Reviewerly/exHarmony huggingface dataset:
AutoWeka4MCPS-AVATAR: Accelerating automated machine learning pipeline composition and optimisation

Automated machine learning pipeline (ML) composition and optimisation aim at automating the process of finding the most promising ML pipelines within allocated resources (i.e., time, CPU and memory). Existing methods, such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods frequently require a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid in the first place, and attempting to execute them is a waste of time and resources. To address this issue, we propose a novel method to evaluate the validity of ML pipelines, without their execution, using a surrogate model (AVATAR). The AVATAR generates a knowledge base by automatically learning the capabilities and effects of ML algorithms on datasets’ characteristics. This knowledge base is used for a simplified mapping from an original ML pipeline to a surrogate model which is a Petri net based pipeline. Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components and input/output simplified mappings. Evaluating this surrogate model is less resource-intensive than the execution of the original pipeline. As a result, the AVATAR enables the pipeline composition and optimisation methods to evaluate more pipelines by quickly rejecting invalid pipelines. We integrate the AVATAR into the sequential model-based algorithm configuration (SMAC). Our experiments show that when SMAC employs AVATAR, it finds better solutions than on its own. This is down to the fact that the AVATAR can evaluate more pipelines within the same time budget and allocated resources.
WSHE: User feedback-based weighted signed heterogeneous information network embedding

Heterogeneous information networks (HINs), which have rich semantic relations, can flexibly model multisource heterogeneous data in recommendation systems. Learning more comprehensive features of users based on HINs is a way to improve recommendation performance. User feedback can truly reflect user preferences. Most meta-path-based HIN embedding methods measure the similarity among users by counting the number of meta-paths and cannot fully learn the polar similarity of user preferences. In this work, we proposed a user feedback-based weighted signed HIN embedding method to learn more comprehensive embeddings of users and items. First, we defined a similarity measure using the weighted meta-path to measure the polar similarities of users. Second, we designed a weighted signed network embedding method based on the weighted sampling random walk. The embeddings of different meta-paths were deeply fused guided by an attention mechanism. The fused embeddings were further fused with attribute information using a pooling operation to capture their interactions. Finally, we utilized the rating prediction task to optimize the model and obtain the final embeddings of users and items. Extensive experiments performed on four datasets demonstrated the effectiveness of the model. In addition, we analyzed the importance of the different semantic meta-paths in the rating prediction task based on the interpretability of the attention mechanism.
Artificial intelligence and education: Addressing the variability in learners’ emotion and motivation with adaptive teaching assistants

One of the main challenges of education in modern societies is to effectively address the variability of students in academic learning settings. Students vary in terms of their individual learning preconditions, such as achievement and preknowledge, but also motivation and emotion. Teachers, in turn, have limited resources to provide each learner with individually tailored instruction. This research overview reviews research on artificially intelligent teaching assistants and their role in providing adaptive learning opportunities in relation to learners’ heterogeneous individual learning preconditions in the field of motivation and emotion.
Neural Network-Based Control of an Adaptive Radar

This article demonstrates the use of a neural network to replace the nonlinear optimization at the core of a fully adaptive radar (FAR) system. Use of the neural network is shown to reduce computational complexity, leading to faster updates of the radar parameters. The feed-forward neural networks used were trained using the Levenberg–Marquardt algorithm along with a generalized regression neural network architecture. Training data was obtained from an existing FAR optimization system using the system’s parameter selections and target states for single-target tracking in range and velocity. The trained networks were then used to perform radar parameter selection based on the predicted (forward propagation of prior probabilities) probabilities represented by the target state (target range, velocity, and signal-to-noise ratio) and error variance (target range and velocity variance). The trained neural network was able to reduce the average optimization time by an order of magnitude while obtaining on average a lower resource consumption and similar radar task performance.
Dynamics of entropy and information of time-dependent quantum systems: exact results

Dynamical aspects of information-theoretic and entropic measures of quantum systems are studied. First, we show that for the time-dependent harmonic oscillator, as well as for the charged particle in certain time-varying electromagnetic fields, the increase of the entropy and dynamics of the Fisher information can be directly described and related. To illustrate these results, we have considered several examples for which all the relations take the elementary form. Moreover, we show that the integrals of (geodesic) motion associated with some conformal Killing vectors lead to the Ermakov–Lewis invariants for the considered electromagnetic fields. Next, we explicitly work out the dynamics of the entanglement entropy of the oscillators coupled by a continuous time-dependent parameter as well as we analyse some aspects of quantum-classical transition (in particular decoherence). Finally, we study in some detail the behaviour of quantum quenches (in the presence of the critical points) for the case of mutually non-interacting non-relativistic fermions in a harmonic trap.
A Composite Metric Routing Approach for Energy-Efficient Shortest Path Planning on Natural Terrains

This paper explores the problem of energy-efficient shortest path planning on off-road, natural, real-life terrain for unmanned ground vehicles (UGVs). We present a greedy path planning algorithm based on a composite metric routing approach that combines the energy consumption and distance of the path. In our work, we consider the Terramechanics between the UGV and the terrain soil to account for the wheel sinkage effect, in addition to the terrain slope and soil deformation limitations in the development of the path planning algorithm. As benchmarks for comparison, we use a recent energy-cost minimization approach, in addition to an ant colony optimization (ACO) implementation. Our results indicate that the proposed composite metric routing approach outperforms the state-of-the-art energy-cost minimization method in terms of the resulting path distance, with a negligible increase in energy consumption. Moreover, our results indicate also that the proposed greedy algorithm strongly outperforms the ACO implementation in terms of the quality of the paths obtained and the algorithm running time. In fact, the running time of our proposed algorithm indicates its suitability for large natural terrain graphs with thousands of nodes and tens of thousands of links.
IChannels: Exploiting Current Management Mechanisms to Create Covert Channels in Modern Processors

To operate efficiently across a wide range of workloads with varying power requirements, a modern processor applies different current management mechanisms, which briefly throttle instruction execution while they adjust voltage and frequency to accommodate for power-hungry instructions (PHIs) in the instruction stream. Doing so 1) reduces the power consumption of non-PHI instructions in typical workloads and 2) optimizes system voltage regulators’ cost and area for the common use case while limiting current consumption when executing PHIs.However, these mechanisms may compromise a system’s confidentiality guarantees. In particular, we observe that multilevel side-effects of throttling mechanisms, due to PHI-related current management mechanisms, can be detected by two different software contexts (i.e., sender and receiver) running on 1) the same hardware thread, 2) co-located Simultaneous Multi-Threading (SMT) threads, and 3) different physical cores.Based on these new observations on current management mechanisms, we develop a new set of covert channels, IChannels, and demonstrate them in real modern Intel processors (which span more than 70% of the entire client and server processor market). Our analysis shows that IChannels provides more than 24× the channel capacity of state-of-the-art power management covert channels. We propose practical and effective mitigations to each covert channel in IChannels by leveraging the insights we gain through a rigorous characterization of real systems.
Pose Estimation and Correcting Exercise Posture

Our posture shows an impact on health both mentally and physically. Various methods have been proposed in order to detect different postures of a human being. Posture analysis also plays an essential role in the field of medicine such as finding out sleeping posture of a patient. Image processing based and sensor based approach are the leading posture analysis approaches. Sensor based approach is used by numerous models to focus on posture detection in which the person needs to wear some particular devices or sensors which is helpful in cases such as fall detection. Image processing based approach helps to analyze postures such as standing and sitting postures. Fitness exercises are exceptionally beneficial to individual health, but, they can also be ineffectual and quite possibly harmful if performed incorrectly. When someone does not use the proper posture, exercise mistakes occur. This proposed application utilizes pose estimation and detects the user’s exercise posture and provides detailed, customized recommendations on how the user can improve their posture. A pose estimator called OpenPose is used in this application. OpenPose is a pre trained model composed of a multi-stage CNN to detect a user’s posture. This application then evaluates the vector geometry of the pose through an exercise to provide helpful feedback. Pose estimation is a method in which spatial locations of key body joints is calculated using image or video of the person. This computer vision technique detects human posture in images or videos and shows the keypoints such as elbow or knee in the output image.
Strategizing secured image storing and efficient image retrieval through a new cloud framework

Nowadays, a major problem in faster page loading through optimizing websites is the absence of having images in their intended sizes. Accordingly, while loading a webpage, most of the time overhead pertains to image-related tasks such as image loading and resizing, which can be optimized substantially through the pre-availability of smaller-size images. Therefore, in this study, we propose a strategy to enable faster and efficient image retrieval from a cloud via the necessary pre-processing of images beyond conventional online processing. We also extend the abilities of cloud file sharing from the conventional “only storing images” to pre-processing along with security reinforcing. Here, we perform the first task of resizing images to several dimensions for covering diversified remote user devices available nowadays. Then, we perform encoding and encryption of images using the P-Fibonacci transform of Discrete Cosine Coefficients (PFCC) algorithm. Afterward, we resize images using the Bicubic interpolation method to both JPEG and Progressive-JPEG (PJPEG) formats by adding a new middleware named iBuck for faster and smooth retrieval. We leverage necessary algorithms and image types after careful evaluation of their performances in our intended framework. Our evaluation covers both objective and subjective evaluations including QoE metrics such as MSE, SSIM, etc. Furthermore, we conduct detailed experimentation over real setups to evaluate the performance of our implemented middleware in a cloud file sharing environment with both custom and standard data sets. Our evaluation reveals substantial performance improvement using our proposed framework compared to that using conventional alternatives.
Differential Evolution-based Neural Network Training Incorporating a Centroid-based Strategy and Dynamic Opposition-based Learning

Training multi-layer neural networks (MLNNs), a challenging task, involves finding appropriate weights and biases. MLNN training is important since the performance of MLNNs is mainly dependent on these network parameters. However, conventional algorithms such as gradient-based methods, while extensively used for MLNN training, suffer from drawbacks such as a tendency to getting stuck in local optima. Population-based metaheuristic algorithms can be used to overcome these problems. In this paper, we propose a novel MLNN training algorithm, CenDE-DOBL, that is based on differential evolution (DE), a centroid-based strategy (Cen-S), and dynamic opposition-based learning (DOBL). The Cen-S approach employs the centroid of the best individuals as a member of population, while other members are updated using standard crossover and mutation operators. This improves exploitation since the new member is obtained based on the best individuals, while the employed DOBL strategy, which uses the opposite of an individual, leads to enhanced exploration. Our extensive experiments compare CenDE-DOBL to 26 conventional and population-based algorithms and confirm it to provide excellent MLNN training performance.
Attribute Prototype Learning for Interactive Face Retrieval

Interactive face retrieval aims at finding target subjects in face databases through human and machine interaction, which involves user feedback based on human perception and machine similarity measure in feature spaces. In this article, we propose an attribute prototype learning method to tackle the semantic gap between human and machine in face perception for fast interactive face retrieval. We reformulate the theoretical explanation of the interactive retrieval model and develop the algorithm of the heuristic solution of the model. Each module of the prototype model is learned with a set of identity-related facial attributes. The outputs of the prototype modules form the semantic representation. To adapt the prototype models across different databases, we propose a transfer selection algorithm based on the coherence measurements in interactive face retrieval. Coherence analysis proves that the proposed attribute prototype representation can effectively narrow down the semantic gap even in the case of cross-database transfer learning. The prototype representation can effectively reduce the feature dimension in the retrieval process. Real user retrieval with the Bayesian relevance feedback model shows that attribute prototype space is superior to low-level feature space and proves that interactive retrieval with attribute prototype representation can converge fast in large face databases.
Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence–Based Computer-Aided Detection

BACKGROUND. Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conserving therapy (BCT) has not been widely investigated. OBJECTIVE. The purpose of our study was to assess the impact of additional DBT or AI-CAD on recall rate and diagnostic performance in women undergoing mammographic surveillance after BCT. METHODS. This retrospective study included 314 women (mean age, 53.3 ± 10.6 [SD] years; four with bilateral breast cancer) who underwent BCT followed by DBT (mean interval from surgery to DBT, 15.2 ± 15.4 months). Three breast radiologists independently reviewed images in three sessions: digital mammography (DM), DM with DBT (DM plus DBT), and DM with AI-CAD (DM plus AI-CAD). Recall rates and diagnostic performance were compared between DM, DM plus DBT, and DM plus AI-CAD using the readers' mean results. RESULTS. Of the 314 women, six breast recurrences (three ipsilateral and three contralateral) had developed at the time of surveillance mammography. The ipsilateral breast recall rate was lower for DM plus AI-CAD (1.9%) than for DM (11.2%) or DM plus DBT (4.1%) (p < .001). The contralateral breast recall rate was significantly lower for DM plus AI-CAD (1.5%, p < .001) than for DM (6.6%) but for not DM plus DBT (2.7%, p = .08). In the ipsilateral breast, accuracy was higher for DM plus AI-CAD (97.0%) than for DM (88.5%) or DM plus DBT (94.8%) (p < .05); specificity was higher for DM plus AI-CAD (98.3%) than for DM (89.3%) or DM plus DBT (96.1%) (p < .05); sensitivity was significantly lower for DM plus AI-CAD (22.2%) than for DM (66.7%, p = .03) but not DM plus DBT (22.2%, p > .99). In the contralateral breast, accuracy was significantly higher for DM plus AI-CAD (97.1%) than for DM (92.5%, p < .001) but not DM plus DBT (96.1%, p = .25); specificity was significantly higher for DM plus AI-CAD (98.6%) than for DM (93.7%, p < .001) but not DM plus DBT (97.5%) (p = .09); sensitivity was not different between DM (33.3%), DM plus DBT (22.2%), and DM plus AI-CAD (11.1%) (p > .05). CONCLUSION. After BCT, adjunct DBT or AI-CAD reduced recall rates and improved accuracy in the ipsilateral and contralateral breasts compared with DM. In the ipsilateral breast, the addition of AI-CAD resulted in a lower recall rate and higher accuracy than the addition of DBT. CLINICAL IMPACT. AI-CAD may help address the challenges of interpreting post-BCT surveillance mammograms.
Assamese VADER: A Sentiment Analysis Approach Using Modified VADER

Sentiment Analysis is a Natural Language Processing (NLP) technique that determines the opinion towards an entity, identifying the opinion as positive, negative, or neutral. Extensive research has taken place for high-resourced Languages like English, whereas for Low resourced Indo-Aryan languages, it is still an area in progress. This paper attempts to perform Sentiment Analysis on Assamese Texts, which is a morphologically rich yet Low-resource Indo-Aryan Language, using the concepts of popular sentiment analyzer named “Vader”, while taking “Bengali-Vader” as its backbone. The process follows that of a traditional Vader tool, with consideration towards creating a dictionary of negative booster words and creating an Assamese Lexicon, pre-processing of data, boosting the valence of each word, valence calculation and sentiment categorization of text based on the valence. The necessity of proper dataset in this method is immense, and even though the lack of good translation tools and resources to feed the model, this model was experimented on a set of Assamese texts from a renowned Assamese novel. The comparison was done by manually translating the Assamese sentences to its Bengali and English forms and it shows significant results when compared with the Bengali and English counterparts.
Automatic consecutive context perceived transformer GAN for serial sectioning image blind inpainting

Serial sectioning is the routine method in histology study. In order to restore the defective images in section stack, and overcome the limitations of manual annotation of broken areas, we developed a fully automatic approach for locating and restoring the defective image stack. We proposed a novel end-to-end framework named automatic consecutive context perceived transformer GAN (ACCP-GAN) for fully automatic serial sectioning image blind inpainting. The first stage network (auto-detection module) was designed to detect the broken areas and repair them roughly, then guided the second stage network (refined inpainting module) to generate these expected patches precisely; therefore, the segmentation part was integrated into restoring part. The transformer module (SPTransformer), based on self-attention mechanism, was introduced to make the refined inpainting module focus on the features from neighboring images to help in correcting inpainting results. Moreover, gated convolution was largely used to extract features from normal parts in the defective image. The framework was trained and validated on the N7 dataset (803 images), and the generalization ability of the model was tested on the E17 (701 images) and N5 (413 images) datasets, all of these images were collected for previous kidney study. N7 dataset was divided into training, validation, and test sets with a ratio of 6:2:2. Our model performed well in broken areas segmentation with the accuracy = 0.9995. The final restoration got the best performance with FSIM = 0.9478, MS-SSIM = 0.9592, PSNR = 29.7903, VIF = 0.8543, and FID = 47.2252 compared to the popular inpainting methods. The model was further tested on E15 and N5 datasets, and the generalization ability was satisfying. Our method could detect and restore the defective serial sectioning image stack automatically, even the broken patches were large on an individual image. The newly designed SPTransformer performed well in feature extraction. This method reduced the workload of manual annotation and improved the analysis or application of large scale sectioning image stack in histology research.
Improved Ternary Gray-Code Phase Unwrapping Algorithm for 3D Measurement Using a Binary Defocusing Technique

By using one-bit binary patterns instead of eight-bit sinusoidal ones, the binary defocusing techniques have been widely applied for high-speed 3D shape measurement. As projector defocusing is required, the phase unwrapping process of these techniques remains challenging. A recently proposed ternary Gray-code method can effectively increase the measurement speed by reducing the number of acquired coding patterns. However, it still has limitations, including the measuring range and a noise problem. To improve these, a new ternary Gray-code method is proposed by utilizing 2D modulation instead of the 1D modulation used in the conventional method. Simulations were conducted to compare these two methods, and the results verify that the proposed method reduces the variance in the introduced intermediate gray value by nearly 90%. Experiments were also carried out with the results verifying the superiority of the proposed method.
A Fuzzy Rule-based System using a Patch-based Approach for Semantic Segmentation in Floor Plans

Semantic segmentation models help with the extraction of information from images. Currently, Convolutional Neural Networks (CNNs) are the state of the art for performing such tasks but the interpretability in their predictions is low. Previous work had proposed the use of Fuzzy Logic Rule-based systems (FRBS) as an explainable AI classifier of pixels for segmentation of images. In this paper, we extend that approach by using the similarity between image patches as context information for our model. The type-1 FRBS that uses the proposed set of context information features reaches an average Intersection over Union (IoU) value 3.51% higher than the type-1 FRBS using colour information. The difference in average IoU is significant due to the importance of colour in the testing images and the already high IoU value from the type-1 FRBS using colour.