Reviewerly/exHarmony huggingface dataset:
Face Phylogeny Tree: Deducing Relationships Between Near-Duplicate Face Images Using Legendre Polynomials and Radial Basis Functions

Photometric transformations such as brightness and contrast adjustment are widely used to enhance the visual aesthetics of face images. A face image can be repeatedly modified using such transformations, resulting in a set of near duplicate images. In this work, we tackle the difficult problem of generating an Image Phylogeny Tree (IPT) from near-duplicate face images. An IPT captures the structure of evolution between images. We first assemble a dataset of near-duplicate face images using random sequences of photometric transformations. Secondly, we develop an algorithm to model the pairwise transformation using Legendre polynomials and Gaussian radial basis functions. Given a set of near-duplicate face images, we utilize these basis functions to model the pairwise relationship between these images. Thirdly, we develop a likelihood-ratio based scheme to distinguish between forward transformations and reverse transformations. The likelihood-ratio value itself is used to develop an asymmetric similarity matrix that depicts the relationship between pairs of images. Finally, we deduce the IPT from this matrix. Experiments conducted on images from the LFW dataset involving 2,727 IPTs convey the promise of our method to confront this challenging problem in image forensics, exhibiting 80% root node identification accuracy for near-duplicate face images.
BPhoton-CNN: An Ultrafast Photonic Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across its many using a backpropagation algorithm (BP). This results in expensive computation and energy overhead during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to develop a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this paper, we introduce a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPhoton-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPhoton-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPhoton-CNN using a commercial CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves at least 35× acceleration in training time, 31× improvement in computational efficiency, and 45× energy savings compared to the state-of-the-art designs, without any loss of accuracy.
ConMerge – Arbitration of Constraint-Based Knowledge Bases

Due to the increasing need to individualize mass products, product configurators are becoming more and more a manifest in the environment of business to customer retailers. Furthermore, technology-driven companies try to formalize expert knowledge to maintain their most valuable asset – their technological know-how. Consequently, insulated and diversified knowledge bases are created leading to complex challenges whenever knowledge needs to be consolidated. In this paper, we present the ConMerge-Algorithm which can integrate two constraint-based knowledge bases by applying redundancy detection and conflict detection. Based on detected conflicts, our algorithm applies resolution strategies and assures consistency of the resulting knowledge bases. Furthermore, the user can choose the operation mode of the algorithm: keeping all configuration solutions of each individual input knowledge base or only solutions which are valid in both original knowledge bases. With this method of knowledge base arbitration, the ability to consolidating distributed product configuration knowledge bases is provided.
An improved Chinese text multi-label classification method based on CNN

Abstract Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm.
Hyperparameters optimization for neural network training using Fractal Decomposition-based Algorithm

This paper introduces the application of the fractal decomposition-based algorithm (FDA) to the optimization of the hyperparameters of deep neural network architecture. FDA is a metaheuristic that was recently proposed to solve high dimensional continuous optimization problems. In this work, we apply FDA to the optimization of well-known architectures such as VGG-16, NasNet, MobileNetV2 and ResNetV2-50. The hyperparameters of those architectures were fine-tuned using FDA on the CIFAR-10 benchmark dataset. The experiments demonstrate the superiority of proposed method over the state-of-art values. Considered approach shows promising results as every architecture was improved with hyperparameters found by using FDA. The experiments were conducted using low computational power with only 3 NVIDIA V100 GPUs, with 16GB of RAM.
A Three-Step Strategy for Recommendation in Mobile Police Video Secure Access

Abstract Mobile police video security access link is an important boundary channel to ensure the secure and trusted access of mobile video in the private video network. Therefore, this paper proposes a three-step strategy for access control, encrypted transmission, and wireless video security inspection. While carrying the ability to carry higher video streams, it is both safe and reliable, and has strong scalability in performance. This paper analyzes the access mechanism of SSL VPN in encryption, identity authentication and resource access control. Proposes a mobile police video security access link solution based on SSL VPN for network clients. Respectively presents a secure access solution for wired video and wireless video. And a load balancing access scheme based on NAT address translation is designed. Finally, a test scenario for the maximum number of concurrent users and server bandwidth performance is designed, implemented and analyzed. In actual application, the trial projects have been highly recognized by public security customers.
Deep Metric Learning Based Histopathological Image Classification

Abstract With the rapid development of deep learning, histopathological image classification models have made remarkable progress. Recent deep learning-based methods have been applied to raw histopathological images to construct end-to-end models, which avoid hand-craft feature engineering. To construct a model that can capture the intrinsic pattern of the histopathological image dataset, we design a model based on deep metric learning which embeds data points into a Euclidean space. The proposed model trains a deep neural network, which embeds an input image into a Euclidean space where dissimilar images are located far away to each other and vice versa. We adopt a BN-Inception network pretrained on ImageNet as the embedding model. Then it is retrained on target datasets with some triplet loss function. A weighted distance-based triplet sampling strategy is designed to generate hard triplets for the training procedure. Evaluations on benchmark datasets indicate that our deep metric learning-based method outperforms recent successful deep learning models.
Applying GANs for Generating Image with Varied Facial Attributes from Sketch

Abstract The rapid development of GANs and its variants has shown remarkable progress in synthesizing realistic images. Image-to-image translation becomes a potent research topic due to its wide application. The translation of face sketch to color images greatly contributes to digital image processing industry. It could also help confirm the identity of the suspect or lost persons. In this paper, a model is implemented by applying GANs for generating an image with varied facial attributes from sketch. The architecture consists of two separate GANs, implemented with Pix2Pix and StarGan2 as bases. The output realistic images can be generated with manipulating a single facial attribute including, wavy hair, straight hair, and wearing glasses.
A low-cost three-dimensional reconstruction and monitoring system using digital fringe projection

In this paper, the design and construction of a low-cost three-dimensional (3D) reconstruction and monitoring system using digital fringe projection (DFP) is proposed, which can perform small- and large-scale measurements in different environments and can be applied to various applications such as intelligent monitoring, 3D online inspection, and obstacle detection for vehicle guidance. The contribution of this paper is threefold: (i) development of a comprehensive 3D measuring system that performs sensors handling, coordinate acquisition, reconstruction, and display process simultaneously and quickly; (ii) proposal of new filters to improve quality and efficiency of the system; and (iii) development of a real-time virtual 3D measurement system to calibrate and analyze the proposed methodologies. Optical and simulation measurement results are presented to verify the feasibility and performance of the developed systems. The observed RMS difference was found to be &lt;0.021 mm in the optical measurement. From measured results, it can be concluded that the proposed systems and adopted methodology are effective in obtaining 3D surface profiles.
U-Map: A Reference Map for Safe Handling of Runtime Uncertainties

“Uncertainty is certain” – a well-stablished fact that challenges design and engineering of dynamic systems. Cyber Physical Systems (CPSs) must function and perform tasks safely in real world contexts that might not be engineered specifically for them. These dynamic contexts are often accompanied by the pervasive presence of uncertainty. The dynamic nature of such systems as well as their ever-growing complexity further complicate safety assurance and require a paradigm shift towards more effective runtime safety assurance techniques. Many of the present runtime safety assurance techniques consider certain dynamic aspects of the system and its context, but not the uncertainty aspects completely. This paper presents results from an ongoing research to effectively handle runtime uncertainties in a model-based approach to assure system safety. In this paper, we propose a reference map called Uncertainty Map (U-Map) that can be used during system design to handle runtime uncertainties and apply it to a case study. The U-Map consists of an exhaustive set of possible uncertainties that are mapped to sets of potentially resulting hazards as well as possible runtime mitigation measures. It is intended to facilitate the identification of uncertainty-induced hazards during early design and contribute to the safe handling of runtime uncertainties.
Haptic Display Using Fishing Rod

Abstract This paper proposes a grounded haptic display that can widely present a force sensation using a fishing rod. The proposed haptic display incorporates a fishing rod to present the force sensation at the user’s fingertip using a thread being reeled from the tip of the fishing rod. The multidirectional force sensation is presented by driving the base supporting the fishing rod using a pan-tilt mechanism, and wending the thread using the reel. The fishing rod is lightweight and moves at high speed. Additionally, it is possible to widely present the force by bending the fishing rod. This device requires conversion from the position of the user’s fingertip and magnitude of the force to the necessary winding force of the thread and the posture of the fishing rod. This study developed a control method and evaluated it by considering a case wherein the force to be presented is exerted in the vertical direction.
Classification and Analysis of Multicriteria Optimization Methods

The problem of optimization is becoming one of the leading in the world of artificial intelligence. It is often representable in the form of an objective function that needs to be optimized (moreover, it is not always defined analytically, and sometimes defined as a "black box"), and a certain set of initial data and constraints on the solution.
The DEKI Account

We now address the issues faced by Representation-As and develop it into a full-fledged account of epistemic representation. The result of this endeavour is what we call the DEKI account, where the acronym is formed from the names of the account’s defining features: denotation, exemplification, keying-up, and imputation. In this chapter we introduce the DEKI account in the context of material models and discuss the account’s basic tenets. In the next chapter we extend the account to non-material models.
Towards a Proof of the Fourier-Entropy Conjecture?

The total influence of a function is a central notion in analysis of Boolean functions, and characterizing functions that have small total influence is one of the most fundamental questions associated with it. The KKL theorem and the Friedgut junta theorem give a strong characterization of such functions whenever the bound on the total influence is $$o(\log n)$$ . However, both results become useless when the total influence of the function is $$\omega (\log n)$$ . The only case in which this logarithmic barrier has been broken for an interesting class of functions was proved by Bourgain and Kalai, who focused on functions that are symmetric under large enough subgroups of $$S_n$$ . In this paper, we build and improve on the techniques of the Bourgain–Kalai paper and establish new concentration results on the Fourier spectrum of Boolean functions with small total influence. Our results include: Our concentration result for the Fourier spectrum of functions with small total influence also has new implications in learning theory. More specifically, we conclude that the class of functions whose total influence is at most K is agnostically learnable in time $$2^{O(K\log K)}$$ using membership queries. Thus, the class of functions with total influence $$O(\log n/\log \log n)$$ is agnostically learnable in $$\mathsf{poly}(n)$$ time.
A Multi-Constraint Strategy Algorithm for Stereo Matching

Abstract An improved multi-constraint stereo matching strategy is proposed herein. First, the original reference images are downsampled to reduce the matching scale. Absolute difference and rank transform are used as the matching cost. We establish the associated disparity map and the matching cost matrix to constrain the matching process. We use the associated disparity map to apply a smoothing constraint on disparity values. We then apply a uniqueness constraint using the associated disparity map and the matching cost matrix. We also apply a continuity constraint to improve the efficiency of the algorithm. Second, for the downsampled disparity map, the disparity map of the original image is obtained by interpolation and filling. Lastly, the final disparity map is obtained using a sub-pixel refinement strategy. The experimental results demonstrate that the algorithm is obviously superior to the traditional method. In addition, a multi-constraint matching process may be extracted as a framework for combination with other algorithms. Compared with the original algorithms, the fusion algorithm improved matching accuracy by 3% on average.
Emotion Recognition Algorithm Based on Panorama-plane Mapping Dataset and VGG16 in Prison Monitoring System

Abstract This paper proposes a digital processing scheme for efficient panoramic-plane image mapping for general-purpose panoramic cameras pictures, and builds an efficient emotion recognition algorithm based on VGG16 for real-time monitoring in prison environment. Panoramic-plane mapping algorithm is used to reduce the distortion of the input images and analyse the collected facial data to predict the sentiment of the prisoners. Compared with traditional monitoring methods, this algorithm is able to automatically sense the emotional changes of prisoners, which might improve the situation that traditional prison monitoring requires a large amount of human and material resources. We have proved that the method shows a good result on reducing distortion, besides our method runs with a small computation load so it is optimized to support real-time processing through experiments. The model trained in the experiments has reached a considerable accuracy up-to 79.75% on emotion classification at the same time. It is hopeful that the research could utilize in the future development of unattended prison monitoring.
Critical fault diagnosis method of small components of power transmission based on optimizational YOLOv3 algorithm

Abstract In order to solve the problem that the image recognition algorithm in UAV (Unmanned Aerial Vehicle) inspection of power transmission has low recognition rate for small, indiscoverable and high-risk component failures, this paper proposes a critical fault diagnosis method based on the optimizational YOLOv3 algorithm. We add SPP net under the basic framework of the original YOLOv3 and extract multi-scale depth features in the same convolutional layer which can transform all the input images into a uniform size and solve the problem caused by different input image sizes and formats. An optimizational scale prediction framework of YOLOv3 is proposed, which expands the original three-scale prediction frameworks to four scales and carries out multi-scale integration. This improvement is used to strengthen the feature extraction of small and indiscoverable fault. The optimizational algorithm is verified by using the dataset of electrical components of transmission system in the actual scene which have small and indiscoverable faults. The results show that the optimizational YOLOv3 improves the recognition rate of the selected categories by 3.98% on average, and increases the recognition rate of the small size and indiscoverable fault by 11.95% on average.
UKF Applied in Target Tracking Based on Single Observation Station

Unscented Kalman filter is used to tackle nonlinear tracking target moving with uniform velocity in straight line, based on a single observation station. Symmetrical set unscented transform is applied to design a target tracking system which includes a two-dimensional and a three-dimensional state-space model.
A Statistical Defense Approach for Detecting Adversarial Examples

Adversarial examples are maliciously modified inputs created to fool Machine Learning algorithms (ML). The existence of such inputs presents a major issue to the expansion of ML-based solutions. Many researchers have already contributed to the topic, providing both cutting edge-attack techniques and various defense strategies. This work focuses on the development of a system capable of detecting adversarial samples by exploiting statistical information from the training-set. Our detector computes several distorted replicas of the test input, then collects the classifier's prediction vectors to build a meaningful signature for the detection task. Then, the signature is projected onto a class-specific statistic vector to infer the input's nature. The class predicted for the original input is used to select the class-statistic vector. We show that our method reliably detects malicious inputs, outperforming state-of-the-art approaches in various settings, while being complementary to other defense solutions.
Research on Key Technologies of Substation Comprehensive Energy Saving in the Background of Smart Grid

Abstract In the current process of social and economic development in my country, electric energy as a basic power has become an important foundation for building economic growth. At present, in my country’s various substation systems, the application of intelligent substations has become the mainstream form of substation construction engineering with the progress of technology. As an important part of the intelligent substation building, the energy-saving system of intelligent substation building can achieve the purpose of high efficiency and energy saving while ensuring the normal operation of the substation. This article will start with a variety of smart substation building structures, and comprehensively analyse the functions, functions, and effects of smart substation energy-saving systems.
Enhanced Interactive Estimation of Distribution Algorithms with Attention Mechanism and Restricted Boltzmann Machine

Interactive Estimation of Distribution Algorithm (IEDA), by integrating users interactions with Estimation of Distribution Algorithm, is powerful for efficient personalized search when the probability model and fitness function are well designed. We here propose an improved IEDA by using attention mechanism strengthened Restricted Boltzmann Machine (RBM). An attention mechanism assisted RBM model is constructed to approximate the user preferences by inputting item features and user generated contents. Then the attention-enhanced probability model of EDA and the fitness function are developed based on the RBM. In the evolutionary process, the attention-based RBM together with the probability model and fitness function are managed according to new interactions and corresponding information. The proposed algorithm is applied to real-world Amazon data sets usually used in the personalized search or recommendation, and its performance is experimentally demonstrated in better predicting the user preferences to improve the searching efficiency and accuracy.
Introduzione alla psicopatologia del trauma: alcune riflessioni

The Author in this preliminary work to the trauma, stresses this kind of problem. Starting from the writings of some English Psychoanalysts who seem to underline the relationship between trauma and its meaning, placing their attitude to the last one specifically referred to the affective patient experience, proposes a comparison with freudian point of view of Phillys Greenacre, who pays attention to Structural derangement. The brilliant idea of Ferenczi that follows his works about trauma, involves the formulation of the concept of “Psicosoma” as a whole integrating component of human person, that’s why the Structure lays itself on the meaning. The psychoanalyst pupil of Ferenczi Elisabeth Severn will conceive the original Ferenczi concept in a wider frame including the ‘Self’. This represent a real change in understanding the trauma phenomenon.