nils-herrmann/clean_scirepeval huggingface dataset:
Building an Automatic Vehicle License-Plate Recognition System

detection module, we propose an efficient boundary line-based method combining the Hough transform and Contour algorithm. This method optimizes speed and accuracy in processing images taken from various positions. Then, we use horizontal and vertical projection to separate plate numbers in VLP segmentation module. Finally, each plate number will be recognized by OCR module implemented by Hidden Markov Model. The system was evaluated in two empirical image sets and has proved its effectiveness (see section IV) which is applicable in real traffic toll systems. The system can also be applied to some other types of VLPs with minor changes.
A low dropout, CMOS regulator with high PSR over wideband frequencies

Modern system-on-chip (SoC) environments are swamped in high frequency noise that is generated by RF and digital circuits and propagated onto supply rails through capacitive coupling. In these systems, linear regulators are used to shield noise-sensitive analog blocks from high frequency fluctuations in the power supply. The paper presents a low dropout regulator that achieves power supply rejection (PSR) better than -40 dB over the entire frequency spectrum. The system has an output voltage of 1.0 V and a maximum current capability of 10 mA. It consists of operational amplifiers (op amps), a bandgap reference, a clock generator, and a charge pump and has been designed and simulated using BSIM3 models of a 0.5 /spl mu/m CMOS process obtained from MOSIS.
Story tracking: linking similar news over time and across languages

The Europe Media Monitor system (EMM) gathers and aggregates an average of 50,000 newspaper articles per day in over 40 languages. To manage the information overflow, it was decided to group similar articles per day and per language into clusters and to link daily clusters over time into stories. A story automatically comes into existence when related groups of articles occur within a 7-day window. While cross-lingual links across 19 languages for individual news clusters have been displayed since 2004 as part of a freely accessible online application (http://press.jrc.it/NewsExplorer), the newest development is work on linking entire stories across languages. The evaluation of the monolingual aggregation of historical clusters into stories and of the linking of stories across languages yielded mostly satisfying results.
FA*IR: A Fair Top-k Ranking Algorithm

In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria.
 Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-k should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above.
 An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.
The evaluation of children in the primary care setting when sexual abuse is suspected.

This clinical report updates a 2005 report from the American Academy of Pediatrics on the evaluation of sexual abuse in children. The medical assessment of suspected child sexual abuse should include obtaining a history, performing a physical examination, and obtaining appropriate laboratory tests. The role of the physician includes determining the need to report suspected sexual abuse; assessing the physical, emotional, and behavioral consequences of sexual abuse; providing information to parents about how to support their child; and coordinating with other professionals to provide comprehensive treatment and follow-up of children exposed to child sexual abuse.
TextFlow: Towards Better Understanding of Evolving Topics in Text

Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms — especially the collaborative filtering (CF)-based approaches with shallow or deep models — usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.
Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology

The rapid growth of investment in information technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation a d management issue. While such acceptance has received fairly extensive attention from previous research, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts. In response, this paper reports a research work that examined the applicability of the Technology Acceptance Model (ΤΑΜ) in explaining physicians' decisions to accept telemedicine technology in the health-care context. The technology, the user group, and the organizational context are all new to IT acceptance/adoption research. The study also addressed a pragmatic technology management need resulting from millions of dollars invested by healthcare organizations in developing and implementing telemedicine programs in recent years. The model's overall fit, explanatory power, and the individual causal links that it postulates were evaluated by examining the acceptance of telemedicine technology among physicians practicing at public tertiary hospitals in Hong Kong. Our results suggested that ΤΑΜ was able to provide a reasonable depiction of physicians' intention to use telemedicine technology. Perceived usefulness was found to be a significant determinant ofattitude and intention but perceived ease of use was not. The relatively low R-square of the model suggests both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context. Based on the study findings, implications for user technology acceptance research and telemedicine management are discussed.
Playing with reality: I. Theory of mind and the normal development of psychic reality.

The authors of this paper discuss changes in the child's perception of psychic reality during normal development, highlighting a major shift in the child's understanding of minds ('theory of mind') at the oedipal stage. They illustrate this transition with material from the analysis of a 4-year-old girl. They maintain that the very young child uses two forms of psychic reality, which they have called 'psychic equivalent' and 'pretend' modes, which differ primarily in the assumed relationship between internal and external realities. The integration of the dual modes into a singular reflective mode is normally completed by about the age of 4, with affect leading cognition: the child first understands that people have different feelings, then that they may have different thoughts about the same external reality. The authors describe normal psychological growth and neurotic pathology, in which the integration of these two modes of functioning has been only partly achieved.
LSHDB: a parallel and distributed engine for record linkage and similarity search

In this paper, we present LSHDB, the first parallel and distributed engine for record linkage and similarity search. LSHDB materializes an abstraction layer to hide the mechanics of the Locality-Sensitive Hashing (a popular method for detecting similar items in high dimensions) which is used as the underlying similarity search engine. LSHDB creates the appropriate data structures from the input data and persists these structures on disk using a noSQL engine. It inherently supports the parallel processing of distributed queries, is highly extensible, and is easy to use.We will demonstrate LSHDB both as the underlying system for detecting similar records in the context of Record Linkage (and of Privacy-Preserving Record Linkage) tasks, as well as a search engine for identifying string values that are similar to submitted queries.
Lucid Data Dreaming for Object Tracking

Convolutional networks reach top quality in pixel-level object tracking but require a large amount of training data (1k∼10k) to deliver such results. We propose a new training strategy which achieves state-of-the-art results across three evaluation datasets while using 20×∼100× less annotated data than competing methods. Instead of using large training sets hoping to generalize across domains, we generate in-domain training data using the provided annotation on the first frame of each video to synthesize (“lucid dream”1) plausible future video frames. In-domain per-video training data allows us to train high quality appearanceand motion-based models, as well as tune the post-processing stage. This approach allows to reach competitive results even when training from only a single annotated frame, without ImageNet pre-training. Our results indicate that using a larger training set is not automatically better, and that for the tracking task a smaller training set that is closer to the target domain is more effective. This changes the mindset regarding how many training samples and general “objectness” knowledge are required for the object tracking task.
Are We There Yet? Research in Commercial Spoken Dialog Systems

In this paper we discuss the recent evolution of spoken dialog systems in commercial deployments. Yet based on a simple finite state machine design paradigm, dialog systems reached today a higher level of complexity. The availability of massive amounts of data during deployment led to the development of continuous optimization strategy pushing the design and development of spoken dialog applications from an art to science. At the same time new methods for evaluating the subjective caller experience are available. Finally we describe the inevitable evolution for spoken dialog applications from speech only to multimodal interaction.
Nonlinear Disturbance Observer Design For Robotic Manipulators

Robotic manipulators are highly nonlinear and coupled systems that are subject to different types of disturbances such as joint frictions, unknown payloads, varying contact points, and unmodeled dynamics. These disturbances, when unaccounted for, adversely affect the performance of the manipulator. Employing a disturbance observer is a common method to reject such disturbances. In addition to disturbance rejection, disturbance observers can be used in force control applications. Recently, research has been done regarding the design of nonlinear disturbance observers (NLDOs) for robotic manipulators. In spite of good results in terms of disturbance tracking, the previously designed nonlinear disturbance observers can merely be used for planar serial manipulators with revolute joints (Chen, W.H., Ballance, D.J., Gawthorp, P.J., O’Reilly,  2000. A nonlinear disturbance observer for robotic manipulators. ), (Nikoobin, A., Haghighi, R., 2009. Lyapunov-based nonlinear disturbance observer for serial n-link manipulators. ). In this paper, a general systematic approach is proposed to solve the disturbance observer design problem for robotic manipulators without restrictions on the number of degrees-of-freedom (DOFs), the types of joints, or the manipulator configuration. Moreover, this design method does not need the exact dynamic model of the serial robotic manipulator. This method also ∗Corresponding author,  ∗∗Principal corresponding author,  +1 (780)492-8935, fax: +1 (780)492-1811 Email addresses: alireza.mohammadi@mail.utoronto.ca (A. Mohammadi), mahdi.tavakoli@ualberta.ca (M. Tavakoli), , farzad.hashemzadeh@ualberta.ca (F. Hashemzadeh)  2012 unifies the previously proposed linear and nonlinear disturbance observers in a general framework. Simulations are presented for a 4-DOF SCARA manipulator to show the effectiveness of the proposed disturbance observer design method. Experimental results using a PHANToM Omni haptic device further illustrate the effectiveness of the design method.
Sound event recognition in unstructured environments using spectrogram image processing

The objective of this research is to develop feature extraction and classification techniques for the task of sound event recognition (SER) in unstructured environments. Although this field is traditionally overshadowed by the popular field of automatic speech recognition (ASR), an SER system that can achieve human-like sound recognition performance opens up a range of novel application areas. These include acoustic surveillance, bio-acoustical monitoring, environmental context detection, healthcare applications and more generally the rich transcription of acoustic environments. The challenge in such environments are the adverse effects such as noise, distortion and multiple sources, which are more likely to occur with distant microphones compared to the close-talking microphones that are more common in ASR. In addition, the characteristics of acoustic events are less well defined than those of speech, and there is no sub-word dictionary available like the phonemes in speech. Therefore, the performance of ASR systems typically degrades dramatically in these challenging unstructured environments, and it is important to develop new methods that can perform well for this challenging task. In this thesis, the approach taken is to interpret the sound event as a two-dimensional spectrogram image, with the two axes as the time and frequency dimensions. This enables novel methods for SER to be developed based on spectrogram image processing, which are inspired by techniques from the field of image processing. The motivation for such an approach is based on finding an automatic approach to “spectrogram reading”, where it is possible for humans to visually recognise the different sound event signatures in the spectrogram. The advantages of such an approach are twofold. Firstly, the sound event image representation makes it possible to naturally capture the sound information in a two-dimensional feature. This has advantages over conventional onedimensional frame-based features, which capture only a slice of spectral information
Linking a domain thesaurus to WordNet and conversion to WordNet-LMF

We present a methodology to link domain thesauri to general-domain lexica. This is applied in the framework of the KYOTO project to link the Species2000 thesaurus to the synsets of the English WordNet. Moreover, we study the formalisation of this thesaurus according to the ISO LMF standard and its dialect WordNet-LMF. This conversion will allow Species2000 to communicate with the other resources available in the KYOTO architecture.
Improving literacy in developing countries using speech recognition-supported games on mobile devices

Learning to read in a second language is challenging, but highly rewarding. For low-income children in developing countries, this task can be significantly more challenging because of lack of access to high-quality schooling, but can potentially improve economic prospects at the same time. A synthesis of research findings suggests that practicing recalling and vocalizing words for expressing an intended meaning could improve word reading skills - including reading in a second language - more than silent recognition of what the given words mean. Unfortunately, many language learning software do not support this instructional approach, owing to the technical challenges of incorporating speech recognition support to check that the learner is vocalizing the correct word. In this paper, we present results from a usability test and two subsequent experiments that explore the use of two speech recognition-enabled mobile games to help rural children in India read words with understanding. Through a working speech recognition prototype, we discuss two major contributions of this work: first, we give empirical evidence that shows the extent to which productive training (i.e. vocalizing words) is superior to receptive vocabulary training, and discuss the use of scaffolding hints to ""unpack"" factors in the learner's linguistic knowledge that may impact reading. Second, we discuss what our results suggest for future research in HCI.
Application-specific resource provisioning for wide-area distributed computing

Some modern distributed applications require cooperation among multiple geographically separated computing facilities to perform intensive computing at the end sites and large-scale data transfers in the wide area network. It has been widely recognized that WDM networks are cost-effective means to support data transfers in this type of data-intensive applications. However, neither the traditional approaches to establishing lightpaths between given source destination pairs nor the existing application-level approaches that only consider computing resources but take the underlying connectivity for granted are sufficient. In this article we identify key limitations and issues in existing systems, and focus on joint resource allocation of both computing resources and network resources in federated computing and network systems. A variety of resource allocation schemes that provide modern distributed computing applications with performance and reliability guarantees are presented.