Reviewerly/exHarmony huggingface dataset:
Machine Learning Model for Group Activity Recognition Based on Discriminative Interaction Contextual Relationship

This paper represents the recognition of group activity in public areas, considering personal actions and interactions between people from the field of computer vision. Modeling the interaction relationships between multiple people is essential for recognizing group activity in the video scene. In artificial intelligence applications, identifying group activities based on human interaction is often a challenging task. This paper proposed a model that formulates a group action context (GAC) descriptor. The descriptor was developed by integrating the focal person action descriptor and interaction joint context descriptor of nearby people in the video frame. The model used an efficient optimization principle based on machine learning to learn the discriminative interaction context relations between multiple persons. The proposed novel group action context descriptor is classified by support vector machine (SVM) to recognize group activity. The proposed technique effectiveness is evaluated for group activity recognition by performing experiments on a publicly available collective activity dataset. The proposed approach infers a group action class when multiple persons are together in the video sequence, especially when the interaction between people is confusing. The overall group action recognition model is interrelated with a baseline model to estimate the performance of interaction context information. The experimental result of the proposed group activity recognition model is comparable and outperforms the previous methods.
Application of graph theory in the library domain—Building a faceted framework based on a literature review

Based on a literature review, we present a framework for structuring the application of graph theory in the library domain. Our goal is to provide both researchers and libraries with a standard tool to classify scientific work, at the same time allowing for the identification of previously underrepresented areas where future research might be productive. To achieve this, we compile graph theoretical approaches from the literature to consolidate the components of our framework on a solid basis. The extendable framework consists of multiple facets grouped into five categories whose elements can be arbitrarily combined. Libraries can benefit from these facets by using them as a point of reference for the (meta)data they offer. Further work on formally defining the framework’s categories as well as on integration of other graph-related research areas not discussed in this article (e.g. knowledge graphs) would be desirable and helpful in the future.
Analysis of Influencing Factors of Technical transformation Project Cost Based on PCA-ISM

Analyzing and digging out the influencing factors of power grid technological transformation project cost is an effective means to achieve cost control. Based on PCA-ISM, the impact mechanism of various influencing factors on the project cost is analyzed in this paper. And the root causes, direct factors and other influencing factors that affect the cost of technical transformation projects are excavated. This will provide a basis for effectively controlling the cost risk of technical transformation projects and optimizing cost management.
Classical and Brain-inspired Neural Networks for Substance Identification and Breast Cancer Detection: The Chaos Challenge

Artificial Neural Networks (ANNs) are largely used in multiple domains and represent an offshoot of pattern recognition paradigms. This work studies the use of three different ANN models, applied as classification technics for breast cancer identification, and as an intelligent data processing module for a multi-wavelength optoelectronic biochemical sensor. The models developed are such as; Multilayer Neural Network (MLNN) trained with the resilient Backprop, a heteroassociative Bidirectional Associative Memory (BAM) and a Chaotic-BAM (CBAM). The later model is tested on 23 different chaotic output functions; the memory fully satisfies the exigencies of a perfect recognition with one specific chaotic map. This work is assessing classification accuracy and investigates the facts that, beyond its biological plausibility, chaos is potential to enhance classification accuracies even in high dimensional application domain. It is resilient to noise present in data; moreover, chaos can face multi class problems with a better efficiency comparing with other classical ANN models. To assess the cited facts, an experimental set up was realized leading to interesting conclusions.
Energy-Efficient Word-Serial Processor for Field Multiplication and Squaring Suitable for Lightweight Authentication Schemes in RFID-Based IoT Applications

Radio-Frequency Identification (RFID) technology is a crucial technology used in many IoT applications such as healthcare, asset tracking, logistics, supply chain management, assembly, manufacturing, and payment systems. Nonetheless, RFID-based IoT applications have many security and privacy issues restricting their use on a large scale. Many authors have proposed lightweight RFID authentication schemes based on Elliptic Curve Cryptography (ECC) with a low-cost implementation to solve these issues. Finite-field multiplication are at the heart of these schemes, and their implementation significantly affects the system’s overall performance. This article presents a formal methodology for developing a word-based serial-in/serial-out semisystolic processor that shares hardware resources for multiplication and squaring operations in GF(2n). The processor concurrently executes both operations and hence reduces the execution time. Furthermore, sharing the hardware resources provides savings in the area and consumed energy. The acquired implementation results for the field size n=409 indicate that the proposed structure achieves a significant reduction in the area–time product and consumed energy over the previously published designs by at least 32.3% and 70%, respectively. The achieved results make the proposed design more suitable to realize cryptographic primitives in resource-constrained RFID devices.
Study and Analysis of Emotion Classification on Textual Data

Emotion analysis plays a part in understanding the feelings of human beings. People's actions and speech express various feelings, behaviors and emotions which can have various impacts. Emotion and sentiment analysis is a broad research area for finding emotion which helps getting useful insight through text and speech. In most of previous work, nearly all projects have focused on analyzing the expression based on positive, negative and neutral classification. This research work analyzes the proposed system by categorizing the text into emotion classes called joy, sadness, anger, fear, love and surprise. This work helps us to label text emotions into multiple classes and categorize the text for better accuracy. This research work represents the enhancement of a novel Deep Learning model scheme, Long Short-Term Memory and Recurrent Neural Network which discuss various categories distribution on knowledgeable data. This will also summarize the previous works done on textual emotional classification based on various sentiment models and approach with comparative survey analysis.
A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients

Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper presents a fully-automated image processing pipeline which quantifies the SC volume of MRI images.In the proposed pipeline, after conducting some pre-processing tasks, a deep convolutional network is utilized to segment the spinal cord cross-sectional area (SCCSA) of each slice. After full segmentation, certain extra slices interpolate between each two adjacent slices using the shape-based interpolation method. Then, a 3D model of the SC is reconstructed, and, by counting the voxels of it, the SC volume is calculated. The performance of the proposed method for the SCCSA segmentation is evaluated on 140 MRI images. Subsequently, to demonstrate the application of the proposed pipeline, we study the differentiations of SC atrophy between 38 Multiple Sclerosis (MS) and 25 Neuromyelitis Optica Spectrum Disorder (NMOSD) patients.The experimental results of the SCCSA segmentation indicate that the proposed method, adapted by Mask R-CNN, presented the most satisfactory result with the average Dice coefficient of 0.96. For this method, statistical metrics including sensitivity, specificity, accuracy, and precision are 97.51%, 99.98%, 99.92%, and 98.04% respectively. Moreover, the t-test result (p-value = 0.00089) verified a significant difference between the SC atrophy of MS and NMOSD patients.The pipeline efficiently quantifies the SC volume of MRI images and can be utilized as an affordable computer-aided tool for diagnostic purposes.
Cassandra: Detecting Trojaned Networks From Adversarial Perturbations

Deep neural networks are being widely deployed for critical tasks. In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan behaviors. These malicious behaviors can be triggered at the adversary’s will, which is a serious security threat. To verify the integrity of a deep model, we propose a method that captures its fingerprint with adversarial perturbations. Inserting backdoors into a network alters its decision boundaries which are effectively encoded by adversarial perturbations. Our proposed Trojan detection network learns features from adversarial patterns and its properties to encode the unknown trigger shape and deviations in the decision boundaries caused by backdoors. Our method works completely without or with limited clean samples for improved performance. Our method also performs anomaly detection to identify the target class of a Trojaned network and is invariant to the trigger type, trigger size, network architecture and does not require any triggered samples. Experiments are performed on MNIST, NIST-TrojAI and Odysseus datasets, with 5000 pre-trained models in total, making this the largest study to date on Trojaned detection and the new state-of-the-art accuracy is achieved.
Describing images using fuzzy mutual position matrix and saliency-based ordering of predicates

Describing the content based on bounding boxes of objects located within the image has recently gained popularity thanks to the fast development of object detection algorithms based on deep learning. Such description, however, does not contain any information on the mutual relations between objects that may be crucial to understand the scene as a whole. In the paper, a method is proposed that extracts, from the set of bounding boxes, a scene description in the form of a list of predicates containing consecutive objects' position, referring them to previously described ones. To estimate bounding boxes' relative position, a fuzzy mutual position matrix is proposed. It contains the complete information on the scene composition stored in fuzzy 2-D position descriptors extracted from fuzzified relative bounding box coordinates by a two-stage fuzzy reasoning process. The descriptors of non-zero membership function values are next considered as potential predicates related to the image content. Their list is ordered using the saliency-based criteria to select the most relevant ones, explaining best the scene composition. From the ordered list, the algorithm extracts the final list of predicates. It contains complete and concise information on the composition of objects within the scene. Some examples of the proposed method illustrate the paper.
Hybrid approach for Data Security using Coverless Image Steganography with AES

We need systems which makes data more secure as threats to data are increasing day by day. Cryptography and Steganography are the techniques which resist security threats from adversary entities. The combination of cryptography and steganography helps in achieving the more security of data instead of using the technique alone at a time. Today we have systems for data security that combines cryptography and steganography but they use covered steganography. Our proposed work uses the coverless image steganography instead of covered image steganography technique with encryption algorithm. Proposed work enhances the level of the data security by using the encryption process at first level and at the second level data is embedded into the container media using coverless image steganography technique. The proposed system uses the 128-bit variant of AES symmetric algorithm for encryption of the data. Here the proposed system uses the coverless image steganography because the conventional covered steganography is hard to resist the steganalysis tools. Coverless image steganography means embedding the secret message by generating a cover image. The proposed system uses the fresh concept of coverless image steganography given by A.H.S Saad & others as the steganographic technique whose payload capacity is maximum than other coverless image steganography algorithms.
Simultaneous estimation of multiple phases in generalised Mach–Zehnder interferometer

Abstract In this work we investigate the problem of simultaneous estimation of phases using generalised three- and four-mode Mach–Zehnder interferometer. In our setup, we assume that the phases are placed in each of the modes in the interferometer, which introduces correlations between estimators of the phases. These correlations prevent simultaneous estimation of all these phases, however we show that we can still obtain the Heisenberg-like scaling of precision of joint estimation of any subset of $$d-1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> phases, d being the number of modes, within completely fixed experimental setup, namely with the same initial state and set of measurements. Our estimation scheme can be applied to the task of quantum-enhanced sensing in three-dimensional interferometric configurations.
Experimental Comparison of Relational and NoSQL Document Systems: The Case of Decision Support

We design and implement an experimental analysis comparing a relational system (PostgreSQL) and two document-based NoSQL systems (MongoDB and Couchbase) on a single server, Decision Support (DSS) scenario. We argue that DSS is becoming an important case study for NoSQL. We experiment with several database designs and several query formulations in order to investigate the effect of physical design and query optimization in document-based stores. Our results show that design is very important for document stores performance, and that query optimization over documents is less sophisticated than in relational systems and needs to improve. Our results offer some guidance in this area.
A Pixel image generation algorithm based on CycleGAN

As people's aesthetic becomes more and more diversified, pixel art is loved by more and more people with its unique artistic form and artistic value. In the creation process of pixel image, every pixel needs to be arranged into a zigzag contour according to the rule, and the contour needs to be continuous and clear. However, the existing algorithms often generate more artifacts, and the contour details are not clear. This paper proposed a new unsupervised algorithm for pixel image generation. By using nested U-Net generator structure, multi-scale feature fusion is used to assist in drawing the contour features of pixel image. In addition, structure combination loss is proposed to further ensure the integrity of linear structures such as contour in pixel images. Experimental results show that the proposed method can generate high-quality pixel-style images, and is superior to the existing methods in preserving image details.
Interpreting Generative Adversarial Networks for Interactive Image Generation

Abstract Significant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. However, there lacks enough understanding of how a realistic image is generated by the deep representations of GANs from a random vector. This chapter gives a summary of recent works on interpreting deep generative models. The methods are categorized into the supervised, the unsupervised, and the embedding-guided approaches. We will see how the human-understandable concepts that emerge in the learned representation can be identified and used for interactive image generation and editing.
MT-MTD: Muti-Training based Moving Target Defense Trojaning Attack in Edged-AI network

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.
Relationship between Code Similarity and Course Semester in Programming Assessments

Some code similarity detectors are designed to address academic integrity in early programming courses by recognising subtle variations, in the assumption that the code similarity in these courses is typically higher than that in later courses. Although the assumption is often used, it has no empirical evidence, and might be misleading. This study empirically investigates the assumption by examining the relationship between code similarity and course semester in seven programming courses with a total of 931 distinct assessment tasks. Our study shows that the argument is not necessarily true since in later courses, some assessment tasks require the students to follow a particular structure, to use external libraries, or to implement specific algorithms taught during the course.
DisSAGD: A Distributed Parameter Update Scheme Based on Variance Reduction

Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based on variance reduction, named DisSAGD, is proposed in this study. DisSAGD corrects the gradient estimate for each iteration by using the gradient variance of historical iterations without full gradient computation or additional storage, i.e., it reduces the mean variance of historical gradients in order to reduce the error in updating parameters. We implemented DisSAGD in distributed clusters in order to train a machine learning model by sharing parameters among nodes using an asynchronous communication protocol. We also propose an adaptive learning rate strategy, as well as a sampling strategy, to address the update lag of the overall parameter distribution, which helps to improve the convergence speed when the parameters deviate from the optimal value—when one working node is faster than another, this node will have more time to compute the local gradient and sample more samples for the next iteration. Our experiments demonstrate that DisSAGD significantly reduces waiting times during loop iterations and improves convergence speed when compared to traditional methods, and that our method can achieve speed increases for distributed clusters.
Unsupervised gender prediction based on deep facial features

Gender prediction is a common topic in machine learning. It can be seen that many approaches have been proposed for this task and achieved certain successes. However, it is necessary to figure out a method that has high performance and reduce time processing. This paper focuses on applying deep neural networks to solve gender prediction based on facial images. Specifically, Convolutional Neural Networks (CNN) and its enhancements are applied to extract features of facial images. Then, K-means clustering is employed to predict gender on a large-scale GenderFace80K dataset with 80,000 facial images with gender annotation.
Shot Segmentation Method Based on Image Similarity and Deep Residual Network

In order to segment the video according to the gradual shots, mutation shots or constant shots, so as to apply to the subsequent video analysis algorithm, this paper designs shot boundary detection method based on image similarity combined with deep residual network. The image similarity feature has better segmentation effect for mutation shots, but poor detection ability for gradual shots. The deep neural network can perform better segmentation of gradual shots, but the amount of calculation is relatively large. Based on the combination of the depth feature of the video frame and the principle of image mutual information, this paper initially extracts video boundary candidate points. At the same time, 3D residual network is designed and the shot boundary segmentation training is carried out according to the Clipshots dataset. Finally, the trained network is used to analyze the real video shot boundary from the candidate points and determine its category. The experimental results show that this method has better shot segmentation effect for VR movies and network user videos.
Monocoque Multirotor Airframe Design with Rotor Orientations Optimized for Direct 6-DoF UAV Flight Control

The search and rescue purposes of many VTOL UAVs are needed but there has been hardly new non-control research efforts recently improving the UAVs' flight agility and ability to survive in those hostile post-disaster environment. This paper introduces the design and analysis of a class of multirotor airframes with the capability of in-plane maneuverability and direct decoupled 6-DoF control which allow for low profile sensor payload integration without the need for a gimbal as well as predictable and safe flight in confined spaces such as tunnels and collapsed buildings. The case study of a hexrotor airframe is presented with its layout optimized via the methodology from the authors' previous work. The actuator combinations are tested and selected based on the performance metrics defined by authors, and utilized to restart the layout optimization. The aerodynamic performance of the platform is evaluated in several closed-quarter flight scenarios using CFD and compared with that of other airframe configurations. The structure of the single hollow monocoque shell is proposed and validated to obtain the desired shell thickness for the monocoque frame based on modal analyses and finite element methods. Finally, the flight controller developed by the authors is implemented and initial flight tests are conducted to validate the full actuation and a few more flight tests are to be done in the near future.