Reviewerly/exHarmony huggingface dataset:
Modeling Long- and Short-Term User Behaviors for Sequential Recommendation with Deep Neural Networks

In e-commerce platforms, a user's next behavior will be affected by his long-term constant interests and short-term temporal needs. Such information is usually hidden in the users' historical online behavior data, so how to capture long-term and short-term patterns becomes the key to design better recommendation models or algorithms. Current mainstream methods such as Markov chain, convolutional neural network, and recurrent neural network cannot well express the mixed dynamic characteristics. In this paper, we propose an attention-based deep neural network (ADNNet) to solve the problem. In ADNNet, a convolutional neural network is used to extract the short-term patterns in the behavior sequences, and a gated recurrent unit is used to mine the long-term patterns in the behavior sequences. The attention mechanism is adopted to help the network automatically learn the best fusion coefficient of these two patterns. Our experimental result on four real public datasets (+0.69% in Hit Ratio and +3.49% in MRR) shows the superiority of our proposed ADNNet compared with other state-of-the-art methods.
Learning to Build High-Fidelity and Robust Environment Models

This paper is concerned with robust learning to simulate (RL2S), a new problem of reinforcement learning (RL) that focuses on learning a high-fidelity environment model (i.e., simulator) for serving diverse downstream tasks. Different from the environment learning in model-based RL, where the learned dynamics model is only appropriate to provide simulated data for the specific policy, the goal of RL2S is to build a simulator that is of high fidelity when interacting with various policies. Thus the robustness (i.e., the ability to provide accurate simulations to various policies) of the simulator over diverse corner cases (policies) is the key challenge to address. Via formulating the policy-environment as a dual Markov decision process, we transform RL2S as a novel robust imitation learning problem and propose efficient algorithms to solve it. Experiments on continuous control scenarios demonstrate that the RL2S enabled methods outperform the others on learning high-fidelity simulators for evaluating, ranking and training various policies.
On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval

The quality of case retrieval in case-based reasoning (CBR) systems depends on assigning appropriate case indices. Defining feature vocabularies for indexing is an important knowledge acquisition problem for CBR, often addressed by hand. The manual process may result in high-quality vocabularies, but at considerable effort and expense, and it may be difficult for non-symbolic input such as images. Recently, the ability of deep learning (DL) to identify important features has made it appealing for learning to assign case features. However, such methods may miss features apparent to knowledge engineers. This paper presents a case study on methods for combining benefits of both engineered and DL-generated features. It considers case-based classification of cases described by both symbolic features and images. It evaluates the power of both types of features individually, examines how quality of engineered feature information affects their combined benefit, and tests network methods to generate weights for their combination. Experimental results show that in the test domain under suitable circumstances, the combined approach can outperform either method individually.
Influence of vapor-phase fluids on the geochemical characterization of hydrothermal sulfides in the shimmering waters of the southern Okinawa Trough

Inverted lakes filled with shimmering water are unique environments within hydrothermal systems. Recently, vapor-phase fluids have been detected on the top of an inverted lake in a mushroom cap-shaped (MC) sulfide structure based on in situ Raman spectra at the Yokosuka Site in the Okinawa Trough (OT). However, the effects of vapor-phase fluids on the migration and metallogenic characteristics of elements remain unclear. To characterize the influence of such fluids, a comparative study of the micro-Raman spectroscopy, electron microprobe analyses (EMPA), polarizing microscopic, element distribution, and S-Pb isotopic composition among the hydrothermal sulfides collected from MC area, the middle Okinawa Trough (MOT) and southern Okinawa Trough (SOT), was conducted. Raman spectra show that the dominant band of chalcopyrite in MC sulfide generated a blue shift, while sphalerite generated a red shift, indicating that compositional differences exist in MC sulfide samples compared to the other two areas. This was also confirmed by EMPA data for these two minerals. Moreover, the full width(s) at half maximum (FWHM) of the Raman spectra of minerals in the MC sulfide samples showed a lower degree of crystallinity influence by the vapor-phase fluids. Elemental analyses further demonstrated that the MC samples share a particular elemental composition. That is, MC sulfides featured high Mn, Ba, and Sr and low Fe, Cu, Zn, Pb, Bi, Mo, Cd, and Ga. The same enrichment trends were observed among Ba–Sr, Cu–Mo, Cu–Bi, Zn–Pb, Zn–Cd, and Zn–Ga. Moreover, the δ34S of 5.44–5.85‰ in these deposits suggest that S was contributed by the underlying basalt, sediments, and seawater. The Pb isotopic composition of MC sulfides exhibited the same compositional patterns as the other sulfides and the underlying sediments at the SOT site, indicating that the sedimentary contribution of Pb has increased. The S–Pb isotopic composition suggests that a hydrothermal source may not serve as the primary cause of differences in the MC deposits. Instead, the unique vapor-phase fluids and other gaseous components may have predominantly affected the precipitation and migration of elements at this site. Although the shimmering water regions of inverted lakes accompanied by vapor-phase fluids are difficult to observe in deep-sea hydrothermal systems, here, we describe the geochemical characteristics of sulfides affected by this particular vapor-phase fluid, providing a unique perspective on the migration and precipitation occurring during sulfide deposit formation.
Nacre: Proactive Recurrent Concept Drift Detection in Data Streams

Concept drift detection is used to signal to a learning algorithm that there has been a change in the underlying distribution of the data stream. However, there is a delay in detecting the actual drifts, leading to performance loss between the start of the drift and the detection point. There are two major challenges in reducing such performance loss, specifically the difficulty in anticipating the location of the next drift point and determining the exact concept that will appear for timely concept adaptation. In this research, we leverage concept recurrences in data streams. We proposed a framework called Nacre, which can perform proactive drift detection and online updates to allow for smooth adaptation of concept drifts. We present a novel technique, called drift coordinator, that anticipates the next drift point and assesses the incoming concept. This will ultimately increase accuracy in the classification performance. We demonstrate that our method is able to learn and predict drift trends in streams with recurring drifts. This allows the anticipation of future changes which enables users and detection methods to be more proactive. We empirically show that our technique outperforms baselines in terms of accuracy, kappa, accuracy gain per drift and cumulative accuracy gain on both synthetic and real-world datasets.
Transformer-Based Online Bayesian Neural Networks for Grant-Free Uplink Access in CRAN With Streaming Variational Inference

We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks to enable fast online machine learning. The proposed distributed inference framework is shown to be robust and suitable for the online scenario. Our simulations demonstrate the robustness of the proposed solution in online propagation environments and its ability to capture uncertainty.
Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality

Many explanation methods have been proposed to reveal insights about the internal procedures of black-box models like deep neural networks. Although these methods are able to generate explanations for individual predictions, little research has been conducted to investigate the relationship of model accuracy and explanation quality, or how to use explanations to improve model performance. In this paper, we evaluate explanations using a metric based on area under the ROC curve (AUC), treating expert-provided image annotations as ground-truth explanations, and quantify the correlation between model accuracy and explanation quality when performing image classifications with deep neural networks. The experiments are conducted using two image datasets: the CUB-200-2011 dataset and a Kahikatea dataset that we publish with this paper. For each dataset, we compare and evaluate seven different neural networks with four different explainers in terms of both accuracy and explanation quality. We also investigate how explanation quality evolves as loss metrics change through the training iterations of each model. The experiments suggest a strong correlation between model accuracy and explanation quality. Based on this observation, we demonstrate how explanations can be exploited to benefit the model selection process—even if simply maximising accuracy on test data is the primary goal.
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator

Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the mutation operator. In this paper, we propose a novel DE algorithm, Clu-DE, that improves the efficacy of DE using a novel clustering-based mutation operator. First, we find, using a clustering algorithm, a winner cluster in search space and select the best candidate solution in this cluster as the base vector in the mutation operator. Then, an updating scheme is introduced to include new candidate solutions in the current population. Experimental results on CEC-2017 benchmark functions with dimensionalities of 30, 50 and 100 confirm that Clu-DE yields improved performance compared to DE.
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?

One of the most concerning threats for modern AI systems is data poisoning, where the attacker injects maliciously crafted training data to corrupt the system's behavior at test time.Availability poisoning is a particularly worrisome subset of poisoning attacks where the attacker aims to cause a Denial-of-Service (DoS) attack.However, the state-of-the-art algorithms are computationally expensive because they try to solve a complex bi-level optimization problem (the "hammer").We observed that in particular conditions, namely, where the target model is linear (the "nut"), the usage of computationally costly procedures can be avoided.We propose a counter-intuitive but efficient heuristic that allows contaminating the training set such that the target system's performance is highly compromised.We further suggest a re-parameterization trick to decrease the number of variables to be optimized.Finally, we demonstrate that, under the considered settings, our framework achieves comparable, or even better, performances in terms of the attacker's objective while being significantly more computationally efficient.
Interdisciplinary Technical Competitions – A Case Study

Bosch Future Mobility Challenge is an international technical contest organized by the Bosch Engineering Centre in Cluj, Romania. The main goal of the competition is the development of autonomous driving algorithms and implementation of them on 1/10 scale vehicle. The vehicle and the computation core are provided by the company. The competition has a clear timeline. During this time, the students must prepare four technical documentations. The subjects integration of the four technical documentations results in the autonomous driving algorithm, and must include lane keeping, intersection crossing logic, sign detection, collision avoidance, while the movement of the vehicle should be smooth. A mentor among the University staff should be designated. In the current paper we will present the educational gains achieved by attending this type of competition. The paper will present the technical challenges, the team results, and how this competition helps illustrating the integration of disciplines forming the curricula, as a tool to motivate students in successfully graduating their studies.electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
An Improved (Adversarial) Reprogramming Technique for Neural Networks

Neural networks can be repurposed via adversarial reprogramming to perform new tasks, which are different from the tasks they were originally trained for. In this paper, we introduce new and improved reprogramming technique that, compared to prior works, achieves better accuracy, scalability, and can be successfully applied to more complex tasks. While prior literature focuses on potential malicious uses of reprogramming, we argue that reprogramming can be viewed as an efficient training method. Our reprogramming method allows for re-using existing pre-trained models and easily reprogramming them to perform new tasks. This technique requires a lot less effort and hyperparameter tuning compared training new models from scratch. Therefore, we believe that our improved and scalable reprogramming method has potential to become a new method for creating neural network models.
Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models

Mixture modeling is a major paradigm for clustering in statistics. In this article, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with the mixture model parameters can represent the original data effectively and much more compactly. We thus cluster the data using the latent states and select variables according to the relationship between the states and the clusters. As true class labels are unknown in the unsupervised setting, we first generate more refined clusters, namely, semi-clusters, for variable selection and then determine the final clusters based on the dimension reduced data. Experiments on simulated and real data show that the new method is highly competitive in terms of clustering accuracy compared with several widely used methods. Supplementary materials for this article are available online.
Augmented Reality in Medical Education, an Empirical Study

Even though we live in a high tech world some of the learning techniques used in medical schools are outdated or are not taking advantage of the latest technologies available. This is partially due to the cost associated with adopting new and ever-changing technologies in emerging economies and lacking awareness of their existence. We created an application that has the potential to help students train themselves better by using Augmented Reality and gamification techniques. It detects the student’s face through the laptop or phone camera and maps realistic skin conditions on the detected face, prompting the student to recognize them. With the overwhelmingly positive response from the surveys, the application should deepen student knowledge of pathologies.
Partial Transfer Learning for Fast Evolutionary Generative Adversarial Networks

Generative Adversarial Networks (GAN) are well known for their capability of generating photo-realistic images or data collections that appear real. One of state-of-the-art approaches in GAN is Evolutionary GAN (E-GAN) which can outperform other GAN methods by leveraging the advantages of evolutionary computing, including population based search, mutation and elitism operators. However evolutionary search is often demanding in terms of resources, e.g. computational power and time. That limits its applicability when resource is limited. Hence we propose Partial Transfer training based E-GAN (PT-EGAN) to improve the efficiency of E-GAN. PT-EGAN aims to train the generator and the discriminator with smaller data sets and transfer the gained features across different stages of training. Our comparative experiments on the CIFAR-10 data set shows that PT-EGAN can reach better performance than E-GAN when using similar resources. Alternatively PT-EGAN requires less resources to achieve a similar performance as E-GAN. PT-EGAN is effective in speeding up generative adversarial learning.
Image Modeling with Deep Convolutional Gaussian Mixture Models

In this conceptual work, we present Deep Convolutional Gaussian Mixture Models (DCGMMs): a new formulation of deep hierarchical Gaussian Mixture Models (GMMs) that is particularly suitable for describing and generating images. Vanilla (i.e., flat) GMMs require a very large number of components to describe images well, leading to long training times and memory issues. DCGMMs avoid this by a stacked architecture of multiple GMM layers, linked by convolution and pooling operations. This allows to exploit the compositionality of images in a similar way as deep CNNs do. DCGMMs can be trained end-to-end by Stochastic Gradient Descent. This sets them apart from vanilla GMMs which are trained by Expectation-Maximization, requiring a prior k-means initialization which is infeasible in a layered structure. For generating sharp images with DCGMMs, we introduce a new gradient-based technique for sampling through non-invertible operations like convolution and pooling. Based on the MNIST and FashionMNIST datasets, we validate the DCGMMs model by demonstrating its superiority over flat GMMs for clustering, sampling and outlier detection.
Privacy-Preserving Audio Classification Using Variational Information Feature Extraction

In this paper we investigate and tackle the privacy risks of deep-neural-network-based feature extraction for sound classification in acoustic sensor networks. To this end, we analyze a single-label domestic activity monitoring and a multi-label urban sound tagging scenario. We show that in both cases, the feature representations designed for sound classification also carry a significant amount of speaker-dependent data, thus posing serious privacy risks for speaker recognition attacks based on feature interception. We then propose to mitigate the aforementioned privacy risks by introducing a variational information feature extraction scheme that allows sound classification while, concurrently, minimizing the feature representation's level of information and hence, inhibiting speaker recognition attempts. We control and analyze the balance between the performance of the trusted and attacker tasks via the resulting model's composite loss function, its budget scaling factor, and latent space size. It is empirically demonstrated that the proposed privacy-preserving feature representation generalizes well to both single-label and multi-label scenarios with vast as well as reduced training-dataset resources. Furthermore, it exhibits robustness against x-vector-based, state-of-the-art speaker recognition attacks.
Imaging targets hidden in scattering and viscous liquid-based media by combining multiple projections and applying a non-local mean filtering algorithm

Scattering and viscous (or sticky) liquid-based media such as that in the human ear can be considered a highly turbid environment through which imaging a target is challenging. Typically, reduced target visibility and image quality together with the limitation of depth through which objects may be resolved occurs because of multiple scattering events. Consequently, several approaches described in the literature have been applied to image objects hidden within turbid liquid media. In this paper, we experimentally demonstrate an approach to enhance image quality during recovery of objects hidden in sticky liquid at different viscosity levels by the fusion of single-shot projections assisted by a non-local mean (NLM) filtering algorithm. The turbidity of the medium was evaluated via attenuation coefficient by Beer's law and the viscosity was measured by a capillary viscometer device. The medium was illuminated with a laser beam and multiple images of the object were obtained from different perspectives using a lens array. Following image processing, each sub-image (projection) was digitally cropped, extracted from the array, filtered, shifted to a common center and then summed with other sub-images to form a single average image. Different setup conditions such as: medium turbidity, target shape, illumination state, and filtering processing were tested and compered during this work. A quantitative image quality metric of visibility commonly used in image processing was adopted to evaluate the quality of the reconstructed targets. The feasibility of the proposed hybrid method is supported by the experimental results which show a reconstructed image with high visibility. This study is a part of our ongoing efforts to enable imaging and reveal objects embedded in turbid media by integrating different strategies from the disciplines of: optics, astrophysics, digital image processing, and medicine.
Human Abnormal Activity Pattern Analysis in Diverse Background Surveillance Videos Using SVM and ResNet50 Model

Today, almost all the places are observed by surveillance cameras. The aim is to monitor the activities in and around, especially for abnormal activities. But it requires manual assistance to watch/monitor. However, manual inspection is a monotonous job, that reflects on information lost. It shows the importance of an automatic abnormal activity detection system. This is considered a difficult task because of object overlapping, light variation, clutter background, camera angle, presence of various activity, and posture variations. It manipulates the human actions in videos. Hence, it is challgening to recognize abnormal human actions. This work examines the concert of the classical SVM and ResNet50 model among four datasets: The 'SAIAZ' (S tudents A ctivities I n A cademic Z one)-Corridor, 'SAIAZ'-Open space, Classroom Violence from YouTube (cc), and Mixed Background Dataset (MBD). The 'SAIAZ' was created by student volunteers of our Institution, and MBD is a collection of selected frames from various videos. Abnormal actions like slapping, punching, kicking, running, and fighting is commonly extant in these datasets. Here, MBD is assumed to adopt various real-world situations. The SVM achieved the classification accuracy of 85%, 92%, 60%, and 44% on SAIAZ-Corridor, SAIAZ-Open space, Classroom Violence, MBD respectively. The ResNet50 achieved significant improvements in all the given datasets.