Miracle-dz/UniFAR-FTUs huggingface dataset:
Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis

Purpose To quantify the levels of performance (symptom severity) of the computer-vision symptom scale (CVSS17), confirm its bifactorial structure as detected in an exploratory factor analysis, and validate its factors as subscales. Methods By partial credit model (PCM), we estimated CVSS17 measures and the standard error for every possible raw score, and used these data to determine the number of different performance levels in the CVSS17. In addition, through discriminant analysis, we checked that the scale's two main factors could classify subjects according to these determined levels of performance. Finally, a separate Rasch analysis was performed for each CVSS17 factor to assess their measurement properties when used as isolated scales. Results We identified 5.8 different levels of performance. Discriminant functions obtained from sample data indicated that the scale's main factors correctly classified 98.4% of the cases. The main factors: Internal symptom factor (ISF) and external symptom factor (ESF) showed good measurement properties and can be considered as subscales. Conclusion CVSS17 scores defined five different levels of performance. In addition, two main factors (ESF and ISF) were identified and these confirmed by discriminant analysis. These subscales served to assess either the visual or the ocular symptoms attributable to computer use.
Joint Channel Estimation, Interference Cancellation, and Data Detection for Ambient Backscatter Communications

This paper deals with the problem of joint channel estimation, interference suppression, and data detection for ambient backscatter communications, where a small device modulates its information by reflecting the radio-frequency signals emitted by a legacy orthogonal frequency-division multiplexing access point (AP). In particular, we study the case when the intended recipient of the backscatter information is integrated into the AP. The proposed technique, which relies on the space alternating generalized expectation maximization algorithm, detects the backscatter symbols iteratively, while the channel parameters are updated in parallel, leading to a receiver that also incorporates interference cancellation capabilities. Its performance is evaluated through Monte Carlo simulations.
Research on Fault Diagnosis Method for Planetary Gear of Wind Turbine Generator Based on MCSA and EMD

Planetary gear box is the key transmission equipment of wind turbine, internal structure is complex, the problems of sensor installation and signal transmission are difficult to be solved by means of vibration diagnosis. For this reason, this paper presents a method of nondestructive fault diagnosis based on stator current analysis of wind turbine generator (MCSA), and the fault features are separated and extracted by empirical mode decomposition (EMD). A simulation experimental platform of wind turbine is built based on the analysis of the structure and typical vibration characteristics of the planetary gear. The stator current signal is decomposed by EMD in the normal and simulated fault state of the planetary gear, and spectrum and instantaneous frequency analysis are performed by extracting effective IMF components. Comparative test shows, the method of MCSA can reflect the main characteristics of complex planetary gearbox faults, and the method of EMD can also be used to separate some characteristic frequency components effectively. Therefore, the combination of these two methods is helpful to widen the application scope of MCSA.
Convolutional Neural Networks with Recurrent Neural Filters

We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as a linear affine transformation followed by a non-linear function, which fails to account for language compositionality. As a result, it limits the use of high-order filters that are often warranted for natural language processing tasks. In this work, we model convolution filters with RNNs that naturally capture compositionality and long-term dependencies in language. We show that simple CNN architectures equipped with recurrent neural filters (RNFs) achieve results that are on par with the best published ones on the Stanford Sentiment Treebank and two answer sentence selection datasets.
FPGA-based PairHMM Forward Algorithm for DNA Variant Calling

One of the main objectives of human genetic research is the identification of DNA variations that may be involved in the development of rare diseases. Thanks to advances in DNA sequencing technologies and to a progressive integration of the available genetic databases, it is now possible to study not only common variants, but also ones occurring at very low frequencies in the population. Despite the presence of consolidated algorithms to perform the analysis of genetic data, the major hurdle is the impossibility to efficiently process the data, and translate them into biologically meaningful information. This prevents the current solutions and architectures from scaling to growing number of individuals, both for the discovery of new variants and the adoption of these methodologies to support diagnosis and treatment of illnesses in a common clinical setting. In this scenario, Field Programmable Gate Arrays (FPGAs) provide a viable alternative to conventional software-based approaches. In particular, they have already proved to efficiently manage huge workloads while reaching outstanding performance over power consumption scores. Therefore, the purpose of this work is exploring novel computing paradigms to tackle the limitations we are facing. In particular, we present an FPGA-based acceleration of the PairHMM Forward Algorithm, the performance bottleneck in the HaplotypeCaller, a variant calling tool in the popular Genome Analysis Toolkit (GATK). Our final architecture is able to achieve 2160x speedup when compared to the Original Java version in GATK, outperforming existing implementation on both CPUs, GPUs and FPGAs.
Modeling and Simulation of an Underwater Planar Eel Robot in Non-inertial Frame

The obtained dynamic equations in inertial frame are highly coupled and fully nonlinear, thus those are not conductive to controller design. Inspired by Autonomous Underwater Vehicle(AUV) modeling in non-inertia frame, this paper defines the non-inertial frame that is always aligned with the eel robot. The kinematic model is derived by using Coriolis' theorem. By using improved Newton's second law and the principle of moment balance, the dynamic equations are derived, in which the hydrodynamic forces and torques take into account current effects, nonlinear drag forces, added mass forces and fluid torque effects. The established dynamic model is analytical and can be utilized in modern model-based control schemes, regardless of the number of modules. Compared with the existed model, the model established in this paper obtains the tangential velocity sub-dynamics directly and achieves the purpose of simplifying the model by the orientation angle parameter decoupling. In order to verify the accuracy of the proposed model, simulations of a eel robot with nine links are presented. Simulation results indicate the proposed model fits well with the proposed model.
Adaptively Banded Smith-Waterman Algorithm for Long Reads and Its Hardware Accelerator

In this paper, we propose hardware-compatible Adaptively Banded Smith-Waterman algorithm (ABSW) to align long genomic sequences. By utilizing banded Smith-Waterman algorithm to align subsequences of fixed lengths, ABSW finds alignment of a pair of arbitrarily long sequences with constant memory. In addition, a heuristic algorithm, dynamic overlapping, is proposed to make overlaps of bands of subsequences to improve accuracy. To enable hardware acceleration of ABSW, we further propose the hardware architecture of banded Smith-Waterman with traceback. Experiments show that ABSW produces near optimal alignment scores for sequences with up to 40% error rates. Our hardware implementation of ABSW demonstrates more than $\pmb{200}\times$ sneedun over software imnlementation.
A Preliminary Study on a Robot's Prediction of Human Intention

A wise man is said to be able to do the right thing at the right time. Thanks to the cognition, human beings are able to make reasonable judgments and then take specific actions accordingly. However, without explicit communication, even the most intelligent robot currently existing in the world may not be able to well understand the intentions of its human companion, not to mention its ability to act according to the human's intentions. In fact, “When to do? ”, “What to do? ”, and “How to do? ” may not only confuse philosophers, but also often puzzle designers of robots. In this preliminary research, we introduce a role of prediction in the context of human-robot interaction. We study how a human pitches a ball by experiment and how a robot blocks the ball accordingly by simulation. The study is to investigate how a robot perceives and learns from the motion of a human participant. This paper proved that with machine learning techniques, a robot is able to accurately predict the human participant's intention of pitching. We also demonstrate how the learned knowledge can enhance the robot's intelligence and action.
A Global Localization System for Mobile Robot Using LIDAR Sensor

This paper demonstrates a set of approaches addressing mobile robot localization problems. The approaches can operate efficiently in 2D occupancy grid map created by robot which is equipped with LIDAR sensor. Given a complex environment and a known map, the robot could be placed at anywhere initially and the global localization system described in this paper can be applied to startup stage in which robot can realize its initial location and orientation in the map without any movement. In the following stage, the system can track the moving robot's position in real time. Thus as long as the robot start the navigation process, the robot system knows where the robot is at the beginning and the robot can be navigated to a reachable place directly. Combined with other sensors, such as cliff sensor, the robot can perceive the kidnapped problem, and then relocate the position to recover from global localization failures. The experimental results show that compared with appointing the initial position and orientation manually, it costs seconds in startup stage to figure out the robot's initial pose in the map, and the system can update the robot's position in motion in real time.
한국판 대학상담센터용 정신건강 평가 척도 (Counseling Center Assessment of Psychological Symptoms) 타당화 연구

본 연구에서는 미국 대학상담센터에서 대학생 내담자의 정신건강척도로써 널리 사용되고 그 유용성을 인정받은 대학상담센터용 정신건강 평가척도-단축형(Counseling Center Assessment of Psychological Symptoms-34; CCAPS-34)의 요인구조와 타당도를 검증하였다. 이를 위해, 먼저 대학상담센터의 내담자190명에게 1회기 전에 한국어로 번안한 CCAPS-34를 실시하여 탐색적 요인분석을 실시한 결과, 한국판 CCAPS는 6개 하위 요인, 27문항이 적절한 것으로 나타났다. 6개 하위요인은 부정정서, 알코올사용 문제, 적대감, 섭식문제, 학업효능감 문제, 사회불안이었다. 다음으로, 대학상담센터의 내담자 178명을 대상으로 확인적 요인분석과 수렴타당도를, 일반 대학생 46명을 대상으로 2주 간격 검사-재검사 신뢰도를 살펴보았다. 그 결과, 한국판 CCAPS 6요인 구조가 지지되었고, 2주 간격 검사-재검사 신뢰도에서 높은 일관성을 확인하였다. 본 연구의 결과는 대학상담센터에서 간편하게 내담자의 정신건강을 측정할 수 있는 도구를 다듬었다는 데에 그 의의가 있다. 본 연구의 상담적 시사점, 한계점 및 후속 연구에 대한 제안점 등을 논의하였다.
Omni-Directional Perception Based on Multiple Cameras: A Feasible Solution to the Limited Sensing of Wireless Multimedia Sensor Networks

In recent years, wireless multimedia sensor networks (WMSNs) attract great attention. Generally, WMSNs node is equipped with only one single camera for visual observation. In order to achieve wider view-angle, existing solutions, such as node rotation and view-angle adjustment, always cause high cost, high energy consumption and non-real-time response. In this paper, we propose a feasible solution by utilizing four wide-angle cameras to form omni-directional visual sensing for WMSNs node. The omni-directional probability sensing model is analyzed and defined. With DSP processor, we carry out the system architecture in details. Energy consumption is analyzed by comparing with rotatable camera. To our best knowledge, it is the first time that omni-directional vision with multiple cameras is introduced for WMSNs node to monitor the surrounding environment in real time. Experiment results show that the proposed omni-directional sensing can achieve better performance on deployment and energy cost than traditional WMSNs node.
Knee osteoarthritis at the early stage: The four-week effect of lateral wedge insole on pain and risk of falls

Background: Knee osteoarthritis (KOA) is associated with a decrease in function, increase in pain and risk of falls. Lateral wedge insole (LWI) is commonly prescribed in KOA to improve pain and function. Our study aimed to 1) evaluate the clinical symptoms and risk of falls in early KOA and compare with controls; 2) evaluate the immediate and four-week effect of LWI. Methods: A sample of 20 Persian dwelling individuals with early KOA and 19 matched controls were recruited. Pain with Visual Analogue Scale (VAS), Quality of life (QOL) with the knee injury and osteoarthritis outcome score, risk of falls with the Timed Up and Go (TUG) and static One-leg Balance (OLB) tests were assessed. The four-week effect of 5º LWI was considered for individuals with KOA. Independent t-test was done to report the between-group differences, and paired t-test was used to report the four-week effect of LWI. Results: At baseline, statistically significant higher scores for pain, lower scores for QOL, and higher risk of falls were observed in KOA compared to controls (p< 0.001). A significant statistical decrease was observed in pain, and risk of falls, and an increase in QOL in KOA after four-week effect of LWI compared to baseline (p< 0.001). Conclusion: People with early KOA showed higher pain and lower level of QOL that were associated with higher risk of falls. LWI may have the potential to improve clinical symptoms and reduce the risk of falls at the early stage of KOA.
Shuffle based Anomaly Detection in Multi-state System

The anomaly events are defined as the points that rare and diverse from the other points in feature space. Conventional anomaly detection methods usually find low-probability events with a learned a probability distribution model, or evaluate the testing samples with the local density of the testing samples. Multi-state system usually has multiple normal states, and state changes at unpredictable points caused by the daily operation such as feed, outlet, flow control, etc. For the multistate system, collecting enough data that contain all possible states are challenging or impossible to users. Furthermore, conventional anomaly detection methods are sensitive to the context of training datasets or the unpredicted phased changes of the testing datasets, or just consider the local density of the testing samples. Motivated by this problem, we transform the model learning problem to a distinction learning problem that learns the familiarity of each testing samples. In order to reduce the effects of the phased changes, we randomly shuffle the testing dataset and use a sliding window to evaluate the familiarity of the testing samples with one-class Support Vector Machine (SVM) method. Our contributions include: (1) reducing the requirement of the prior knowledge; (2) handling the phased changes of the testing datasets, (3) considering the global familiarity of the testing samples. Our proposed method is evaluated on the synthetic datasets, and the real datasets, and experiments results show that our proposed method is superior than the state-of-the-art methods.
Bacterial Biohybrid Microswimmers

Over millions of years, Nature has optimized the motion of biological systems at the micro and nanoscales. Motor proteins to motile single cells have managed to overcome Brownian motion and solve several challenges that arise at low Reynolds numbers. In this review, we will briefly describe naturally motile systems and their strategies to move, starting with a general introduction that surveys a broad range of developments, followed by an overview about the physical laws and parameters that govern and limit motion at the microscale. We characterize some of the classes of biological microswimmers that have arisen in the course of evolution, as well as the hybrid structures that have been constructed based on these, ranging from Montemagno's ATPase motor to the SpermBot. Thereafter, we maintain our focus on bacteria and their biohybrids. We introduce the inherent properties of bacteria as a natural microswimmer and explain the different principles bacteria use for their motion. We then elucidate different strategies that have been employed for the coupling of a variety of artificial microobjects to the bacterial surface, and evaluate the different effects the coupled objects have on the motion of the “biohybrid. ” Concluding, we give a short overview and a realistic evaluation of proposed applications in the field.
Give Me Some Slack: Efficient Network Measurements

Many networking applications require timely access to recent network measurements, which can be captured using a sliding window model. Maintaining such measurements is a challenging task due to the fast line speed and scarcity of fast memory in routers. In this work, we study the impact of allowing \emph{slack} in the window size on the asymptotic requirements of sliding window problems. That is, the algorithm can dynamically adjust the window size between $W$ and $W(1+\tau)$ where $\tau$ is a small positive parameter. We demonstrate this model's attractiveness by showing that it enables efficient algorithms to problems such as MAX and GENERAL-SUM that require $\Omega(W)$ bits even for constant factor approximations in the exact sliding window model. Additionally, for problems that admit sub-linear approximation algorithms such as BASIC-SUMMING and COUNT-DISTINCT, the slack model enables a further asymptotic improvement.
Effects of task-specific movement patterns during resistance exercise on the respiratory functions and thickness of abdominal muscles of children with cerebral palsy: randomized placebo-controlled double-blinded clinical trial

[Purpose] This study was conducted to examine the effects of task-specific movement patterns during resistance exercise program, which are applied to children with cerebral palsy, on respiratory functions and thickness of abdominal muscles. [Participants and Methods] This study was conducted with randomized double-blinded controlled research was pursued since it is a clinical trial with minors with disabilities as the participants. Seventeen children with cerebral palsy were randomly allocated to both experimental group and placebo group by means of simple randomized sampling. The experimental group wore weighted vest to which loaded-resistance was applied by means of sand bag while the placebo group wore weighted vest without loaded-resistance. Task-specific movement patterns during resistance exercise were performed for 40 minutes 2 times a week over a period of 12 weeks for the participants in both groups. Differences in respiratory functions and thickness of abdominal muscles measured prior to and after 12 weeks of the experiment were compared. [Results] All the measurement values for the respiratory functions and abdominal muscle thickness displayed statistically significant changes between those prior to and after the exercise in both of the experimental group and the placebo group. There were statistically significant differences in the changes prior to and following the exercise between the two groups. [Conclusion] Therefore, task-specific movement patterns in anatomical plane, diagonal patterns and combined forms during resistance exercise program on for children with cerebral palsy can be considered as an efficient intervention method in improving respiratory capacity.
Interference Mitigation Via Rate-Splitting in Cloud Radio Access Networks

Cloud-radio access networks (C-RAN) help overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs), and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, and evaluates rate-splitting (RS) and common-message decoding techniques, as a means to enable large-scale interference management. To this end, the paper proposes splitting the message of each user at the CP into a private part decodable at one user, and a common part decodable at a subset of users for the sole purpose of interference mitigation. The paper then focuses on maximizing the weighted sum-rate subject to backhaul capacity and transmission power constraints, so as to determine the RS mode of each user, and the associated beamforming vectors. The paper proposes solving such a complicated non-convex optimization problem using an inner-convex approximation approach, which guarantees achieving a stationary solution to the problem. Numerical results show that the proposed method provides significant gain compared to classical interference mitigation techniques that do not rely on RS and common message decoding.
Operation of the Classical CMOS Schmitt Trigger As an Ultra-Low-Voltage Amplifier

In this brief, we analyze the operation of the classical Schmitt trigger (ST) circuit as an ultra-low-voltage (ULV) amplifier. Small-signal analysis is employed in order to determine optimum relative transistor sizes, which maximize the voltage gain. A comparison of the classical ST with the standard inverter highlights the relative benefits and drawbacks of each for operation as an amplifier in ULV applications. It is shown that the theoretical minimum supply voltage of the ST for voltage amplification (absolute voltage gain ≥ 1) is 31.5 mV at 300 K, which is below the well-known 36 mV limit of the standard inverter. Two test chips in a 180 nm technology with ST circuits built from transistors with different relative sizes were fabricated. For ${\boldsymbol {V}}_{\mathrm{ DD}} = 60$ mV and 100 mV, the values measured for the voltage gain of the ST were 6 and 32 dB, respectively, which are significantly higher than the values of 4.4 and 15 dB simulated for the standard inverter at the same supply voltages.
Solving Traveling Salesman Problem with Hybrid Estimation of Distribution Algorithm

A hybrid distribution estimation algorithm for traveling salesman problem is proposed. Firstly, based on the distributed estimation algorithm, a new effective probability model is proposed to solve the traveling salesman problem. Secondly, in order to speed up the optimization of the algorithm to prevent the algorithm falling into the local optimal, the extreme optimization algorithm is combined to form a hybrid distribution estimation algorithm to improve the effectiveness of the algorithm. Then through the public TSPLIB data set, it is proved that the hybrid distribution estimation algorithm is effective, and the algorithm can solve this kind of problem well. Finally, a new idea is proposed to verify the validity of the traveling salesman problem, and the algorithm is tested by the proposed algorithm. The experimental results show that the proposed hybrid distribution estimation algorithm has a good performance in solving the traveling salesman problem.