Miracle-dz/UniFAR-FTUs huggingface dataset:
DIC: Deep Image Clustering for Unsupervised Image Segmentation

Unsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are sensitive to the parameters such as the segmentation numbers or of high training and inference complexity. Encouraged by neural networks’ flexibility and their ability for modelling intricate patterns, an unsupervised segmentation framework based on a novel deep image clustering (DIC) model is proposed. The DIC consists of a feature transformation subnetwork (FTS) and a trainable deep clustering subnetwork (DCS) for unsupervised image clustering. FTS is built on a simple and capable network architecture. DCS can assign pixels with different cluster numbers by updating cluster associations and cluster centers iteratively. Moreover, a superpixel guided iterative refinement loss is designed to optimize the DIC parameters in an overfitting manner. Extensive experiments have been conducted on the Berkley Segmentation Database. The experimental results show that DCS is more effective in aggregating features during the clustering procedure. DIC has also proven to be less sensitive to varying segmentation parameters and of lower computation costs, and DIC can achieve significantly better segmentation performance compared to the state-of-the-art techniques. The source code is available on https://github.com/zmbhou/DIC.
FMCW Radar-Based Anomaly Detection in Toilet by Supervised Machine Learning Classifier

With the increase in the population of elderly, a technique for monitoring them is receiving more and more attention all over the world. In particular, it is highly demanded to develop the system that detects a fainting elderly in a toilet, since such an accident is likely to get worse due to the late detection. In this paper, we propose an anomaly detection method in toilet using an FMCW(Frequency Modulated Continuous Wave) radar, which can measure the distance between an object and an FMCW radar. As anomalies, we determine as the situations where a subject faints while stooping on the toilet seat, leaning on the backrest, leaning on the side wall, and lying on the floor after the fall. With an FMCW radar, a fainting subject could be detected based on whether the estimated distance between a subject and an FMCW radar is almost constant. However, a subject might be sitting still, and also the distance to not a subject but a floor could be estimated depending on a subject's posture. To address this problem, in the proposed method, the time when the estimated distance is almost constant is detected, and then the detected time is classified into the time due to a faint or a non-faint by a supervised machine learning classifier. The used features are extracted based on the data, e.g., the estimated distance, just before the detected time. To evaluate the anomaly detection accuracy of the proposed method, we conducted the experiments to observe not only the aforementioned abnormal behavior but also the normal behavior, e.g., the button operation and sitting down. As a result, in terms of the classification of abnormal and normal behavior, our proposed method achieved the F- measure of 0.91, where F-measure is the overall score of recall and precision rates.
Deep Neural Network Task Partitioning and Offloading for Mobile Edge Computing

The surging Deep Neural Network (DNN) based applications are becoming increasingly popular in mobile computing. However, they impose significant challenges for mobile computing, as DNN tasks lead to much more computation complexity and data volume compared with traditional tasks. To alleviate this, mobile edge computing (MEC) provides a feasible approach through task partitioning and offloading. In this paper, we investigate a DNN based MEC scheme considering multiple mobile devices and one MEC server. To facilitate task partitioning, we first develop a processing delay prediction mechanism for typical DNN tasks. To achieve the minimal processing delay as well as to release the computing burden of mobile devices, a mixed integer linear programming (MILP) based DNN task partitioning and offloading mechanism is presented. Evaluations show that our mechanism can achieve up to 90.5% and 69.5% processing delay reduction compared with MEC server only and mobile device only schemes respectively.
Rethinking TCP and UDP on Shared Satellite Links

The Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) are the two main traditional transport protocols on the Internet. TCP's congestion control algorithms allow TCP senders to "back off" when they detect packet loss, a feature that UDP does not implement. TCP is thus generally regarded as being "fair" towards other TCP connections, in contrast to UDP. Internet Service Providers (ISPs) with upstream narrowband satellite connectivity often block UDP on their satellite links as it is perceived to be disruptive in the presence of TCP. This paper argues that in practice, many TCP flows on satellite links are too short-lived to back off, and that therefore, these TCP flows behave like UDP. This poses the question as to how much is gained by blocking UDP, a transport protocol that has gained in importance in recent years with the emergence of QUIC and UDP-based peer-to-peer applications.
Integrated Task Caching, Computation Offloading and Resource Allocation for Mobile Edge Computing

Applications with more sensitive delay and larger data volumes, such as interactive gaming and augmented reality, have become popular recently. Computation offloading is expected as a promising technique to meet low latency for mobile users. However, computation offloading requires communication between mobile users and the mobile edge computing (MEC) server, the delay and energy consumption caused by the transmission are considerable expenses for users. Motivated by this, we consider joint computation offloading and task caching optimization in a cellular network where users can proactively cache and offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. By formulating the problem as mixed-integer non-linear programming, we propose to find the optimal solution by three steps. Through which we have obtained the optimal computing resource allocation, the optimal task caching scheme and an algorithm which yields the optimal computation offloading scheme. Simulation results show that in comparison to the other three benchmark methods, the proposed scheme can effectively reduce the system cost.
A Compact Wireless Charger Design with Decoupled Quadruple-D Inductor for LCC-Series Topologies

For inductive power transfer (IPT), inductor¬capacitor-capacitor-series (LCC-S) compensation network is one of the most prevalent topologies. In LCC-S IPT, by performing inductor integration, which integrates the input inductor to the primary-side coil, the system can become more compact. Nonetheless, one drawback of inductor integration is that the mutual coupling between the input inductor and the IPT coils can degrade system efficiency and output, especially under misaligned conditions. This paper purposes a decoupled quadruple-D inductor design that achieves inductor integration while has negligible coupling to the IPT coils within the whole operating region. A 1kW LCC-series compensated IPT prototype with the purposed inductor design has been developed, and the experimental results verify that the purposed design has almost identical electrical performance as conventional stand-alone inductors, while requiring no additional space.
Characterisation of HIV-specific Immunity in a Cohort of High-risk Kenyan Sex Workers

A clearer understanding of HIV-specific immune responses in highly-exposed, persistently seronegative (HEPS) subjects is important in developing models of HIV-l protective immunity, and ultimately for vaccine development. Various HIV-specific immune responses have been described in HEPS cohorts, including cytotoxic T lymphocyte (CTL) responses, T helper (Th) responses, and IgA specific for HIV-1 envelope proteins. The presence and correlates of these responses were examined in a Kenyan cohort of HEPS sex workers, and the frequency, specificity, and clinical significance of HTV-specific CD8+ responses were studied. Systemic HIV-1 Env-specific CTL and CD8+ lymphocyte responses against predefined CTL epitopes were present in most HEPS sex workers, as were HIV-1 Env-specific IgA and Th responses. The proportion of HEPS women with HIV-specific CTL or CD8+ responses increased with the duration of uninfected sex work, suggesting that responses were acquired over time. CD8+ responses were also found in the genital tract of HEPS sex workers, where they were enhanced in comparison to seropositive women. Overall, while systemic CD8+ responses in HEPS women were approximately tenfold weaker than those in infected women, they also targeted different epitopes. This suggests that qualitative rather than quantitative differences may explain HIV-1 protection. Several HIV-1 ‘resistant’ sex workers became infected by HTV-1 despite pre-existing HIV-specific immune responses (CTL, CD8+ responses, and/or IgA), possibly related to the waning of HIV-specific immunity after a temporary break from sex work. These findings imply that vaccine-induced protective HIV immunity may be possible, but that vaccine strategies of boosting or persistent antigen may be necessary for long-lived protection.
Performance Analysis of CP-Reuse based Estimation Technique for High Mobility Node

Orthogonal frequency division multiplexing (OFDM) bears most desirable features that make it fit for adaptation to the breakthrough technologies, like Long Term Evolution (LTE), LTE-Adv, Internet of Things (IoT) etc. Bandwidth scarcity and high speed communication are area where most of development aligned in the current era of mobile telephony. CP-Reuse based Estimation technique gives a bandwidth effective and energy efficient solution to fulfill the demand of current mobile network expansion. Highly efficient technologies deemed fit to support multiple applications realized over OFDM to deliver high performance in terms of throughput and data speed. This paper presents the performance evaluation of CP-Reuse based Estimation technique to control Inter Carrier Interference (ICI) over a high mobility node under AWGN channel. A comparative analysis against Self-Cancellation (SC) and Maximum Likelihood Estimation (MLE) techniques demonstrates the high bandwidth gain and superior BER performance to justify the performance enhancements over high mobility environment.
Robust Beamforming Design for SWIPT-Enabled Hierarchical Cognitive Radio Networks

In this paper, we investigate the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled hierarchical cognitive radio (HCR) where the primary receiver (PR) is allowed to harvest energy when the secondary system (SS) radiates its information to the secondary receiver (SR). The design objective is to maximize the transmission rate of SS provided that the harvested energy and outage probability of the primary system (PS) are guaranteed. The optimization problem, however, is not convex due to the probability-based constraints introduced by the imperfect channel state information (CSI). To obtain the tractable solution, we apply the Bernstein-type inequality and sphere bounding so that the problem can be approximated by convex formulations. Then, the resultant problems can be efficiently solved with CVX tools. Simulation results demonstrate that the designs can effectively improve the system performance under imperfect CSI environments.
Low-Complexity Hybrid Analog-Digital Signal Detector for Uplink Multiuser Massive MIMO Systems

In this paper, we propose a low-complexity hybrid analog-digital signal detector for uplink multiuser massive multiple-input multiple-output (MIMO) systems. Specially, both the hardware cost and computation load can be reduced. The analog part consists of phase shifters only which are tuned by the idea of equal-gain combining (EGC) for each data stream. Thus, no additional optimization is required, and the number of radio-frequency (RF) chains decreases at the same time. In the digital part, the Gauss-Seidel (GS) iteration with decision is then used to extract the transmitted symbols from all uplink users. Simulation results show that our proposed hybrid processing method provides similar bit-error rate (BER) performance as compared with the fully-digital minimum mean-squared error (MMSE)/zero-forcing (ZF) detection, and is even better for some cases. When considering the complexity reduction, it is an attractive way for giving a good compromise between the error rate and complexity.
Design considerations for high-performance Time Sensitive Networking switches

This article contains the problem statement and numerical estimations regarding design of high-performance Time Sensitive Networking (TSN) switching nodes. Performance metrics and delay components are introduced and the impact of each design choice on them is investigated. An architecture supporting the TSN features of Credit Based Shaping (CBS) and Time Aware Shaper (TAS) in a high-performance input queued switch is presented, together with supporting scheduling algorithm. The paper additionally contains a set of recommendations for design of high-capacity TSN switches.
Improved Covariance Matrix Estimators by Multi-Penalty Regularization

In this paper, we deal with the problem of estimating a covariance matrix in limited observation scenarios. We revisit the regularization of Gaussian likelihood function and investigate the multi-penalty regularization strategies to improve the flexibility of covariance matrix estimators. Firstly, for an arbitrary target matrix, we jointly consider two penalty terms based on ridge type and Frobenius norm, and obtain a covariance matrix estimator in closed form through maximizing the corresponding multiply penalized log-likelihood function. Secondly, we generalize the existing regularized estimators by simultaneously employing multiple target matrices. The proposed regularized estimators enjoy various desirable statistical properties including positive definiteness (even when the dimensionality exceeds the number of observations), asymptotical unbiasedness and consistency in large sample scenarios. Moreover, we choose the involved tuning parameters in the sense of minimizing an approximate mean squared error based on cross-validation method. Some numerical simulations and an example application to direction-of-arrival estimation are provided for illustrating the performance of proposed estimators.
Development of An Adaptive Staircase System Actuated by Facial-, Object-, and Voice-Recognition

This paper details a proof-of-concept development of an adaptive staircase system-type capable of user-specific mechanical reconfigurations actuated by facial-, object-, and voice-recognition. The system is described via two variation-prototypes— developed at Technology Readiness Level 4—as instances of the same system-type. Accordingly, each prototype is informed by the same use-case considerations and requirements. Nevertheless, by means of their mechanical particulars, advantages and disadvantages specific to each variation are identified and explored. The present adaptive staircase system-type consists of two main components, one computational and the other mechanical. The computational component is built upon an inherited System Architecture previously developed and implemented by the authors. More specifically, the computational component uses Google’s TensorFlow for facial-recognition; BerryNet for multi-object detection; and VoiceIt for voice-recognition. These three cloud-compatible, -based, or -dependent recognition mechanisms are used to ascertain the identity three user-types: (1) a person without perceivable physical disabilities; (2) a person reliant on a walking-cane; and (3) a person on a wheelchair. With the exception of the first case, the computational component proceeds to actuate mechanical transformations pertinent to each variety of disabilities depending on which user-type is identified. The objective of this implementations is to present an intuitive and automated vertical mobility solution capable of supporting users with varying degrees of reduced mobility.
Riding the Waves: Decoding Asynchronous Multi-User MISO via Time-Variant Zero-Forcing

Future wireless communication systems are envisioned to meet stringent latency requirements to support control applications and machine interactions. One important step in this direction is to eliminate waiting times in the protocol stack, which can be achieved by letting multiple nodes transmit the same data concurrently just when the data becomes available. This communication paradigm essentially enables wide-range broadcasting and low-latency flooding. However, imperfect transmitter synchronization may arise in such schemes, which causes signal incoherence, which in turn prohibits reception with conventional decoders. In this paper, we introduce time-variant zero-forcing (TVZF), a novel equalization technique that deals with non-stationary interferences. In doing so, TVZF relaxes the requirements for time and frequency synchronization in concurrent multi-user transmissions. We present a receiver design for asynchronous MU-MISO with IEEE 802.11g compliant signals and evaluate it with over-the-air transmissions in a WARP SDR testbed in LOS and NLOS scenarios. The relative timing and frequency offsets between concurrent transmitters are systematically tuned to assess our receiver's performance in comparison to commodity hardware. For timing offsets up to the length of the cyclic prefix and frequency offsets up to 5 kHz, our receiver achieves average frame reception rates of 97 % and 92 % for LOS and NLOS, respectively.
The development and pilot of an instrument for measuring nurse managers’ leadership and management competencies

This paper describes the development and piloting of an instrument for measuring nurse managers’ leadership and management competencies. No previous instruments exist that comprehensively measure these dimensions. The instrument was developed in three phases. Following a literature review, the items of the instrument were created. Second, the face and content validity of the instrument were evaluated by an expert panel. Finally, the developed instrument was pilot-tested with a test survey of 22 nurse managers in two organisations. The validity and reliability of the scale was analysed using statistical methods. The resulting Nurse Managers Leadership and Management Competencies scale is a web-based self-assessment test consisting of 194 five-point Likert scale items. Competence is divided into general competence and special competence. The instrument makes it possible to evaluate nurse managers’ leadership and management competencies in public healthcare, and to obtain information for the further development of these attributes and factors.
Device and Circuit-Level Assessment of GaSb/Si Heterojunction Vertical Tunnel-FET for Low-Power Applications

This article investigates the performance of a vertically grown GaSb/Si tunnel field effect transistor (V-TFET) with a source pocket to enhance the performance of the device. The commercially available Silvaco TCAD has been used for simulating the proposed V-TFET structure. A low bandgap material, GaSb, is used in the source region for the first time to enhance the carrier tunneling through the source (GaSb)-channel (Si) heterojunction. The proposed V-TFET with a pocket shows the improved subthreshold swing (SS) of 26 mV/decade at ${V}_{{\textit {DS}}}= 0.5$ V over the V-TFET without any pocket. The effects of temperature on SS and ${I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}}$ ratio along with the analog/RF figures of merit (FOMs) are also analyzed for V-TFETs with and without a pocket. The results are also compared with some recently reported TFETs. The dc and analog/RF performances of V-TFET with a pocket are shown to be better than those of the V-TFET without a pocket and other reported TFET structures. Finally, the applications of V-TFETs with and without a pocket in designing inverter and ring oscillator circuits have been demonstrated. The dc and transient responses of the V-FET-based inverter and ring oscillator circuits have been analyzed using the Verilog-A model in the CADENCE tool.
Using Trusted Networks to Detect Anomaly Nodes in Internet of Things

The increase of Internet of Things has insert many challenges on networks studies. This kind of network suffer with problems like wide traffic, fault and monitoring problems. Regarding this, the concept of trust has gained increasing attention on academia by its comprehensiveness through the nodes behavior such as energy consumption, data transmission, and processing time. If a node has a different behavior from its neighbours it can be considered an anomalous node. In this paper, we propose a method for identifying if nodes are anomalous, using only their own monitored data by computing trust values for each node. Also, a data compression method is applied to help reduce the network traffic. This method is capable of signaling and separating anomalous data coming from different nodes in order to maintain the lowest possible interference level due to errors, frauds or malicious attacks. Our objective is to avoid errors in a posterior phase. For trust measurement we have used Subjective Logic that has been recently explored for these purposes. Experiments demonstrate that our method is feasible and help to reduce package size and spare energy.
Unsupervised Fact-finding with Multi-modal Data in Social Sensing

This paper develops unsupervised fact-finding algorithms that combine consideration of multi-modal microblog content features with analysis of propagation patterns to determine veracity of microblog observations. In contrast to prior solutions that use labeled examples to learn content features that are correlated with veracity, our approach is entirely unsupervised. Hence, given no prior training data, we jointly learn the importance of different content features together with the veracity of observations using propagation patterns as an indicator of perceived content reliability. To offer robustness, we describe fact-finding extensions that handle the existence of malicious colluding sources. We evaluate the performance of the proposed algorithms on real-world data sets collected from Twitter. The evaluation results demonstrate that the proposed algorithms outperform the existing fact-finding approaches, and offer tunable knobs for controlling robustness/performance trade-offs in the presence of malicious sources.
Machine Learning Based Information Extraction for Diabetic Nephropathy in Clinical Text Documents

Diabetic nephropathy is common complication of diabetes mellitus, it's important to intervene early. For building a predictive model for diabetic nephropathy. In order to extract relevant information as a prediction risk factor, we construct a golden standard corpus. 3422 admission summary notes from 2013 to 2017 in a tertiary hospital were included in the study. An information extraction method based on machine learning models is proposed to extract important information from unstructured medical record texts, in which Adaboost on Duration of Diabetes has best performance (F1=0.97), and Family history of heart disease extraction is most challenge, F1 value of best model result SVM is 0.73. The best performance of the other six types of information extraction model is between 0.85 and 0.96, and the practical application is feasible.