Reviewerly/exHarmony huggingface dataset:
Detecting Anomalous User Behavior in Remote Patient Monitoring

The growth in Remote Patient Monitoring (RPM) services using wearable and non-wearable Internet of Medical Things (IoMT) promises to improve the quality of diagnosis and facilitate timely treatment for a gamut of medical conditions. At the same time, the proliferation of IoMT devices increases the potential for malicious activities that can lead to catastrophic results including theft of personal information, data breach, and compromised medical devices, putting human lives at risk. IoMT devices generate tremendous amount of data that reflect user behavior patterns including both personal and day-to-day social activities along with daily routine health monitoring. In this context, there are possibilities of anomalies generated due to various reasons including unexpected user behavior, faulty sensor, or abnormal values from malicious/compromised devices. To address this problem, there is an imminent need to develop a framework for securing the smart health care infrastructure to identify and mitigate anomalies. In this paper, we present an anomaly detection model for RPM utilizing IoMT and smart home devices. We propose Hidden Markov Model (HMM) based anomaly detection that analyzes normal user behavior in the context of RPM comprising both smart home and smart health devices, and identifies anomalous user behavior. We design a testbed with multiple IoMT devices and home sensors to collect data and use the HMM model to train using network and user behavioral data. Proposed HMM based anomaly detection model achieved over 98% accuracy in identifying the anomalies in the context of RPM.
XAI Handbook: Towards a Unified Framework for Explainable AI

The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus regarding its terminology. In particular, each new contribution seems to rely on its own (and often intuitive) version of terms like "explanation" and "interpretation". Such disarray encumbers the consolidation of advances in the field towards the fulfillment of scientific and regulatory demands e.g., when comparing methods or establishing their compliance w.r.t. biases and fairness constraints. We propose a theoretical framework that not only provides concrete definitions for these terms, but it also outlines all steps necessary to produce explanations and interpretations. The framework also allows for existing contributions to be re-contextualized such that their scope can be measured, thus making them comparable to other methods. We show that this framework is compliant with desiderata on explanations, on interpretability and on evaluation metrics. We present a use-case showing how the framework can be used to compare LIME, SHAP and MDNet, establishing their advantages and shortcomings. Finally, we discuss relevant trends in XAI as well as recommendations for future work, all from the standpoint of our framework.
Heterogeneous Blockchain and AI-Driven Hierarchical Trust Evaluation for 5G-Enabled Intelligent Transportation Systems

The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.
Comparison of three machine learning models for the prediction of hourly PV output power in Saudi Arabia

The optimum integration of photovoltaic (PV) technologies into existing power systems necessitates accurate PV performance planning, which is critical for both plant operators and the grid. This study investigates the application of different machine learning (ML) models to predict the PV power output at Jubail Industrial City, Kingdom of Saudi Arabia. Specifically, three techniques have been explored which include k nearest neighbour (kNN), Multiple regression and decision tree regression each with its own set of hyper-parameters & functions. Using the Ostwald’s technique, large dataset comprising the hourly solar irradiance and temperature covering a three-year period, i.e. 2016 to 2019, has been initially used to estimate the PV power for Jubail. Then, the PV power was predicted using the aforementioned ML models. The kNN outperformed the other models, with root mean square error, mean absolute error and normalized root mean square error of 18.68%, 80.6%, and 13.2%, respectively. The other two ML techniques i.e. MLR and the DTR performed reasonably well. As a further contribution, the kNN was applied to forecast the day ahead PV output power for Jubail and the results shows a good agreement between the predicted the actual values. The results imply that using ML techniques, it is possible to predict PV output power across Saudi Arabia, and that this data may be used as a reference for predicting PV output power in different regions of the country.
A robust compressed sensing image encryption algorithm based on GAN and CNN

In this paper, a robust compressed sensing image encryption algorithm based on generative adversarial network, convolutional neural network (CNN) denoising network and chaotic system is developed. Firstly, we use a sampling network to get the measurement of plain image. Second, the cipher image is obtained by scrambling the measurement through the Logistic-Tent chaotic system. After getting cipher image, the decryption party obtains decrypted measurement by inverse scrambling the cipher image, and then sends it to the reconstruction network to obtain decrypted reconstructed image. Finally, by using the CNN denoiser, the image quality and the visual expression of final decrypted image can be improved. In this scheme, the dual denoiser structure based on reconstruction network and CNN denoiser can effectively resist noise attacks. Besides, the proposed training strategy with noise injection can further improve the robustness of network. Experiments show our method has high reconstruction quality, efficiency, robustness and security.
A hybridization of distributed policy and heuristic augmentation for improving federated learning approach

Modifying the existing models of classifiers' operation is primarily aimed at increasing the effectiveness as well as minimizing the training time. An additional advantage is the ability to quickly implement a given solution to the real needs of the market. In this paper, we propose a method that can implement various classifiers using the federated learning concept and taking into account parallelism. Also, an important element is the analysis and selection of the best classifier depending on its reliability found for separated datasets extended by new, augmented samples. The proposed augmentation technique involves image processing techniques, neural architectures, and heuristic methods and improves the operation in federated learning by increasing the role of the server. The proposition has been presented and tested for the fruit image classification problem. The conducted experiments have shown that the described technique can be very useful as an implementation method even in the case of a small database. Obtained results are discussed concerning the advantages and disadvantages in the context of practical application like higher accuracy.
Decision Making of Curriculum Attainment Degree for Engineering Geology Based on Fuzzy Set Theory

The study of engineering geology emphasizes the combination of theory and practice, and it highlights the comprehensive cultivation of curricular theory, curricular practice, and comprehensive skills. It is necessary to establish a set of effective methods to evaluate the achievement of training objectives to track, test, and improve the quality of curricular learning. In this paper, the principle of the fuzzy analytic hierarchy process based on entropy is used to construct an evaluation index system for the goal achievement degree of engineering geology courses, and the primary indicators mainly include knowledge, ability, and quality. Based on the actual situation, five curricular achievement levels are determined, that is, the comment set V = {very low, low, medium, high, very high}. Relying on the engineering geology course resource database of Xuchang University, the course goal achievement degree is evaluated, and the results show that the engineering geology course achievement degree is high. This method is suitable not only for the evaluation of the achievement of curricular objectives but also for the evaluation of the achievement of engineering education graduation requirements. This method can also help us find different courses and teaching weak links supporting the index points, indicate the direction and provide support for teachers to continuously improve their teaching and management methods, and effectively promote a continuous improvement in teachers’ teaching level and teaching quality.
Empirical investigation of hyperparameter optimization for software defect count prediction

Prior identification of defects in software modules can help testers to allocate limited resources efficiently. Defect prediction techniques are helpful for this situation because they allow testers to identify and focus on defect prone parts of the software. The regression approach is a machine learning technique used to find the defect count in software segments. These regression techniques are more effective if their hyperparameters are adjusted. However, limited studies are available for hyperparameter optimization of regression techniques. In this paper, we investigated the impact of hyperparameter optimization on defect count prediction. In an empirical analysis on 15 software defect datasets, we find that hyperparameter optimization of learning techniques: (1) improves the prediction performance for MLPR, Lasso, DTR, Hubber, and SVR, by 16.96%, 8.31%, 8.16%, 6.01% and 5.22%, respectively; (2) linear regression is not optimization-sensitive; (3) overall grid search optimization improved the prediction performance by 4.42% while random search optimization by 3.36%.; (4) non-significant classifier have also changed their ranking substantially, and (5) logistic regression obtained the highest ranking concerning hyperparameter optimization. While both random and grid search performed well, but concerning the default parameter, the grid search always obtained better outcomes; however, it may not with random search. This emphasizes the importance of exploring the parameter space when using parameter-sensitive regression techniques.
Full scene underwater imaging with polarization and an untrained network

The veiling effect caused by the scattering and absorption of suspending particles is a critical challenge of underwater imaging. It is possible to combine the image formation model (IFM) with the optical polarization characteristics underwater to effectively remove the veiling effect and recover a clear image. The performance of such methods, to a great extent, depends on the settings of the global parameters in the application scenarios. Meanwhile, learning-based methods can fit the underwater image information degradation process nonlinearly to restore the images from scattering. Here, we propose for the first time a method for full scene underwater imaging that synergistically makes use of an untrained network and polarization imaging. By mounting a Stokes mask polarizer on the CMOS camera, we can simultaneously obtain images with different polarization states for IFM calculation and optimize the imaging automatically by an untrained network without requiring extra training data. This method makes full use of the nonlinear fitting ability of a neural network and corrects the undesirable imaging effect caused by imperfect parameter settings of the classical IFM in different scenes . It shows good performance in removing the impact of water scattering and preserving the object information, making it possible to achieve clear full scene underwater imaging.
Improving image captioning with Pyramid Attention and SC-GAN

Most of the existing image captioning models mainly use global attention, which represents the whole image features, local attention, representing the object features, or a combination of them; there are few models to integrate the relationship information between various object regions of the image. But this relationship information is also very instructive for caption generation. For example, if a football appears, there is a high probability that the image also contains people near the football. In this article, the relationship feature is embedded into the global-local attention to constructing a new Pyramid Attention mechanism, which can explore the internal visual and semantic relationship between different object regions. Besides, to alleviate the exposure bias problem and make the training process more efficient, we propose a new method to apply the Generative Adversarial Network into sequence generation. The greedy decoding method is used to generate an efficient baseline reward for self-critical training. Finally, experiments on MSCOCO dataset show that the model can generate more accurate and vivid captions and outperforms many recent advanced models in various prevailing evaluation metrics on both local and online test sets.
High-Performance Hardware Implementation of CRYSTALS-Dilithium

Many currently deployed public-key cryptosystems are based on the difficulty of the discrete logarithm and integer factorization problems. However, given an adequately sized quantum computer, these problems can be solved in polynomial time as a function of the key size. Due to the future threat of quantum computing to current cryptographic standards, alternative algorithms that remain secure under quantum computing are being evaluated for future use. One such algorithm is CRYSTALS-Dilithium, a lattice-based digital signature scheme, which is a finalist in the NIST Post Quantum Cryptography (PQC) competition. As a part of this evaluation, high-performance implementations of these algorithms must be investigated. This work presents a high-performance implementation of CRYSTALS-Dilithium targeting FPGAs. In particular, we present a design that achieves the best latency for an FPGA implementation to date. We also compare our results with the most-relevant previous work on hardware implementations of NIST Round 3 post-quantum digital signature candidates.
Personal information inference from voice recordings: User awareness and privacy concerns

Abstract Through voice characteristics and manner of expression, even seemingly benign voice recordings can reveal sensitive attributes about a recorded speaker (e. g., geographical origin, health status, personality). We conducted a nationally representative survey in the UK (n = 683, 18–69 years) to investigate people’s awareness about the inferential power of voice and speech analysis. Our results show that – while awareness levels vary between different categories of inferred information – there is generally low awareness across all participant demographics, even among participants with professional experience in computer science, data mining, and IT security. For instance, only 18.7% of participants are at least somewhat aware that physical and mental health information can be inferred from voice recordings. Many participants have rarely (28.4%) or never (42.5%) even thought about the possibility of personal information being inferred from speech data. After a short educational video on the topic, participants express only moderate privacy concern. However, based on an analysis of open text responses, unconcerned reactions seem to be largely explained by knowledge gaps about possible data misuses. Watching the educational video lowered participants’ intention to use voice-enabled devices. In discussing the regulatory implications of our findings, we challenge the notion of “informed consent” to data processing. We also argue that inferences about individuals need to be legally recognized as personal data and protected accordingly.
Dynamical time series embeddings in recurrent neural networks

Time series forecasting has historically been a key research problem in science and engineering. In recent years, machine learning algorithms have proven to be a very successful data-driven approach in this area. In particular, Recurrent Neural Networks (RNNs) represent the state-of-the-art algorithms in many sequential tasks. In this paper we train Long Short Term Memory networks (LSTM), which are a type of RNNs, to make predictions in time series corresponding to the observation of a single variable of a chaotic system. We show that, under certain conditions, networks learn to generate an embedding of the data in their inner sate that is topologically equivalent to the original strange attractor. Remarkably, this resembles standard forecasting methods from nonlinear science in which the time series is embedded in a multi-valued space using Takens's delay embedding mechanism.
Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars

In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We used ResNet based feature extractor, dilated convolutional layers in down-sampling part, atrous convolutional layers in the upsampling part and used concat operation to merge them. A new attention module is proposed to encode more contextual information and enhance the receptive field of the network. We present an in depth theoretical analysis of our network with training and optimization details. Our network was trained and tested on the Camvid dataset and Cityscapes dataset using mean accuracy per class and Intersection Over Union (IOU) as the evaluation metrics. Our model outperforms previous state of the art methods on semantic segmentation achieving mean IOU value of 74.12 while running at >100 FPS.
EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning

Missing data is a considerable problem in knowledge extraction where the completeness and the quality of the data play a major role in data analysis. In many applications, ignoring the records with missing values may adversely affect the prediction process and creates a significant bias in the resulting data. Therefore, Missing Data Imputation (MDI) has become mandatory to tackle the negative consequences of the presence of missing data. However, different features show different behaviours to data imputation, as the imputation of some features can enhance the learning process while others may lead to worse results according to the feature properties. This paper proposes the use of evolutionary algorithms to evaluate the usefulness of the imputation for each feature on the performance of the prediction model, in order to select the best subset of incomplete features that can enhance the learning process and maximize the prediction power of the model after it has been handled properly. This paper proposes a new approach for handling missing values while performing feature selection simultaneously to enhance the model’s learning performance and reduce the negative consequences of imputation. The performance of the proposed method was evaluated using 10 bench-marking datasets under 10-folds cross validation test. The results were compared with five classical imputation methods (mean, median, multiple imputation, expectation maximization, and K-nearest neighbours). The proposed methodology significantly outperformed other methods in terms of accuracy, sensitivity, specificity, geometric means, and the area under the curve. Moreover, the effectiveness of the proposed method was compared against three recent evolutionary based imputation methods, where the proposed methodology outperformed other methods in terms of accuracy in 75% of the datasets.
Investigating the Vision Transformer Model for Image Retrieval Tasks

This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation. The description method utilizes the recently proposed Vision Transformer network while it does not require any training data to adjust parameters. In image retrieval tasks, the use of Handcrafted global and local descriptors has been very successfully replaced, over the last years, by the Convolutional Neural Networks (CNN)-based methods. However, the experimental evaluation conducted in this paper on several benchmarking datasets against 36 state- of-the-art descriptors from the literature demonstrates that a neural network that contains no convolutional layer, such as Vision Transformer, can shape a global descriptor and achieve competitive results. As fine-tuning is not required, the presented methodology’s low complexity encourages adoption of the architecture as an image retrieval baseline model, replacing the traditional and well adopted CNN-based approaches and inaugurating a new era in image retrieval approaches.
Deep learning for Aspect-based Sentiment Analysis

With the popularity of the Internet and the amount of information people can get from the Internet increases exponentially, sentiment analysis depending on text information has become a rather important tool in daily life, which helps people improve the quality of products and their lifestyle. According to the different granularity of sentiment text, sentiment analysis can be divided into three levels: word, sentence, and document level. While coarse-grained sentiment analysis only focuses on the polarity of emotions and neglects the finer-grained tendency of emotions. Driven by this demand, some researchers put forward the aspect-based sentiment analysis. ABSA can be categorized into two basic steps: aspect extraction and sentiment classification, on which researchers are mainly focusing to improve its performance. Deep learning has gradually become a research hotspot in recent years and has been widely used in CV and NLP fields. Recently, some efforts have begun to apply deep learning to ABSA tasks. In this paper, we further summarize and analyze the recent achievements of ABSA in the application of different deep learning models in terms of their completeness, accuracy, and detail. We also summarize ABSA's current challenges and difficulties and look forward to the future development and improvement of ABSA based on the existing experience.