Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Machine Learning Applied to Radiomic Image Features for Segmenting Tumour Structures

Lung cancer (LC) was the predicted leading cause of Australian cancer fatalities in 2018 (around 9,200 deaths). Non-Small Cell Lung Cancer (NSCLC) tumours with larger amounts of heterogeneity have been linked to a worse outcome. Medical imaging is widely used in oncology and non-invasively collects data about the whole tumour. The field of radiomics uses these medical images to extract quantitative image features and promises further understanding of the disease at the time of diagnosis, during treatment and in follow up. It is well known that manual and semi-automatic tumour segmentation methods are subject to inter-observer variability which reduces confidence in the treatment region and extent of disease. This leads to tumour under- and over-estimation which can impact on treatment outcome and treatment-induced morbidity. This research aims to use radiomic features centred at each pixel to segment the location of the lung tumour on Computed Tomography (CT) scans. To achieve this objective, a Decision Tree (DT) model was trained using sampled CT data from eight patients. The data consisted of 25 pixel-based texture features calculated from four Gray Level Matrices (GLMs) describing the region around each pixel. The model was assessed using an unseen patient through both a confusion matrix and interpretation of the segment. The findings showed that the model accurately (AUROC = 83.9%) predicts tumour location within the test data, concluding that pixel based textural features likely contribute to segmenting the lung tumour. The prediction displayed a strong representation of the manually segmented Region of Interest (ROI), which is considered the ground truth for the purpose of this research.
Social Media Users Send Promotional Links to Strangers: Legitimate Promotion or Security Vulnerability?

Nowadays, many users make money by publishing content on social media platforms. In order to attract users’ attention, they often take measures to promote themselves. The security vulnerabilities in social media platforms may provide convenience for their user promotion work. We call this type of vulnerability the user promotion security vulnerability (UPSV). UPSV may cause unfair competition and endanger the interests of legitimate users and the social media platforms. Therefore it has great research significance to find and fix this vulnerability. In this paper, we propose a UPSV which widely exists in the function of sending messages to strangers of in-app chatting of many social media platforms. We first analyzed this vulnerability in some apps, and then YY app (China’s largest live streaming platform) was chosen as the research object to verify the actual effect of the vulnerability on illegitimate user promotion. We took the method of promoting a target YY streamer through sending promotional links to viewers, and to improve promotion effect, we used the method of user preference learning to select viewers for promotion. The experimental results show that among the promoted viewers, more than 44% entered the target streamers’ channels to watch live streaming, more than 21% followed the target steamers, and more than 13% gave gifts to the target steamers. It fully proves that this UPSV is real, exploitable and harmful, and we also proposed some fix suggestions to help the platforms to fix it.
A Primal-Dual Smoothing Framework for Max-Structured Non-Convex Optimization

We propose a primal-dual smoothing framework for finding a near-stationary point of a class of nonsmooth nonconvex optimization problems with max-structure. We analyze the primal and dual gradient complexities of the framework via two approaches, that is, the dual-then-primal and primal-the-dual smoothing approaches. Our framework improves the best-known oracle complexities of the existing method, even in the restricted problem setting. As an important part of our framework, we propose a first-order method for solving a class of (strongly) convex-concave saddle-point problems, which is based on a newly developed non-Hilbertian inexact accelerated proximal gradient algorithm for strongly convex composite minimization that enjoys duality-gap convergence guarantees. Some variants and extensions of our framework are also discussed. Funding: R. Zhao’s research is partially supported by AFOSR [Grant FA9550-22-1-0356].
Design and Implementation of Real-Time Visualization of Sound Source Positions by Drone Audition

Disaster robotics, in particular, unmanned aerial vehicle (hereinafter, drone) is expected to improve the promptness and effectiveness of search and rescue mission. Although the most common sensor for drones is vision, its drawbacks such as ill-illumination condition and occlusion should be compensated by other modalities. This paper focuses on sound source localization and position estimation by drone audition. The most critical issue of robot audition with a microphone array is the suppression of ego-noise caused by rotors and airflow around the drone. Another issue is handling of multiple sound sources in addition to ego-noise. If multiple sound sources are actually or virtually crossing due to movement of sound sources and/or the drone, tracking may suffer from uncertainty in data association. In addition, user interface is also critical for the mission, which has not been discussed to date. This paper focuses on the design and implementation of real-time visualization of sound source positions for search and rescue mission. The design follows the guideline proposed by Murphy and Tadokoro. As a proof of concept, public demonstrations at ImPACT Tough Robotics Challenge (TRC) are reported.
Adaptive Matching Pursuit Method Based on Auxiliary Residual for Sparse Signal Recovery

Greedy pursuit methods are widely used for compressive sensing (CS) and sparse signal recovery due to their low computational complexity. In this paper an adaptive matching pursuit is proposed, which is based on the backtracking-based adaptive orthogonal matching pursuit (BAOMP) and uses auxiliary residual to make correlation test to add more correct atoms per iteration. The proposed method can be regarded as an improved BAOMP. The simulation results show that it has better performance to those of some other greedy pursuit methods. Finally the experiment of CS-based ISAR imaging verifies the effectiveness of the proposed method.
Efficient and Secure Ciphertext-Policy Attribute-Based Encryption Without Pairing for Cloud-Assisted Smart Grid

Cloud-assisted smart grid has been broadly deployed to improve the economics, efficiency, sustainability, and reliability of electricity services. The new revolution of technology will also bring new challenges to data security, particularly data confidentiality. As a promising encryption method for supporting fine-grained access control, attribute-based encryption (ABE) is widely given attention by the academia and industry. However, most existing schemes suffer from the efficiency problem limiting its deployment for the cloud-assisted smart grid, especially for resource-limited users. To address this problem, this paper proposes two efficient pairing-free ciphertext-policy attribute-based schemes that eliminate the computation-intensive bilinear pairing operation. The basic scheme only uses some simple scalar multiplications based on elliptic curves, and the enhanced scheme provides an important usability feature, namely the users and attributes revocation. The security of the proposed schemes is reduced to the elliptic curve decisional Diffie-Hellman problem. Finally, the performance analysis and extensive experiments demonstrate that our proposed schemes are suitable for the cloud-assisted smart grid.
Observational detection of correlation between galaxy spins and initial conditions

Galaxy spins can be predicted from the initial conditions in the early Universe through the tidal tensor twist. In simulations, their directions are well preserved through cosmic time, consistent with expectations of angular momentum conservation. We report a $\sim 3 \sigma$ detection of correlation between observed oriented directions of galaxy angular momenta and their predictions based on the initial density field reconstructed from the positions of SDSS galaxies. The detection is driven by a group of spiral galaxies classified by the Galaxy Zoo as (anti-)clockwise, with a modest improvement from adding galaxies from MaNGA and SAMI surveys. This is the first such detection of the oriented galaxy spin direction, which opens a way to use measurements of galaxy spins to probe fundamental physics in the early Universe.
3D Pose Estimation for the Object with Knowing Color Symbol by Using Correspondence Grouping Algorithm

Color feature matching for point cloud data with color 2D code is proposed to recognize the object whose unique feature is difficult to be made by only using shape information. When a specific object such as a commercial product is recognized, it is possible to inquire the product information by attaching a known marker registered in the network server. However, even when a known marker is attached, it is difficult to acquire the accurate marker position and orientation unless the image is taken from a direction that allows good recognition. On the other hand, the position / orientation estimation method using point cloud information cannot be effective unless the shape information is easy to appear. In the proposed method, in order to obtain product information and accurately measure the position and orientation of the product, a marker that is given a characteristic color is attached to a known location on the target object so that the color information as well as shape information can be evaluated simultaneously. In the case of packaged products, it is not necessary to give color information in particular. However, the artificial known marker could be the important evaluation item to estimate object posture since the products that are covered with a transparent film such as a sandwich and a rice ball in the convenience store have different textures for each individual. In order to evaluate the accuracy of the proposed method, the recognition results are compared in a 3D virtual environment. For the evaluation of matching with corresponding grouping algorithm, two major feature-based matching algorithm are implemented; one is 3D-HV (Hough Voting) which returns acceptable inlier searching performance in real-time, and another is RANSAC (Random Sample Consensus) which is known as the high precision performance. In addition, 2.5D and complete 3D reference model are prepared to compare the computational time and the accuracy of the posture estimation. As a result of the experiment, the method using the CSHOT feature, the RANSAC and the complete 3D model as the search method has the highest accuracy and is practical as the search time.
Teacher-Student BLSTM Mask Model for Robust Acoustic Beamforming

Microphone array beamforming has been approved to be an effective approach for suppressing adverse interferences. Recently, acoustic beamformers employing neural networks (NN) for time-frequency (T-F) mask prediction, termed as Mask-BF, have received tremendous interest and shown great benefits as a front-end for distant automatic speech recognition (ASR). However, our preliminary experiments using Mask-BF for ASR task show that the mask model trained with only simulated training data underperforms when the real-recording data appears in the testing stage, where a data mismatch problem occurs. In this study, we aim at reducing the impact of the data mismatch on the mask model. Our research is quite intuitive that the real-recording data can be used together with the simulated data to make the mask model more robust against data mismatch problem. Specifically, two bi-directional long short-term memory (BLSTM) models, are designed as a teacher mask model and a student mask model, respectively. The teacher mask model is trained with simulated data, and it is then employed to generate the soft mask labels for both simulated and real-recording data separately. Then, the simulated data and the real-recording data with generated soft mask labels form the new training data to train the student mask model. As a result, a novel T-S mask BF is developed accordingly. Our T-S mask BF is evaluated as a frontend for ASR on the CHiME-3 dataset. Experimental results show that the generalization ability of our T-S mask BF is enhanced where we obtain relative 4% word error rate (WER) reduction compared to the baseline Mask-BF in the real-recording test set.
A LSTM-Based Joint Progressive Learning Framework for Simultaneous Speech Dereverberation and Denoising

We propose a joint progressive learning (JPL) framework of gradually mapping highly noisy and reverberant speech features to less noisy and less reverberant speech features in a layer-by-layer stacking scenario for simultaneous speech denoising and dereverberation. As such layers are easier to learn than mapping highly distorted speech features directly to clean and anechoic speech features, we adopt a divide-and-conquer learning strategy based on a long short-term memory (LSTM) architecture, and explicitly design multiple intermediate target layers. Each hidden layer of the LSTM network is guided by a step-by-step signal-to-noise-ratio (SNR) increase and reverberant time decrease. Moreover, post-processing is applied to further improve the enhancement performance by averaging the estimated intermediate targets. Experiments demonstrate that the proposed JPL approach not only improves objective measures for speech quality and intelligibility, but also achieves a more compact model design when compared to the direct mapping and two-stage, namely denoising followed dereverberation approaches.
DIRAC: A Hybrid Approach to Customer Demographics Analysis for Advertising Campaigns

Digital advertising is a critical task for any business sectors to announce their new products and attract customers (audience). Digital advertising requires efficient market segmentation methods (i.e., customer clustering) to group customers into different categories based on age, gender, and other particulars so that ads can reach the appropriate target audience. However, such personal particulars are usually not available for advertisers to collect due to the privacy issue or the audience does not want to reveal it. To overcome this challenge, existing approaches analyze customer demographics by applying unsupervised machine learning algorithms to crawling data obtained when customers browse Internet websites. Crawling data includes customer browsing habit (e.g., browsing time) and hobby (e.g., frequently-visited websites). However, such algorithms are not robust against outliers or abnormal data. Further, they require a pre-defined number of clusters that may not be available in practice. To address the above challenges, in this paper, we develop DIRAC, a hybrid approach to customer demographics analysis for advertising campaigns. DIRAC consists of 4 phases that include data preprocessing, determination of the optimal number of clusters, clustering and clustering optimization. We carry out extensive experiments on a realistic dataset to evaluate the performance of DIRAC. The experimental results show that DIRAC provides useful insights on customer demographics and satisfies the expectation of advertisers while requiring 38% less execution time on the examined dataset.
3-D-Printed Modified Butler Matrix Based on Gap Waveguide at W-Band for Monopulse Radar

This article presents the design and fabrication of low-loss, -weight, and -cost, modified Butler matrix in groove gap waveguide technology at W-band, implemented by additive manufacturing. The design, simulations, and measurements of each of the elements that conform to the modified Butler matrix are presented. The proposed modified Butler matrix is asymmetric and oriented to an easy assembly with a radial antenna in order to obtain simultaneous sum and difference patterns for a narrowband radar application. The measured individual components of the modified Butler matrix fit very well with the simulations. As for the full modified Butler matrix, measurements provide a 0.7% bandwidth below −20 dB for sum and difference ports centered at 94 GHz. The amplitude and phase imbalance are below 1.5 dB and 20°, respectively. This narrow bandwidth is due to the difference in length between input and output ports due to the asymmetry of the design. The total losses of the modified Butler matrix are below 1 dB.
Social-Aware Content Delivery in Low Latency D2D Caching Networks

Proactive caching at the users is an attractive strategy to unload the core network during peak hours. The main principle is to cache some of the most popular contents during off-peak hours, and then exploit the usersâ€™ social interactions to distribute the desired contents via device-to- device (D2D) connections. In this work, we introduce an optimal content caching placement and delivery policy based on social interactions between mobile users under a reliability constraint. We adopt the inter-contact mobility model to represent users meeting and mobility patterns. The Service provider (SP) captures users demand and mobility statistics to decide which contents to be cached. Content delivery occurs when users, having social relationships, meet together. Having several nearby users, each user has multiple potential interfaces to get the desired content. We find the optimal caching policy that maximizes contents hit probability and guarantees reliable data transmission.
Vibration-Based Multi-Axis Force Sensing: Design, Characterization, and Modeling

It is strongly desirable but challenging to obtain force sensing mechanisms that are low-cost, volumetrically compact, away from contact location, and can be easily integrated into existing and emerging robot systems. For example, having a bulky force sensor near the tip of surgical robot tools may be impractical as it may require a large incision, infect biological tissues, and negatively affect surgeon's operation. In this letter, a new vibration-based approach was proposed to measure the force applied to a structure utilizing the structure's acceleration signals. By exciting the structure using a vibration motor, the structure's acceleration signals in time domain showed discernible ellipse-shaped profiles when a force was applied. For the first time, these acceleration profiles were characterized via regression and employed for estimating the direction and magnitude of the applied force. Experimental results showed that, the achieved resolutions with the proposed approach in estimating the direction and magnitude of the applied force were 10° and 0.098 N, respectively. The sensing errors were within the range of 8–18%. This force-sensing approach has strong potential for a wide area of robotic applications.
Novel Switchable Filtering Circuit With Function Reconfigurability Between SPQT Filtering Switch and Four-Way Filtering Power Divider

In this article, we propose a circuit model for designing the switchable filtering circuit with reconfigurable functions between a single-pole quad-throw (SPQT) filtering switch (State 1) and a four-way filtering power divider (State 2) without additional switchable impedance matching network (IMN). The proposed circuit model can be utilized as the feeding network of a switchable directive and omni-directional antenna system to eliminate the omni-directional antenna in conventional solutions to reduce the size and cost. It consists of four filter channels with a common port. In State 1, it works as a SPQT filtering switch, where only one channel is turned on to realize good filtering responses and the impedances at the other three channels are adapted to infinity to realize high isolation. The four channels are turned on simultaneously in State 2 to work as a four-way filtering power divider, and the input impedance of the common port is the same as that in State 1. Thus, a switchable IMN is not required for port matching when switching between the two states. Transmission zeros are generated to enhance the skirt selectivity. For verification, the filtering circuit is constructed using high- $Q$ coaxial resonators. Experimental results show excellent performance in both states acting as a SPQT filtering switch and a four-way filtering power divider.
Optimization-Based Fundus Image Decomposition for Diagnosis Support of Diabetic Retinopathy

Diabetes mellitus often leads to a serious eye disease called diabetic retinopathy, which is one major cause of blindness among adults. Since this blindness can be prevented if the diabetic retinopathy is detected at an early stage and appropriate medical treatment is provided, routine screening tests with fundus images are very important. However, as the number of diabetic patients increases, the routine screening tests are becoming big burdens for ophthalmologists. To reduce these burdens, in this paper, we propose a diagnosis support method by using convex optimization. The proposed method decomposes a green channel fundus image into a basic image composed of non-disease parts, a positive image including exudates, and a negative image including hemorrhages. Numerical experiments show the effectiveness of our method.
Redundancy Analysis and Elimination on Access Patterns of the Windows Applications Based on I/O Log Data

In this paper, we analyze I/O log data monitored in the Windows operating system for improving the system performance. Especially, we focus on the I/O operations to the Windows registry. As a result, we identify redundant access patterns of the Windows applications. To find all the possible redundant patterns from the large-scale log data, we propose the redundancy detection algorithm. Then, we propose the two-level redundancy elimination method to remove unnecessary redundant operations. We also present an event-driven method that guarantees that the result of redundancy elimination is equivalent to that of the original program. Through experiments, we show that the proposed redundancy elimination method improves the performance of the original program having redundant access patterns by up to 90.25% for individual access patterns; by 8.93% ~ 26.21% when the multiple programs having combined access patterns are running concurrently.
Suitable-Compensation Circuit Design for a PAM4 Transmitter in 180-nm CMOS

This paper presents compensation circuit design for four-level pulse amplitude modulation (PAM4) transmitters with feed forward equalizing (FFE) in data transmission systems. The signal amplitude in PAM4 is small, particularly when the supply voltage is low. This leads to signal degradation. To maintain signal quality in PAM4 transmitters, we developed a design that performed within the appropriate combiner parameters. We conducted a post-layout simulation in an 180-nm CMOS process to confirm the advantage of the circuit. The rising and falling times were reduced by 7.4% and 2.4% compared with those of a conventional circuit.