Reviewerly/exHarmony huggingface dataset:
Modified Multiway Pixel-Value Differencing Methods Based on General Quantization Ranges for Image Steganography

A modified multiway pixel-value differencing method for image steganography using general quantization ranges of pixel pairs&#x2019; difference values is proposed, where the widths of the quantization ranges are not limited to be powers of two. The method can be employed to embed a secret message into a cover image with a higher embedding rate. The cover image is partitioned into non-overlapping complete or incomplete blocks using <inline-formula> <tex-math notation="LaTeX">$1 \times 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2 \times 3$ </tex-math></inline-formula>, or <inline-formula> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> block templates. The message bitstream, whose bit positions are randomized in advance by use of a random number generator, can be embedded in the resulting blocks. More specifically, one pixel in each block is set as a shared pixel, and the least-significant-bit substitution method with the optimal pixel adjustment process is employed to embed message bits into the shared pixel. Next, the shared pixel in the block is combined with each remaining pixel to form a pixel pair, which is then utilized to embed message bits by a new pixel-value differencing method. Also, the use of non-power-of-two range widths is accomplished by a multiple-based number conversion mechanism, and the shared pixel in each block, as well as each related pixel pair whose two grayscale values pass a falling-off-boundary check, are used to embed the digits of the resulting multiple-based number. The experimental results show that the embedding rates yielded by the proposed method are higher than those yielded by existing multiway pixel-value differencing methods. The stego-images resulting from embedding secret messages retain good image quality, and the RS steganalysis process cannot detect the presence of the embedded secret messages.
Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI

Statistical artificial intelligence based upon machine learning is facing major challenges such as machine bias, explainability, and verifiability problems. Resolving them would be of the utmost importance for ethical, safe, and responsible AI for social good. In this paper we propose to address these problems with the (recently rapidly developing) methodology of category theory, a powerful integrative scientific language from pure mathematics, and discuss, in particular, the possibility of the categorical integration of statistical (inductive) with symbolic (deductive) artificial intelligence. Categorical artificial intelligence arguably has the potential to resolve the aforementioned urgent problems, thus being beneficial to make artificial intelligence more ethical, verifiable, responsible, and so more human, which would be crucial for AI-pervasive society.
A web-based application for face detection in real-time images and videos

Abstract Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.
Personalization of Information using Graph Convolutional Network

There exists several researches that have been done on link-based search engines for instance, Clever and Google. They involve the use of link structure to getprecise results. Generally, search engines based on link structure give users high-quality results than search engines which are text based. However, those search engines encounter difficulty producing the result fitting to a specific user’s profile. Personalization means knowing the user intimately enough to not only meet their needs but also predict them. This paper presents an analogy to a personalized search engine using an already existing GCN (Graph Convolutional Network) architecture on (Cora) the paper citation dataset (similar to web pages) and additionally followed by KNN algorithm to rank the personalized citations in best consonance with a user’s profile.
BroadGAN: Generative adversarial networks of discriminating separate features based on broad learning

Since the generative adversarial network (GAN) was proposed in 2014, it has rapidly become a hot topic in the field of deep learning. In recent years, there are many optimizations for GAN, which are divided into three kinds, including the optimization of loss functions, external structure of network and internal structure of network. Few people optimizes GAN from internal structure of network, and because of the process of game, slow training speed is one of the biggest problems of GAN. This paper introduces the ideology of broad learning algorithm (Chen and Liu, 2017) to put forward the multi-judge generative adversarial network method based on different random features to enhance the quality of generative result and increase the training speed of GAN (Goodfellow et al., 2014). This model provides a method which reduce the input information of each discriminator to optimize the training process of GAN. The experiments on cifar10 and anime face dataset find that our model obtains a better performance than the GMAN model and Base model. This paper finds a group of hyper-parameters to enhance the BroadGAN.
An Investigation of Automatic Saccade and Fixation Detection from Wearable Infrared Cameras

Eye movement plays an important role in cognition and perception, and the detection of saccade and fixation has been studied for human-computer applications, however often under conditions where head movement is constrained, and often using calibration-dependent gaze information rather than the raw pupil position. In order to investigate the performance of saccade and fixation detection using gaze and pupil data, three representative saccade detection algorithms are applied to both pupil data and gaze data collected with and without head movement, and their performance is evaluated against a stimulus-induced ground truth under different measures. Results indicate that saccade/fixation detection using pupil data generally provides better performance than using gaze data with an 8.6% improvement in Cohen’s Kappa (averaged across the three algorithms), even when moderate head movement is involved. Hence, pupil data can be used as an alternative to gaze data for saccade and fixation detection in wearable contexts with less effort in calibration and higher accuracy.
Binary PSO Variants for Feature Selection in Handwritten Signature Authentication

In this paper we propose modifications of the well-known algorithm of particle swarm optimization (PSO). These changes affect the mapping of the motion of particles from continuous space to binary space for searching in it, which is widely used to solve the problem of feature selection. The modified binary PSO variations were tested on the dataset SVC2004 dedicated to the problem of user authentication based on dynamic features of a handwritten signature. In the example of k-nearest neighbours (kNN), experiments were carried out to find the optimal subset of features. The search for the subset was considered as a multicriteria optimization problem, taking into account the accuracy of the model and the number of features.
A Machine Learning Approach for Heart Attack Prediction

There are lot of activities in the human body which are unpredictable in general form. Heart attack is one of them, and it is so serious activity in human body which cause in death of human. But this action gives some sim-terms before it committed. Although it is not highly noticeable in normal conditions, it committed suddenly. So this is one of the highly unpredictable occurrences in human body. With the technology advancement some of the data mining algorithms were developed to predict the heart attack. In such continuation, different data mining algorithms, with machine learning approach are able to predict heart attack occurrence in human body. It is one of the typical diagnosis tasks, but it should be achieved accurately and efficiently with the help of machine learning. The present research paper is an attempt to model and solve the heart attack prediction problem. Different machine algorithms such as KNN, SVC, RFC, decision tree and logistic regression are taken here to form a model for the study. The forte of the projected model is sufficient and is competent enough to predict the occurrence of heart attack in the human body by the compilation of some sim-terms occurring in the body. So machine learning approach is a good approach to predict occurrence of heart attack timely and we may go for proper treatment and precaution for the same.
Towards Self-Guided Remote User Studies - Feasibility of Gesture Elicitation using Immersive Virtual Reality

Gesture Elicitation Studies (GES) are a widely used empirical method to develop gesture vocabularies, interaction models and methods for gesture-based systems in different contexts. While GES show great promise to identify user-defined gestures, there are inherent problems with current methods used for GES. Especially during the ongoing pandemic, it has been nearly impossible to conduct in-person, in-lab GES, while ensuring the safety and well-being of the participants, and complying with social distancing regulations. Further, with prevailing experiment designs, increasing the number of participants is time consuming, while in-lab environments also limit ecological validity. This study explores an intuitive way of conducting self-guided GES using immersive Virtual Reality (VR), utilizing its capability to simulate various contexts to enhance ecological validity. We present a methodology and a tool set that use an immersive VR environment to conduct ecologically valid GES (as a use case) while requiring minimal involvement by the investigator. We evaluate our method using the case of a smart home environment and measure participant acceptance and discuss opportunities and challenges involved in this method. We believe that this study will help HCI research to move forward with participatory design research, even when lab experiments are difficult to conduct.
The Impact of Covid-19 on Blockchain Adoption Time of Shipowners

The outbreak of Covid-19 has accelerated the need for digitalisation in maritime supply chains. Blockchain is a promising digital technology for post-Covid-19 recovery because of its immutability, transparency, traceability and security. This paper aims to analyse the impact of Covid-19 on blockchain adoption time among shipowners. A game theoretical model is built to solve the problem. In numerical applications, the impact of Covid-19 is observed by comparing the results to those without Covid-19. It is found that Covid-19 has a positive effect on accelerating blockchain adoption for a few small shipowners. This indicates that Covid-19 provides a good business opportunity for blockchain developers to market their products.
Modelling and Simulation of Cloud Service Cost Analysis using Resource Scheduling

Organizations are avoiding investments in infrastructure because of 3 reasons, age of technology, maintenance and scalability. This has created a huge market for Cloud service providers like AWS, Azure, Google etc. Cloud computing services are usually sorted into three groups SaaS, PaaS and IaaS. Each service belongs to a specified group and is offered at specific prices. The Cloud service providers (CSP's) offer services in small modules, and users feel their investment is low; on the other hand, these CSP's provision resources in a manner to maximize their revenues in the context of cloud resource management.
Comprehensive evaluation of the intelligence levels for unmanned swarms based on the collective OODA loop and group extension cloud model

Considering the increasing complexity of application scenarios, the evaluation of the intelligence levels for unmanned swarms has attracted scholarly attention. However, the existing evaluation studies cannot suitably reflect the intelligence of unmanned swarms, and they rarely provide a specific evaluation process. By introducing the collective intelligent behavior model for unmanned swarms based on the collective Observe–Orient–Decide–Act (OODA) loop, this study constructs a comprehensive evaluation index system that can systematically reflect the overall intelligence of unmanned swarms in complex scenarios. Considering the fuzziness and randomness in the processes of weight calculation and level evaluation, this study proposes a comprehensive evaluation method of the intelligence levels for unmanned swarms based on a group extension cloud model. This method calculates the weights of various evaluation indexes at different layers by adopting the group extension analytic hierarchy process. Moreover, it obtains the comprehensive evaluation conclusion of the intelligence levels for unmanned swarms by adopting the cloud model. Applying the proposed method, this study evaluates two specific types of unmanned aerial vehicle swarms. The results show that the proposed method can more flexibly and accurately evaluate the intelligence levels of unmanned swarms than the previous fully qualitative methods.
Disease Correlation Enhanced Attention Network for ICD Coding

The automatic ICD coding task is to assign the most relevant International Classification of Disease diagnosis codes (ICD codes) to the patient’s clinical text, which is defined as a multi-label text classification problem. In practical applications, the clinical text is lengthy and the distribution of ICD codes is skewed with a long tail, making it difficult to predict the labels for rare disease. To tackle the problem, we propose a new deep learning framework which combines diseases correlation and multi-attention mechanism into Bidirectional Recurrent Neural Network. In particular, we apply the Graph Convolutional Network to capture the disease relevance between ICD codes, which can be highly valuable for those diseases with limited data. In addition, we leverage multi-head attention and label-wise attention to encode clinical text, thus our model can learn label-specific representations and consider disease correlation in the same time. Extensive experiments demonstrate that our proposed model is effective. On the MIMIC-III-full dataset, our model outperformed the state-of-the-art model in 5 out of 7 evaluation metrics. On the MIMIC-III-50 dataset, our model outperformed the state-of-the-art model in 4 out of 7 evaluation metrics.
An Efficient Reconfigurable Neural Network on Chip

Neural networks are being used in several domains and applications. One of the main challenges is the hardware implementation of neural networks. The hardware design is fixed in the number of nodes and layers that make the network applicable to specific applications. This paper presents a reconfigurable neural network where the number of layers and nodes can be changed according to the applications. The proposed method is based on the Network on Chip (NoC) configuration, which is used for routing data between different layers and nodes. Each router in NoC is connected to m nodes that can represent a part or complete layer. According to the reconfiguration, the number of routers can be selected to present each layer, and the number of needed nodes per layer is decided by the number of nodes in each router. The proposed method is implemented on FPGA Altera 10 GX, and it achieves an accuracy of 97% for using the MNIST dataset. The throughput and delay of the proposed method are more efficient compared to the traditional method.
MCMS-STM: An Extension of Support Tensor Machine for Multiclass Multiscale Object Recognition in Remote Sensing Images

The support tensor machine (STM) extended from support vector machine (SVM) can maintain the inherent information of remote sensing image (RSI) represented as tensor and obtain effective recognition results using a few training samples. However, the conventional STM is binary and fails to handle multiclass classification directly. In addition, the existing STMs cannot process objects with different sizes represented as multiscale tensors and have to resize object slices to a fixed size, causing excessive background interferences or loss of object’s scale information. Therefore, the multiclass multiscale support tensor machine (MCMS-STM) is proposed to recognize effectively multiclass objects with different sizes in RSIs. To achieve multiclass classification, by embedding one-versus-rest and one-versus-one mechanisms, multiple hyperplanes described by rank-R tensors are built simultaneously instead of single hyperplane described by rank-1 tensor in STM to separate input with different classes. To handle multiscale objects, multiple slices of different sizes are extracted to cover the object with an unknown class and expressed as multiscale tensors. Then, M-dimensional hyperplanes are established to project the input of multiscale tensors into class space. To ensure an efficient training of MCMS-STM, a decomposition algorithm is presented to break the complex dual problem of MCMS-STM into a series of analytic sub-optimizations. Using publicly available RSIs, the experimental results demonstrate that the MCMS-STM achieves 89.5% and 91.4% accuracy for classifying airplanes and ships with different classes and sizes, which outperforms typical SVM and STM methods.
AI-based Real-time Classification of Human Activity using Software Defined Radios

Real-time monitoring is an essential part in the development of healthcare monitoring systems. Research has shown that human movement affects the propagation of radio frequencies, as signals will reflect off the human body. Machine Learning techniques have been used in research to classify patterns observed in the signal propagation. This paper makes use of universal software radio peripheral devices to create a wireless communication link where the signal propagation data, known as channel state information, is collected while a user moves or remains still. A machine learning model which achieved an accuracy result of 93.25 % is used to classify between movement and no activity. Inference is then used to decide if the human position is sitting or standing and detected movements are used to differentiate between the two positions. The testbed implements cloud storage and a web-interface to present a visualisation of the human position.
Less Is Better: Fooling Scene Text Recognition with Minimal Perturbations

Scene text recognition (STR) has made tremendous progress in the era of deep learning. However, the attack of the sequential STR does not attract sufficient scholarly attention. The very few existing researches to fool STR belong to white-box attacks and thus would have limitations in practical applications. In this paper, we propose a novel black-box attack on STR models, only using the probability distribution of the model output. Instead of disturbing most pixels like existing STR attack methods, our proposed approach only disturbs very few pixels and utilizes own characteristics of recurrent neural networks (RNNs) to propagate perturbations. Experiments validate the effectiveness and superiority of our attack approach.
DynaPosGNN: Dynamic-Positional GNN for Next POI Recommendation

Location-based offline user activity data plays a key role to understand user interest and context in various user-friendly services. Especially, the next Point-Of-Interest (POI) recommendation task is focusing on the user&#x2019;s next location based on previous user trajectory. Previous studies for the next POI recommendations generally utilized the order of each visit as temporal characteristic and positions as spatial characteristic based on the Recurrent Neural Networks (RNN) mechanism. However, they are just interested in the next location right after a user&#x2019;s current trajectory not considering specific arrival time or the prediction time. In this paper, we propose Dynamic-Positional Graph Neural Network (DynaPosGNN), a novel next POI recommendation model considering specific arrival time in offline user activities. DynaPosGNN can predict users&#x2019; next location by analyzing the correlation between arrival time and two spatial dynamic graphs called &#x2019;User-POI graph&#x2019; and &#x2019;POI-POI graph&#x2019;. Our experiments conducted on two real-world datasets show that DynaPosGNN outperforms existing next POI recommendation models.
FAM: Fully Attention Module for Medical Image Segmentation

Semantic segmentation plays crucial role in image analysis, which needs rich spatial information and contextual representations. Recently, deep convolution neural networks have achieved much progress in semantic segmentation. However, it still faces many challenges due to the complexity of images, like the blur, noise, and similarity, which imped the progress of the image segmentation. In this paper, we rethink the feature fusion attention methods, such as spatial attention and channel attention, and propose a novel Fully Attention Module (named FAM) based on every pixel of feature map without reducing the size of the input feature maps. We integrate the proposed FAM into U-Net and Fast-SCNN to assess its effectiveness on Synapse dataset. The extensive experiments show that FAM could improve the performance of both two architectures with acceptable cost in terms of speed and total number of parameters. Specifically, we improve the average Dice Similarity Coefficient (DSC) 4.31% / 1.07% and average Hausdorff Distance (HD) 17.62mm / 8.21mm comparing to U-Net and FastSCNN, respectively. In summary, our proposed FAM could boost the segmentation performance by extracting the fully attention of each pixel in the feature maps. The code will be made publicly available at https://github.com/apple1986/FAM.
Secure hybrid replacement policy: Mitigating conflict-based cache side channel attacks

Conflict-based attacks on the last-level cache (LLC) are the most threatening cache side channel attack scenarios, where attackers exploit cache set conflicts as a channel to obtain sensitive cache accesses of victims. In this paper, we observe that conflict-based attacks cause exploited cache sets to experience an abnormally large number of accesses. Protecting these sets with suspicious access patterns effectively interferes with the attacker's observation and thus mitigates such attacks. Based on this insight, we propose Secure Hybrid Replacement Policy (SHRP). SHRP captures suspicious sets by counting the number of accesses and alters their replacement policy to introduce noise into the attacker's probe. Specifically, When a request occurs an LLC hit, SHRP has a certain probability of sending an invalidation request to the suspicious set. If a request triggers an LLC replacement, SHRP has a certain probability of turning the request uncacheable. Because of these probabilistic obscuring actions, attackers are impossible to accurately infer the victim's behaviors on the protected cache set. The evaluation demonstrates that SHRP efficiently mitigates conflict-based attacks and induces negligible performance impacts and acceptable storage overhead.