Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Attention Graph Convolution Neural Network of Point Cloud Segmentation for Defect Detection

The traditional defect detection algorithms are suitable for regular defects, but not for workpiece defects with fuzzy features and various shapes. Point cloud segmentation is effective for 3D workpiece defect detection. In this paper, a point cloud segmentation method based on Dynamic Attention Graph convolution neural network is designed and applied to workpiece surface defect detection. Because the edge convolution module cannot capture the direction of the vector between adjacent points, we propose a new method of constructing neighbor graph based on the fusion distance. In the feature encoding stage, an attention mechanism is introduced to extract features for segmentation, and finally output the category score of each point. Experimental results show that the average prediction accuracy of the new model is improved compared to the original model.
Post-Click Behaviors Enhanced Recommendation System

To predict users’ interests, the traditional recommendation system (RS) relies on exploring the explicit user-item ratings and macro implicit feedbacks (e.g., whether or not a user clicks the item). In this work, fine-grained post-click behaviors (e.g., mouse behaviors, keyboard events, and page scrolling events) are integrated to alleviate the data sparsity problem of explicit feedback and the data accuracy problem of macro implicit feedback. In the deployed article recommendation pipeline, a variety of post-click behaviors are combined to create a reading pattern model. The reading patterns are leveraged by the recommendation system to estimate users’ preference levels. As compared with existing click-based (macro implicit feedback) and dwell time-based (single micro implicit feedback) recommendation systems, the test performance of our designed reading pattern-based RS has been significantly improved in terms of rating prediction and ranking.
Neural Restaurant-aware Dish Recommendation

Food is the first necessity of the people. Due to the fast-paced modern life, people usually choose to dine out for convenience. While existing methods have paid efforts for the food recommendation, they are mainly limited in inferring users’ personal preferences for online recipes, and ignore the dish ordering process in dine-out scenarios. Given the same recipe, different restaurants may produce various tastes due to food cuisines or chefs’ cooking habits. In the current restaurant, users’ general favored dish may have bad word-of-mouth. Thus, apart from their personal taste preferences, users also turn to restaurant specialties to guarantee the dish quality. As such, the restaurant-related dish quality and users’ personal taste should be considered simultaneously. To address this task, we propose a neural restaurant-aware dish recommender to infer users’ preferences for dishes in a specific restaurant. Given a dish in the current restaurant, whether to order it or not is mainly decided by two factors: users’ personal taste and the dish quality in this restaurant. Our proposed model can: 1) capture users’ personal diet preferences by the strong expressiveness of neural networks; 2) evaluate how good the current restaurant is at cooking certain dishes. To show the effectiveness of our proposed model, we conduct extensive experiments on a real dataset, demonstrating significant improvements over the several competing models, such as NCF with an average improvement of 36%, and PITF with 3.4%.
Container Image Access Control Architecture to Protect Applications

A container platform allows various applications to be deployed or run after installation. A user can download or execute a container image with the required application. To apply the configuration management system, a container image uses a union filesystem composed of multiple layers. To provide stability, important application files must be protected from unauthorized access. However, the container image used for distributing an application does not have its own protection function, and it is not protected by the container platform. The access control function provided by the operating system cannot protect the applications because the container environment is not considered. In this study, a container image access control architecture is proposed that can ensure a safe application operating environment by denying unauthorized direct access to container images. The proposed architecture enforces the access control function after the container image is downloaded, denying unauthorized access to the container image layer directory. Because the access control function is provided at the kernel level, there is a security advantage that users cannot bypass. To verify this approach, the functions and performance were determined empirically according to the proposed architecture. Functional verification confirmed that the proposed architecture denies unauthorized access to the container base image and allows access only to authorized users. It was also confirmed that the proposed architecture ensures the performance of the container platform in the same way as before, and that the proposed container image access control architecture is sufficiently effective.
A Novel Technique for Optimizing 270°-Bent Electron Gun for Evaporation Applications

In this article, a novel approach for optimizing a practical 270° bent electron gun has been proposed by using JUPYTER NOTEBOOK, an open-source web application utilizing PYTHON script, aiding faster data processing and mathematical computations. For this purpose, a 3-D cost function has been formulated to optimize the position of the anode with respect to the filament which aims at maximizing the beam power density, maximizing beam parallelism or laminar flow and optimizing beam shape. CST Studio-Particle Tracking Module is used for gun design, magnetic field design, and for the study and analysis of generated electron beam trajectories by performing both electrostatic and electromagnetic 3-D gun simulations. Unlike the conventional 270° bent transverse electron guns using Helmholtz coils for generating uniform magnetic fields, here permanent magnets have been used for generating nonuniform magnetic fields making the overall system compact along with electromagnets and magnetic shunts for precise beam maneuvering. Magnetic fields are experimentally measured by using Gauss meter which match well with that of the simulated values. The electron gun was fabricated and trial experiments were conducted under high vacuum environment. Experimental validation was done by puncturing a stainless steel plate kept at 90° and 270° positions above the crucible and the results match well with the simulated results.
Heat Transfer and Two-Phase Flow Regimes in Manifolded Microgaps: Performance Comparison Between R245fa and FC-72

Two-phase embedded cooling has great potential for meeting the increasing thermal management needs of high-heat flux electronics. The use of manifold-microchannels further enhances the effectiveness of embedded cooling while maintaining low pressure drop and pumping power. Previous research has shown that thermodynamic quality drives the thermo-fluid phenomena in the channel, including the prevailing flow regime, the transition to dryout, and the local heat transfer coefficients. The flow phenomena are also strongly dependent on the properties of the working fluid. The current study investigates the performance of FC-72 and R245fa in an enlarged visualization manifold-microchannel, referred to as a "microgap" channel.
Detecting Spam Tweets Using Lightweight Detectors on Real-Time Basis and Update the Models Periodically in Batch Mode

the majority of accessible technique for spam detection on Twitter purpose to recognize and block user who put up spam tweet. Here this document, we suggest a semi-supervised spam detection structure for spam discovery at tweet-stage. Planned structure contains of two essential modules: spam discovery module working in concurrent mode and method update module working in batch mode. The spam detection module consists of 4 frivolous detectors: 1) blacklisted area detector to label tweets include blacklisted URLs; 2) close to-reproduction detector to label tweets which can be close to-duplicates of expectantly relabeled tweets; 3) dependable ham detector in the direction of label tweets which are published by means of trusted customers and that do not incorporate spammy words; and 4) multiclassifier primarily based totally detector labels the closing tweets. The data needful thru the detection detail is up to date in batch mode primarily based on the tweets which can be categorized inside the preceding moment in time windowpane. Experiment on top of a massive-scale records set display that the method adaptively learn styles of recent spam actions and hold appropriate accuracy for spam detection in a tweet torrent.
An Event-Centric Prediction System for COVID-19

As COVID-19 evolved into a pandemic, a lot of effort has been made by scientific community to intervene in its spread. One of them was to predict the trend of the epidemic to provide a basis for the decision making of both the public and private sectors. In this paper, a system for predicting the spread of COVID-19 based on detecting and tracking events evolution in social media is proposed. The system includes a pipeline for building Event-Centric Knowledge Graphs from Twitter data streams about COVID-19, and uses the graph statistics to obtain a more accurate prediction based on the simulation of epidemic dynamic models. Experiments of 128 countries or regions conducted on the data set released by Johns Hopkins University on COVID-19 confirmed the effectiveness of the system. At the same time, the guidance our system provided to the plan of return-to-work for an enterprise has attracted the attention of and reported by top influential media.
Agent Abstraction via Forgetting in the Situation Calculus

In an earlier paper, Banihashemi et al. proposed a general framework for agent abstraction based on the situation calculus. They used basic action theories (BATs) to represent agents’ behavior, and mappings to specify how high-level BATs relate to low-level ones. They then deﬁned the concepts of sound/complete abstractions of BATs based on the notion of bisimulation between high-level and low-level models. However, they didn’t address the issue of the construction of an abstraction from a low-level action theory when given a mapping. It turns out that their concept of abstraction is closely related to the well-explored notion of forgetting. In this paper, we explore agent abstraction via forgetting. Firstly, we show that a correct (i.e., sound and complete) abstraction can be obtained via forgetting low-level symbols from the low-level action theory together with axioms for bisimulation. Secondly, we show how to compute via forgetting a correct abstraction in the form of a generalized BAT (i.e., where the initial database, action preconditions and successor state descriptions can be second-order formulas) under a suitable Markovian restriction. Finally, we show that in the propositional case, under the suitable Markovian restriction, correct abstractions are always computable.
Feature-Based Laser Odometry for Autonomous Surface Vehicles utilizing the Point Cloud Library

This paper proposes a pipeline for feature-based laser odometry for autonomous surface vehicles (ASVs) operating in urban environments. In particular, we investigate the suitability of several keypoint extractors and keypoint descriptors available through the Point Cloud Library (PCL). The complete odometry system, using the different extractors and descriptors, is implemented using the iSAM2 framework, and validated on real lidar data recorded onboard the autonomous ferry prototype MilliAmpere. The results demonstrate that accuracy similar to a standard Global Navigation Satellite System (GNSS) receiver can be achieved after 10 minutes even without loop closure. This study can be used as a starting point for future research on Simultaneous Localization And Mapping (SLAM) for ASVs.
Utilizing Knowledge Distillation in Deep Learning for Classification of Chest X-Ray Abnormalities

Automatic screening and diagnosis of lung abnormalities from chest X-ray images has been recently drawing attention from the computer vision and medical imaging communities. Previous studies of deep neural networks have predominantly demonstrated the effectiveness of lung disease binary classification procedures. However, large numbers of medical images—which can be labeled with a variety of existing or suspected pathologies—are required to be interpreted and reported upon daily by an individual radiologist; this poses a challenge in maintaining a consistently high diagnosis accuracy. In this paper, we present a competitive study of knowledge distillation (KD) in deep learning for classification of abnormalities in chest X-ray images. This method aims to either distill knowledge from cumbersome teacher models into lightweight student models or to self-train these student models, to generate weakly supervised multi-label lung disease classifications. Our approach was based on multi-task deep learning architectures that, in addition to multi-class classification, supported the visualizations utilized in saliency maps of the pathological regions where an abnormality was located. A self-training KD framework, in which the model learned from itself, was shown to outperform both the well-established baseline training procedure and the normal KD, achieving the AUC improvements of up to 6.39% and 3.89%, respectively. Through application to the publicly available ChestX-ray14 dataset, we demonstrated that our approach efficiently overcame the interdependency of 14 weakly annotated thorax diseases and facilitated the state-of-the-art classification compared with the current deep learning baselines.
First Results on How to Certify Subsumptions Computed by the EL Reasoner ELK Using the Logical Framework with Side Conditions

The generation of proof certiﬁcates and the use of proof checkers is nowadays standard in ﬁrst-order automated theorem proving and related areas. They have, to the best of our knowledge, not yet been employed in Description Logics, where the focus was on detecting and repairing errors in the ontology, rather than on catching erroneous consequences created by an incorrect reasoner. This paper reports on ﬁrst steps towards remedying this deﬁcit for subsumptions computed by the DL reasoner Elk . We use an existing tool for generating proofs of consequences from Elk , and transform these proofs into a format that is accepted as certiﬁcates by our proof checker. The checker is obtained as an instance of a generic certiﬁcation tool based on the Logical Framework with Side Conditions (LFSC), by formalizing the inference rules of Elk in LFSC. We report on the results of applying this approach to the classiﬁcation of a large number of real-world OWL 2 EL ontologies.
A Novel Medium Access Control Algorithm for Ad Hoc Networks Based on Ising Model

Medium Access Control (MAC) scheduling in ad hoc networks is a challenging task due to the trade-offs between fairness, delay and throughput. In this work, we propose a distributed link scheduling algorithm based on the Ising Model from statistical mechanics, by associating a novel Hamiltonian measure with the network such that its optimization yields a feasible schedule. This work overcomes the shortcomings of previous Ising Model based algorithms by incorporating queuing and servicing dynamics as well as fairness measures for improving aggregate throughput and network latency. Our simulations show considerable improvement in performance compared to existing benchmarks over a variety of traffic arrival patterns and network topologies.
Design and characterization of differential Signal Integrity interconnects at Millimeter-Wave PCB

In this paper, a Signal Integrity test board is designed to evaluate differential calibration and de-embedding methods at millimeter-Wave frequency band: Differential Crosstalk TRL (Thru-Reflect-Line) and Differential 2x-Thru de-embedding. For DUT's result, S21 phase delay are same, but differential Crosstalk TRL shows non-causality issue together with higher error at S21 amplitude. Finally 2x- Thru is selected at several typical de-embedding scenarios: PCB parameters extraction (Dk, Df and Surface Roughness) up to 67 GHz; Ultralow loss PCB material evaluation (Panasonic Meg 7, TUC TU933+, Nelco MW4000 and Isola Tachyon-100G); Multichannel skew test (less than 1 ps) with one port de-embedding.
An X/Ku Dual-Band Switch-Free Reconfigurable GaAs LNA MMIC Based on Coupled Line

This article presents an X/Ku dual-band switch-free reconfigurable GaAs low-noise amplifier (LNA) realized by inter-stage and output-stage coupled lines. This article is the first switch-free reconfigurable LNA design in the coupled lines structure. After amplified by the broadband drive stage, the input signal is divided into two parallel single-band stages (consists of a high-band stage and a low-band stage) by the proposed inter-stage coupled line. Two split-band signals are combined by the proposed output-stage coupled line into the output port after amplified by the single-band stages. The proposed coupled lines are also included in LNA matching networks. Dual-band operation of the proposed reconfigurable LNA is achieved by turning off drain voltages of the unused single-band transistors. The reconfigurable LNA is designed in a 0.15-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> E-mode GaAs pHEMT process. The fabricated LNA features small-signal gains of 25-25.2/20.1-28 dB, noise figure (NF) of 1.28-1.41/1.23-1.51 dB, and output 1-dB compression point of 0.5-1.7/2.2-5 dBm over 8-10/12-20 GHz, while consuming 76/91 mA of dc current, with a size of <inline-formula> <tex-math notation="LaTeX">$2.0\times 1.8$ </tex-math></inline-formula> mm<sup>2</sup>.
Enforcing fairness in logistic regression algorithm

Machine learning has been subject to discussion from the legal and ethical points of view in recent years. Automation of the decision-making process can lead to unethical acts with legal consequences. There are examples where the decision made by machine learning systems was unfairly biased toward some group of people. This is mainly because data used for model training were biased and thus developed a predictive model inherited that bias. Therefore, the process of learning a predictive model must be aware and account for the possible bias in the data. In this paper, we propose a modification of the logistic regression algorithm that adds one known and one novel fairness constraints into the process of model learning, thus forcing the predictive model not to create disparate impact and allow equal opportunity for every subpopulation. We demonstrate our model on real-world problems and show that a small reduction in predictive performance can yield a high improvement in disparate impact and equality of opportunity.
Computationally Efficient Methods for Estimating Unknown Input Forces on Structural Systems

We consider the problem of estimating unknown input forces on structural systems using only noisy acceleration measurement data. This is an important task for condition monitoring, for example, to predict fatigue damage in a structure's body or to reduce transmission of vibrations in marine vessels. In this paper, we propose a new idea to estimate an input force with a sinusoidal form by formulating a force identification problem without a direct feed-through system. Consequently, the minimum variance unbiased (MVU) filter can be implemented coupled with a fast Fourier transform algorithm to estimate unknown input forces accurately in real-time. Moreover, when the input force is completely unknown, the ensemble sampling method combined with an augmented Kalman filter can be formulated to significantly reduce computation time. Experimental results confirm the effectiveness of our proposed methods and show that the formulations investigated outperform other state-of-the-art methods in term of computational cost whilst not compromising estimation performance.
Integrated Sparse Coding With Graph Learning for Robust Data Representation

Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies. While the traditional graph sparse coding preserves the neighborhood structure of the data, this study integrates the low-rank representation(LRR) to fix the inconsistency of sparse coding by holding the subspace structures of the high-dimensional observations. The proposed method is dubbed low-rank graph regularized sparse coding (LogSC), which learns sparse codes and low-rank representations jointly rather than the traditional two-step approach. Since the two data representations share a dictionary matrix, the resulted sparse representation on this dictionary could be benefited from LRR. We solved the optimization problem of LogSC by using the linearized alternating direction method with adaptive penalty. Experimental results show the proposed method is discriminative in feature learning and robust to various noises. This work provides a one-step approach to integrating graph embedding in representation learning.
Characterization of Enclosed Layout Transistors for Analog Applications on a130nm Technology

Enclosed Layout Transistors (ELT) comprise a feasible design alternative to hardening CMOS circuits against total ionizing dose effects. This design technique significantly reduces the leakage paths produced by trapped charges in the field oxide of regular NMOS devices. This work presents the characterization of several ELT devices designed and fabricated on a 130nm technology aiming at analog applications. Standard (rectangular) transistors with equivalent aspect ratio were also considered for comparison. The experimental data are compared with simulations performed using a commercial design tool, considering the aspect ratio (W/L) extracted from the layout. ELTs with the drain designed both as the inner and outer terminal were characterized and compared. Additionally, in order to improve the achievable aspect ratio range of the square ELTs, parallel and series associations of enclosed devices were implemented as well. According to obtained results, there may be significant differences between the W/L extracted from the design tool and its actual value in the fabricated device. For all enclosed devices, the simulation overestimates the current values. A different pattern regarding the influence of drain location (inner or outer terminal) on current performance is also observed when comparing measured data with simulations. Additionally, associations of ELT showed to be feasible to widen the range of possible aspect ratio of this particular transistor.