Reviewerly/exHarmony huggingface dataset:
Face age verification for access control application

The age verification is an important task in various context of applications like access control in spaces in hotels which are prohibited for children and teenagers, in dangerous spaces for children and in public area during a spread of virus among others. In fact, the age verification consists in classifying the face images into different age groups while dealing with the face appearance variation affected by occlusion, pose variation, low resolution, scale variation and illumination variation. This work introduced an access control application based on the age verification in an uncontrolled environment. In fact, we proposed a new two-level age classification method based on deep learning in order to classify the face images into eight age groups. Actually, the two-level classification strategy help reducing the confusion between the inter and intra age groups. Our experiments were performed on the multi-constrained Adience benchmark. The obtained results illustrate the effectiveness and robustness of the proposed age classification method in an uncontrolled environment.
VReason Grasp: An Ordered Grasp Based on Physical Intuition in Stacking Objects

The supporting semantics plays a vital role in the robotic grasping task. However, it is difficult to identify the spatial relationship of the objects and grasp them orderly without falling. This paper presents a Visual Reasoning Grasp Framework (VRGF) to address the problems mentioned above. The VRGF consists of two parts: 3D semantic segmentation and Supporting Relationship Reasoning (SRR). We adopt the fused projection for 3D semantic segmentation, which can reduce the processing time and promote the performance of the reasoning framework. The SRR can distinguish how objects support each other in the stacking scenarios. We also design a complete robotic system to verify the robustness and generalization of the framework in the real world. The experiment results demonstrate the reliability and robustness of the VRGF’s grasping strategy for stacking objects.
Few-shot object detection via metric learning

To achieve good results with the existing target detection framework, a large amount of annotated data is often needed. However, the acquisition of annotated data is a laborious process. It is even impossible to obtain sufficient annotated data in some categories. To reduce the dependence of deep learning model on large-scale data, a new end-to-end single-stage detector (FSSSD) network is proposed based on metric learning method. In this way, the objects that are not seen during training can be detected under the condition of providing a small number of samples. The main innovation of this paper is that the traditional single-stage detection model FCOS is improved and the Class Feature Extractor module is added to make it become the Correlation Detector. Thus, the model can extract the feature distribution of the support category from the small number of pictures provided by the support set. Thereafter, the model converts the query set feature map into the object probability distribution map, and fuses it with the original feature map to enhance the feature representation of the potential objects consistent with the supporting category on the feature map, so that the model pays more attention to the objects consistent with the supporting category in classification and regression. The method in this paper does not need fine-tuning or retraining at all when recognizing objects of a new category, and only needs to provide supporting pictures of the corresponding category during testing. At the same time, our modules are flexible and easy to migrate, theoretically suitable for all target detection models, and can improve the performance of these models on few-shot problems.
Amplitude-squared squeezing of Schrödinger cat states via postselected von Neumann measurement

We know that the original Schr\"odinger cat states has no amplitude-squared squeezing. In this paper, we investigate the amplitude-squared squeezing of Schr\"odinger cat states using postselected von Neumann measurement. The results show that after postselected von Neumann measurement, the amplitude-squared squeezing of Schr\"odinger cat states change dramatically and this can be considered a result of weak value amplification. The squeezing effect also investigated by studying the Wigner function of Schr\"odinger cat states after postselected measurement.
Risk Assessment of Electronic Archive Services using Octave Allegro Method (Case Study: SIKN JIKN)

Abstract The National Archives Information System (SIKN) and the National Archives Information Network (JIKN) are the government national priority programs that aimed at providing web-based electronic archive treasury information facilities to the public. However, threat and disruption of critical information asset on electronic archives can cause interruption strategic objectives goals, vision, and mission of The National Archives of Indonesia (ANRI) in organizing archives that impact the network node, and the public. Therefore this research aims to analyze and identify risks in the SIKN JIKN electronic archive service and determine the potential risk of threats to important information assets. The OCTAVE Allegro method used to measure information security on five important information assets of the SIKN JIKN electronic archive information service. The results showed 64 areas of concerns analyzed, as many as 40 had to mitigate while the remaining 24 could be deferred or postponed. The conclusion of the risk assessment results in recommendations that must take to manage and protect critical assets in electronic archive services and supporting material for policymaking.
Distance regularization energy terms in level set image segment model: A survey

The level set is a classical image segmentation model. In order to achieve its stable evolution, the level set function should be a signed distance function (SDF). However, due to the common appearance of irregularities, it must periodically be initialized in order to remain a SDF near the zero level set. Distance regularization terms have been used to maintain the stable evolution of the level set function. We provide a survey of the various distance regularization potential functions. Firstly, we summarize many kinds of distance regularization potential functions studied in the literature. We then divide them into five classes according to the type of potential function. Secondly, we analyze the properties of every class of potential functions and their diffusion rate functions. Finally, to demonstrate the effectiveness of the distance regularization potential functions, we apply them with a region based level set energy functional for image segmentation. Experimental analyses are conducted to compare the segmentation performance of various distance regularization potential functions when combined with the classical Chan Vese model.
Fast convex optimization of vehicle trajectories based on improved trust domain

Abstract The re-entry process of lift vehicles faces a variety of complex constraints and path limitations, so fast and accurate trajectory optimization for them is an important topic. This paper proposes a fast trajectory generation method based on improved sequential convex optimization, which simplifies the equations of motion with energy as the independent variable, takes the sine and cosine function of the bank angle as the control variable, and convexities multiple constraints to obtain a computational model that satisfies the optimization conditions. On this basis, the "jitter" phenomenon, which is easy to occur in the optimization process, is improved, and the method of variable trust domain and weight function is proposed to reduce the jump problem in the iterative process, and also to improve the terminal accuracy requirement. Finally, numerical simulations show that the algorithm can efficiently and rapidly solve the re-entry trajectory optimization problem under the inclusion of no-fly zone.
Depth estimation of the light field image based on convolutional neural network

Light field imaging uses a single camera to capture an array of images from different angles. It can obtain the spatial information and angle information of the light scene by placing a micro-lens array between the lens and the image sensor to, which is widely used in computer vision, industrial inspection, medical imaging. The image depth estimation algorithm affects the spatial resolution of reconstructed images. A convolution neural network algorithm based on Epipolar Image (EPI) was developed and optimized by adjusting the parameters. i.e. learning rate and activation function. The depth information of light field image was predicted with an accuracy of 67.5%. The convolution neural network algorithm effectively improved the efficiency of the depth estimation and realized the 3D image reconstruction.
Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks

In this study, a wavelet recurrent fuzzy neural network is used to conduct in-depth research and analysis on the real-time regulation of physical training intensity. Firstly, an inter-process control technique is proposed to solve the problem of incomplete control flow graph construction caused by the inability to effectively collect all program control flow information in the process of static analysis, in preparation for the research of fuzzy testing technique. Next, a wavelet recursive fuzzy neural network-guided fuzzy testing technique is proposed to solve the problem of fuzzy tests falling into invalid variation due to the lack of directionality in the fuzzy testing process. Each neuron in the feedforward network is divided into different groups according to the order of receiving information. Each group can be regarded as a neural layer. The neurons in each layer receive the output of the neurons in the previous layer and output to the neurons in the next layer. The empirical data show that injury-preventive fitness training can effectively improve all physical qualities in the first phase of preparation and can effectively maintain the physical state and effectively contribute to their abilities during the competition period, and its injury-preventive fitness training interventions were verified by statistical analysis to have a dangerous main effect on their pre and post-test performance. Therefore, it is still not possible to determine its correlation with the coordination and improvement of the athletes' physical fitness, and the integration of the basic physical training and rehabilitation physical training systems, making this theory a new special training theory.
Preface

prefatory Share on PrefaceProbabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages xxv–xxvihttps://doi.org/10.1145/3501714.3501715Online:04 March 2022Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Adaptive one-pass passive-aggressive radial basis function for classification problems

This paper presents a novel adaptive one-pass Passive-Aggressive Radial Basis Function (APARBF) for classification problems. The APARBF uses elliptic Gaussian neurons followed by a Softmax layer and cross-entropy loss function. This network tries to overcome the elasticity-plasticity dilemma by using the Passive-Aggressive (PA) algorithm and adapting the hidden layer structure. The weight updates have to be plastic to acquire the most information from each sample and at the same time need to be elastic to retrain the information from the past instances. Inspired by PA, a novel update formula for cross-entropy loss minimization has been derived. The adaptive design of the network lets it start with zero hidden neurons and grow or shrink according to the data. For kernel parameters, the adaptive structure determines the correct number of hidden neurons and updates recursively each neuron’s center and covariance matrix. To evaluate our network, we perform two series of experiments. The first experiments compare the proposed APARBF with other recently developed one-pass algorithms (i.e., OVIG, OBHT, SCW, AROW, OGD, and PA). The subsequent experiments include comparing the proposed algorithm with some online adaptive structures such as FGAP-RBF, C-Mantec, McNN, and PBL-McNN, based on their mean classification error and the number of hidden neurons. Wilcoxon sign rank test and Friedman test clearly show the superiority of the proposed network’s results compared with its competitors in one-pass classification problems.
DQN Based Exit Selection in Multi-Exit Deep Neural Networks for Applications Targeting Situation Awareness

Smart infrastructure targeting situation awareness for first responders enables intelligent and faster response time. Such an application requires an edge device to process information. The edge hardware is limited by computation capability for running Deep Neural Networks (DNNs). Multi-exit DNNs are used to provide static exit selection for faster inference. We propose using Deep Q Network (DQN) based technique for dynamic exit selection in multi-exit DNNs. The DQN learns an optimal policy using hardware and multi-exit DNN based state information. Our system achieves performance improvement with 63.5 &#x0025; decrease in inference time, 33 &#x0025; reduction in energy and classifies 2.2 x more inputs.
Semantic Similarity of Inverse Morpheme Words Based on Word Embedding

Inverse morpheme words are compound words that have the same morphemes but are arranged in the opposite order. The majority of related works on the subject have focused on a narrow investigation of dictionary definitions, with few studies based on large-scale corpora. We used the People’s Daily corpus (1946–2017) to add and delete words from a base list and obtained a word list of 668 pairs of inverse morpheme words. Furthermore, the cosine similarity is computed by using word embedding based on the distributed representation, and the Pearson correlation coefficient between it and the manually annotated value is 0.907, indicating that this method can measure the semantic similarity of inverse morpheme words very close to human judgment. We also discovered that 76% of inverse morpheme words have a cosine similarity of 0.4 or higher and that word formation, part-of-speech, and frequency all have an impact on semantic similarity.
Classification and Generation of Virtual Dancer Social Behaviors Based on Deep Learning in a Simple Virtual Environment Paradigm

This work examines one possibility of using a deep neural network to control a virtual dance partner behavior. A neural-network-based system is designed and used for classification, evaluation, prediction, and generation of socially emotional behavior of a virtual actor. The network is trained using deep learning on the data generated with an algorithm implementing the eBICA cognitive architecture. Results show that, in the selected virtual dance paradigm, (1) the functionality of the cognitive model can be efficiently transferred to the neural network using deep learning, allowing the network to generate socially emotional behavior of a dance partner similar to a human participant behavior or the behavior generated algorithmically based on eBICA, and (2) the trained neural network can correctly identify the character types of virtual dance partners based on their behavior. When considered together with related studies, our findings lead to more general implications extending beyond the selected paradigm.
Ancient Tibetan Word Segmentation: Dataset and Methods

Tibetan ancient literature is an important literature material for the study of ancient Tibetan culture, history, and the development of Sino Tibetan language family. However, the lack of work on ancient Tibetan word segmentation tools seriously restricts the research of ancient Tibetan literature. In view of this situation, this paper first utilizes ancient Tibetan interlaced contrast tagging data to extract the ancient Tibetan word segmentation dataset. Based on this dataset, we conduct numerous experiments for the task of ancient Tibetan word segmentation. Experimental results show that BiLSTM + CRF word segmentation algorithm can achieve the best performance, and the performance of ancient Tibetan word segmentation can be further improved through model ensemble. And the results show that the unknown words, insufficient training data and word ambiguity restrict the performance of ancient Tibetan word segmentation.
A Survey on Various Techniques and Proposals for Improving the Cryptocurrency Protocol

It is the time where the cryptocurrencies are gaining more and more attention among many individuals and company. Because they have some advantages over centralized currency system, like they are scarce, divisible, transportable, durable, counterfeit able, there are a variety of cryptocurrencies, and a number of protocols which lie behind the working of these coins, with block chain and cryptography as the underlying technology and concepts, but there are many advantages and disadvantages of each protocols, and the factors which are taken into account are privacy, scalability, efficiency, anonymity, efficiency, speed of transaction, security. These factors are very crucial and necessary in deciding the choice of cryptography protocol, and in this paper, we summarize the various protocols and its features and techniques which govern the working of various cryptocurrencies.
I–M

Approach for the administration of identities and permissions in companies, which is usually based on a separate →application system or module that is linked to other →application systems of the company. The aim of the master data, which are usually managed on a central →IDM server, is to achieve a high degree of consistency and integrity of the data, so that →front-, →middle- and →back-office areas, for example, can access the current data stocks for →authentication and →authorization purposes. Identity management is usually a prerequisite for channel strategies (→multi-channel, →omni-channel) and →SSO. With the opening towards →multi-bank relationships (e.g., due to →PSD2) and the use of digital identities in other areas of life (e.g., health, public administration), services for identity management have become established. In this field of application, also known as →KYC, there are centralized solutions (e.g., PostIdent, Verimi, Veriff or WebID) as well as decentralized solutions based on →blockchain technology such as Hyperledger Indy, Sovrin or Vetri (→SSI).
Quantization Algorithm in Neural Network Training

In the continuous development of model quantification, many experts try to start with quantification in training process. This paper mainly explains Quantification Aware Training. The basic concept of quantification aware training and the basic algorithm are introduced to make the reader have a basic understanding of quantification aware training. In addition, this paper provides a further understanding of quantification aware training by citing four experiments on the development of relevant algorithms. The four algorithm experiments are Hessian AWare Quantifification, Uncertainty Quantifification, Knowledge Distillation- Training and Quantization-Retraining and weight activation quantization. These four innovative algorithms can effectively reduce the calculation parameters on the basis of little change in accuracy. It can also make readers better understand some specific work related to algorithm development in this field and provide reference for the future research work in this field.
Few-shot object detection with anti-confusion grouping

Recent approaches have achieved excellent results on few-shot object detection. However, most detectors are easily confused by visually similar classes, leading to misclassification of interesting objects. In this work, we introduce an anti-confusion grouping mechanism for this problem. Our model can refine the results of the major multi-class classifier of the few-shot object detector with an anti-confusion module. Instead of maximizing the feature distribution distance of similar classes in the feature space, our approach uses additional auxiliary grouping module to distinguish similar classes on the same feature space as in base training phase. Concretely, the class groups are obtained according to the class visual similarity, and then they are utilized to train the auxiliary module. The main classifier, regressor and auxiliary anti-confusion module are end-to-end trained based on a multi-task loss. In the test phase, the auxiliary module is combined with the main classifier to provide the final classification result. Through extensive experiments, we demonstrate that our model outperforms well-established baselines for few-shot object detection. We also present analysis on various aspects of our model, aiming to provide some inspiration for future few-shot detection works.
Comprehensive Assessment of Neural Network Synthetic Training Methods using Domain Randomization for Orbital and Space-based Applications

The continued advancement of neural networks and other deep learning architectures have fundamentally changed the definition of "State of the art" (SOTA) in a wide and ever-growing range of disciplines. Arguably the most impacted field of study is that of computer vision, providing flexible and general function-approximation frameworks capable of accurately and reliably performing object identification and classification on a wide range of image datasets. However, the impressive gains achieved by deep learning methods come at a cost. The incredibly large number of images required to train a deep network makes them prohibitive for certain applications where large image datasets are limited or simply do not exist. Collecting the required image data is often too expensive, too dangerous, or too cumbersome to gather for many problem sets. Space-based applications are a perfect example of an imagery limited domain due to its complex and extreme environment. Conversely, breakthroughs in space-based computer vision applications would enable a wide range of fundamental capabilities required for the eventual automation of this critical domain, including robotics-based construction and assembly, repair, and surveying tasks of orbital platforms or celestial bodies. To bridge this gap in capability researchers have started to rely upon 3D rendered synthetic image datasets generated from advanced 3D rasterization software.Generating synthetic data is only step one. Space-Based computer vision is more complex than traditional terrestrial tasks due to the extreme variances encountered that can confuse or degrade optical sensors and CV and ML algorithms. These include orientation and translation tumble in all axes, inability for a model to orient on a horizon, and extreme light saturation on lit sides of celestial bodies and occlusion on dark sides and in shadows. To produce a more reliable and robust result for computer vision architectures, a new method of synthetic image generation called domain randomization has started to be applied to more traditional computer vision problem sets. This method involves creating an environment of randomized patterns, colors, and lighting while maintaining rigid structures for objects of interest. This may prove a promising solution to the variance space-based problem. This paper explores computer vision, domain randomization, and the necessary computational hardware required to apply them to space-based applications.