-known method, Frustum ConvNet, our method achieved competitive results, with an improvement of 0.27%, 0.43%, and 0.36% in Average Precision (AP) for 3D object detection in easy, moderate, and hard cases, respectively, and an improvement of 0.21%, 0.27%, and 0.01% in AP for Bird’s Eye View (BEV) object detection in easy, moderate, and hard cases, respectively, on the KITTI detection benchmarks. Our method also obtained the best results in four cases in AP on the indoor SUN-RGBD dataset for 3D object detection.
Construction of English Network Teaching Platform Relying on Computer Big Data

Abstract In the application of data and information in recent years, a large number of large Internet English teaching databases have gradually been established and the requirements for data management and collection have become more mature. Related Internet companies need to be able to build a huge database based on the collected data. Data system, so as to better carry out simple English teaching big data application and query work, but because the quality and efficiency of current data collection are not high enough, the difficulty of collection is very large, so the construction of the database is extremely difficult and relevant Of personnel can find targeted data collection methods. At the same time, because the number of visits to English teaching big data is extremely large and the related visits can reach an astonishing millions at the peak, it also poses an inevitable test for the management of the collection department for combing and it needs to pass reasonable data management. And applications to build a huge data management system, so as to better support the English teaching large database to complete data query and management actions and achieve the optimization of database load balance.
A Novel Scheme for Real-Time Max/Min-Set-Selection Sorters on FPGA

Partial sorting, selecting M largest/smallest numbers from N inputs, is of interest in many applications. Thus, this brief presents a novel scheme for real-time max/min-set-selection sorters on field-programmable gate arrays (FPGAs). Our scheme has good expansibility but no close relation with the vector length, whose basic idea is reducing the programming complexity through the essence of sorting networks for Bitonic sequences. In addition, two modified forms of our scheme are proposed to solve the high data rate sequence and a multiple of 2 max/min-set-selection problem respectively. Finally, the effectiveness of our scheme is demonstrated on one Xilinx XC7VX690T FPGA by performing comparisons with state-of-the-arts.
Obstacle Avoidance Path Planning of Manipulator Based on Improved RRT Algorithm

Rapidly-exploring Random Tree (RRT) algorithm is suitable for solving path planning problems under high-dimensional space and complex constraints. To solve the problem of the RRT algorithm such as strong randomness, path redundancy, an improved-RRT algorithm with target probability offset and variable step size control is proposed in this paper. The improved-RRT algorithm can improve the operation efficiency and optimize the path. The average planning time of the improved RRT algorithm is reduced by 70.05%. At the same time, the end effector of the manipulator can reach the target position and orientation and each joint will not collide with obstacles in space. The correctness and effectiveness of the proposed method are demonstrated and validated via a MATLAB simulation.
Reconstruction of 3D flight trajectories from ad-hoc camera networks

We present a method to reconstruct the 3D trajectory of an airborne robotic system only from videos recorded with cameras that are unsynchronized, may feature rolling shutter distortion, and whose viewpoints are unknown. Our approach enables robust and accurate outside-in tracking of dynamically flying targets, with cheap and easy-to-deploy equipment. We show that, in spite of the weakly constrained setting, recent developments in computer vision make it possible to reconstruct trajectories in 3D from unsynchronized, uncalibrated networks of consumer cameras, and validate the proposed method in a realistic field experiment. We make our code available along with the data, including cm-accurate groundtruth from differential GNSS navigation.
Assisted Mobile Robot Teleoperation with Intent-aligned Trajectories via Biased Incremental Action Sampling

We present a method to assist the operator in teleoperation of mobile robots by generating trajectories such that the vehicle completes the desired task with ease in unstructured environments. Traditional assisted teleoperation methods have focused on reactive methods to avoid collisions, but neglect the operator's intention in doing so. Instead, we generate long horizon, smooth trajectories that follow the operator's intended direction while circumventing obstacles for a seamless teleoperation experience. For mobile robot teleoperation, an explicit goal in the state space is often unclear in cases such as exploration or navigation. Therefore, we model the intent as a direction and encode it as a cost function. As trajectories of various lengths can satisfy the same directional objective, we iteratively construct a tree of sequential actions that form multiple trajectories along the intended direction. We show our algorithm on a real-time teleoperation task of a simulated hexarotor vehicle in a dense random forest environment. By doing so, our approach allows operator to achieve the navigation task while requiring less effort than reactive methods.
Multi-party Private Set Intersection in Vertical Federated Learning

Vertical federated learning (VFL) is a privacy-preserving machine learning framework in which the training dataset is vertically partitioned and distributed over multiple parties, i.e., for each sample each party only possesses some attributes of it. In this paper we address the problem of computing private set intersection (PSI) in VLF, in which a private set denotes the data possessed by a party satisfying some distinguishing constraint. This problem actually asks how the parties jointly compute the common IDs of their private sets, which plays a key role in many learning tasks such as Decision Tree Learning. Currently all known PSI protocols, to our knowledge, either involve expensive cryptographic operations, or are designed for the two-party scenario originally which will leak privacy-sensitive information in multi-party scenario if applied to each pair of parties gradually. In this paper we propose a new multi-party PSI protocol in VFL, which can even handle the case that some parties drop out in the running of the protocol. Our protocol achieves the security that any coalition of corrupted parties, which number is less than a threshold, cannot learn any secret information of honest parties, thus realizing the goal of preserving the privacy of the involved parties. Moreover, it only relies on light cryptographic primitives (i.e. PRGs) and thus works more efficiently compared to the known protocols, especially when the sample number of dataset gets larger and larger. Our starting point to solve the PSI problem in VFL is to reduce it to computing the AND operation of multiple bit-vectors, each held by one party, which are used to identify parties' private sets in their data. Then our main technical contribution is to present an efficient protocol for summing up these vectors, called MulSUM, and then adapt it to a desired protocol, called MulAND, to compute the AND of these vectors, which result actually identifies the intersection of private sets of all (online) parties, thus accomplishing the PSI issue.
Practical Secure Two-Party EdDSA Signature Generation with Key Protection and Applications in Cryptocurrency

In cryptocurrency and blockchain-based distributed ledgers, transfer of money (digital coins) can be presented as a transaction. Due to the irreversibility nature of blockchain transactions, a single fraudulent use of private key (used to sign transactions) could have significant consequences (e.g. financial loss). Key protection alone is not adequate in protecting cryptocurrencies, and threshold signature is a viable method to avoid fraudulent key usage or key theft. In this paper, we focus on the Edwards-curve digital security algorithm (EdDSA), which has been applied in several cryptocurrencies (e.g. Cardano, Zcash, and Decred) and design the first efficient two-party EdDSA signing protocol. Unlike standard secret sharing, a valid signature is generated using an interactive protocol without the original key ever being exposed. We mathematically prove the security of our proposed protocol. Findings from the performance evalation of the protocol show that it achieves good performance for curve Ed25519, with a single signing operation in the malicious setting taking approximately 3.32 ms between two devices.
Unbounded Key-Policy Attribute-Based Encryption with Black-Box Traceability

Attribute-based encryption received widespread attention as soon as it was proposed. However, due to its specific characteristics, some restrictions on attribute set are not flexible enough in actual operation. In addition, since access authorities are determined according to users' attributes, users sharing the same attributes are difficult to be distinguished. Once a malicious user makes illicit gains by their decryption authorities, it is difficult to track down specific user. This paper follows practical demands to propose a more flexible key-policy attribute-based encryption scheme with black-box traceability. The scheme has a constant size of public parameters which can be utilized to construct attribute-related parameters flexibly, and the method of traitor tracing in broadcast encryption is introduced to achieve effective malicious user tracing. In addition, the security and feasibility can be proved by the security proofs and performance evaluation in this paper.
The accuracy of interrater reliability estimates found using a subset of the total data sample: A bootstrap analysis

Interrater reliability (IRR) assesses the stability of a coding protocol over time and across coders. For practical reasons, it is often difficult to assess IRR for an entire dataset, so researchers sometimes calculate the IRR for a subset of the total data sample. The purpose of this study is to investigate the accuracy of such subset IRRs. Using bootstrapping, we determined the effects of sample size (10%, 25%, &amp; 40% of the total dataset) and IRR measure type (percent agreement, Krippendorff’s alpha, &amp; the G Index) on the bias and percent error of subset IRRs. Results support the use of calculating IRR from subsets of the total data sample, though we discuss how the accuracy of subset IRR values may depend on aspects of the dataset such as total sample size and coding methodology.
Research on the Reform of Music Education Mode in Colleges and Universities Based on Computer Music Technology

Abstract With the continuous development of computer technology in the world today, this technology has been used in many fields, including music education. At present, music education in China still uses the traditional way of education. The teaching effect of the new computer music technology is undoubtedly much better than the traditional teaching method. This study discusses the development status and advantages of computer music technology and its specific application in music education.