nils-herrmann/clean_scirepeval huggingface dataset:
Speech Emotion Recognition with Emotion-Pair Based Framework Considering Emotion Distribution Information in Dimensional Emotion Space

In this work, an emotion-pair based framework is proposed for speech emotion recognition, which constructs more discriminative feature subspaces for every two different emotions (emotion-pair) to generate more precise emotion bi-classification results. Furthermore, it is found that in the dimensional emotion space, the distances between some of the archetypal emotions are closer than the others. Motivated by this, a Naive Bayes classifier based decision fusion strategy is proposed, which aims at capturing such useful emotion distribution information in deciding the final emotion category for emotion recognition. We evaluated the classification framework on the USC IEMOCAP database. Experimental results demonstrate that the proposed method outperforms the hierarchical binary decision tree approach on both weighted accuracy (WA) and unweighted accuracy (UA). Moreover, our framework possesses the advantages that it can be fully automatically generated without empirical guidance and is easier to be parallelized.
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective

Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed.
High-voltage high-frequency inverter using 3.3kV SiC MOSFETs

High voltage SiC MOSFETs enable high switching frequency operation that would otherwise be only possible with more complex architectures such as multilevel or interleaved inverters. In this paper, a full bridge inverter using 3.3kV SiC MOSFETs is presented to achieve high-voltage (2100V dc bus), high-frequency (62.5kHz) operation with a simple hard switching PWM technique. The switching characteristics of the 3.3kV SiC device are presented and the effect of the parasitic parameters is analyzed in details. The analysis shows that the tiny stray output capacitor, which is mainly introduced by the bus-bars implemented with a PCB, can have a significant impact on the switching waveforms of the SiC devices. The reported experimental results validate the performance of the proposed inverter.
Packet Injection Attack and Its Defense in Software-Defined Networks

Software-defined networks (SDNs) are novel networking architectures that decouple the network control and forwarding functions from the data plane. Unlike traditional networking, the control logic of SDNs is implemented in a logically centralized controller which provides a global network view and open programming interface to the applications. While SDNs have become a hot topic among both academia and industry in recent years, little attention has been paid on the security aspect. In this paper, we introduce a novel attack, namely, packet injection attack, in SDNs. By maliciously injecting manipulated packets into SDNs, attackers can affect the services and networking applications in the control plane, and largely consume the resources in the data plane. The consequences could be the disruption of applications built on the top of the topology manager service and rest API, as well as a huge consumption of network resources, such as the bandwidth of the OpenFlow channel. To defend against the packet injection attack, we present PacketChecker, a lightweight extension module on SDN controllers to effectively detect and mitigate the flooding of falsified packets. We implement a prototype of PacketChecker in floodlight controller and conduct experiments to evaluate the efficiency of the defense mechanism. The evaluation shows that the PacketChecker module can effectively mitigate the attack with a minor overhead to the SDN controller.
An architecture of diversity for commonsense reasoning

Although computers excel at certain bounded tasks that are difficult for humans, such as solving integrals, they have difficulty performing commonsense tasks that are easy for humans, such as understanding stories. In this Technical Forum contribution, we discuss commonsense reasoning and what makes it difficult for computers. We contend that commonsense reasoning is too hard a problem to solve using any single artificial intelligence technique. We propose a multilevel architecture consisting of diverse reasoning and representation techniques that collaborate and reflect in order to allow the best techniques to be used for the many situations that arise in commonsense reasoning. We present story understanding—specifically, understanding and answering questions about progressively harder children’s texts—as a task for evaluating and scaling up a commonsense reasoning system.
Smart Cameras as Embedded Systems

I ncreasingly powerful integrated circuits are making an entire range of new applications possible. Complementary metal-oxide semiconductor (CMOS) sensors, for example, have made the digital camera a commonplace consumer item. These light-sensitive chips, positioned where film would normally be, capture images as reusable digital files that users can upload to their computer, manipulate with software, and distribute electronically. Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Fortunately, Moore's law provides an increasing pool of available computing power to apply to real-time analysis. Smart cameras leverage very large-scale integration (VLSI) to provide such analysis in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these systems run a wide range of algorithms to extract meaning from streaming video. Because they push the design space in so many dimensions, smart cameras are a leading-edge application for embedded system research. has developed a first-generation smart camera system that can detect people and analyze their movement in real time. Although there are many approaches to real-time video analysis, we chose to focus initially on human gesture recognition—identifying whether a subject is walking, standing, waving his arms, and so on. Because much work remains to be done on this problem, we sought to design an embedded system that can incorporate future algorithms as well as use those we created exclusively for this application. As Figure 1 shows, our algorithms use both low-level and high-level processing. The low-level component identifies different body parts and categorizes their movement in simple terms. The high-level component, which is application-dependent, uses this information to recognize each body part's action and the person's overall activity based on scenario parameters. The system captures images from the video input, which can be either uncompressed or compressed (MPEG and motion JPEG), and applies four different algorithms to detect and identify human body parts. Region extraction. The first algorithm transforms the pixels of an image, like that shown in Figure 2a, into an M × N bitmap and eliminates the background. It then detects the body part's skin area using a YUV …
A survey of frequent subgraph mining algorithms

Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.
Linking received packet to the transmitter through physical-fingerprinting of controller area network

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.
A Content-Based User Model Generation and Optimization Approach for Movie Recommendation

Personalization has become a powerful approach for constructing more precise and easy to use information search and recommendation systems. The quality of the personalization is heavily dependent on the accuracy of the user models created by the system and it is very important to incorporate content information of the working domain in order to enrich these models. This paper proposes a content based movie recommendation algorithm to make recommendations for the target user through building content based user models from collaborative-based user models and characteristics of the movie domain. Constructed user models are fine-tuned through “highly liked”, “highly not liked”, and “don’t care” flags. The user models are presented to the users in terms of the most important features and dimensions in their profile. This makes explicit the users’ implicit and unknown preferences of the movie domain. The system is evaluated and the results are presented using decision-support metrics.
A 5Gb/s low-power PCI express/USB3.0 ready PHY in 40nm CMOS technology with high-jitter immunity

A PCI Express 2.0/1.0 compatible SERDES system had been fabricated in TSMC 40nm CMOS technology. With the implementation of one lane transceiver, PLL, and PCS, the experimental results have shown this test chip passes PCI Express 2.0/1.0 TX compliance test and RX compliance test. It also achieves receiver jitter tolerance up to 0.33UI at BER of 10-12 with stressing all spec. specified jitter sources. A compact area of 510um×710um for one lane has been achieved while consuming only 125mW from 0.9V supply.
Theory application by school-based occupational therapists.

Recent literature indicates that there is an inconsistent use of theory to guide clinical actions by occupational therapists, including those working in pediatrics. The purpose of this study was to describe school-based therapists' theory application by collecting information about what frames of reference they used and why.



Of the 72 school-based therapists in the mid-Atlantic states who agreed to respond to a questionnaire, 51 (70.8%) returned the questionnaire. Information about demographics, what frames of reference were used, and why they were used was obtained from the questionnaire.



Respondents reported using a multitheoretical approach, with sensory integration theory and neurodevelopmental theory being the predominate frames of reference applied but not the only ones used. The frames of reference were used on the basis of several factors, including the children's needs and the respondent's education.



Formal and continuing education seems to have a great effect on school-based occupational therapists as they develop their personal conceptual frameworks.
REVISED NOTE ON LEARNING QUADRATIC ASSIGNMENT WITH GRAPH NEURAL NETWORKS

Inverse problems correspond to a certain type of optimization problems formulated over appropriate input distributions. Recently, there has been a growing interest in understanding the computational hardness of these optimization problems, not only in the worst case, but in an average-complexity sense under this same input distribution.In this revised note, we are interested in studying another aspect of hardness, related to the ability to learn how to solve a problem by simply observing a collection of previously solved instances. These ‘planted solutions’ are used to supervise the training of an appropriate predictive model that parametrizes a broad class of algorithms, with the hope that the resulting model will provide good accuracy-complexity tradeoffs in the average sense.We illustrate this setup on the Quadratic Assignment Problem, a fundamental problem in Network Science. We observe that data-driven models based on Graph Neural Networks offer intriguingly good performance, even in regimes where standard relaxation based techniques appear to suffer.
Mayer-Rokitansky-Kuster-Hauser Syndrome: Embryology, Genetics and Clinical and Surgical Treatment

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is a pathological condition characterized by primary amenorrhea and infertility and by congenital aplasia of the uterus and of the upper vagina. The development of secondary sexual characters is normal as well as that the karyotype (46,XX). Etiologically, this syndrome may be caused by the lack of development of the Müllerian ducts between the fifth and the sixth weeks of gestation. To explain this condition, it has been suggested that in patients with MRKH syndrome, there is a very strong hyperincretion of Müllerian-inhibiting factor (MIF), which would provoke the lack of development of the Müllerian ducts from primitive structures (as what normally occurs in male phenotype). These alterations are commonly associated with renal agenesis or ectopia. Specific mutations of several genes such as WT1, PAX2, HOXA7-HOXA13, PBX1, and WNT4 involved in the earliest stages of embryonic development could play a key role in the etiopathogenesis of this syndrome. Besides, it seems that the other two genes, TCF2 (HNF1B) and LHX1, are involved in the determinism of this pathology. Currently, the most widely nonsurgical used techniques include the "Frank's dilators method," while the surgical ones most commonly used are those developed by McIndoe, Williams, Vecchietti, Davydov, and Baldwin.
Exploring New Backbone and Attention Module for Semantic Segmentation in Street Scenes

Semantic segmentation, as dense pixel-wise classification task, played an important tache in scene understanding. There are two main challenges in many state-of-the-art works: 1) most backbone of segmentation models that often were extracted from pretrained classification models generated poor performance in small categories because they were lacking in spatial information and 2) the gap of combination between high-level and low-level features in segmentation models has led to inaccurate predictions. To handle these challenges, in this paper, we proposed a new tailored backbone and attention select module for segmentation tasks. Specifically, our new backbone was modified from the original Resnet, which can yield better segmentation performance. Attention select module employed spatial and channel self-attention mechanism to reinforce the propagation of contextual features, which can aggregate semantic and spatial information simultaneously. In addition, based on our new backbone and attention select module, we further proposed our segmentation model for street scenes understanding. We conducted a series of ablation studies on two public benchmarks, including Cityscapes and CamVid dataset to demonstrate the effectiveness of our proposals. Our model achieved a mIoU score of 71.5% on the Cityscapes test set with only fine annotation data and 60.1% on the CamVid test set.
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks

Alzheimer’s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer’s disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is exacting due to the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.
Millimeter-wave antenna in package on low-cost organic substrate for flip chip chip scale (FCCSP) package

This work proposes a broadband microstrip patch antenna array with H-shaped aperture-coupled stacked rectangular patches is implemented to low-cost and low-loss organic substrate for flip chip chip scale (FCCSP) package for millimeter-wave antenna in package (AiP) application. The proposed antenna array is built on a low-cost and low-loss multilayer organic substrate for flip chip chip scale (FCCSP) package. The return loss of < −10 dB bandwidth of the proposed antenna array is from 55 to 69 GHz, which covers the requirement of IEEE 802.11ad standard. The cross-polarization levels in both and planes are better than 30 dB. The front-to-back ratio of the antenna radiation pattern is better than 20 dB. The prospoed microstrip patch antenna can achieve a wide bandwidth, low cross-polarization levels, low backward radiation levels, and easy integration with radio frequency (RF) circuits. This work provides a cost-effective approach for fifth-generation mobile communications (5G), IEEE 802.11ad, such as smart phone, laptops, and wireless router.
0.9–10GHz low noise amplifier with capacitive cross coupling

This paper presents a 0.9GHz-10GHz Ultra Wideband Low Noise Amplifier (LNA) designed for software-defined-radios (SDR). Capacitive cross coupling (CCC) is used at both input stage and cascade stage for wideband input impedance matching and small noise figure (NF). A combination of inductor peaking load and series inductor between the cascade stages of LNA is employed for a flat gain and enhanced input matching. This LNA is implemented in a standard 0.18µm CMOS technology. For 0.9GHz-10GHz ultra wideband applications, it achieves a maximum gain of 14dB, 3.5dB∼4.1dB NF and +1.8dBm IIP3. It consumes only7.6mW from a 1.8V power supply.
Vertical-Edge-Based Car-License-Plate Detection Method

This paper proposes a fast method for car-license-plate detection (CLPD) and presents three main contributions. The first contribution is that we propose a fast vertical edge detection algorithm (VEDA) based on the contrast between the grayscale values, which enhances the speed of the CLPD method. After binarizing the input image using adaptive thresholding (AT), an unwanted-line elimination algorithm (ULEA) is proposed to enhance the image, and then, the VEDA is applied. The second contribution is that our proposed CLPD method processes very-low-resolution images taken by a web camera. After the vertical edges have been detected by the VEDA, the desired plate details based on color information are highlighted. Then, the candidate region based on statistical and logical operations will be extracted. Finally, an LP is detected. The third contribution is that we compare the VEDA to the Sobel operator in terms of accuracy, algorithm complexity, and processing time. The results show accurate edge detection performance and faster processing than Sobel by five to nine times. In terms of complexity, a big-O-notation module is used and the following result is obtained: The VEDA has less complexity by K2 times, whereas K2 represents the mask size of Sobel. Results show that the computation time of the CLPD method is 47.7 ms, which meets the real-time requirements.
The Modified Abbreviated Math Anxiety Scale: A Valid and Reliable Instrument for Use with Children

Mathematics anxiety (MA) can be observed in children from primary school age into the teenage years and adulthood, but many MA rating scales are only suitable for use with adults or older adolescents. We have adapted one such rating scale, the Abbreviated Math Anxiety Scale (AMAS), to be used with British children aged 8-13. In this study, we assess the scale's reliability, factor structure, and divergent validity. The modified AMAS (mAMAS) was administered to a very large (n = 1746) cohort of British children and adolescents. This large sample size meant that as well as conducting confirmatory factor analysis on the scale itself, we were also able to split the sample to conduct exploratory and confirmatory factor analysis of items from the mAMAS alongside items from child test anxiety and general anxiety rating scales. Factor analysis of the mAMAS confirmed that it has the same underlying factor structure as the original AMAS, with subscales measuring anxiety about Learning and Evaluation in math. Furthermore, both exploratory and confirmatory factor analysis of the mAMAS alongside scales measuring test anxiety and general anxiety showed that mAMAS items cluster onto one factor (perceived to represent MA). The mAMAS provides a valid and reliable scale for measuring MA in children and adolescents, from a younger age than is possible with the original AMAS. Results from this study also suggest that MA is truly a unique construct, separate from both test anxiety and general anxiety, even in childhood.