Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-aware Graph Attention Network with Distributed & Cross Learning for Collaborative Recommendation

Nowadays, side-information is widely used to rein-force the user-item interaction and helps to handle the sparsity issue and cold start problem of conventional recommendation algorithms. Due to the overlook of the relationship between items and entities and the higher-order connectivity information, most existing approaches are hard to get users' deep preferences. In this paper, we propose KGANCL, a Knowledge-aware Graph Attention Network with Distributed & Cross Learning. It focuses on using different forms of the knowledge graph to strengthen both users' and items' embedding representations, respectively. Firstly, Graph Attention Network is adopted for user embedding learning, which can give different importance scores to different neighbors, and a user-item KG graph is used to integrate adjacent information to enhance the representation. Secondly, a cross module is used for item embedding learning, which shares the high-order interaction between the recommender system and the knowledge graph. We also use the idea of distributed processing for embeddings in different entities to improve the learning efficiency. Experimental results demonstrate that KGANCL can provide better recommendations compared with the state-of-the-art baseline models. Our model can also maintain superior prediction accuracy even in little-known interaction scenarios.
TSEvo: Evolutionary Counterfactual Explanations for Time Series Classification

With the increasing predominance of deep learning methods on time series classification, interpretability becomes essential, especially in high-stake scenarios. Although many approaches to interpretability have been explored for images and tabular data, time series data has been mostly neglected. We approach the problem of interpretability by proposing TSEvo, a model-agnostic multiobjective evolutionary approach to time series counterfactuals incorporating a variety of time series transformation mechanisms to cope with different types and structures of time series. We evaluate our framework on both uni- and multivariate benchmark datasets.
Accelerated Regeneration for Markov Chain Simulations

This paper describes a generalization of the classical regenerative method of simulation output analysis. Instead of blocking a generated sample path on returns to a fixed return state, a more general scheme to randomly decompose the path is used. In some cases, this decomposition scheme results in regeneration times that are a supersequence of the classical regeneration times. This “accelerated” regeneration is advantageous in several simulation contexts. It is shown that when this decomposition scheme accelerates regeneration relative to the classical regenerative method, it also yields a smaller asymptotic variance of the regenerative variance estimator than the classical method. Several other contexts in which increased regeneration frequency is beneficial are also discussed.
EVDD - A Novel Dataset For Embedded System Vulnerability Detection Mechanism

In the digital world where the smart device is a ubiquitous feature of modern life, embedded systems are almost everywhere. From the smart home, to the smart office, to the smart restaurant, and even the smart traffic system, this technology has become a crucial aspect of the modern experience. Although these systems are typically quite efficient, the rapid growth of embedded systems in smart cities has created inconsistencies and misalignments between secured and unsecured systems, which has created a need for secure, invulnerable embedded systems to protect modern users from the dangers of hacking. To address this problem, we have developed a large, novel, and unbiased dataset for embedded system vulnerability detection and modifying an advanced machine learning model for Linux Kernel. This unique dataset will provide the landscape of embedded systems with a multitude of ways to predict the vulnerabilities in the Linux Core and to detect all vulnerabilities during the development process. This research paper discusses the collection, filtering, correlation, and statistics associated with the dataset for Linux Embedded system.
UAV Indoor Localization Using 3D Laser Radar

At present, unmanned aerial vehicle (UAV) mainly relies on GPS to achieve positioning in outdoor environment. However, the GPS signal is weak and easy to be lost in complex indoor environment. This paper presents an indoor positioning system for UAV using 3D laser radar. First, we receive the point cloud data through the UAV airborne lidar, subsequently, its edge feature points and plane feature points are extracted, and then the motion of UAV is estimated through the feature matching between the point cloud data at two consecutive times, In the meantime, a point cloud map is constructed. Finally, the public data set is used to test in the UAV simulation environment. The simulation results show that the localization accuracy of UAV can achieve 0.09m in indoor environment.
An HDL Simulator with Direct Register Access for Improving Code Coverage

When debugging a DUT (Device Under Test) written in HDL (Hardware Description Language) code in simulation, code coverage is one of the most important evaluation metrics because it indicates how many unchecked statements remain where bugs could be hidden. A typical random test-pattern generator can be used very easily for debugging; however, it could fail to obtain enough code coverage of DUTs because it does not provide effective strategies for code coverage. In this paper, we propose an HDL simulator to improve branch coverage of DUTs up to 100%. A key idea behind our simulator is to directly write values to registers of DUTs for intentionally transfer a state to an unchecked state in the state machine of DUTs. This improves code coverage by executing statements corresponding to an unchecked state. Our simulator uses an SMT (Satisfiability Modulo Theories) solver to obtain the values written to registers from the condition (e.g., if and case) corresponding to an unchecked state. With the evaluation, we confirmed that our simulator successfully obtained a branch coverage of 100% for each of three open-sourced IP (Intellectual Property) core modules. As a bench mark, we also used a random test-pattern generator for those modules.
A 0.5-to-400MHz Programmable BAW Oscillator with Fractional Output Divider Achieving 4ppm Frequency Stability over Temperature and <95fs Jitter

Bulk-Acoustic-Wave (BAW)-technology-based oscillators have recently been introduced that combine a BAW resonator die with a CMOS circuit die containing oscillator and temperature compensation circuits [1]. In this work, we leverage the BAW technology to create a user programmable oscillator with attractive characteristics such as long-term reliability, miniaturization and low cost. Unlike low-frequency crystal/MEMS-based programmable resonators that require a fractional PLL to generate higher output frequencies [4–6], BAW resonators, due to a high resonant frequency (~2.5GHz), allow programmability by making use of a Fractional Output Divider (FOD), as developed in this work.
Understanding Consumer Advertising via Audio Analytics of Sports Videos

This research develops and applies a research process integrating advanced data analytics methods, including machine learning, to test a research model for a number of existing and new theoretical relationships between voice, music, and content features and net positive affect from sports video advertisements. This research uses YouTube video data from the most frequently advertised products and brands in Super Bowls from 2000 to 2020. The findings outline the most effective audio and music features which will have a greater impact on net positive affect. The results have methodological and theoretical implications and consequences for practitioners aiming to develop the most affective advertisements for viewers.
Light transport model in a n-layered mismatched tissue

The steady-state and time-domain analytical solutions of the diffusion equation for photon migration through a highly scattering n-layered mismatched medium have been obtained. The effect of the refractive-index mismatch is taken into account, and the extrapolated boundary condition has been considered. A Monte Carlo code for photon migration through an n-layered mismatched medium has also been developed. Comparisons with the results of Monte Carlo simulations showed that the analytical solutions correctly describe the photon migration, while reflectance is observed.
Analyzing Gender Detection in Speech in Adult Filipino Citizens

Being one of the most distinct and essential social characteristics and features of humans, speech is commonly used for communication. Whether it would be for the purpose of the communication of ideas, exchanging information, etc. With this, new approaches are made in order to create a more efficient and easier lifestyle for everybody. This would include gender detection or prediction for improving user experience. Affective computing, as well as artificial intelligence, is used for tasks related to recognition, including gender recognition. The study concentrates on improving gender recognition from speech, specifically Filipino adult voices. With this in mind, selecting the features that are most important in finding a speaker’s gender can be done to improve the accuracy of other algorithms as well as K-Nearest Neighbor, Random Forest, and Artificial Neural Network algorithms being used. The study then aims to explore the use of Kernel PCA instead of the classical PCA when it comes to reducing data as well as when MFCC is combined with several short term and long-term features to detect gender in speech. 250 Filipino voices from Metro Manila were gathered, cleaned, and 25 features were then extracted. Features such as MFCC, pitch, zero-crossing rate, energy, spectral centroid, spectral flux, and age were extracted. The classifiers used are Random Forest, K-Nearest Neighbor, and Artificial Neural Network. 4 models in total were created wherein Kernel PCA and PCA were applied. Out of the 4 models, those without Kernel PCA performed had a higher accuracy compared to the models that used Kernel PCA due to the information loss when lowering the dimensions. Moreover, the best performing model had an Artificial Neural Network classifier being the most accurate. This could be due to its computational power gathered from its interconnected group of nodes which is needed for speech recognition.
TFE-NET: Time and Feature focus Embedding Network for Multivariate-to-Multivariate Time Series Forecasting

Multivariate-to-multivariate time series forecasting is being studied in various fields because it is important in many fields such as finance and transportation. The important point in multivariate time series forecasting is to simultaneously capture and reflect the interrelationships that exist between variables and the dynamic patterns over time. However, existing proposed multivariate time series prediction methods are difficult to consider potential dependencies existing in the data at once. In this study, we propose a time and feature focus embedding network (TFE-Net) designed for multivariate-to-multivariate time series prediction. TFE-Net consists of feature focus embedding that captures interdependencies, which is a complex relationship between variables, and time focus embedding that captures dynamic temporal dependencies in parallel, and each embedding is extracted using attention with a different structure. mechanism is used. In addition, an autoregressive (AR) model is additionally used for consideration of scale consistency of data and linearity of prediction. As a result of comparing the proposed method with other models, it shows the best performance in terms of accuracy.
Sound field computations in a stratified, moving medium

A numerical method for computing the sound field in a stratified, moving medium is described. The method is quite similar to the well‐known fast‐field program (FFP), except that the wave equation is modified to account for fluid and/or source motion. The computation requires a two‐dimensional horizontal wave‐number spectrum rather than a one‐dimensional radial wave‐number spectrum as required by previous FFPs. As an example, the sound pressure produced by a point source in a neutrally buoyant atmospheric shear layer is calculated. A far‐field approximation, which reduces the calculation to a one‐dimensional spectrum, is also discussed.
An Unsupervised Approach For Detecting Lateral Movement Logins Based On Knowledge Graph

The lateral movement that relies on privacy theft is receiving increasing attention. This type of attack can be achieved by Cross-Host Login. Constructing an authentication graph between hosts is a common way to detect lateral movement, but the previous graph-based detection methods lack an effective means to integrate the account information into graph-based features. We abstract the login events as edges on the knowledge graph and account as the relations on the edges. Then we utilize a graph auto-encoder model comprised of an entity encoder and a scoring function (decoder), where entities are represented as low-dimensional vectors learned from the relational graph neural network. The decoder scores the edges to get the likelihood of the edges belonging to normal behaviors. We also propose a novel negative sample approach to reduce the false alarm rate. We apply this technique to authentication data derived from a large-scale real-world environment. The results show our approach can detect malicious authentication events associated with lateral movement with a true positive rate of 95 % and false positive rate of 10%, compared to 85% and 0.9% by the previous random walk approach as a baseline.
Parameterization of Microphysical Processes in Convective Clouds in Global Climate Models

AbstractThe microphysical processes inside convective clouds play an important role in climate. They directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts. The detrained water substance in turn affects the anvil cloud formation, upper-tropospheric water vapor distribution, and thus the atmospheric radiation budget. In global climate models, convective parameterization schemes have not explicitly represented microphysics processes in updrafts until recently. In this paper, the authors provide a review of existing schemes for convective microphysics parameterization. These schemes are broadly divided into three groups: tuning-parameter-based schemes (simplest), single-moment schemes, and two-moment schemes (most comprehensive). Common weaknesses of the tuning-parameter-based and single-moment schemes are outlined. Examples are presented from one of the two-moment schemes to demonstrate the performance of the scheme in simulating the hydrometeor distribution in convect.
A 1mW Always-on Computer Vision Deep Learning Neural Decision Processor

Syntiant®'s, NDP200 is a specially architected edge Al processor, bringing the ultra-lowpower, high performance, deep neural network processing capabilities of Syntiant Core 2™ (SC2) to computer vision (CV) applications. Ideally suited for low-latency inferences on power-constrained devices such as doorbell cameras, the NDP200 is equipped with an 8-bit direct video port (DVP), l2C, and SPl interfaces to interface directly with lowpower image sensors, as well as Pulse Density Modulation (PDM) and Inter-IC Sound interface (l2S) to facilitate use cases requiring both video and audio processing.
1-AID: A Web Application Tool for First Aid and Homeopathic Treatment with Vulnerability Security Scanning

In the Philippines, most injury cases were not given first aid treatment because of the lack of knowledge about performing first aid techniques in some injuries. This study provides a web application that will assist its users in performing first aid treatments for injuries, finding supplies, contacting health services, and locating the nearest hospitals. It aims to measure the effectiveness of the web application in providing accurate information. This study uses a quantitative descriptive approach to illustrate the data obtained from the survey results and interviews with medical professionals. The results from the validity questionnaires are positive based on the response of medical professionals since the first aid steps provided by the web application are valid, effective, and safe to perform by laypeople. 1-AID web application also received a satisfactory rating and an average of 0.5932 on its usability testing. This study also received positive feedback from the users based on their answers to the assessment questionnaires. Based on the data collected, the study concluded that 1-AID is effective in providing safe and reliable first aid treatments, and positively affects the design of a web application intended to provide various medical information.
A 32kHz-Reference 2.4GHz Fractional-N Nonuniform Oversampling PLL with Gain-Boosted PD and Loop-Gain Calibration

A 32.768kHz-reference (REF) phase-locked loop (PLL) can help eliminate the high-frequency crystal oscillator in a wireless system-on-chip (SoC), which would significantly reduce the cost and improve power efficiency [1–3]. With the conventional $1{\times}-\text{sampling}$ PLL, the loop bandwidth of the PLL is limited to 1/10 of the REF frequency $(\mathrm{f}_{\text{REF}})$. To break the limitation of the narrow bandwidth and poor jitter caused by low $\mathrm{f}_{\text{REF}}$, the reference-oversampling PLL (OSPLL) was studied [3]. However, different from the high $\mathrm{f}_{\text{REF}}$ [4], [5], the 32kHz-REF PLL severely suffers from jitter transferred from voltage noise (mainly from a comparator) from a slow signal slope, which degrades the total jitter performance. To overcome the above issues, a nonuniform OSPLL is proposed with a 32kHz REF to realize a 2.4GHz output with a fractional-N operation. The proposed DAC-and-DTC-assisted nonuniform OSPLL works with a proposed gain-boosted phase detector (PD) and an adaptive loop-gain calibration. It realizes a 200kHz loop bandwidth, 4.95ps rms jitter with 3.8mW power consumption in a 65nm CMOS technology.
A Review on Rate-Splitting Multiple Access-Assisted Downlink Networks: Energy Optimizations

Rate-splitting multiple access (RSMA) is acknowledged as a promising solution for improving the capacity of dense downlink networks designed to meet the severe criteria of networks beyond 5G, in which a large number of users may be simultaneously supplied by a single bandwidth spectrum. Numerous studies have focused on establishing acceptable RSMA solutions for energy optimization measures like energy efficiency (EE) and weighted power consumption of all users under the acceptance of perfect or imperfect channel state information at transmitter (CSIT). This evaluation focuses on the technical features of newly published papers and their relevance in a variety of situations. We also cover the difficulties and unresolved concerns of RSMA applications in future heterogeneous downlink networks.
Two-Stage Online Reinforcement Learning based Distributed Optimal Resource Allocation for Multiple RIS-assisted Mobile Ad-Hoc Network

In this paper, distributed optimal resource allocation problem for multiple Reconfigurable Intelligent Surface (RIS) assisted wireless mobile ad-hoc networks (MANET) has been studied. A novel resource allocation technique needs to be developed that cannot only optimize the overall RIS-assisted MANET network quality, e.g. maximizing energy efficiency, minimizing power consumption, etc., but also be capable of handling the uncertainty in wireless MANET environment, such as time-varying wireless channel, in real time. Therefore, a novel two-stage online reinforcement learning based optimal distributed resource allocation algorithm has been developed. At Stage I, an online distributed cooperative actor-critic reinforcement learning is developed along with neural networks (NNs) to learn the optimal transmit power control, RIS phase shift control for multiple users with a given RIS selection policy. Then, in stage II, the second reinforcement learning algorithm is activated to find the optimal RIS selection policies for the system Using alternative optimization theory, the distributed optimal resource allocation can be achieved while two-stage reinforcement learning algorithms are converging along with time. Eventually, numerical simulations have been provided to demonstrate the effectiveness of the developed scheme.
Machine Learning Experiments with Artificially Generated Big Data from Small Immunotherapy Datasets

Big data and machine learning result in agile and robust healthcare by expanding raw data into useful patterns for data-enhanced decision support. The available datasets are mostly small and unbalanced, resulting in non-optimal classification when the algorithms are implemented. In this study, five novel machine learning experiments are conducted to address the challenges of small datasets by expanding these into big data and then utilising Random Forests. The experiments are based on personalised adaptable strategies for both balanced and unbalanced datasets. Multiple datasets from cryotherapy and immunotherapy are considered, however, hereby only immunotherapy is used. In the first experiment, artificially generated data is presented by increasing the observations of the dataset, each new data is four-time larger than the previous one, resulting in better classification. In the second experiment, the effect of volume on classification is considered based on the number of attributes. The attributes of each new dataset are built based on conditional probabilities. It did not make any difference, in obtained classification, when the number of attributes is increased to more than 879. In the third simulation experiment, classes of data are classified manually by dividing the data into a two-dimensional plane. This experiment is first performed on small data and then on expanded big data: by increasing observations, an accuracy of 73.68% is attained. In the fourth experiment, the visualisation of the enlarged data did not provide better insights. In the fifth experiment, the impact of correlations among datasets’ attributes on classification is observed, however, no improvements in performance are achieved. The experiments generally improved performance by comparing the classification results using the original and artificial data.
Dejà vu: Recurrent Neural Networks for health wearables data forecast

Wearable devices are a useful and widely used source of continuous and temporal dependant data. In contrast to the traditional clinical environment, these devices allow time series data collection in an individual’s daily living environment. However, missing data can occur while using them. Many techniques have been applied to solve these data gaps; nonetheless, missing time series data poses extra challenges, such as maintaining the temporal dependency. In this article, we addressed the forecast of sleep trackers data (sleeping heart rate (HR) and time asleep) for 2 main reasons: (1) to design models capable of accurately forecasting missing data from those devices, and (2) to apply those models to empower sleep interventions that may increase its quality, by forecasting future sleep events. We collected wearables data over 290 days (per individual) from 12 participants using a smartwatch and made this dataset publicly available. We then explored several hyperparameters of 2 Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). We further elaborated and compared the performance of 3 approaches to training those RNNs. Although similar performance, slightly more accurate results were obtained after training a GRU network on an entire population’s dataset, which was able to forecast the average, minimum, and maximum sleeping HR with a root-mean-squared error (RMSE) of 4.4 (± 1.4), 4.9 (± 2.6), and 12.1 ( 4.0) beats per minute, respectively. However, the total time ±asleep was impossible to forecast with low error.