nils-herrmann/clean_scirepeval huggingface dataset:
Prescribed performance control of quadruped robot

Due to the complex and high nonlinear structure of the quadruped robots, the dynamic control of quadruped robots has long been a big challenge for the researchers. In this paper, a guaranteed performance adaptive control algorithm is proposed for tracking control of a quadruped robot with parameter uncertainties. The controller is designed based on the sliding mode theory, and incorporates a transformed error, which includes the performance indices. Parameter uncertainties are estimated by a linear network. By choosing a proper Lyapunov function, the prescribed performance with the proposed controller is proven in the scenes of the parameter uncertainties and the environment constraint. Numerical simulations demonstrate the effectiveness and superiority of the proposed controller.
A Latent Semantic Indexing-based approach to multilingual document clustering

The creation and deployment of knowledge repositories formanaging, sharing, and reusing tacit knowledgewithin an organization has emerged as a prevalent approach in current knowledge management practices. A knowledge repository typically contains vast amounts of formal knowledge elements, which generally are available as documents. To facilitate users' navigation of documents within a knowledge repository, knowledge maps, often created by document clustering techniques, represent an appealing and promising approach. Various document clustering techniques have been proposed in the literature, but most deal with monolingual documents (i.e., written in the same language). However, as a result of increased globalization and advances in Internet technology, an organization often maintains documents in different languages in its knowledge repositories, which necessitates multilingual document clustering (MLDC) to create organizational knowledge maps. Motivated by the significance of this demand, this study designs a Latent Semantic Indexing (LSI)-based MLDC technique capable of generating knowledge maps (i.e., document clusters) from multilingual documents. The empirical evaluation results show that the proposed LSI-based MLDC technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision, and is capable of maintaining a good balance between monolingual and cross-lingual clustering effectiveness when clustering a multilingual document corpus.
Correlating driver gaze with the road scene for driver assistance systems

A driver assistance system (DAS) should support the driver by monitoring road and vehicle events and presenting relevant and timely information to the driver. It is impossible to know what a driver is thinking, but we can monitor the driver’s gaze direction and compare it with the position of information in the driver’s viewfield to make inferences. In this way, not only do we monitor the driver’s actions, we monitor the driver’s observations as well. In this paper we present the automated detection and recognition of road signs, combined with the monitoring of the driver’s response. We present a complete system that reads speed signs in real-time, compares the driver’s gaze, and provides immediate feedback if it appears the sign has been missed by t ©
Analyzing Resource Behavior Using Process Mining

It is vital to use accurate models for the analysis, design, and/or control of business processes. Unfortunately, there are often important discrepancies between reality and models. In earlier work, we have shown that simulation models are often based on incorrect assumptions and one example is the speed at which people work. The “Yerkes-Dodson Law of Arousal” suggests that a worker that is under time pressure may become more efficient and thus finish tasks faster. However, if the pressure is too high, then the worker’s performance may degrade. Traditionally, it was difficult to investigate such phenomena and few analysis tools (e.g., simulation packages) support workload-dependent behavior. Fortunately, more and more activities are being recorded and modern process mining techniques provide detailed insights in the way that people really work. This paper uses a new process mining plug-in that has been added to ProM to explore the effect of workload on service times. Based on historic data and by using regression analysis, the relationship between workload and services time is investigated. This information can be used for various types of analysis and decision making, including more realistic forms of simulation.
Large-scale deep learning at Baidu

In the past 30 years, tremendous progress has been achieved in building effective shallow classification models. Despite the success, we come to realize that, for many applications, the key bottleneck is not the qualify of classifiers but that of features. Not being able to automatically get useful features has become the main limitation for shallow models. Since 2006, learning high-level features using deep architectures from raw data has become a huge wave of new learning paradigms. In recent two years, deep learning has made many performance breakthroughs, for example, in the areas of image understanding and speech recognition. In this talk, I will walk through some of the latest technology advances of deep learning within Baidu, and discuss the main challenges, e.g., developing effective models for various applications, and scaling up the model training using many GPUs. In the end of the talk I will discuss what might be interesting future directions.
A study of deep convolutional auto-encoders for anomaly detection in videos

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. Convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since they learn features automatically during the training process. From the anomaly detection perspective, the Convolutional Autoencoder (CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE architecture, we also propose a method for aggregating high-level spatial and temporal features with the input frames and investigate how they affect the CAE performance. An easy-to-use measure of video spatial complexity was devised and correlated with the classification performance of the CAE. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area.
Operational capabilities development in mediated offshore software services models

The paper expands theoretical and empirical understanding of capabilities development in the mediated offshore outsourcing model whereby a small or a medium-sized firm delivers offshore software services to a larger information technology firm that in turn contracts and interfaces with the actual end-client onshore firms. Such a mediated model has received little prior research attention, although it is common particularly among Chinese firms exporting services to Japan, the largest export market for Chinese software services. We conducted case studies in four China-based software companies to understand the mechanisms used to develop their operational capabilities. We focused on client-specific, process, and human resources capabilities that have been previously associated with vendor success. We found a range of learning mechanisms to build the capabilities in offshore firms. Results show that the development of human resources capabilities was most challenging in the mediated model; yet foundational for the development of the other capabilities. This paper contributes to the information systems literature by improving our understanding of the development of operational capabilities in smalland medium-sized Chinese firms that deploy the mediated model of offshore
Full-wave design of H-plane contiguous manifold output multiplexers using the fictitious reactive load concept

In this paper, a new procedure for the design of contiguous band manifold output multiplexers in H-plane configuration is presented. Such a configuration eliminates the slots between the T-junctions and the filters allowing high power handling, as well as dramatically reducing the risk of multipactor. This paper pursues the division of the design into tasks with low computational effort. The approach comprises: 1) the singly terminated synthesis of the channel filters and their corresponding full-wave responses; 2) the manifold design using fictitious reactive loads simulating the phase response of every channel out of its corresponding band; and 3) the final full-wave optimization of the whole structure using the simulated-annealing method. In order to validate the above procedure, a five-channel Ku-band multiplexer has been designed, manufactured, and measured.
Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN

Chest radiography (chest X-ray) is a low-cost yet effective and widely used medical imaging procedures. The lacking of qualified radiologist seriously limits the applicability of the technique. We explore the possibility of designing a computer-aided diagnosis for chest X-rays using deep convolutional neural networks. Using a real-world dataset of 16,000 chest X-rays with natural language diagnosis reports, we can train a multi-class classification model from images and preform accurate diagnosis, without any prior domain knowledge.
Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry

User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use.
Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.
Kullback-Leibler Divergence Constrained Distributionally Robust Optimization

In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) divergence. We consider DRO problems where the ambiguity is in the objective function, which takes a form of an expectation, and show that the resulted minimax DRO problems can be formulated as a one-layer convex minimization problem. We also consider DRO problems where the ambiguity is in the constraint. We show that ambiguous expectation-constrained programs may be reformulated as a one-layer convex optimization problem that takes the form of the Benstein approximation of Nemirovski and Shapiro (2006). We further consider distributionally robust probabilistic programs. We show that the optimal solution of a probability minimization problem is also optimal for the distributionally robust version of the same problem, and also show that the ambiguous chance-constrained programs (CCPs) may be reformulated as the original CCP with an adjusted confidence level. A number of examples and special cases are also discussed in the paper to show that the reformulated problems may take simple forms that can be solved easily. The main contribution of the paper is to show that the KL divergence constrained DRO problems are often of the same complexity as their original stochastic programming problems and, thus, KL divergence appears a good candidate in modeling distribution ambiguities in mathematical programming.
Safe Planes for Injection Rhinoplasty: A Histological Analysis of Midline Longitudinal Sections of the Asian Nose

Dorsal nasal augmentation is an essential part of injection rhinoplasty on the Asian nose. Aesthetic physicians require detailed knowledge of the nasal anatomy to accurately and safely inject filler. One hundred and thirty-five histological cross sections were examined from 45 longitudinal strips of soft tissue harvested from the midline of the nose, beginning from the glabella to the nasal tip. Muscles and nasal cartilage were used as landmarks for vascular identification. At the nasal tip, a midline longitudinal columellar artery with a diameter of 0.21 ± 0.09 mm was noted in 14 cadavers (31.1 %). At the infratip, subcutaneous tissue contained cavernous tissue similar to that of the nasal mucosa. The feeding arteries of these dilated veins formed arteriovenous shunts, into which retrograde injection of filler may be possible. All of the nasal arteries present were identified as subcutaneous arteries. They coursed mainly in the superficial layer of the subcutaneous tissues, with smaller branches forming subdermal plexuses. A substantial arterial anastomosis occurred at the supratip region, in which the artery lay in the middle of the subcutaneous tissue at the level of the major alar cartilages. These arteries had a diameter ranging between 0.4 and 0.9 mm and were found in 29 of 45 specimens (64.4 %). This was at the level midway between the rhinion above the supratip and the infratip. This anastomotic artery also crossed the midline at the rhinion superficial to the origin of the procerus on the lower end of the nasal bone. Here the arterial diameter ranged between 0.1 and 0.3 mm, which was not large enough to cause arterial emboli. Fascicular cross sections of the nasalis muscle directly covered the entire upper lateral cartilage. The subdermal tissue contained few layers of fat cells along with the occasional small artery. The procerus arose from the nasal bone and was continuous with the nasalis in 16 cadavers (35.6 %). There was fatty areolar tissue between the procerus and the periosteal layer and no significant arteries present. The procerus ascended beyond the brow to insert into the frontalis muscle with very few cutaneous insertions. The supratrochlear vessels and accompanying nerve were occasionally found on the surface of the frontalis muscle. Most nasal arteries found in the midline are subcutaneous arteries. Filler should be injected deeply to avoid vascular injury leading to compromised perfusion at the dorsum or filler emboli at the nasal tip. This journal requires that the authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Kinematic analysis and design considerations for optimal base frame arrangement of humanoid shoulders

It is well known that kinematics can significantly affect the manipulation capabilities of robotic arms, traditionally illustrated by performance indices such as workspace volume, kinematic and force manipulability, and isotropy within the arm workspace. In the case of dual-arm systems and bimanual manipulation tasks, the kinematics effects to the above indices becomes even more apparent. However, in spite of the large number of dual-arm systems developed in the past, there is a little literature on the kinematic design analysis for the development of such systems. Particularly, the effects of configuration/ orientation of the shoulders' placement with respect to the torso structure have not sufficiently studied or considered, while many dual-arm systems with upward and/or forward tilt angle in shoulder base frame have been introduced. This paper addresses this problem and quantifies the effect of shoulders base frame orientation in a dual-arm manipulation system by looking at its effect on several important manipulation indices, such as the overall and common workspace, redundancy, global isotropy, dual-arm manipulability, and inertia ellipsoid index within the common workspace of the two arms. Consequently, a range of upward and forward tilt angles for the shoulder frames is identified for the design of a dual-arm torso system to render the most desired manipulation performance.
Direct-on-line-start permanent-magnet-assisted synchronous reluctance machine with ferrite magnets

The possibility of applying low-cost ferrite magnets in a direct-on-line-start permanent-magnet-assisted synchronous reluctance machine (DOL PMaSynRM) for industrial applications, such as pump or ventilation systems was studied. The main target of this machine topology was to achieve a higher efficiency class compared to a counterpart induction machine (IM). Because of the weak properties of the ferrite magnets the motor had to be optimized for a low magnetic stress starting from the permanent magnets' point of view. The optimization procedure includes finding of a suitable rotor construction with a proper number of barriers, thickness of the barriers and thickness of the ferrite magnets. More detailed attention was dedicated to the analysis and elimination of irreversible demagnetization risk of ferrite magnets during start up period.
A Formal Analysis of the ISO 9241-210 Definition of User Experience

User Experience (UX) is a major concept in HCI and a variety of different UX definitions have been suggested within the scientific community. An ISO UX definition has been presented to standardize the term from an industry perspective. We introduce methods from formal logic in order to formalize and analyze the ISO UX definition with regard to consistency and ambiguities and present recommendations for an improved version. Although this kind of formalization is not common within the CHI community, we show that quasi-formal methods provide an alternative way of analyzing widely discussed HCI terms, such as UX, to deepen its understanding.
Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks

Heterogeneous information network (HIN) is a general representation of many different applications, such as social networks, scholar networks, and knowledge networks. A key development of HIN is called PathSim based on meta-path, which measures the pairwise similarity of two entities in the HIN of the same type. When using PathSim in practice, we usually need to handcraft some meta-paths which are paths over entity types instead of entities themselves. However, finding useful meta-paths is not trivial to human. In this paper, we present an unsupervised meta-path selection approach to automatically find useful meta-paths over HIN, and then develop a new similarity measure called KnowSim which is an ensemble of selected meta-paths. To solve the high computational cost of enumerating all possible meta-paths, we propose to use an approximate personalized PageRank algorithm to find useful subgraphs to allocate the meta-paths. We apply KnowSim to text clustering and classification problems to demonstrate that unsupervised meta-path selection can help improve the clustering and classification results. We use Freebase, a well-known world knowledge base, to conduct semantic parsing and construct HIN for documents. Our experiments on 20Newsgroups and RCV1 datasets show that KnowSim results in impressive high-quality document clustering and classification performance. We also demonstrate the approximate personalized PageRank algorithm can efficiently and effectively compute the meta-path based similarity.
A CMOS Pixel With Embedded ADC, Digital CDS and Gain Correction Capability for Massively Parallel Imaging Array

In the paper, a CMOS pixel has been proposed for imaging arrays with massively parallel image acquisition and simultaneous compensation of dark signal nonuniformity (DSNU) as well as photoresponse nonuniformity (PRNU). In our solution the pixel contains all necessary functional blocks: a photosensor and an analog-to-digital converter (ADC) with built-in correlated double sampling (CDS) integrated together. It is implemented in standard <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text {m}$ </tex-math></inline-formula> CMOS technology. The size of the pixel with 9-bit resolution is <inline-formula> <tex-math notation="LaTeX">$21~\mu \text {m}\,\,\times 21\,\,\mu \text {m}$ </tex-math></inline-formula>. Measurements of the <inline-formula> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula> pixel array confirm functionality of the proposed solution. CDS reduces dark FPN from 12 LSB (3%) to 0.8 LSB (0.2%) and light FPN from 14 LSB (3.7%) to 7 LSB (1.8%). Further reduction of the light FPN (to ~1 LSB) was achieved by compensating PRNU using massively parallel innovative digital multiplication which features good resolution (1/511), does not disturb CDS executed at the same time, and can be implemented within a small pixel area.