Reviewerly/exHarmony huggingface dataset:
Recommender Systems Challenges and Solutions Survey

Today's Recommender system is a relatively new area of research in machine learning. The recommender system's main idea is to build relationship between the products, users and make the decision to select the most appropriate product to a specific user. There are four main ways that recommender systems produce a list of recommendations for a user - content-based, Collaborative, Demographic and hybrid filtering. In content-based filtering the model uses specifications of an item in order to recommend additional items with similar properties. Collaborative filtering uses past behavior of the user like items that a user previously viewed or purchased, In summation to any ratings the user gave those items rate and similar conclusions made by other user's items list. To predicts items that the user may find interesting. Demographic filtering is view user profile data like age category, gender, education and living area to find similarities with other profiles to get a new recommender list. Hybrid filtering combines all three filtering techniques. This paper introduces survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges.
Detection of GPS Spoofing Attacks on Unmanned Aerial Systems

Unmanned Aerial Systems (UAS) have received a huge interest in military and civil applications. Applications of UAS are dependent on successful communications of these systems with different entities in their networks. A UAS network can include the UAS, ground control station, navigation satellite system, and automatic dependent surveillance-broadcast (ADS-B) receiver. Through these entities, a UAS is vulnerable to different cyber-attacks such as GPS spoofing. In this attack, a malicious user transmits fake signals to the GPS receiver on the UAS. The fake signals can mislead not only the aircraft but also air traffic controllers, leading to serious problems. These problems range from aircraft hijacking to collisions and human casualties. This paper proposes a supervised machine learning method based on the artificial neural network to detect GPS spoofing signals. Different features such as pseudo range, Doppler shift, and signal-to-noise ratio (SNR) are used to perform the classification of GPS signals. We examine and compare the efficiency of one-and two-hidden-layer neural networks with various numbers of hidden neurons. The results show that our proposed method provides a high probability of detection and a low probability of false alarm.
Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

In this paper, we propose a multi-level cooperative fusion approach to address the online multiple human tracking problem in a Gaussian mixture probability hypothesis density (GM-PHD) filter framework. The proposed fusion approach consists essentially of three steps. First, we integrate two human detectors with different characteristics (full-body and body-parts), and investigate their complementary benefits for tracking multiple targets. For each detector domain, we then propose a novel discriminative correlation matching model, and fuse it with spatio-temporal information to address ambiguous identity association in the GM-PHD filter. Finally, we develop a robust fusion center with virtual and real zones to make a global decision based on preliminary candidate targets generated by each detector. This center also mitigates the sensitivity of missed detections in the generalized covariance intersection fusion process, thereby improving the fusion performance and tracking consistency. Experiments on the MOTChallenge Benchmark demonstrate that the proposed method achieves improved performance over other state-of-the-art RFS-based tracking methods.
SoK: General Purpose Compilers for Secure Multi-Party Computation

Secure multi-party computation (MPC) allows a group of mutually distrustful parties to compute a joint function on their inputs without revealing any information beyond the result of the computation.This type of computation is extremely powerful and has wide-ranging applications in academia, industry, and government.Protocols for secure computation have existed for decades, but only recently have general-purpose compilers for executing MPC on arbitrary functions been developed.These projects rapidly improved the state of the art, and began to make MPC accessible to non-expert users.However, the field is changing so rapidly that it is difficult even for experts to keep track of the varied capabilities of modern frameworks.In this work, we survey general-purpose compilers for secure multi-party computation.These tools provide high-level abstractions to describe arbitrary functions and execute secure computation protocols.We consider eleven systems: EMP-toolkit, Obliv-C, ObliVM, TinyGarble, SCALE-MAMBA (formerly SPDZ), Wysteria, Sharemind, PICCO, ABY, Frigate and CBMC-GC.We evaluate these systems on a range of criteria, including language expressibility, capabilities of the cryptographic back-end, and accessibility to developers.We advocate for improved documentation of MPC frameworks, standardization within the community, and make recommendations for future directions in compiler development.Installing and running these systems can be challenging, and for each system, we also provide a complete virtual environment (Docker container) with all the necessary dependencies to run the compiler and our example programs.
Can Children Understand Machine Learning Concepts?

Machine Learning services are integrated into various aspects of everyday life. Their underlying processes are typically black-boxed to increase ease-of-use. Consequently, children lack the opportunity to explore such processes and develop essential mental models. We present a gesture recognition research platform, designed to support learning from experience by uncovering Machine Learning building blocks: Data Labeling and Evaluation. Children used the platform to perform physical gestures, iterating between sampling and evaluation. Their understanding was tested in a pre/post experimental design, in three conditions: learning activity uncovering Data Labeling only, Evaluation only, or both. Our findings show that both building blocks are imperative to enhance children's understanding of basic Machine Learning concepts. Children were able to apply their new knowledge to everyday life context, including personally meaningful applications. We conclude that children's interaction with uncovered black boxes of Machine Learning contributes to a better understanding of the world around them.
BPIIoT: A Light-Weighted Blockchain-Based Platform for Industrial IoT

Blockchain is experiencing rapid development and has the full potential of revolutionizing the IoT platform in the industrial field. In this paper, a light-weighted Blockchain-based platform for IIoT is presented to address security, trust, and island connection problem in the process of IIoT ecosystem construction. BPIIoT is comprised of the on-chain network and the off-chain network. All transactions are processed in the on-chain network such as including digital signature based on access control and programmable permission. The off-chain network deals with the storage, complex data processing, and other problems that blockchain cannot solve. The smart contract is utilized as the service contract of consumers and manufacture resources, providing on-demand manufacturing service. Two smart application cases, manufacturing equipment data sharing and maintenance service sharing from smart manufacturing, are implemented to explain the smart contract for equipment maintenance service and status data sharing service throughout maintenance, repair, and operation service network by the BPIIoT.
The scalability challenge of ethereum: An initial quantitative analysis

The concerns held on the scalability of permissionless Blockchain platforms are a significant hindrance to their wider adoption. To address this issue rigorously, we consider it opportune to assess the current implementation and the associated improvement proposals, within a single coherent evaluation framework. This work addresses this specific question in the particular context of Ethereum, a prominent implementation of Blockchain, using a threefold approach. First, it maps the internal constituents of Ethereum onto a layered architecture inspired in the ISO/OSI model, so that its provisioning organization can be better understood. Second, it employs the AKF Scale Cube to analyze the pros and cons of the present specification of Ethereum, as well as of the current improvement proposals, so that the scalability challenges can be reasoned about in an orderly fashion. Finally, it uses an extensible test environment with synthetic benchmarks so that the transaction throughput of the current implementation of Ethereum can be evaluated in a private scenario, when no smart contract is to run. Our conclusions suggest that Ethereum respects the scalability trilemma being versed on security and decentralization renouncing scalability. These limits can be mitigated by adopting novel solutions such as Plasma and Sharding which enable a significant increment of performance by partitioning the data, and, ultimately, unlocking parallel execution of the transactions.
Novel IoT-Based Privacy-Preserving Yoga Posture Recognition System Using Low-Resolution Infrared Sensors and Deep Learning

In recent years, the number of yoga practitioners has been drastically increased and there are more men and older people practice yoga than ever before. Internet of Things (IoT)-based yoga training system is needed for those who want to practice yoga at home. Some studies have proposed RGB/Kinect camera-based or wearable device-based yoga posture recognition methods with a high accuracy; however, the former has a privacy issue and the latter is impractical in the long-term application. Thus, this paper proposes an IoT-based privacy-preserving yoga posture recognition system employing a deep convolutional neural network (DCNN) and a low-resolution infrared sensor-based wireless sensor network (WSN). The WSN has three nodes (x, y, and z-axes) where each integrates 8 × 8 pixels' thermal sensor module and a Wi-Fi module for connecting the deep learning server. We invited 18 volunteers to perform 26 yoga postures for two sessions each lasted for 20 s. First, recorded sessions are saved as .csv files, then preprocessed and converted to grayscale posture images. Totally, 93200 posture images are employed for the validation of the proposed DCNN models. The tenfold cross-validation results revealed that F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -scores of the models trained with xyz (all 3-axes) and y (only y-axis) posture images were 0.9989 and 0.9854, respectively. An average latency for a single posture image classification on the server was 107 ms. Thus, we conclude that the proposed IoT-based yoga posture recognition system has a great potential in the privacy-preserving yoga training system.
Human-Centered Reinforcement Learning: A Survey

Human-centered reinforcement learning (RL), in which an agent learns how to perform a task from evaluative feedback delivered by a human observer, has become more and more popular in recent years. The advantage of being able to learn from human feedback for a RL agent has led to increasing applicability to real-life problems. This paper describes the state-of-the-art human centered RL algorithms and aims to become a starting point for researchers who are initiating their endeavors in human-centered RL. Moreover, the objective of this paper is to present a comprehensive survey of the recent breakthroughs in this field and provide references to the most interesting and successful works. After starting with an introduction of the concepts of RL from environmental reward, this paper discusses the origins of human-centered RL and its difference from traditional RL. Then we describe different interpretations of human evaluative feedback, which have produced many human-centered RL algorithms in the past decade. In addition, we describe research on agents learning from both human evaluative feedback and environmental rewards as well as on improving the efficiency of human-centered RL. Finally, we conclude with an overview of application areas and a discussion of future work and open questions.
Side Window Filtering

Local windows are routinely used in computer vision and almost without exception the center of the window is aligned with the pixels being processed. We show that this conventional wisdom is not universally applicable. When a pixel is on an edge, placing the center of the window on the pixel is one of the fundamental reasons that cause many filtering algorithms to blur the edges. Based on this insight, we propose a new Side Window Filtering (SWF) technique which aligns the window's side or corner with the pixel being processed. The SWF technique is surprisingly simple yet theoretically rooted and very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under the SWF framework. Extensive analysis and experiments show that implementing the SWF principle can significantly improve their edge preserving capabilities and achieve state of the art performances in applications such as image smoothing, denoising, enhancement, structure-preserving texture-removing, mutual-structure extraction, and HDR tone mapping. In addition to image filtering, we further show that the SWF principle can be extended to other applications involving the use of a local window. Using colorization by optimization as an example, we demonstrate that implementing the SWF principle can effectively prevent artifacts such as color leakage associated with the conventional implementation. Given the ubiquity of window based operations in computer vision, the new SWF technique is likely to benefit many more applications.
Fuzzy topic modeling approach for text mining over short text

In this era, the proliferating role of social media in our lives has popularized the posting of the short text. The short texts contain limited context with unique characteristics which makes them difficult to handle. Every day billions of short texts are produced in the form of tags, keywords, tweets, phone messages, messenger conversations social network posts, etc. The analysis of these short texts is imperative in the field of text mining and content analysis. The extraction of precise topics from large-scale short text documents is a critical and challenging task. The conventional approaches fail to obtain word co-occurrence patterns in topics due to the sparsity problem in short texts, such as text over the web, social media like Twitter, and news headlines. Therefore, in this paper, the sparsity problem is ameliorated by presenting a novel fuzzy topic modeling (FTM) approach for short text through fuzzy perspective. In this research, the local and global term frequencies are computed through a bag-of-words (BOW) model. To remove the negative impact of high dimensionality on the global term weighting, the principal component analysis is adopted; thereafter the fuzzy c-means algorithm is employed to retrieve the semantically relevant topics from the documents. The experiments are conducted over the three real-world short text datasets: the snippets dataset is in the category of small dataset whereas the other two datasets, Twitter and questions, are the bigger datasets. Experimental results show that the proposed approach discovered the topics more precisely and performed better as compared to other state-of-the-art baseline topic models such as GLTM, CSTM, LTM, LDA, Mix-gram, BTM, SATM, and DREx+LDA. The performance of FTM is also demonstrated in classification, clustering, topic coherence and execution time. FTM classification accuracy is 0.95, 0.94, 0.91, 0.89 and 0.87 on snippets dataset with 50, 75, 100, 125 and 200 number of topics. The classification accuracy of FTM on questions dataset is 0.73, 0.74, 0.70, 0.68 and 0.78 with 50, 75, 100, 125 and 200 number of topics. The classification accuracies of FTM on snippets and questions datasets are higher than state-of-the-art baseline topic models.
State-of-the-art analysis of geochemical data for mineral exploration

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyse and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc-related Cu–Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with a similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17
Long short-term memory neural network based fault detection and isolation for electro-mechanical actuators

In the new generation of aircraft, electro-mechanical actuators (EMA) have been replacing the conventional hydraulic versions. Despite the fact that a failure of this system can seriously affect the safety of vehicles and their operators, there are few studies that focus on fault diagnosis for the units. In this paper, we present an innovative fault detection and isolation method for the EMA. Our method is tested and verified against three types of failures. This novel fault diagnosis method works by creating a model for sensor data by using the known time series. It utilizes an advanced Long Short-term Memory (LSTM) neural network, which can effectively handle time series data in this domain. A modification to the LSTM network is applied in order to take advantage of the correlation between sensors. In addition, the algorithm uses a sliding window to improve performance of LSTM applied to fault isolation. Our research has revealed that the proposed algorithm is better able to detect faults when compared to traditional neural networks. We also compare our performance with the support vector machine algorithm and the typical LSTM algorithm. Ultimately, the proposed method performs superiorly for the task of fault isolation.
Efficient invisible speculative execution through selective delay and value prediction

Speculative execution, the base on which modern high-performance general-purpose CPUs are built on, has recently been shown to enable a slew of security attacks. All these attacks are centered around a common set of behaviors: During speculative execution, the architectural state of the system is kept unmodified, until the speculation can be verified. In the event that a misspeculation occurs, then anything that can affect the architectural state is reverted (squashed) and re-executed correctly. However, the same is not true for the microarchitectural state. Normally invisible to the user, changes to the microarchitectural state can be observed through various side-channels, with timing differences caused by the memory hierarchy being one of the most common and easy to exploit. The speculative side-channels can then be exploited to perform attacks that can bypass software and hardware checks in order to leak information. These attacks, out of which the most infamous are perhaps Spectre and Meltdown, have led to a frantic search for solutions.
Designing Convolutional Neural Network Architecture by the Firefly Algorithm

This paper presents firefly algorithm framework for designing convolutional neural network architecture. Convolutional neural networks can be classified as a special category of deep neural networks that in most cases consist of several convolution, fully connected (dense) and pooling layers. Wide set of image classification tasks and problems from the computer vision domain were successfully tackled by convolutional neural networks. One of the most challenging tasks from this domain is to find the convolutional neural network architecture that obtains the best performance for the specific application. The values of network's hyper-parameters have significant influence on the overall network performance. Research shown in this paper deals with convolutional neural network hyper-parameters optimization that define the network's architecture and structure. The hyper-parameters that were taken into account for this research include the number of convolutional and dense layers, the number of kernels per layer and the kernel size. We performed hyper-parameters optimization by the well-known firefly algorithm that belongs to the group of swarm intelligence metaheuristis. Solution's quality, robustness and performance of our proposed framework was tested against the MNIST dataset. Obtained empirical results showed that the proposed framework achieves promising performance in this domain.
Optimal Stochastic Vehicle Path Planning Using Covariance Steering

This letter addresses the problem of vehicle path planning in the presence of obstacles and uncertainties, a fundamental robotics problem. While several path planning algorithms have been proposed over the years, many of them have dealt with only deterministic environments or with only open-loop uncertainty, i.e., the uncertainty of the system state is not controlled and, typically, increases with time because of exogenous disturbances. This may lead to potentially conservative nominal paths. The typical approach to deal with disturbances and reduce uncertainty is to use a lower level feedback controller. We advocate the premise that, if a path planner can consider the closed-loop evolution of the system uncertainty, it can lead to less conservative, but still feasible, paths. To this end, in this letter, we develop an approach that is based on optimal covariance steering, which explicitly steers the state covariance for stochastic linear systems. We verify the proposed framework using extensive numerical simulations.