Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Head Attentional Point Cloud Classification and Segmentation Using Strictly Rotation-Invariant Representations

Point cloud processing plays an increasingly essential role in three-dimensional (3D) computer vision target, scene parsing, environmental perception, etc. Compared with using aligned point cloud data for classification and segmentation, the strictly rotation-invariant representations show enough robustness. Inspired by the great success of deep learning, we propose a novel neural network called Multi-head Attentional Point Cloud Classification and Segmentation Using Strictly Rotation-invariant Representations. Our research focuses on processing the point cloud rotated in any direction effectively and precisely. First of all, the strictly rotation-invariant point cloud representations are obtained through point projection. Then we apply a multi-head attentional convolution layer (MACL) using attention coding to develop the performance of point cloud feature extraction. Finally, our network assigns different responses and recognizes the overall geometry well through a key point descriptor, adding to the global feature. Our method can explore more in-depth information for accuracy enhancement with attention pooling and multi-layer perceptron (MLP) based on an advanced DenseNet. Our network enjoys 90.63% and 87.50% classification accuracy testing on ModelNet10 and ModelNet40, and 75.15% intersection over union metric (mIoU) evaluating on ShapeNet Part dataset, remaining under any rotation. Rotating experimental results indicate that our framework realizes better point cloud classification and segmentation performance than most state-of-the-art methods.
A 94GHz Scalable 2 × 2 Phased-Array Receiver in SiGe BiCMOS for High Data-Rate Communication

Due to the low atmospheric attenuation and considerably wide bandwidth resource, W-band frequencies have become a strong candidate for various applications, such as high data-rate communication, radar, and imaging. Advanced SiGe BiCMOS technologies provide a low-cost and high-integrated solution to multichannel chipsets, which are desirable to implement phased-array systems. Several W-band highly-integrated phased arrays have been demonstrated [1 – 4]. However, most of the reported works exhibit small IF bandwidth (BW) and frequency-dependent phase shifting characteristic, which limits the overall system performance.
Evaluating the Energy Efficiency of UE-to-Network Relay Assisted Device-to-Device Communication

Device-to-Device (D2D)-enabled LTE allows direct communication between users without traversing the cell base station. This paper evaluates the energy efficiency of UE-to-Network relay assisted D2D and the feasibility of using it to reduce energy consumed in data transmission between users. A simulation is developed to simulate a 5G network and calculate the energy consumption of transmitting and receiving data between two users under two scenarios. The first scenario simulates the conventional LTE network, where each device is directly connected to the base station. The second scenario employs D2D where one device acts as a relay to the second. The system is evaluated using different values for the simulation time, packet transmission interval and distance. Attained results show that UE-to-Network Relay Assisted D2D reduces the total energy consumption of devices by up to 40% compared to LTE, with a side effect of slightly increasing the energy consumption of the relay device. Results also show that the energy consumption of network transmission chains under D2D is up to 45% lower compared to LTE.
Decision tree-based Design Defects Detection

Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on UML diagrams instead of code level. Unfortunately, we find in literature many defects that are described textually and there is no consensus on how to decide if a particular design violates model quality. Defects could be quantified as metrics based rules that represent a combination of software metrics. However, it is difficult to find manually the best threshold values for these metrics. In this paper, we propose a new approach to identify design defects at the model level using the ID3 decision tree algorithm. We aim to create a decision tree for each defect. We experimented our approach on four design defects: The Blob, Data class, Lazy class and Feature Envy defect, using 15 Object-Oriented metrics. The rules generated using decision tree give a very promising detection results for the four open source projects tested in this paper. In Lucene 1.4 project, we found that the precision is 67% for a recall of 100%. In general, the accuracy varies from 49%, reaching for Lucene 1.4 project 80%.
Neural-Guided Inductive Synthesis of Functional Programs on List Manipulation by Offline Supervised Learning

Synthesizing intended programs from user-specified input-output examples, also known as Programming by Examples (PBE), is a challenging problem in program synthesis, and has been applied to a wide range of domains. A key challenge in PBE is to efficiently discover a user-intended program in the search space that can be exponentially large. In this work, we propose a method for automatic synthesis of functional programs on list manipulation, by using offline-trained Recurrent Neural Network (RNN) models to guide the program search. We adopt miniKanren, an embedded domain-specific language for flexible relational programming, as an underlying top-down deductive search engine of candidate programs that are consistent with input-output examples. Our approach targets an easy and effective integration of deep learning techniques in making better PBE systems and combines two technical ideas on generating diverse training dataset and designing rich feature embeddings of probable subproblems for synthesis generated by deductive search. The offline-learned model is then used in PBE to guide the top-down deductive search with specific strategies. To practically manipulate data structures of lists, our method synthesizes functional programs with popular higher-order combinators including <inline-formula> <tex-math notation="LaTeX">$\texttt {map}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\texttt {foldl}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\texttt {foldr}$ </tex-math></inline-formula>. We have implemented our method and evaluated it with challenging program synthesis tasks on list manipulation. The experiments show promising results on the performance of our method compared to related state-of-the-art inductive synthesizers.
Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

Safety guarantees and regulatory approval for autonomous vehicles remain an ongoing challenge. In particular, software that is frequently adapted or contains complex, non-transparent components, such as artificial intelligence, is exceeding the limits of safety standards. This paper presents a detailed implementation of an online verification module – the Supervisor – that copes with these challenges. The presented implementation focuses on autonomous race vehicles without loss of generality. Following an identified holistic list of safety-relevant requirements for a trajectory, metrics are developed to monitor whether the trajectory can safely be executed. To evaluate safety with respect to dynamic objects in a semi-structured and highly dynamic racing environment, rule-based reachable sets are presented. As a result, the pure reachable set is further constrained by applicable regulations. Real-time capability and effectiveness are demonstrated in fault-injected scenario-based tests and on real-world run data. The implemented Supervisor will be publicly available on GitHub.
Improvement of Xception-ResNet50V2 Concatenation for COVID-19 Detection on Chest X-Ray Images

Novel coronavirus disease (COVID-19) has globally become a pandemic since the first quarter of 2020 whose growth rate must immediately be controlled. One of strategy to reduce the growth rate of sufferers is to break the chain of the spread by detecting it and carrying out quarantine. X-ray imaging can be used as a modality to detect the COVID-19 in suspected patients’ lungs as a clinical diagnostic tool. One of the challenges of this task is the difficulty in distinguishing the characteristics of COVID-19 from other diseases with similar features of the images resulted from the X-ray of the chest. To reduce the problems that will be faced, machine learning or deep learning is embedded in an automatic computer-aided diagnosis (CAD) to increase efficiency and accuracy. Several deep learning-based artificial intelligence systems can be used in diagnosis, one of the most popular is using the previously proposed Convolutional Neural Network (CNN), which has promising accuracy in detecting COVID-19 confirmed patients using CXR images. In this study, to detect COVID-19 confirmed patients by classifying them into 4 classes, we propose a modified combination of two CNN architectures named Xception and ResNet50V2 which makes the system powerful using multiple feature extraction capabilities. The proposed method achieves high accuracy, precision, recall, and F1-Score, reaching 93.412%, 96.6%, 99.6%, and 98%, respectively. Overall, the proposed method can be used as an automatic diagnosis system that can be utilized by clinical practitioners and radiologists to diagnose, validate, and follow-up of COVID-19 suspected cases.
Band broadening phenomena in liquid phase ion mobility spectrometry

by Manuja Rasangi Lamabadusuriya, Ph.D. Washington State University December 2014 Chair: Herbert H. Hill, Jr. Growing demand for mobile analytical tools to detect explosives, illicit drugs and other forensic related compounds has led to the development of several miniaturized ion mobility spectrometers, yet many have limited performance. Liquid phase ion mobility spectrometry (LPIMS) has the potential to become a highly efficient, but small separation device. However, in recent work high resolving powers have not been achieved promised by LPIMS theory. Thus, the primary focus of the research presented here was to identify the band broadening processes that has led to low resolving powers in the practice of LPIMS and to suggest methods in which the resolving power of LPIMS can be improved. Using four unique ion mobility spectrometers, which were all constructed during the process of this work, electric field inhomogeneity, ion cluster formation, gate pulse efficiency, and Coulombic repulsion were investigated as possible.
Assessment of Video Accessibility by Students of a MOOC on Digital Materials for All

The assessment of multimedia accessibility is a relevant, complex and time-consuming task, which takes more than simply checking whether the video has audiodescription and captions or not. In our study, we face this challenge through the: 1) involvement of a cohort of novice evaluators, who previously took part in a MOOC on the accessibility of digital content and 2) the division of the accessibility assessment into the application of a set of criteria. Two groups of novice accessibility testers were asked to evaluate the accessibility of two similar videos, one video per group. While both videos were equivalent in terms of their pedagogical content, only one of them had non-severe accessibility barriers for people with low vision and for blind people. Each participant was asked to rate qualitatively a set of statements extracted from the WCAG 2.1 success criteria, one generic statement about the video accessibility, and a set of statements on the quality perception and the aspects of personal preference. The largest differences in ratings occurred for statements whose success criteria had been improved. It was also the case for one success criterion that is understandable but hardly applicable by novice evaluators, according to the literature. However, the difference was statistically significant only for the success criterion with more salient differences between both videos. As a main conclusion, a group of novice evaluators can identify accessibility problems in videos when using specific accessibility statements.
Verification of Feature Extraction from a Visuomotor Integration Model with Convolutional Neural Network

Manipulation control technology for multi-fingered robot hands is an important requirement of life-support robots operating in living spaces. However, Hand movements with many degrees of freedom are difficult to replicate. When humans grasp an object, they internally represent the object in their brain by integrating the sensory information with the motor commands acquired from past grasping of the object. This information process can be modeled by a modular auto-encoder. Here, such a model is constructed and verified on a real multi-fingered robot manipulator. Previously, we confirmed that a five-layered perceptron can internally represent the shape and position of an object without an explicit supervised signal. After the learning, the robot can grasp the object from a camera image alone. In this study, we introduce a convolutional neural network (CNN) that extracts the object-shape features and compares the model with one without CNN. The results confirmed that the CNN is effective for this feature extraction model.
Multi-Points Indoor Air Quality Monitoring Based on Internet of Things

Indoor air quality monitoring is of great importance to human health as people typically spend more than 90% of their time in indoor environments. An indoor air quality detector (IAQD) enabling measurement of CO2, PM2.5, temperature, humidity, has been designed and tested in residential buildings based on Internet of Things (IoT). The hardware and software design of IAQD are described in detail, and seven IAQDs with Zigbee wireless module embedded were deployed in the target building for a continuous period of one month in winter. The gateway collects sensor data from each IAQD in turn at an interval of two minutes, and transmits data to server in the cloud via GPRS/4G. The authorized users can access cloud platform via mobile apps or the Web browser. Monitoring data analysis results reveal that the maximum PM2.5 concentrations is 10 times than usual during cooking period, and CO2 concentrations with the door closed will rise to 2500 ppm compared to the door opened at night, which threatens the health of occupants.
A Diode-Based Fault Detection, Classification, and Localization Method for Photovoltaic Array

In this research work, a diode-based circuit and an algorithm are proposed to detect, classify, and localize faults in small- or medium-scale photovoltaic (PV) array with blocking diodes. Using a single voltage sensor for the whole PV array, the proposed method can detect line-to-line (L-L) and line-to-ground (L-G) faults and find the approximate number of faulty modules. In addition, the proposed circuit uses one voltage sensor to classify faults and one to detect the exact faulty string. Alongside, the proposed algorithm is capable of detecting low and high impedance faults. In contrast to complicated strategies, easy circuit analysis is done to examine the behavior of PV arrays in the event of faults. The proposed algorithm and circuit has been thoroughly tested through a hardware prototype.
3-D Printed Microjet Impingement Cooling for Thermal Management of Ultrahigh-Power GaN Transistors

Future GaN-based radio frequency (RF) high-electron-mobility-transistors (HEMTs) can enable increased areal power dissipation by, for example, integrating GaN device layers with high thermal conductivity diamond substrates. To maximize the benefit of the ultrahigh-power-density electronic devices, improved package-level cooling methods are needed to prevent the package and heatsink becoming a thermal bottleneck. We demonstrate that 3-D printed polymeric microjet liquid impingement cooling can reduce the thermal resistance at the package level by ~60% with respect to GaN RF HEMTs mounted on conventional packaging.
Bringing Network Coding into SDN: Architectural Study for Meshed Heterogeneous Communications

Modern communications have moved away from point-to-point models to increasingly heterogeneous network models. In this article, we propose a novel controller-based architecture to deploy adaptive causal network coding in heterogeneous and highly meshed communication networks. Specifically, we consider using the software-defined network as the main controller. We first present an architecture for highly meshed heterogeneous multi-source multi-destination networks that represent the practical communication networks encountered in the fifth generation of wireless networks and beyond. Next, we present a promising solution to deploy network coding over the new architecture. We also present a new controller-based setting with which network coding modules communicate to attain the required information. Finally, we briefly discuss how the proposed architecture and network coding solution provide a good opportunity for future technologies.
Electric/Hybrid-Electric Aircraft Propulsion Systems

The idea of electric propulsion for transportation is not new; indeed, the first cars, nearly 200 years ago, were electric. However, our dependence on fossil fuels over the last 100 years is now being questioned, and as a global society, we are moving toward more-electric transportation solutions. Electric propulsion of aircraft is part of this trend, either all-electric or through a large variety of the proposed hybrid propulsion systems. This article considers some of these systems, their technological requirements, and the ongoing research and development in motors and drives necessary to make this technological change a feasible option for the future of passenger flight.
Convolutional Neural Networks on Multichannel Time Series of Smartphone Applications for Gender or Age Range Classification

In this study, we developed a classification method for gender and age ranges using convolutional neural networks (CNNs). We trained an architecture using a time series of smartphone application-connecting durations and demographics of datasets containing gender and age ranges. The inputs were in the form of big data for all application categories of the time series, automatically collected from a single source panel of individuals with smartphones. The demographics of gender and age ranges were used for prediction. To identify the demographics of individuals, this study extracted effective features of mobile website access activities. We proposed a feature learning model from the raw inputs to solve the problem of classifying gender and age ranges using a deep convolutional neural network, which is an important but challenging task in digital marketing. For companies that can obtain only web application logs but not the demographics of gender and age ranges, this research may provide a possible solution that can be applied in digital marketing.
Analysis of User Acceptance for Rumah Belajar Mobile Application

Rumah Belajar is an online learning portal developed by Indonesia’s Education and Culture Ministry with a total user of 2,968,689. However, such a number is far from the total number of students in Indonesia, 45,073,778 students. It is still far from the Ministry of Education and Culture’s expectation that all students in Indonesia can use it. This study aims to analyze the factors that influence the acceptance of Rumah Belajar’s application based on students’ perspectives, especially high school students using the UTAUT as a basic model. This quantitative study was conducted by distributing online questionnaires that were analyzed further by using PLSSEM to determine the factors influencing the application’s acceptance. It revealed performance expectancy, facilitating conditions, information quality, enjoyment, and playfulness significantly and positively affect the intention to use the application. Other variables, such as effort expectancy, selfmanaged learning, and system quality, showed no significant impact on the intention to use the application. Furthermore, intention to use also showed a significant and positive effect on the application’s actual use.
A GDPR-Compliant Framework for IoT-Based Personal Health Records Using Blockchain

An up-to-date personal health record (PHR) system is crucial for people’s health. Achieving a reliable PHR system in the e-Health and m-Health era is still a challenge concerning data integration from different EHRs, data interoperability, and enforcing that access to data is fully under the patient’s control. We address these challenges by proposing an electronic health wallet (EHW) system that uses emergent decentralized technologies like blockchain and IPFS and adopts health data interoperability standards and technologies like FHIR’s APIs. The EHW stands on a GDPR-compliant framework for IoT-based PHR systems that ensures both data privacy and interoperability. The proposed conceptual framework and system architecture provide a comprehensive solution for a patient-centered IoT-based PHR system that preserves data privacy and satisfies the data interoperability needs. By encouraging patients to share their data in a controlled way, also enables health big data analytics by utilizing the IoT data in a privacy-preserving fashion.
A Novel Probability Confidence CNN Model and Its Application in Mechanical Fault Diagnosis

The development of artificial intelligence has brought new opportunities and challenges in the field of mechanical fault diagnosis. Especially, data-driven intelligent fault diagnosis (DIFD) methods are favored by researchers. However, commonly used DIFD methods can only recognize the fault of a known class instead of an unknown class, which results in the DIFD’s inability to self-learning. To handle this problem, novel probability confidence convolutional neural network (PCCNN) is proposed in this article. The probability of belonging to each known class calculated by the PCCNN and the confidence of each known class is used to recognize the known and unknown classes. At the same time, the ability to recognize a new class is achieved by updating the architecture and parameters of the PCCNN model with a self-learning method. The bearing experiment dataset, the gearbox experiment dataset, and the rotating machinery failure data are used in the case studies. The study results show that the proposed method achieves an average accuracy of 97.42% and 96.87% for recognizing unknown and known classes, respectively, exhibiting at least 5.18% better performance than the 12 comparison methods. All the code and experimental data are available on the website.