Reviewerly/exHarmony huggingface dataset:
Implementation and Evaluation of a MLaaS for Document Classification with Continuous Deep Learning Models

This paper indicates an approach of a continuous training pipeline to enhance deep learning models and assessing their feasibility based on an evaluation. The purpose of this research is to analyze the quality effect of a continuously learning neural network algorithm for document classification by taking user feedback into account. The hypothesis implies that user feedback through active learning increases the precision and thus makes the process of document classification more efficient. For this purpose, based on a utility analysis, the available technologies are identified, and necessary ones are selected for designing a software concept. TensorFlow as a deep learning framework, Tesseract as an OCR engine, and Apache Airflow for the life cycle management and for orchestrating the elements for the continuous training pipeline are used. This implementation of a machine learning as a service prototype allows for exploration into the synergistic effect between the use of active learning, in the form of user feedback, and the quality of document classification achieved by deep learning. In an experiment, the implemented service is used to analyze the models behavior based on three different states. This includes synthetic data and active learning in the form of user feedback through data from data augmentation and simulated realistic data. The result shows that active learning enhanced models indicate a higher accuracy than artificially generated models. The evaluation experiment confirms the hypothesis that user feedback with continuously learning models perform better in terms of generalizing within the document classification. In conclusion, the paper demonstrates the technical requirements for implementing a machine learning as a service and affirms that the use of active learning can be integrated into existing industrial systems.
Person Attribute Recognition using Hybrid Transformers for Surveillance Scenarios

Recognition of person attributes has been an emerging research topic and also have drawn extensive attention in the area of video surveillance. It is a very important and challenging task to notice the regions of a person’s attributes. Existing methods are applied to primary convolutional neural networks to localize the region related to person attribute. In this paper we adopted a co-scale Conv-Attentional image transformer to decipher the most discriminative attribute and region at multiple levels.Serial and parallel building blocks are introduced wherein serial blocks consists of conv-attention and feed forward network and parallel blocks have two strategies which are attention with feature interpolation and direct cross layer attention. From our results we observe that hybrid transformers are better than pure transformers. Extensive experimental result shows that proposed hybrid method outperforms the existing methods on four different personal attribute datasets i.e., RapV2, RapVl, PETA, PA100K.
Adversarial Attacks Against Audio Surveillance Systems

The recent rise of adversarial machine learning highlights the vulnerabilities of various systems relevant in a wide range of application domains. This paper focuses on the important domain of automatic space surveillance based on the acoustic modality. After setting up a state of the art solution using log-Mel spectrogram modeled by a convolutional neural network, we systematically investigate the following four types of adversarial attacks: a) Fast Gradient Sign, b) Projected Gradient Descent, c) Jacobian Saliency Map, and d) Carlini & Wagner <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{\infty}$</tex> . Experimental scenarios aiming at inducing false positives or negatives are considered, while attacks' efficiency are thoroughly examined. It is shown that several attack types are able to reach high success rate levels by injecting relatively small perturbations on the original audio signals. This underlines the need of suitable and effective defense strategies, which will boost reliability in machine learning based solution.
Context Feature Guided Network for Image Super-resolution

Convolutional neural networks (CNNs) have achieved remarkable advance in single image super-resolution (SISR). While seeking for better performance, most existing SR methods prone to be deeper or wider structure are usually computationally intensive, which thus limits the application of SR networks in resource-limited devices. In this paper, a novel lightweight SR network named Context Feature Guided Network (CFGN) is proposed for a better trade-off between performance and running efficiency. Specifically, to fully exploit semantic features, a novel Context Feature Guided Convolution (CFGC) is designed as the main building block for CFGN. The proposed CFGC consists of multiple stacked two-path fusion convolutions (TFCs), which could significantly enlarge the receptive field of each layer and learn more discriminative feature representations. To further consider rich multi-scale information, we design a context feature guided group (CFGG) as the main building block of CFGN by progressively stacking feature extraction distillation module and CFGCs. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art lightweight SR models in terms of both quantitative metrics and visual quality.
Hardware Implementation of Masked SKINNY SBox with Application to AEAD

Hardware masking is an important countermeasure for cryptographic schemes. In this paper, we study the hardware implementations of the SKINNY SBox using first-order Boolean masking. We implement the SKINNY 8-bit SBox using a wide range of masking schemes, and show the different security goals achieved by each implementation using formal verification. We develop and adapt a practical unit testing framework based on the Sasebo-GII FPGA board, and identify an issue with all the considered masking schemes. Based on the explanations in literature to similar observations, we propose a new implementation of the SBox that can be verified/validated using TVLA even in high SNR environments. We provide a full implementation of two of the Romulus AEAD modes, which can be configured with any of the SBox implementations proposed. We provide synthesis results using ASIC.
Computer algebra methods for polynomial system solving at the service of image-based visual servoing

Visual servoing refers to different methods used for robot motion control based on data from computer vision, in general the projection of a number of characteristics from the scene on a camera mounted on the robot. When the control is carried out in the space of parameters extracted from the image, we refer to Image-Based Visual Servoing (IBVS).
BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis

Generative models for audio-conditioned dance motion synthesis map music features to dance movements. Models are trained to associate motion patterns to audio patterns, usually without an explicit knowledge of the human body. This approach relies on a few assumptions: strong music-dance correlation, controlled motion data and relatively simple poses and movements. These characteristics are found in all existing datasets for dance motion synthesis, and indeed recent methods can achieve good results. We introduce a new dataset aiming to challenge these common assumptions, compiling a set of dynamic dance sequences displaying complex human poses. We focus on breakdancing which features acrobatic moves and tangled postures. We source our data from the Red Bull BC One competition videos. Estimating human keypoints from these videos is difficult due to the complexity of the dance, as well as the multiple moving cameras recording setup. We adopt a hybrid labelling pipeline leveraging deep estimation models as well as manual annotations to obtain good quality keypoint sequences at a reduced cost. Our efforts produced the BRACE dataset, which contains over 3 h and 30 min of densely annotated poses. We test state-of-the-art methods on BRACE, showing their limitations when evaluated on complex sequences. Our dataset can readily foster advance in dance motion synthesis. With intricate poses and swift movements, models are forced to go beyond learning a mapping between modalities and reason more effectively about body structure and movements.
A Cross-Project Defect Prediction Model Based on Deep Learning With Self-Attention

Cross-project defect prediction technique is a hot topic in the field of software defect research because of the huge difference in data distribution between source project and target project.The previous defect prediction technique used manually defined parameters to extract the features of the project, which were used by classifiers to construct a defect prediction model.However, traditional features lack sufficient semantic information that exists between source codes, resulting in poor performance of the prediction models.So, driven by the idea that sufficient semantic information helps to construct more accurate prediction models, we propose a cross-project defect prediction framework named BSLDP, which implements semantic extraction of source code files through a bidirectional long and short-term memory network with self-attention mechanism.In particular, we use the proposed semantic extractor named ASL to extract source code semantics based on source code files, and then we employ the proposed classification algorithm fed the semantic information of source project and target project, namely BSL, to build a prediction model.Furthermore, we propose an equal meshing mechanism that ASL generates semantic information on small fragments by dividing the numerical token vector to further improve the performance of the proposed model.We evaluated the performance of the proposed model on a publicly available PROMISE dataset.Compared with the four state-of-the-art methods, the experimental results indicate that on average BSLDP improves the performance of cross-project defect prediction in terms of F1 by 14.2%, 34.6%, 32.2% and 23.6%, respectively.
Review of image encryption and compression techniques

This paper presents a survey on image encryption and compression techniques. The main aim of using encryption and compression together is to reduce the size; to ensure a quality during reconstruction; to reduce bandwidth requirement, to increase the speed of data communication over a network; to reduce the computational time, and to ensure its safety. To carry out the Image compression and encryption together three different ways are there. The three existing combinations are as encryption technique followed by compression, in this method compression ratio is more, but there are some security issues as compared with next. The next combination is a compression technique followed by encryption technique, in this method, researchers focus more on security of the image rather than to compress the size of the image. The last combination is joint encryption and compression strategies in this strategy encryption and compression happens at the same time either to some extent or as compressive detecting. With low computational time it gives a decent security. The compression procedure utilized in every one of the blends can be lossless, lossy, or mix of both.
A Graph Architecture Search Method Based On Grouped Operations

Graph data is ubiquitous in the real world and graph neural networks (GNNs) are effective for modeling the complex relationships and dependencies between the entities. However, it's difficult to design data-specific GNNs. Recently, researchers have started to apply neural architecture search (NAS) to design GNNs. In this work, we propose a graph architecture search method to decrease the instability with a large number of candidate operations. Following SANE(Search to Aggregate NEighborhood), we focus on searching to aggregate neighbourhoods but we divide the candidate operations into groups. We use a continuous relaxation of our search space and optimize the hyper-networks with a gradient-based algorithm. Extensive experiments on several node-level and graph-level tasks demonstrate that our method achieves a promising performance.
JPEG compression coding optimization based on NEON instruction

NEON technology is the implementation of the ARM Advanced Single Instruction Multiple Data(SIMD) extension. The purpose of NEON technology is to provide flexible and powerful acceleration capability for consumer multimedia applications and the trade off between the complexity of hardware implementation and so-obtained performance improvement. In this paper, we design a JPEG compression coding system that uses NEON instruction set. It uses Discrete Cosine transform (DCT) is AAN algorithm. This design is validated on a processor FT2000/4 made in China and a Debian operating system platform. The convincing results show that the performance of JPEG compression coding system with the NEON instruction has been greatly improved, compared with the not optimized compression coding system.
Device Agent Assisted Blockchain Leveraged Framework for Internet of Things

Blockchain (BC) is a burgeoning technology that has emerged as a promising solution to peer-to-peer communication security and privacy challenges.As a revolutionary technology, blockchain has drawn the attention of academics and researchers.Cryptocurrencies have already effectively utilized BC technology.Many researchers have sought to implement this technique in different sectors, including the Internet of Things.To store and manage IoT data, we present in this paper a lightweight BC-based architecture with a modified raft algorithm-based consensus protocol.We designed a Device Agent that executes a novel registration procedure to connect IoT devices to the blockchain.We implemented the framework on Docker using the Go programming language.We have simulated the framework on a Linux environment hosted in the cloud.We have conducted a detailed performance analysis using a variety of measures.The results demonstrate that our suggested solution is suitable for facilitating the management of IoT data with increased security and privacy.In terms of throughput and block generation time, the results indicate that our solution might be 40% to 45% faster than the existing blockchain.
Improving Virtual Machine Migration Effects in Cloud Computing Environments Using Depth First Inspired Opportunity Exploration

The cloud platform has established itself as the de-facto standard in IT outsourcing. This is resulting in large-scale migration of infrastructure and development platforms from in-house to cloud service providers. Many recent proposals on cloud platforms have addressed several issues that appeared on the cloud horizon. VM placement (VMP) has been a serious concern when it comes to placement of VMs after migration or VM reallocation. Most of the recent works have lacked multiple VM placement (MVMP) problem instances. A recently researched idea of MVMP through depth first opportunistic exploration (DFOE) is proposed in this paper. The performance of MVMP is compared with existing single VM placement benchmark algorithm. Improvement in terms of number of VM migrations, energy consumption, and VM reallocation is reported through simulation of real-time load scenario. Cloud environments can benefit from MVMP and improve operating margins in terms of power saving and load balancing.
Research on Intuitive Gesture Recognition Control and Navigation System of UAV

UAVs (unmanned aerial vehicles) are mostly equipped with GPS and additional sensors. However, the control of the UAV still depends on the skill of the operator. Inexperienced control often leads to accidents that damage the UAV and harm the environment, pedestrians, and buildings. In this research, we propose a superior control of the UAV through intuitive gestures based on deep learning of gesture recognition, which reduces the difficulty of UAV control. To improve the flight control technology of the UAV and reduce accidents caused by improper UAV control, an intuitive gesture recognition control system is constructed. The gesture recognition control is operated by a series of gesture recognition and LSTM (Long Short-Term Memory) neural networks which output the label as the control commands of the UAV. Eight different control commands are defined and generated for the control. After identifying the pickup gesture, the coordinates of the index finger are projected to the UAV's screen, and the target can be easily positioned to identify objects. The system is expected to be used for subsequent automatic flight navigation. The gesture recognition system achieves 99.54% accuracy in the training set and 99.17% accuracy in the testing set. The method to realize the control of the UAV is to send a control command back to the UAV after the computer recognizes a frame of the input hand image. The original output screen of gesture recognition has only 10‒12 FPS, and the control of the drone has a latency of about 83.33‒100 ms. After using multi-threaded processing, the FPS is increased to 15‒16, which reduces the delay, so that the latency is only about 62.5‒66.67 ms. Through a high-accuracy and low-latency intuitive gesture recognition control system, we have enough confidence to replace the method of controlling UAVs with remote control for easy control.
Cinematic Storytelling with Virtual Reality for Cultural Heritage Marketing and Accessibility

This chapter addresses the potential of virtual reality (VR) as a storytelling medium for experiential marketing and the accessibility of cultural heritage sites through a case study of a cinematic VR storytelling aimed to enhance the visitor experience at the Archeological Park of Cuma (Italy). The immersive storytelling was used both as a persuasive tool in the pre-visit stage and as a core element of the on-site visit experience for improved accessibility. The VR storytelling was created through a user-centred design process involving users and in collaboration with heritage curators. The case study illustrates how the cinematic immersive narrative is used to engage viewers, developing a connection between two VR experiences that experiment different narratives in relation to their functions in the visitor experience. Theoretical, methodological, and managerial implications are discussed for the design of visitor-centric experiences and the development of experiential marketing strategies.
A Multi-Objective Dynamically Optimized Fleet Algorithm for Misty Fields Exploration

The Fleet algorithm is a new meta-heuristic swarm intelligence algorithm inspired by the behavior of fleet exploring unknown regions, collecting and distributing geo-data and deciding on global voyage data acquisition in the Age of Discovery. To address the problem of solving the optimal path for multi-objective path planning in the exploration of the misty aread, this paper proposes and constructs a dynamic path planning model for a fleet of ships exploring global route geo-data. The global optimal path can be effectively planned by fitting the algorithm model description with the historical archival data for deduction.
Analysis of Methods for Classifying and Segmenting 3D Images

Issues related to the classification and segmentation of three-dimensional images in the modern era of the transition of technological and economic systems to the digital space are a very relevant and attractive vector of research from a scientific point of view. This article presents the main methods of semantic classification and segmentation of three-dimensional images, describes their main features and analyzes the effectiveness of their work on several data sets.
Construction of an Instrument for the Quantitative Assessment of Experience in the Use of Conversational Agents

Relational conversational agents represent a new frontier in human-computer interaction. They can be handy, for example, in interacting with the elderly public. Considering the increase in life expectancy, longevity came to be seen as a gain and a concern for society. The number of older people grows at a more significant proportion than the number of professionals specialized in their care, often causing an overload in the work of these professionals. In this context, we argue that the technology of voice assistants has the potential to become a promising solution to alleviate problems such as the greater demand for care services for the elderly, contributing not only to assisting caregiver’s activities as well as for the recovery of the independence of the elderly. However, the lack of tools for evaluating users’ experiences with these voice interfaces is remarkable, mainly when used by the elderly. Thus, this work contributes to filling this gap, providing as its main contribution an instrument that quantitatively measures the experience obtained by an older user when using a conversational agent. The application of a tool like this can decisively help design and evolve a voice interface. Applying a tool like this can decisively help plan and grow a voice interface. Furthermore, our instrument can help from observing points of improvement in the system to the degree of suitability of the product to the user, being helpful to get validation with the user from the beginning of the development cycle.
Blockchain-Based Medical Image Processing Mechanism

Blockchain is an emerging technology that provides privacy and security to the user data, ultimately leading to user trust. This paper consists of basic key features of blockchain technology, various kinds of blockchain technology, basic blockchain technology, and the use of a cryptographic function. The blockchain uses a cryptographic hash function that converts an arbitrary size into a fixed-length example: MD5, SHA-256, etc., and proof-of-work to validate the block creation. The blockchain is a decentralized architecture with various applications, such as sharing medical data in healthcare, voting mechanism, personal identity security, supply chain, and cross-border transactions. Blockchain is not limited to only digital currency examples: Bitcoin (BTC), but also has massive applications in the medical field and health sector. The paper’s primary purpose is to explore the current work of blockchain in a medical image. Blockchain deals with the privacy and security issues in transmitting, storing or retrieving medical photos.
Large-scale gradient-based training of Mixtures of Factor Analyzers

Gaussian Mixture Models (GMMs) are a standard tool in data analysis. However, they face problems when applied to high-dimensional data (e.g., images) due to the size of the required full covariance matrices (CMs), whereas the use of diagonal or spherical CMs often imposes restrictions that are too severe. The Mixture of Factor analyzers (MFA) model is an important extension of GMMs, which allows to smoothly interpolate between diagonal and full CMs based on the number of factor loadings l. MFA has successfully been applied for modeling high-dimensional image data [1]. This article contributes both a theoretical analysis as well as a new method for efficient high-dimensional MFA training by stochastic gradient descent, starting from random centroid initializations. This greatly simplifies the training and initialization process, and avoids problems of batch-type algorithms such Expectation-Maximization (EM) when training with huge amounts of data. In addition, by exploiting the properties of the matrix determinant lemma, we prove that MFA training and inference/sampling can be performed based on precision matrices, which does not require matrix inversions after training is completed. At training time, the methods requires the inversion of l × l matrices only. Besides the theoretical analysis and proofs, we apply MFA to typical image datasets such as SVHN and MNIST, and demonstrate the ability to perform sample generation and outlier detection.