Reviewerly/exHarmony huggingface dataset:
End to End Product Recommendation system with improvements in Apriori Algorithm

Product Recommendation plays a major role in the revenue of an E-commerce application. But the chance of the wrong recommendation is very high and the cost of such recommendations are also very high. Why a birthday cap is always required along with the Christmas cake? - This is a kind of question that has to be considered while using the Apriori algorithm for product recommendation purpose. There might be a chance to recommend unrelated items. This paper proposes a system, which uses an improved apriori algorithm for a product recommendation, which takes the context of purchase into account.
Designing an Effective Learning Environment for Language Learning During the Covid-19 Pandemic

The rapid development in IT in the twenty-first century have along with the COVID-19 pandemic have had effects on the field of education, with the need to use technology for educational purposes arising. Mobile phones, which hold a more important place than computers, also became a part of education. In today's world, it is possible to access educational content without any limitation of time and space. Individuals who desire to learn a new language but cannot do so due to financial difficulties or free time have moved toward mobile learning methods. "YIT101" is a mobile application designed to teach Turkish as a foreign language independent from time and location and targets basic Turkish A1 level course content. This application enables users to access information when needed and to learn Turkish as a foreign language outside the classroom environment independent from a formal instructor, allowing them to save time.
XAI-Based Microarchitectural Side-Channel Analysis for Website Fingerprinting Attacks and Defenses

Website Fingerprinting attacks aim to track the visited websites in browsers and infer confidential information about users. Several studies showed that recent advancements in Machine Learning (ML) and Deep Learning (DL) algorithms made it possible to implement website fingerprinting attacks even though various defense techniques are present in the network. Nevertheless, trained models for website detection are not analyzed deeply to identify the leakage sources which are not always visible to both attackers and Cyber Threat Intelligence engineers. This study focuses on explaining ML and DL models in the context of microarchitecture-based website fingerprinting attacks. In the attack model, performance counters and cache occupancy side-channels are implemented on Google Chrome and Tor browsers. After ML and DL models are trained, LIME and saliency map XAI methods are applied to examine the leakage points in the side-channel data. In order to match the leakage samples in the measurements to the network traces, a novel dataset is collected by utilizing Google Chrome and Firefox browser developer tools. Next, the efficiency of explainable methods are analyzed with XAI metrics. Finally, an XAI-based obfuscation defense technique is proposed as a countermeasure against microarchitecture-based website fingerprinting attacks
Self-Supervised Object Detection via Generative Image Synthesis

We present SSOD – the first end-to-end analysis-by-synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real-world images without bounding box annotations to learn to synthesize and detect objects. We leverage controllable GANs to synthesize images with pre-defined object properties and use them to train object detectors. We propose a tight end-to-end coupling of the synthesis and detection networks to optimally train our system. Finally, we also propose a method to optimally adapt SSOD to an intended target data without requiring labels for it. For the task of car detection, on the challenging KITTI and Cityscapes datasets, we show that SSOD outperforms the prior state-of-the-art purely image-based self-supervised object detection method Wetectron. Even without requiring any 3D CAD assets, it also surpasses the state-of-the-art rendering-based method Meta-Sim2. Our work advances the field of self-supervised object detection by introducing a successful new paradigm of using controllable GAN-based image synthesis for it and by significantly improving the baseline accuracy of the task. We open-source our code at https://github.com/NVlabs/SSOD.
An Emotion-aided Gender Prediction System

The gender of a user plays a very important role in the development of the personalized online services. However, due to privacy, and anonymity, gender information is usually not available for many users. Since male and female users have differences in their message contents, the messages posted by users can be analyzed for finding their genders. Users with different genders express their emotions differently too. In the current paper, we introduce an emotion-aided gender prediction system. The intuition behind our approach is to predict the gender of a user based on emotional clues. The proposed approach consists of two neural network branches, one for gender and the other for emotion. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Gender and emotion networks are trained over our own annotated dataset having gender and emotion labels. Experimental results show the effectiveness of using emotion for predicting the gender of a user.
An Enhanced Local Descriptor (ELD) for Face Recognition

The influence of light changes makes the task of feature extraction more difficult for the local descriptors. Most of local descriptors sacrifices their performance in harsh lightning variations. Some uses pre-processing approach & some uses gradient based methods (with local descriptors) to improve accuracy. In this work, a novel Enhanced Local Descriptor (ELD) is introduced by taking advantages of two well behaved descriptors in harsh lightning changes. These 2 are Compound Local Binary Pattern (CLBP) & Median Robust Extended LBP based on Neighborhood Intensity (MRELBP-NI). CLBP is characterized by Sign & Magnitude details, and MRELBP-NI is characterized by Median & Mean statistics. Both of them are very essential in controlling harsh light variations. By merging features of both a discriminant descriptor ELD is gained. FLDA is taken further for size contraction & SVMs is used for matching. ELD achieves stupendous outcomes on Extended Yale B (EYB) dataset. ELD wholly outstrip the singly implemented descriptors & many methods from literature. ELD secure best accuracy of 93.42%. There is no pre-processing & the gradient based methods are used.
Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation

Cardiac motion estimation plays an essential role in motion-compensated cardiac Magnetic Resonance (MR) image reconstruction. In this work, we propose a robust and lightweight self-supervised deep learning registration framework, termed MRAFT, to estimate non-rigid cardiac motion. The proposed framework combines an efficient architecture with a novel degradation-restoration (DR) loss term, and an enhancement mask derived from a pre-trained segmentation network. This framework enables the prediction of both small and large cardiac motion more precisely, and allows us to handle through-plane motion in a 2D registration setting via the DR loss. The quantitative and qualitative experiments on a retrospective cohort of 42 in-house acquired 2D cardiac CINE MRIs indicate that the proposed method outperforms the competing approaches substantially, with more than 25% reduction in residual photometric error, and up to 100\(\times \) faster inference speed compared to conventional methods.
Multi-Scale Guided Attention Network for Crowd Counting

The CNN-based crowd counting method uses image pyramid and dense connection to fuse features to solve the problems of multiscale and information loss. However, these operations lead to information redundancy and confusion between crowd and background information. In this paper, we propose a multi-scale guided attention network (MGANet) to solve the above problems. Specifically, the multilayer features of the network are fused by a top-down approach to obtain multiscale information and context information. The attention mechanism is used to guide the acquired features of each layer in space and channel so that the network pays more attention to the crowd in the image, ignores irrelevant information, and further integrates to obtain the final high-quality density map. Besides, we propose a counting loss function combining SSIM Loss, MAE Loss, and MSE Loss to achieve effective network convergence. We experiment on four major datasets and obtain good results. The effectiveness of the network modules is proved by the corresponding ablation experiments. The source code is available at https://github.com/lpfworld/MGANet.
Modelling Accuracy and Trustworthiness of Explaining Agents

Current research in Explainable AI includes post-hoc explanation methods that focus on building transparent explaining agents able to emulate opaque ones. Such agents are naturally required to be accurate and trustworthy. However, what it means for an explaining agent to be accurate and trustworthy is far from being clear. We characterize accuracy and trustworthiness as measures of the distance between the formal properties of a given opaque system and those of its transparent explanantes. To this aim, we extend Probabilistic Computation Tree Logic with operators to specify degrees of accuracy and trustworthiness of explaining agents. We also provide a semantics for this logic, based on a multi-agent structure and relative model-checking algorithms. The paper concludes with a simple example of a possible application.
Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks

Slimmable neural networks provide a flexible trade-off front between prediction error and computational requirement (such as the number of floating-point operations or FLOPs) with the same storage requirement as a single model. They are useful for reducing maintenance overhead for deploying models to devices with different memory constraints and are useful for optimizing the efficiency of a system with many CNNs. However, existing slimmable network approaches either do not optimize layer-wise widths or optimize the shared-weights and layer-wise widths independently, thereby leaving significant room for improvement by joint width and weight optimization. In this work, we propose a general framework to enable joint optimization for both width configurations and weights of slimmable networks. Our framework subsumes conventional and NAS-based slimmable methods as special cases and provides flexibility to improve over existing methods. From a practical standpoint, we propose Joslim, an algorithm that jointly optimizes both the widths and weights for slimmable nets, which outperforms existing methods for optimizing slimmable networks across various networks, datasets, and objectives. Quantitatively, improvements up to 1.7% and 8% in top-1 accuracy on the ImageNet dataset can be attained for MobileNetV2 considering FLOPs and memory footprint, respectively. Our results highlight the potential of optimizing the channel counts for different layers jointly with the weights for slimmable networks. Code available at https://github.com/cmu-enyac/Joslim.
Virtualization-Aware Traffic Control for Soft Real-Time Network Traffic on Xen

As the role of virtualization technology becomes more prevalent, the range of applications deployed in virtualized systems is steadily growing. This increasingly includes applications with soft real-time requirements that benefit from low and predictable latency, even when co-located with other virtualized hosts with arbitrary traffic patterns. In this paper, we examine the policies and mechanisms affecting communication latency between virtual machines based on the Xen platform, and identify limitations that can result in long or unpredictable network stack latency for virtual machines deployed on this platform. To address these limitations, we propose and implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VATC</i> , a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Virtualization-Aware Traffic Control</i> framework that supports differentiation (via rate-limited prioritization) of outbound and inbound network traffic from co-located virtualized hosts. Results of our experiments show how and why VATC can offer predictable (soft) latency guarantees to applications running on virtualized hosts with minimum overhead.
Secured communication using efficient artificial neural synchronization

In this paper, an efficient artificial neural group synchronization is proposed for secured neural key exchange over public channels. To share the key over a public network, two Artificial Neural Networks (ANNs) are coordinated by mutual learning. The primary issue of neural coordination is assessing the synchronization of two parties' ANNs in the absence of weights from the other. There is a delay in coordination measurement in existing techniques, which affects the confidentiality of neural coordination. Furthermore, research into the mutual learning of a cluster of ANNs is limited. This paper introduces a mutual learning methodology for measuring the entire synchronization of the set of ANNs quickly and efficiently. The measure of coordination is determined by the frequency with which the two networks have had the same outcome in prior rounds. When a particular threshold is reached, the hash is used to decide whether all networks are properly coordinated. The modified methodology uses has value of the weight vectors to achieve full coordination between two communicating entities. This technique has several advantages, including (1) Generation of session key via complete binary tree-based group mutual neural synchronization of ANNs over the public channel. (2) Unlike existing methods, the suggested method allows two communication entities to recognize full coordination faster. (3) Brute force, geometric, impersonation, and majority attacks are all considered in this proposed scheme. Tests to validate the performance of the proposed methodology are carried out, and the results show that the proposed methodology outperforms similar approaches already in use.
Dropout’s Dream Land: Generalization from Learned Simulators to Reality

A World Model is a generative model used to simulate an environment. World Models have proven capable of learning spatial and temporal representations of Reinforcement Learning environments. In some cases, a World Model offers an agent the opportunity to learn entirely inside of its own dream environment. In this work we explore improving the generalization capabilities from dream environments to real environments (Dream2Real). We present a general approach to improve a controller's ability to transfer from a neural network dream environment to reality at little additional cost. These improvements are gained by drawing on inspiration from Domain Randomization, where the basic idea is to randomize as much of a simulator as possible without fundamentally changing the task at hand. Generally, Domain Randomization assumes access to a pre-built simulator with configurable parameters but oftentimes this is not available. By training the World Model using dropout, the dream environment is capable of creating a nearly infinite number of different dream environments. Previous use cases of dropout either do not use dropout at inference time or averages the predictions generated by multiple sampled masks (Monte-Carlo Dropout). Dropout's Dream Land leverages each unique mask to create a diverse set of dream environments. Our experimental results show that Dropout's Dream Land is an effective technique to bridge the reality gap between dream environments and reality. Furthermore, we additionally perform an extensive set of ablation studies (The code is available at https://github.com/zacwellmer/DropoutsDreamLand).
Ring-Based Crossovers in Genetic Algorithms: Characteristic Decomposition and Their Generalization

Effective crossover methods are essential for genetic algorithms because they enhance population diversity, resulting in an increase in the search space to be explored and the mitigation of trapping at local optima [1].Ring-based crossover methods are designed to address the shortcomings of traditional crossover techniques; for example, the ends of binary-encoded chromosomes tend to remain unaltered, even when their fitness values are low.By conjoining the leftmost and rightmost parts of a chromosome to form a ring, the ring-based crossovers allow offspring bits to be inherited from parent bits at the front and rear positions.Such a ring-based technique helps increase population diversity while requiring a relatively low number of fitness evaluations.To the best of our knowledge, there has been no comparative study or comprehensive analysis of ring-based crossover techniques.In this paper, we study and compare the characteristics of various existing ring-based crossover techniques (i.e., circle-ring (CRC), annular (AC), ring (RC) and front-rear crossover (FRC)).Although they each have distinct characteristics, they share a common crossover concept: bits at different positions can be swapped.We propose a generalized ring-based crossover (GRC) as an umbrella of the ring-based crossovers, embodying all of their beneficial characteristics.Chromosome shifting, ring forming, chromosome exchange and offspring creation are integrated into the proposed model.The experimental results show that GRC outperforms the other ring-based crossover methods in all experiments, and these results are consistent with the results of behavioral analysis.In a trap problem, GRC outperformed the state-of-the-art BOA method and require 40-times fewer fitness evaluations.GRC tends to maintain a variety of candidates in populations and to preserve the building blocks of solution, and it requires 93% and 57% fewer fitness evaluations (on average) compared to traditional crossover and other ring-based crossover methods.
Hybrid [ACPSO] Integration Approach based on Particle Swarm Optimization and Ant Colony Optimization to Improve Lifetime of Wireless Sensor Network

[ACPSO] is integration approach of traditional particle Swarm Optimization with ant Colony Optimization. We also developed new sink mobility algorithm to reduce the lifetime of the network efficiently. Particle Swarm Optimization has gone through many research paradigms as it is older Optimization technique. Many of the variants are proposed for particle Swarm Optimization to optimize it in a better way. We analyses integration algorithm [ACPSO] with different parameters and background. The analysis is displayed which shows betterment in energy optimization of the network. Further future enhancement and scope of extended work is written. Analysis prove that this integration algorithm along with sink mobility effective in optimization of energy in wireless sensor network. The main objective of this paper is to use both methods to minimize the limitations and maximize the advantages of particle Swarm Optimization and ant Colony Optimization.
Poly-NL: Linear Complexity Non-local Layers With 3rd Order Polynomials

Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions. Such pairwise functions underpin the effectiveness of non-local layers, but also determine a complexity that scales quadratically with respect to the input size both in space and time. This is a severely limiting factor that practically hinders the applicability of non-local blocks to even moderately sized inputs. Previous works focused on reducing the complexity by modifying the underlying matrix operations, however in this work we aim to retain full expressiveness of non-local layers while keeping complexity linear. We overcome the efficiency limitation of non-local blocks by framing them as special cases of 3rd order polynomial functions. This fact enables us to formulate novel fast Non-Local blocks, capable of reducing the complexity from quadratic to linear with no loss in performance, by replacing any direct computation of pairwise similarities with element-wise multiplications. The proposed method, which we dub as "Poly-NL", is competitive with state-of-the-art performance across image recognition, instance segmentation, and face detection tasks, while having considerably less computational overhead.
Edge Container for Speech Recognition

Containerization has been mainly used in pure software solutions, but it is gradually finding its way into the industrial systems. This paper introduces the edge container with artificial intelligence for speech recognition, which performs the voice control function of the actuator as a part of the Human Machine Interface (HMI). This work proposes a procedure for creating voice-controlled applications with modern hardware and software resources. The created architecture integrates well-known digital technologies such as containerization, cloud, edge computing and a commercial voice processing tool. This methodology and architecture enable the actual speech recognition and the voice control on the edge device in the local network, rather than in the cloud, like the majority of recent solutions. The Linux containers are designed to run without any additional configuration and setup by the end user. A simple adaptation of voice commands via configuration file may be considered as an additional contribution of the work. The architecture was verified by experiments with running containers on different devices, such as PC, Tinker Board 2, Raspberry Pi 3 and 4. The proposed solution and the practical experiment show how a voice-controlled system can be created, easily managed and distributed to many devices around the world in a few seconds. All this can be achieved by simple downloading and running two types of ready-made containers without any complex installations. The result of this work is a proven stable (network-independent) solution with data protection and low latency.
LDDS: Python package for computing and visualizing Lagrangian Descriptors for Dynamical Systems

Chief among these phase space structures are the invariant manifolds that form a barrier between dynamically distinct solutions.In most nonlinear systems, the invariant manifolds are computed using numerical techniques that rely on some form of linearization around equilibrium points followed by continuation and globalization.However, these methods become computationally expensive and challenging when applied to the high-dimensional phase space of vector fields defined analytically, from numerical simulations or experimental data.This points to the need for techniques that can be paired with trajectory calculations, without the excessive computational overhead, and that at the same time can allow visualization along with trajectory data.The Python package LDDS serves this need for analyzing deterministic and stochastic, continuous and discrete high-dimensional nonlinear dynamical systems described either by an analytical vector field or from data obtained from numerical simulations or experiments.To the best of our knowledge, no other open-source software exists for computing Lagrangian descriptors.However, a variety of computational tools are available for obtaining phase space structures in fluid mechanics, such as the identification of Lagrangian coherent structures via finite-time Lyapunov exponents (Briol & d'Ovidio (2011), Nelson & Jacobs (2016), Onu et al. (2015), Finn & Apte (2013), Dabiri Lab (2009), Haller et al. (2020)), finite-size Lyapunov exponents (Briol & d'Ovidio (2011)), and Eulerian coherent structures (Katsanoulis & Haller ( 2018)).Our goal with this software is to make Lagrangian descriptors available to the wider scientific community and enable the use of this method for reproducible and replicable computational dynamical systems.