nils-herrmann/clean_scirepeval huggingface dataset:
Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network

Silent speech recognition SSR converts nonaudio information such as articulatory movements into text. SSR has the potential to enable persons with laryngectomy to communicate through natural spoken expression. Current SSR systems have largely relied on speaker-dependent recognition models. The high degree of variability in articulatory patterns across different speakers has been a barrier for developing effective speaker-independent SSR approaches. Speaker-independent SSR approaches, however, are critical for reducing the amount of training data required from each speaker. In this paper, we investigate speaker-independent SSR from the movements of flesh points on tongue and lips with articulatory normalization methods that reduce the interspeaker variation. To minimize the across-speaker physiological differences of the articulators, we propose Procrustes matching-based articulatory normalization by removing locational, rotational, and scaling differences. To further normalize the articulatory data, we apply feature-space maximum likelihood linear regression and i-vector. In this paper, we adopt a bidirectional long short-term memory recurrent neural network BLSTM as an articulatory model to effectively model the articulatory movements with long-range articulatory history. A silent speech dataset with flesh-point articulatory movements was collected using an electromagnetic articulograph from 12 healthy and two laryngectomized English speakers. Experimental results showed the effectiveness of our speaker-independent SSR approaches on healthy as well as laryngectomy speakers. In addition, BLSTM outperformed the standard deep neural network. The best performance was obtained by the BLSTM with all the three normalization approaches combined.
Facial Expression Analysis

The face is one of the most powerful channels of nonverbal communication. Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 15 years, there has been increasing interest in automated facial expression analysis within the computer vision and machine learning communities. This chapter reviews fundamental approaches to facial measurement by behavioral scientists and current efforts in automated facial expression recognition. We consider challenges, review databases available to the research community, approaches to feature detection, tracking, and representation, and both supervised and unsupervised learning.  Facial expression analysis, Action unit recognition, Active Appearance Models, temporal clustering.
Total vaginectomy and urethral lengthening at time of neourethral prelamination in transgender men

For transgender men (TGM), gender-affirmation surgery (GAS) is often the final stage of their gender transition. GAS involves creating a neophallus, typically using tissue remote from the genital region, such as radial forearm free-flap phalloplasty. Essential to this process is vaginectomy. Complexity of vaginal fascial attachments, atrophy due to testosterone use, and need to preserve integrity of the vaginal epithelium for tissue rearrangement add to the intricacy of the procedure during GAS. We designed the technique presented here to minimize complications and contribute to overall success of the phalloplasty procedure. After obtaining approval from the Institutional Review Board, our transgender (TG) database at the University of Miami Hospital was reviewed to identify cases with vaginectomy and urethral elongation performed at the time of radial forearm free-flap phalloplasty prelamination. Surgical technique for posterior vaginectomy and anterior vaginal wall-flap harvest with subsequent urethral lengthening is detailed. Six patients underwent total vaginectomy and urethral elongation at the time of radial forearm free-flap phalloplasty prelamination. Mean estimated blood loss (EBL) was 290 ± 199.4 ml for the vaginectomy and urethral elongation, and no one required transfusion. There were no intraoperative complications (cystotomy, ureteral obstruction, enterotomy, proctotomy, or neurological injury). One patient had a urologic complication (urethral stricture) in the neobulbar urethra. Total vaginectomy and urethral lengthening procedures at the time of GAS are relatively safe procedures, and using the described technique provides excellent tissue for urethral prelamination and a low complication rate in both the short and long term.
Performance evaluation in obstacle avoidance

No quantitative procedure currently exists to evaluate the obstacle avoidance capabilities of robotic systems. Such an evaluation method is not only needed to compare different avoidance methods, but also to determine the operational limits of autonomous systems. This work proposes an evaluation framework which can find such limits. The framework comprises two types of tests: detection tests and avoidance tests. For each type, both environment and performance metrics need to be defined. For detection tests such metrics are well known, but for avoidance tests such metrics are not readily available. Therefore a new set of metrics is proposed. The framework is applied to a UAV that uses stereo vision to detect obstacles. Three different avoidance methods are compared in environments of varying difficulty.
Men's voices and women's choices

I investigated the relationship between male human vocal characteristics and female judgements about the speaker. Thirty-four males were recorded uttering five vowels and measures were taken, from power spectrums, of the first five harmonic frequencies, overall peak frequency and formant frequencies (emphasized, resonance, frequencies within the vowel). Male body measures were also taken (age, weight, height, and hip and shoulder width) and the men were asked whether they had chest hair. The recordings were then played to female judges, who were asked to rate the males' attractiveness, age, weight and height, and to estimate the muscularity of the speaker and whether he had a hairy chest. Men with voices in which there were closely spaced, low-frequency harmonics were judged as being more attractive, older and heavier, more likely to have a hairy chest and of a more muscular body type. There was no relationship between any vocal and body characteristic. The judges' estimates were incorrect except for weight. They showed extremely strong agreement on all judgements. The results imply that there could be sexual selection through female choice for male vocal characteristics, deeper voices being preferred. However, the function of the preference is unclear given that the estimates were generally incorrect.
A Facial Pore Aided Detection System Using CNN Deep Learning Algorithm

Many people are concerned about their facial skin maintenance. Rough pore is one of the facial skin problems which annoyed many people. The size of facial pore is tiny, and it has various shapes. Therefore, it is difficult to recognize facial pore by using traditional image processing. In this paper, we propose an approach based on convolutional neural networks (CNNs) to develop a facial pore aided detection system. We use the LeNet−5 model as our benchmark architecture, and investigate the performance of different depths network on our facial pore dataset. The facial pore aided detection system will help people understand more about their facial skin problems and properly keep their facial skin well.
Advanced 0.13um smart power technology from 7V to 70V

This paper presents BCD process integrating 7V to 70V power devices on 0.13um CMOS platform for various power management applications. BJT, Zener diode and Schottky diode are available and non-volatile memory is embedded as well. LDMOS shows best-in-class specific Ron (R<sub>SP</sub>) vs. BV<sub>DSS</sub> characteristics (i.e., 70V NMOS has R<sub>SP</sub> of 69mΩ-mm<sup>2</sup> with BV<sub>DSS</sub> of 89V). Modular process scheme is used for flexibility to various requirements of applications.
Joint Application of Speech and Speaker Recognition for Automation and Security in Smart Home

This paper describes the deployment of speech technologies in STARHome, a fully functional smart home prototype. We make use of speech and speaker recognition technologies to provide three voice services, namely, voice command for controlling home appliances, voice biometric for entrancedoor access control, and service customization (speaker-loaded command control). Voice applications for STARHome have been designed to deal with short utterances and low SNR.
Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression.

This study takes advantage of continuing advances in the precision of magnetic resonance imaging (MRI) to quantify hippocampal volumes in a series of human subjects with a history of depression compared with controls. We sought to test the hypothesis that both age and duration of past depression would be inversely and independently correlated with hippocampal volume. A sample of 24 women ranging in age from 23 to 86 years with a history of recurrent major depression, but no medical comorbidity, and 24 case-matched controls underwent MRI scanning. Subjects with a history of depression (post-depressed) had smaller hippocampal volumes bilaterally than controls. Post-depressives also had smaller amygdala core nuclei volumes, and these volumes correlated with hippocampal volumes. In addition, post-depressives scored lower in verbal memory, a neuropsychological measure of hippocampal function, suggesting that the volume loss was related to an aspect of cognitive functioning. In contrast, there was no difference in overall brain size or general intellectual performance. Contrary to our initial hypothesis, there was no significant correlation between hippocampal volume and age in either post-depressive or control subjects, whereas there was a significant correlation with total lifetime duration of depression. This suggests that repeated stress during recurrent depressive episodes may result in cumulative hippocampal injury as reflected in volume loss.
Data Mining With Computational Intelligence

In recent years, data mining has been one of the hotter topics in computing due to the accumulation of large volumes of data carrying valuable but nonetheless hidden information. Short of being a new discipline equipped with a new core of knowledge, data mining often builds on an interdisciplinary bundle of specialized techniques from fields such as statistics, artificial intelligence, machine learning, data bases, pattern recognition, computer-based visualization, and, more recently, also from the computational intelligence (CI) field that encompasses neural, fuzzy, and genetic approaches to data analysis.  Mining with Computational Intelligence by L. Wang and X. Fu enables readers, including both intermediate and more experienced data analysts, to understand data mining processes and improve related practices at many steps of model design and its implementation using CI methods. As shown in this book, the aim of data mining techniques is to discover hidden information from data sets and to present it in an understandable way. This book reviews main data mining stages, such as data preprocessing, data dimensionality reduction, data modeling, classification, elements of knowledge discovery, and linguistic rule extraction. Data Mining with Computational Intelligence features ten chapters and systematically presents how to utilize fuzzy neural networks, multilayer perceptron (MLP) neural networks, radial basis function (RBF) neural networks, wavelet transformation, genetic algorithms, and support vector machines (SVMs) in data mining tasks. In the introductory chapter, data mining tasks including data dimensionality reduction, classification and clustering, and rule extraction are
A novel PSIM and Matlab co-simulation approach to particle swarm optimization tuning of PID controllers

Particle swarm optimization (PSO) has proven to be a particularly popular evolutionary algorithm for the tuning of PID controllers, due to its relative simplicity and suitability as a function approximator. This paper presents a new modeling technique, featuring co-simulation between Matlab and PSIM, a specialist control and power electronics simulation environment. The tuned PID is applied to a buck converter, driven by a PV cell. The novel solution is compared with and evaluated against a system which is tuned using a traditional method. The simulation results indicate that the controller developed using the PSO algorithm achieves a faster response time without adversely affecting peak errors. There are added benefits of this new approach, in terms of a shorter time scale for conceptual model development.
Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning

Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community. A considerable amount of videos on the web are associated with rich but noisy contextual information, such as the title, which provides weak annotations or labels about the video content. To leverage the big noisy web labels, this paper proposes a novel method called WEbly-Labeled Learning (WELL), which is established on the state-of-the-art machine learning algorithm inspired by the learning process of human. WELL introduces a number of novel multi-modal approaches to incorporate meaningful prior knowledge called curriculum from the noisy web videos. To investigate this problem, we empirically study the curriculum constructed from the multimodal features of the videos collected from YouTube and Flickr. The efficacy and the scalability of WELL have been extensively demonstrated on two public benchmarks, including the largest multimedia dataset and the largest manuallylabeled video set. The comprehensive experimental results demonstrate that WELL outperforms state-of-the-art studies by a statically significant margin on learning concepts from noisy web video data. In addition, the results also verify that WELL is robust to the level of noisiness in the video data. Notably, WELL trained on sufficient noisy web labels is able to achieve a comparable accuracy to supervised learning methods trained on the clean manually-labeled data.
Agonal sequences in a filmed suicidal hanging: analysis of respiratory and movement responses to asphyxia by hanging.

The forensic literature on the pathophysiology of human hanging is still limited. Therefore, forensic pathologists often feel uncomfortable when confronted with related questions. Here presented is the filmed suicidal hanging of a 37-year-old man. This recording allows a unique analysis of agonal movement sequences: loss of consciousness (13 sec), convulsions (15 sec), decortication rigidity (21 sec), decerebration rigidity (46 sec), second decortication rigidity (1 min 11 sec), loss of muscle tone, (1 min 38 sec) and last isolated muscle movement (4 min 10 sec). As for respiratory responses, very deep respiratory attempts started at 20 sec. Respiratory movements progressively decreased and completely stopped at 2 min. Despite the fact that extending the presented data on all cases of hanging asphyxia would be a mistake, this case gives a very interesting insight into movement and respiratory response to asphyxia by hanging.
One selector-one resistor (1S1R) crossbar array for high-density flexible memory applications

Lack of a suitable selection device to suppress sneak current has impeded the development of 4F2 crossbar memory array utilizing stable and scalable bipolar resistive-switching. We report a high-performance nonlinear bipolar selector realized by a simple Ni/TiO2/Ni MIM structure with a high current density of 105 A/cm2, and a Ni/TiO2/Ni/HfO2/Pt vertically stacked 1S1R cell capable of gigabit memory implementation. Furthermore, the demonstration of 1S1R array fabricated completely at room temperature on a plastic substrate highlights the promise of future extremely low-cost flexible nonvolatile memory.
Multi-task Coupled Attentions for Category-specific Aspect and Opinion Terms Co-extraction

In aspect-based sentiment analysis, most existing methods either focus on aspect/opinion terms extraction or aspect terms categorization. However, each task by itself only provides partial information to end users. To generate more detailed and structured opinion analysis, we propose a finer-grained problem, which we call category-specific aspect and opinion terms extraction . This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms. To this end, we propose an end-to-end multitask attention model, where each task corresponds to aspect/opinion terms extraction for a specific category. Our model benefits from exploring the commonalities and relationships among different tasks to address the data sparsity issue. We demonstrate its state-of-the-art performance on three benchmark datasets.
Structural Deep Network Embedding

Network embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the underlying network structure is complex, shallow models cannot capture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used by the unsupervised component to capture the global network structure. While the first-order proximity is used as the supervised information in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structure and is robust to sparse networks. Empirically, we conduct the experiments on five real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network significantly better and achieves substantial gains in three applications, i.e. multi-label classification, link prediction and visualization.
Obstacle Detection on Railway Track by Fusing Radar and Image Sensor

Novel technology to recognize the situation in distant place is necessary to develop a railway safety monitoring system by which human being having fallen onto the tracks from a platform and obstacles in the level crossing can be detected. In this research, we propose a method for detecting a stationary or moving obstacle by the technology which employs the super resolution radar techniques, the image recognition techniques, and the technology to fuse these techniques. Our method is designed for detecting obstacles such as cars, bicycles, and human on the track in the range up to hundreds of meters ahead by using sensors mounted on a train. In super resolution radar techniques, novel stepped multiple frequency radar is confirmed to provide the expected high resolution performance by experimental study using software defined radar. We designed the parameters of the stepped multiple frequency radar. The signal processing processor which employs A/Ds, D/As of high-sampling rate and the highest performance FPGAs has been designed and developed. In image recognition techniques, the main algorithm which detects a pair of rails is based on information about rail edges and intensity gradients. As the obstacle exists in the vicinity of rails, all the detection processes can be limited to the region in the vicinity of rails. As an example, we detect an obstacle by estimating image boundaries which enclose a group of feature points exactly on the obstacle. In order to determine the group of feature points, feature points of the whole image are detected at each frames and tracked over image sequence. For robustness estimation of boundary, we use radar so as to detect the obstacle’s rough position in an image region where an obstacle would exist; the motion segmentation technique is applied to the tracks that is located in the region. Note that an obstacle's position detected by radar is remarkably rough because radar has low transverse resolution. Nevertheless, the position detected by radar contributes to rapid and robust estimation of satisfactory image boundaries. To allow for monitoring in large distances, a prototype for an onboard pan/tilt camera platform with directional control was designed and manufactured as required in a railway situation. To detect obstacles in real time, we have built a high-performance test machine with a GPU and an image processing environment.
Calculating web service similarity using ontology learning with machine learning

The Web is a popular, easy and common way to propagate information today and according to the growth of the Web, Web service discovery has become a challenging task. Clustering Web services into similar clusters through calculating the semantic similarity of Web services is one way for overcome this issue. Several methods are used for current similarity calculation process such as knowledge based, information-retrieval based, text mining, ontology based and context-aware based methods. Through this paper, present a method for calculating Web service similarity using both ontology learning and machine learning that uses a support vector machine for similarity calculation in generated ontology instead of edge count base method. Experimental results show that our hybrid approach of combining ontology learning and machine learning works efficiently and give accurate results than previous two approaches.
High performance 32nm logic technology featuring 2nd generation high-k + metal gate transistors

A 32nm logic technology for high performance microprocessors is described. 2nd generation high-k + metal gate transistors provide record drive currents at the tightest gate pitch reported for any 32nm or 28nm logic technology. NMOS drive currents are 1.62mA/um Idsat and 0.231mA/um Idlin at 1.0V and 100nA/um Ioff. PMOS drive currents are 1.37mA/um Idsat and 0.240mA/um Idlin at 1.0V and 100nA/um Ioff. The impact of SRAM cell and array size on Vccmin is reported.