Reviewerly/exHarmony huggingface dataset:
Fraud and Deception Detection: Text-Based Data Analytics

With the trend of increasingly complex big data, how to handle and improve the authenticity of data has become an important issue related to the credibility of data. This chapter discusses how to imitate and detect similar applications and how to identify fake reviews by machine learning and various statistical methods using deceptive applications and fake reviews as examples.
An Underwater Vision Measurement Method Based on Segmented Synchronous Rotational Scanning

Aiming at the influence of refraction, an underwater vision measurement method based on segmented synchronous rotational scanning is proposed in this paper. First, the underwater measurement refraction model is established. When the optical axis of the projector is perpendicular to the refraction plane and the light plane passes through the projection optical axis, the calibration can be done in the air, which simplifies the calibration process. Then the structure projection mode is designed to realize the segmented synchronous rotational scanning of the underwater area. Finally, we perform 3D measurements of underwater flat objects. The experimental results show that the measurement method proposed in this paper can achieve a measurement accuracy of 1.19mm when the measurement distance is within 1000mm.
Area Efficient Asynchronous Circuits for Side Channel Attack Mitigation

Synchronous sequential or clocked digital circuits are susceptible to synchronized side channel attacks (SCAs). By distributing (in time) data processing, one method used to mitigate or defend against SCAs is clockless, asynchronous circuit design. A problem often associated with clockless, asynchronous circuit design methods, like Null Convention Logic (NCL), is the large area for logically equivalent circuits. Typical asynchronous circuits are 2.5 to 3.5x the size of their synchronous sequential counterparts. This work develops a data-path design methodology based on a library of unique hybrid cells (part conventional and part NCL). The new hybrid method has shown a significant reduction in transistor count for asynchronous circuits. It results in logically equivalent asynchronous circuits with only an average transistor count increase of 6% while maintaining distributed (in time) processing advantages. The method, hybrid gate description, and comparison for several benchmark circuits are presented.
BOBCAT: Behaviors, Objectives and Binary states for Coordinated Autonomous Tasks

We present our framework Behaviors, Objectives and Binary states for Coordinated Autonomous Tasks (BOBCAT), a multi-agent decision making and task management system for autonomous robots. BOBCAT builds on behavior-based systems and the Belief-Desire-Intention model for decision-making, by using mission objectives as the basis for selecting tasks, or behaviors. We describe how BOBCAT is formulated and present the results of a real-world implementation in the context of exploring austere underground environments in the DARPA Subterranean Challenge.
Using Python and Machine Learning Algorithms to Create a Spam Classifier

Many email systems now feature spam filters that can distinguish between spam and non-spam communications. As a result of the high demand and vast user base, there is a surge in unwanted emails, sometimes known as spam emails. On several occasions, more than half of the emails received were spam. People continue to lose millions of dollars as a result of phishing emails. The goal of this study is to create a spam filter. SVM is a prominent classifier, thus it was utilised alongside two other machine learning algorithms, random forest and radial basis function, and when compared, random forest outperformed the other classifiers by 94 percent.
Human Interaction Recognition with Skeletal Attention and Shift Graph Convolution

Human interaction recognition has wide applications including intelligent surveillance, intelligent transportation and the analysis of sports videos. In recent years, benefiting from the development of action recognition based on deep learning, the performance of human interaction recognition has been boosted. This paper tackles two vital issues in recognizing human interactions, namely target missing and inadequate feature expression. To this end, we first design a data preprocessing method using skeleton estimation and multi-object tracking, which effectively reduces the chance of missing detection. Second, we propose a two-stream network composing of an appearance branch and a pose branch. The appearance branch extracts features enhanced via part affinity maps and part confidences maps, while the pose branch trains a customized Shift-GCN to extract skeletal features from people-pairs. Appearance and pose features are then fused to generate a more powerful representation of human interactions. Extensive experiments on two existing benchmarks, UT and BIT-Interaction, as well as a new dataset crafted by us, namely Campus-Interaction (CI), demonstrate the superior performance of the proposed approach over the state-of-the-arts.
Online Convolutional Neural Network for Image Streams Classification

In recent years, convolutional neural networks (CNN) have been rapidly developed for offline learning and have shown powerful capabilities in areas such as image classification. However, since these models require sufficient time and batch data learning convergence in offline learning mode, this is not scalable for many realistic scenarios where new data arrives sequentially in streams. In response, we propose a new model: Online Convolutional Neural Network (OCNN). Unlike traditional CNN model, the OCNN is very flexible where its structure can change dynamically as data streams come in, allowing it to work well online. In particular, we use CNN as the feature extractor of the model, and a fully connected neural network (DNN) is connected after each feature extractor as the classifier. By using Hedge Backpropagation method to assign weights to each classifier and get the prediction results, OCNN can dynamically learn to adaptive depth from a sequence of data streams. We validated the efficacy of our model on several large-scale datasets: we improved the classification accuracy by 4.95 and 20.74 compared to baseline model on Fashion-MNIST and CIFAR-10 data streams.
APTKG: Constructing Threat Intelligence Knowledge Graph from Open-Source APT Reports Based on Deep Learning

Advanced persistent threats (APTs) pose a serious threat to the security of cyberspace. Recently, more and more security organizations and vendors have been focusing on cyber threat intelligence (CTI) to tackle APTs. Particularly, some researchers have introduced knowledge graphs to aggregate CTIs and explore the potential intelligence value. This paper presents APTKG, a framework for automatically extracting CTI triples from open-source APT reports and constructing APT group- focused knowledge graphs. Specially, first, we model the STIX- based ontology, APTOnt, which aggregates malicious information from hierarchical levels and can be applied to threat hunting, group profiling, APT tracing and other tasks. Then, we propose a Multi-Feature based CTI recognition method to improve the accuracy of various CTIs extraction. Finally, we apply a BiGRU based Inter-Sentence method to extract document-level CTI relationships. The experimental results confirm that the proposed CTI entity recognition model and relationship extraction method exhibit excellent performance, and the joint model of the two is also effective for CTI triples extraction. In addition, APTKG has stimulated exploration and research on CTI-based APT attack defense and analysis.
Zero Knowledge Proofs Towards Verifiable Decentralized AI Pipelines

We are witnessing the emergence of decentralized AI pipelines wherein different organisations are involved in the different steps of the pipeline. In this paper, we introduce a comprehensive framework for verifiable provenance for decentralized AI pipelines with support for confidentiality concerns of the owners of data and model assets. Although some of the past works address different aspects of provenance, verifiability, and confidentiality, none of them address all the aspects under one uniform framework. We present an efficient and scalable approach for verifiable provenance for decentralized AI pipelines with support for confidentiality based on zero-knowledge proofs (ZKPs). Our work is of independent interest to the fields of verifiable computation (VC) and verifiable model inference. We present methods for basic computation primitives like read only memory access and operations on datasets that are an order of magnitude better than the state of the art. In the case of verifiable model inference, we again improve the state of the art for decision tree inference by an order of magnitude. We present an extensive experimental evaluation of our system.
The Analysis of Explainable AI via Notion of Congruence

Explainable AI has become a popular topic that draws the attention of many researchers, and since the inception of this field, the most popular method of model explanation is undoubtedly using Decision Tree to approximate black box model. However, Decision Tree is single-dimensional model without any nuance in the decision making which may be intrinsic to the behaviors of the black box model itself. With this, we propose using a structured framework in the form of Probabilistic Assumption-based Argumentation (PABA) framework in place of Decision Tree. This paper will demonstrate an application of the aforementioned framework in model explanation problem by establishing the notion of congruence between an example black box model and its structured argumentation framework predictor counterpart.
Fusion of Novelty Detectors Using Deep and Local Invariant Visual Features for Inspection Task

In this study, a novel framework using multiple novelty detection filters is developed to learn a model of the normality of a robot’s visual perception, which is called multichannel novelty detection. Subsequently, the acquired model was used to highlight dissimilar perceptions when the robot explored an environment. The main purpose of fusing multiple novelty filters is that each novelty filter performs well in detecting specific types of novelties; therefore, a new framework is proposed that demonstrates a new way to combine multiple different purposed novelty detection filters together in order to yield an overall more robust novelty status on the visual features. To develop a multichannel novelty detection system, expectation- and appearance-based novelty detection models were used in this study. To become experts in detecting different types of novelty using these models, different features from the input image were extracted as inputs for the models. The expectation-based novelty detection model uses the MobileNetV2 deep network to extract the deep features of the input image, which is subsequently used to learn a sequential and temporal model of normality to detect novelty. By contrast, the appearance-based novelty detection model uses speeded up robust features (SURF), which provide more region-focused features within the input image, to identify whether a specific region of the image is novel. The proposed multichannel novelty detection system is a completely online and real-time approach that is very important for mobile robotics applications. The proposed framework was tested in three novel environments, and it was reported that the proposed multichannel novelty detection system performs better than expectation-based and appearance-based novelty filters separately. Statistically, in the three novel environments, the Matthews correlation coefficients are reported to be 0.94, 0.97, and 0.93, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> scores are reported to be 0.95, 0.97, and 0.93, respectively, which proves that and can be concluded as almost perfect statistically.
Sequential thermal image-based adult and baby detection robust to thermal residual heat marks

The awareness for preserving privacy in in-home monitoring robots is increasing. Although several studies have proposed privacy-preserved in-home monitoring robot systems for adults, only a limited amount of attention has been paid attention to research on privacy-preserved in-home monitoring of babies. Like previous studies, thermal infrared image-based methods could ensure a privacy-preserved monitoring of babies, yet when existing detection methods were applied to thermal images to detect babies and adults, we discovered a frequent occurrence of misdetection due to the presence of thermal residual heat marks. In this research, we propose a sequential thermal image-based detection that conjugated the characteristics of thermal residual heat marks. The proposed detection reduced misdetection caused by thermal residual heat marks by 98.7% when compared to RetinaNet. In addition, we open-source our collected thermal image-based baby and adult dataset via: https://github.com/donkeymouse/ThermalAdultandBaby.
Re-balance, a Perception-Enabled Chair for Multi-species Domestic Agency

The paper presents the theoretical, conceptual, and technical framework of ‘Re-Balance: the chair that doesn’t know it is a chair’, an automated kneeling chair able to construct interactions in various domains of action. This pet furniture is designed to extend our daily life ecologies and to enrich their interactive experiences both on the physical and emotional levels. A unique acoustic and motion language expresses different personalities willing to create special relationships with human and nonhuman actors (living beings or algorithmic intelligence). The project resides at the intersection of posthuman design and human-robot interaction, studying contemporary multi-species cohabitation and social design modes. This chairbot is thought to be an actual commercial product, as a platform for collective research. At the time of writing this paper, the project is at the initial stages of the development of mock-ups and prototypes for the ergonomic studies and the communication skills of the chairbot via movement and sounds.
A Content-Based Approach to Medical Image Retrieval

Medical images stored in distributed and centralized servers are referred to for knowledge, teaching, information, and diagnosis. Content-based image retrieval (CBIR) is used to locate images in vast databases. Images are indexed and retrieved with a set of features. The CBIR model on receipt of query extracts same set of features of query, matches with indexed features index, and retrieves similar images from database. Thus, the system performance mainly depends on the features adopted for indexing. Features selected must require lesser storage, retrieval time, cost of retrieval model, and must support different classifier algorithms. Feature set adopted should support to improve the performance of the system. The chapter briefs on the strength of local binary patterns (LBP) and its variants for indexing medical images. Efficacy of the LBP is verified using medical images from OASIS. The results presented in the chapter are obtained by direct method without the aid of any classification techniques like SVM, neural networks, etc. The results prove good prospects of LBP and its variants.
C2F-CFN: Coarse-to-Fine ClothFlow Network for High-Fidelity Virtual Try-On

Image-based virtual try-on refers to the task of transferring a target clothing item onto the corresponding area of a person. While deep generative models have made considerable progress in virtual try-on, current methods are incapable of handling the rendering of try-on details, and they generally suffer from unrealistic try-on images with artifacts due to the improper warping of clothing. In this paper, we propose a novel virtual try-on network, called C2F-CFN, which models the per-pixel appearance flow via a coarse-to-fine manner. Specifically, the affine transformation is learned first to obtain the coarse warped clothing, and then the texture and geometric details of clothing are refined through appearance flow estimation. Furthermore, the oracle flow supervision is introduced to produce more natural and reasonable flow maps. We evaluate our model on the VITON dataset, and demonstrate that our approach outperforms the state-of-the-art techniques quantitatively and qualitatively.
Early Identification of Faults using Hybrid CNN Model for Industrial Internet of Things

A hybrid deep learning model will be used in the conducted study to prevent errors on the Industrial Internet of Things for a thorough evaluation of defect, a deep convolution neural network-based technique is recommended. Furthermore, an Industrial Internet program for real-time failure prediction and characterization is built and tested using a pilot-size commercial motor. Whenever the research reveals that a particular vibration measurement is to blame for the issue, a hybrid convolutional neural network (CNN) is used to identify the imbalance or carrying defective condition. For the training process, a hybrid deep convolutional neural network model was employed. This unique model’s primary goal is to improve predictive performance, and test results indicate that the prediction time as well as detection effectiveness of the suggested technique are superior to those of previous machine learning and deep learning approaches.
METHODOLOGICAL APPROACHES TO THE JUSTIFICATION OF THE ESSENCE OF THE VALLEOLOGICAL CULTURE OF THE FUTURE TEACHER

The general scientific methodological approaches to substantiation of the essence of valeological culture of the future teacher are analyzed. The expediency of using a systematic approach in the formation of valeological culture of the future teacher is d
On The Design Of Rationalised Bi-orthogonal Wavelet Using Reversible Logic

This paper presents a novel approach to obtain rational bi-orthogonal wavelet filter coefficients based on Perfect Reconstruction (PR) in Half-Band Polynomial (HBP) by removing Vanishing Moments (VM). A delta-error approach is adopted to design wavelet filter coefficients that reduce the error using an iterative method. This error equation is formulated in terms of generalized HBP, where the odd values are equated to zero and the filter coefficients are constrained with complete dyadic conditions. This produces a generalized design for bi-orthogonal filter with perfect reconstruction. Furthermore, in order to realize power-efficient designs, we propose a reversible logic based implementation for the proposed bi-orthogonal wavelet filter bank. The implementation efficiency is measured in terms of Gate Count (GC), Quantum Cost (QC), Ancilla Input (AI) and Garbage Output (GO). The proposed Design 2 is able to reduce (GC, QC, AI, GO) by (34.3%, 31.6%, 33.9%, 31.1%) when compared to existing design. The effectiveness of the proposed wavelet Filter Banks (FBs) is verified using an image compression application. The experimental results show that, the proposed rationalised wavelet FBs are able to achieve an average increase of 16% in PSNR when compared to existing designs.
Fault Resilience Techniques for Flash Memory of DNN Accelerators

Deep neural networks (DNNs) are being widely used in smart appliances, face recognition and autonomous driving. The trained weight data are usually stored in flash memory which suffers from reliability and endurance issues. Owing to the inherent error tolerability for DNN applications, adaptive address remapping techniques are proposed for protecting weight data stored in flash memory. Bit significances are first analyzed to determine the priority of weight bits which should be protected. Thereafter, a novel weight transposer and an address remapper are proposed for remapping significant weight bits to fault-free or much reliable flash cells. A bipartite graph model is developed for modeling address remapping and evaluating error score. The corresponding hardware architectures for address remapping are also proposed. We use the deep learning framework pytorch for evaluating inference accuracy for different DNN models. Experimental results show that based on 0.01 % injected BER in the weight data, the accuracy losses of widely used DNN models are less than 1 % with negligible hardware overhead.
Design and implementation of Aut 64 block cipher for car keyless entry

Over some time, the evolution of automobile key, from using it for not only opening the doors but also as an immobilizer to a remote keyless entry, has always faced the threat of getting hacked and increasing the un-authorized entry into the vehicle. Hackers can even run commands, forcing a vehicle to obey the hacker instead of the driver. Hence in the present generation, there is remote keyless entry with remote keyless ignition and so by this, there is an increasing threat in un-authorized entry into a vehicle. And that tells us the need to have a safety measure to secure the automobile key systems. Cryptography is a method in cyber security that helps in encrypting the important data so that the hacker cannot decode the message and thereby allowing only that person for whom the information is intended to read and process it by the method of decryption. We are proposing AUT64 cipher to implement our solution. Aut64 is a 64-bit automotive cipher with a 120-bit secret key used in many security-sensitive applications like keyless entry systems and vehicle immobilizer. In this report, we are going to explain the details about AUT64 which includes a brief description of the usage of a block cipher and its implementation using Hardware Description Language (HDL).
Image Creation Based on Transformer and Generative Adversarial Networks

To address the problem of low authenticity of generated images in existing generative models, the transformer super-resolution generative adversarial network(TransSRGAN) model based on the generative adversarial network is proposed. The generator of the model uses the transformer encoder sub-module as the basic module. The features of the input vector are extracted. low-definition images are generated through the transformer encoder submodule, and the low-definition image is up-sampled by the convolutional neural network to complete the image generation. The discriminator of this model uses the convolutional neural network as the basic module. To discriminate the real samples from the generated fake samples, the discriminator extracts the image features by the convolutional neural network. The experimental results show that the TransSRGAN model brings the distribution of the generated samples closer to the training samples, effectively raises the quality of the generated samples, improves the authenticity of the generated samples, and enriches the diversity of the generated samples. During the training process, there was no mode collapse or instability.
Structural Estimation of Partially Observable Markov Decision Processes

Partially observable Markov decision processes (POMDPs) is a well-developed framework for sequential decision-making under uncertainty and partial information. This article considers the (inverse) structural estimation of the primitives of a POMDP based upon data in the form of sequences of observables and implemented actions. We analyze the structural properties of an entropy regularized POMDP and specify conditions under which the model is identifiable without knowledge of the state dynamics. We consider a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft</i> policy gradient algorithm to compute a maximum likelihood estimator, and illustrate the approach with an equipment replacement problem.