Reviewerly/exHarmony huggingface dataset:
An 8-bit precision cipher for fast image encryption

Abstract Implementing chaos based ciphers usually involves 32-bit floating-point arithmetics that is hardware resources costly. The limitation of the computational precision is hardware imposed and transforms chaotic orbits into limit cycles with short periods, hence alters their randomness. In cryptographic applications, short period dynamics and weak randomness result in security issues. In order to address this concern, we propose an 8-bit precision cipher that can be implemented with low-end microprocessors running 8-bit integer arithmetics. The cipher includes a quantized pseudo-random number generator (QPRNG) based on a 16-dimensional quantized Arnold’s cat map (QACM). We used entropy measure, statistical, sensitivity and key space analyses to evaluate its security level under limited computational precision. Simulation results attest that it is as highly secure as those involving real-number arithmetics, even for only 8-bit precision. We also showed that the period of the proposed QACM can be chosen such that T x &gt; 10 27 , which is very large as compared to existing QACM. Such a large period implies a high randomness of the derived QPRNG that is confirmed by statistical NIST tests. Contrary to existing ciphers that include other chaotic systems than the QACM for strengthening the security level, ours is exclusively based on the QACM and is fast, despite the included high-dimensional QACM.
Neighborhood-Exact Nearest Neighbor Search for face retrieval

Features extracted by deep convolutional neural network perform well for face recognition. The crux of large-scale face retrieval using such features stems from the trade-off between speed and accuracy: search efficiently while preserving acceptable accuracy. Traditional methods perform Nearest Neighbor Search (NNS) over the entire gallery set based on feature matching in the continuous space, which is able to achieve exact (optimized) retrieval results for given features. However, it is not feasible for large-scale face retrieval due to unaffordable time consumption. A typical way of speeding up the face retrieval is to conduct Approximate Nearest Neighbor Search (ANNS) at the expense of accuracy without guarantee of search quality. To circumvent this dilemma, we define a new problem w.r.t. large-scale face retrieval, which is referred to as Neighborhood-Exact Nearest Neighbor Search (NENNS). NENNS demands efficient search while guaranteeing the search exactness within a specified neighborhood around the query. Further, we propose a heuristic method for face retrieval, which is able to perform NENNS efficiently. Specifically, it first discretizes the continuous features of samples into binary codes by our designed Angular Binary-Encoding Mechanism. Then it performs fast yet precise exclusion of bad candidates in the binary feature space based on our defined metric (binarized Cosine similarity) to reduce search space significantly. Finally the accurate searching results conforming to NENNS are achieved by conducting precise search in continuous feature space. We perform theoretical proof and extensive experiments to validate the correctness and effectiveness of the proposed method.
Path Planning Method of Mobile Robot Using Improved Deep Reinforcement Learning

A mobile robot path planning method based on improved deep reinforcement learning is proposed. First, in order to conform to the actual kinematics model of the robot, the continuous environmental state space and discrete action state space are designed. In addition, an improved deep Q-network (DQN) method is proposed, which takes the directly collected information as the training samples and combines the environmental state characteristics of the robot and the target point to be reached as the input of the network. DQN method takes the Q value at the current position as the output of the network model and uses <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>ε</mi> </math> -greedy strategy for action selection. Finally, the reward function combined with the artificial potential field method is designed to optimize the state-action space. The reward function solves the problem of sparse reward in the environmental state space and makes the action selection of the robot more accurate. Experiments show that compared with the classical DQN method, the average loss function value is reduced by 36.87% and the average reward value is increased by 12.96%, which can effectively improve the working efficiency of mobile robot.
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively.
Is Non-IID Data a Threat in Federated Online Learning to Rank?

In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for future work in this new and largely unexplored research area of Information Retrieval. In the FOLTR process, clients participate in a federation to jointly create an effective ranker from the implicit click signal originating in each client, without the need to share data (documents, queries, clicks). A well-known factor that affects the performance of federated learning systems, and that poses serious challenges to these approaches, is that there may be some type of bias in the way data is distributed across clients. While FOLTR systems are on their own rights a type of federated learning system, the presence and effect of non-IID data in FOLTR has not been studied. To this aim, we first enumerate possible data distribution settings that may showcase data bias across clients and thus give rise to the non-IID problem. Then, we study the impact of each setting on the performance of the current state-of-the-art FOLTR approach, the Federated Pairwise Differentiable Gradient Descent (FPDGD), and we highlight which data distributions may pose a problem for FOLTR methods. We also explore how common approaches proposed in the federated learning literature address non-IID issues in FOLTR. This allows us to unveil new research gaps that, we argue, future research in FOLTR should consider.
An Alternative Framework For Quantifying Coherence Of Quantum Channels

We present an alternative framework for quantifying the coherence of quantum channels, which contains three conditions: the faithfulness, nonincreasing under sets of all the incoherent superchannels and the additivity. Based on the characteristics of the coherence of quantum channels and the additivity of independent channels, our framework provides an easier way to certify whether a function is a bona fide coherence measure of quantum channels. Detailed example is given to illustrate the effectiveness and advantages of our framework.
Measurement of Ability in Adaptive Learning and Assessment Systems when Learners Use On-Demand Hints

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners' progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints.
Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods

Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields of computer vision and image-processing, particularly human action recognition. Many methods have been developed to recognize human activity, which is limited to effectiveness, efficiency, and use of data modalities. Very few methods have used depth sequences in which they have introduced different encoding techniques to represent an action sequence into the spatial format called dynamic image. Then, they have used a 2D convolutional neural network (CNN) or traditional machine learning algorithms for action recognition. These methods are completely dependent on the effectiveness of the spatial representation. In this article, we propose a novel ranking-based approach to select key frames and adopt a 3D-CNN model for action classification. We directly use the raw sequence instead of generating the dynamic image. We investigate the recognition results with various levels of sampling to show the competency and robustness of the proposed system. We also examine the universality of the proposed method on three benchmark human action datasets: DHA (depth-included human action), MSR-Action3D (Microsoft Action 3D), and UTD-MHAD (University of Texas at Dallas Multimodal Human Action Dataset). The proposed method secures better performance than state-of-the-art techniques using depth sequences.
Assessing the Impact of Membership Inference Attacks on Classical Machine Learning Algorithms

In the last decade, machine learning has been widely adopted in many areas and the trend not only continues but accelerates. This has raised many issues ranging from ethics and security to privacy. In particular, it has been shown that in some settings an attacker can infer if a given element has been used to train a machine learning model. This is not acceptable in many applications that deal with sensitive data and thus designers need to take privacy into account when implementing machine learning based systems. In this paper, we study the impact of such membership inference attacks on traditional machine learning algorithms. Our goals are 1) to identify if there are some algorithms that are more vulnerable than others, and 2) if privacy can be guaranteed by selecting the algorithm parameters and if so at what cost on performance. Our results show that ensemble averaging models, especially Extra-Trees, are vulnerable to this attack. This information can be used by designers as an additional input to guide the machine learning algorithm selection process so that privacy is incorporated as a design goal from the beginning.
A two-step algorithm for acoustic emission event discrimination based on recurrent neural networks

We present an algorithm for seismic event discrimination and event approximate location based on multi-station seismograms. A deep learning approach was applied using a two-step algorithm: (i) signal onsets were identified in individual tracks based on the use of long-short-term memory neural network layers; (ii) if a sufficient number of onsets were reliably identified, a preliminary location was determined. We adopted a "reverse location approach" where the time sense of a seismogram is reverted and the origin time is predicted using a neural network approach based on previously determined onsets. Successful location or origin time prediction also served as a feedback for confirming previous onset identification. The method was tested using a data set of Acoustic Emission generated from the uniaxial loading of a Westerly Granite specimen. Accuracy of the method was better than 97%. Discriminated events were automatically located and their seismic moment tensor was determined. Both types of results were in good agreement with the baseline data set. With respect to the particular nature of processed data, we provide a demo code which shows examples presented in the article. In addition, a detailed description of the algorithm, including the control parameter values, is provided in the text. Based on this information the method can be applied on any data.
Hybrid Model for Human Activity Recognition Using an Inflated I3-D Two Stream Convolutional-LSTM Network with Optical Flow Mechanism

Recently, human activity recognition (HAR) and classification have driven interest in the industry and academic studies due to a huge application in the area. However, monitoring of activity from video and images is challenging due to the nature of video data, different weather conditions, the uncertain movement of human being, and availability of suitable datasets. There is very few research on deep learning-based activity recognition as the area is in its early stages. However, existing study was able to detect single individual known activities but unable to detect unknown and complex activities. This can be extended from single person to multi-person classification. Additionally, in the existing 1Dimentional Convolutional Neural Network (1D CNN), the core goes in a single way, thus, the input and output data of 1D CNN is 2-dimensional. Therefore, this technique is more suitable for time series data than images or videos. However, optical flow is a best way to abstract and define shadows for an active video background. Hence, this research further explores activity recognition using multiple activities frames, optical flow, I3D CNN, LSTM and SoftMax Classifier. In this work, LSTM is employed to aid in detecting information based on HAR recognition. The i3D-CNN model, optical flow approach and LSTM algorithm are employed in the feature extraction step. Experimental results on MATLAB 2021a shows that, improving the number of hidden layers more than the number of layers can decrease the accuracy in the test set, thereby making the network to fit to the training set. It was further observed that the proposed model achieved best and stable recognition accuracy when the configuration settings were 10 LSTM layers and 256 hidden units. In this case the proposed model achieved the best-case scenario for the recognition accuracy with 97.8% for draw sword, 94.8% for shoot bow, 9.81% for shoot gun, 94.85% for Hit and 95.9% for punch respectively. On comparison with other base line classifiers, the SoftMax classifier attained the best recognition accuracy of 97.8% as against the SVM with 94.35% and KNN with 92.91%.
CNN for Elderly Wandering Prediction in Indoor Scenarios

This work proposes a way to detect the wandering movement of Alzheimer's patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we have manually generated a dataset of 220 paths using our developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results in finding patterns mainly on images. The Convolutional Neural Network model was trained with the generated data representing the hourly analysis and achieved an F1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on the validation slice. For comparative purposes, we have also trained the model with a 30-min interval of analysis and achieved an F1 score of 57.14%, a recall of 80% and a precision of 44.44%.
Part-facial relational and modality-style attention networks for heterogeneous face recognition

The purpose of heterogeneous face recognition (HFR) is to match face images of the same person from different modalities. Most HFR methods bridge the cross-modality variations with feature alignment by global feature representation learning, but ignore the content information of local features and modality-style information of face image for each modality, which limits the performance for HFR. The content information of local features not only contains the invariance of modality face features, but also can improve the stability of global face features, i.e., local features such as eyes, nose and mouth are steady and invariant. With this motivation, we propose a cross-modality dual-constraint (CMDC) approach that includes the part-facial relational attention network (PRAN) and modality-style attention network (MSAN). First, PRAN is designed to estimate the intrinsic structural relationships of local content features on each modality. It can extract discriminative local face features by capturing correlations within the face space of individual modality, and strengthen representations by contextual relationships across modalities. Secondly, we design the MSAN to capture the modality-style information for each modality, and then reduce the inter-modality differences by minimizing the distance of two modality-style features. Thirdly, to alleviate cross-modality variances and enhance intra-class compactness and inter-class divisibility, we propose the cross-modality dual-constrained loss (DCLoss) in the CMDC approach, which adds a global constraint to each sample distribution in the embedding space. Meanwhile, on the basis of focusing on modality-style information, DCLoss emphasizes the significance of category information. Extensive experiments on four datasets demonstrate the superior performance of our approach over the existing state-of-the-art. The code is available at https://github.com/JianYu777/CMDC.
Comparative Study Of Various Scraping Tools: Pros And Cons

As technology keeps on improving and data is still considered the topmost priority, people need to know how they can make the use of old data available, and impact their worlds. The data these days may be raw data, refined data, structured or even the unstructured data. These data finally result into a huge amount of data with varying volume and velocity, generating the concept of Bigdata. Data Extraction has shifted the way we view the world and as it is so necessary, we need to know certain tools available for this service. Web scraping has become so important for businesses to grow, scientific research, or even to get the knowledge of the most read article on the internet. In this paper, we cover the most powerful known web scraping tools available to date and study them. We have compared these tools and clarified their benefits as well as drawbacks concerning various applications.
Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach

User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.
AR HMD for Remote Instruction in Healthcare

In the following position paper we introduce the use of AR HMD for remote instruction in healthcare and present the challenges our team has faced in achieving this application in two contexts: surgical telementoring and paramedic teleconsulting. After the presentation of how these challenges come to be and indications on how to address them, we argue that those who wish to pursue this area of research must be grounded in best practices from the field of CSCW integrated with technical innovations in AR interaction development. This is a truly interdisciplinary research and development area that has many challenging topics to tackle through collaborative efforts.
Solar Tracking System with Photovoltaic Cells: Experimental Analysis at High Altitudes

There is currently an urgent need to study the application of solar energy to photovoltaic systems due to the need to produce electricity; indeed, maximizing the performance of solar energy promotes efficient and sustainable energy systems. The objective of this study was to determine the photovoltaic performance of a dual-axis solar tracker based on photovoltaic cells with different inclination angles at high altitudes above 3800 m.a.s.l. A solar tracking system activated by two linear actuators was implemented to automatically follow the trajectory of the sun during the day, and the results were compared with those from a fixed photovoltaic system. In addition, due to the climatic variation in the area, photovoltaic cells installed at different inclination angles were used to maximize electricity production and processed by a programmable logic controller (PLC). Finally, principal component analysis (PCA) was used to determine the factors that influenced the performance of the photovoltaic system during the experimental period. The results showed that the maximum monthly performance of the solar tracker was 37.63% greater than that of the fixed system, reaching 10.66 kWh/m2/d on sunny days in peak sun hours (PSH). On days with frequent rain and clouds, the partial yield was less than 14.38%, with energy production during PSH of 6.54 kWh/m2/d. Therefore, in this high-altitude area, the performance of the solar tracker was greater from July to October; from November to February, the performance was reduced due to the occurrence of rain.