nils-herrmann/clean_scirepeval huggingface dataset:
Inferring Destination from Mobility Data

Destination prediction in a moving vehicle has several applications such as alternative route recommendations even in cases where the driver has not entered their destination into the system. In this paper a hierarchical approach to destination prediction is presented. A Discrete Time Markov Chain model is used to make an initial prediction of a general region the vehicle might be travelling to. Following that a more complex Bayesian Inference Model is used to make a fine grained prediction within that destination region. The model is tested on a dataset of 442 taxis operating in Porto, Portugal. Experiments are run on two maps. One is a smaller map concentrating specificially on trips within the Porto city centre and surrounding areas. The second map covers a much larger area going as far as Lisbon. We achieve predictions for Porto with average distance error of less than 0.6 km from early on in the trip and less than 1.6 km dropping to less than 1 km for the wider area.
Smart home care network using sensor fusion and distributed vision-based reasoning

A wireless sensor network employing multiple sensing and event detection modalities and distributed processing is proposed for smart home monitoring applications. Image sensing and vision-based reasoning are employed to verify and further analyze events reported by other sensors. The system has been developed to address the growing application domain in caregiving to the elderly and persons in need of monitored living, who care to live independently while enjoying the assurance of timely access to caregivers when needed. An example of sensed events is the accidental fall of the person under care. A wireless badge node acts as a bridge between the user and the network. The badge node provides user-centric event sensing functions such as detecting falls, and also provides a voice communication channel between the user and the caregiving center when the system detects an alert and dials the center. The voice connection is carried over an IEEE 802.15.4 radio link between the user badge and another node in the network that acts as a modem. Using signal strength measurements, the network nodes keep track of the approximate location of the user in the monitoring environment.The network also includes wall-mounted image sensor nodes, which are triggered upon detection of a fall to analyze their field-of-view and provide the caregiving center with further information about the user 's status. A description of the developed network and several examples of the vision-based reasoning algorithm are presented in the paper.
Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method

Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute value-based collaborative filtering service recommendation mainly includes two important steps. One is the similarity computation, and the other is the prediction for QoS attribute value, which the user has not experienced. In previous studies, the performances of some methods need to be improved. In this paper, we propose a ratio-based method to calculate the similarity. We can get the similarity between users or between items by comparing the attribute values directly. Based on our similarity computation method, we propose a new method to predict the unknown value. By comparing the values of a similar service and the current service that are invoked by common users, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large data set of real web services. Experimental results show that our method obtains better prediction precision, lower mean absolute error ( <inline-formula><tex-math notation="LaTeX">$MAE$</tex-math><alternatives> <inline-graphic xlink:href="wu-ieq1-2479228.gif"/></alternatives></inline-formula>) and faster computation time than various reference schemes considered.
Digital Libraries and Document Image Retrieval Techniques: A Survey

Nowadays, Digital Libraries have become a widely used service to store and share both digital born documents and digital versions of works stored by traditional libraries. Document images are intrinsically non-structured and the structure and semantic of the digitized documents is in most part lost during the conversion. Several techniques related to the Document Image Analysis research area have been proposed in the past to deal with document image retrieval applications. In this chapter a survey about the more recent techniques applied in the field of recognition and retrieval of text and graphical documents is presented. In particular we describe techniques related to recognition-free approaches.
The 2 nd Workshop on Arabic Corpora and Processing Tools 2016 Theme :

Speech datasets and corpora are crucial for both developing and evaluating accurate Natural Language Processing systems. While Modern Standard Arabic has received more attention, dialects are drastically underestimated, even they are the most used in our daily life and the social media, recently. In this paper, we present the methodology of building an Arabic Speech Corpus for Algerian dialects, and the preliminary version of that dataset of dialectal arabic speeches uttered by Algerian native speakers selected from different Algeria’s departments. In fact, by means of a direct recording way, we have taken into acount numerous aspects that foster the richness of the corpus and that provide a representation of phonetic, prosodic and orthographic varieties of Algerian dialects. Among these considerations, we have designed a rich speech topics and content. The annotations provided are some useful information related to the speakers, time-aligned orthographic word transcription. Many potential uses can be considered such as speaker/dialect identification and computational linguistic for Algerian sub-dialects. In its preliminary version, our corpus encompasses 17 sub-dialects with 109 speakers and more than 6 K utterances.
The Polar Parallel Coordinates Method for Time-Series Data Visualization

Parallel coordinates is a very important visualization method, but the dimensions it can express are limited by the length or width of the screen. In this paper, we present the polar parallel coordinates method. First, we define the polar parallel coordinates as the coordinate whose axes exist between the same degree and show the expression of the axis. We give the algorithms for the polar parallel coordinates generation and data shown in the polar parallel coordinates. Then we point out two feathers of the polar parallel coordinates. It can express more axes than parallel coordinates and reflect the characteristics of time-series data. Based on this, we compare the parallel coordinates with the polar parallel coordinates by the time-series data visualization.
A critical survey of state-of-the-art image inpainting quality assessment metrics

7 Image inpainting is the process of restoring missing pixels in digital images in a plausible way. Research in image inpainting has received considerable attention in di erent areas, including restoration of old and damaged documents, removal of undesirable objects, computational photography, retouching applications, etc. The challenge is that the recovery processes themselves introduce noticeable artifacts within and around the restored image regions. As an alternative to subjective evaluation by humans, a number of approaches have been introduced to quantify inpainting processes objectively. Unfortunately, existing objective metrics have their own strengths and weaknesses as they use di erent criteria. This paper provides a thorough insight into existing metrics related to image inpainting quality assessment, developed during the last few years. The paper provides, under a new framework, a comprehensive description of existing metrics, their strengths, their weaknesses, and a detailed performance analysis on real images from public image inpainting database. The paper also outlines future research directions and applications of inpainting and inpainting-related quality assessment measures.
Microstrip bandpass filters for Ultra-Wideband (UWB) wireless communications

A new technique is developed for designing a composite microstrip bandpass filter (BPF) with a 3 dB fractional bandwidth of more than 100%. The BPF is suitable for ultra-wideband (UWB) wireless communications. The design utilizes embedding individually designed highpass structures and lowpass filters (LPF) into each other, followed by an optimization for tuning in-band performance. The stepped-impedance LPF is employed to attenuate the upper stopband and quarter-wave short-circuited stubs are used to realize the lower stopband. Two such BPFs are fabricated and measured to demonstrate the performance.
ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.
The Timbre Model

This paper presents the timbre model, a signal model which has been built to better understand the relationship between the perception of timbre and the musical sounds most commonly associated with timbre. In addition, an extension to the timbre model incorporating expressions is introduced. The presented work therefore has relation to a large field of science, including auditory perception, signal processing, physical models and the acoustics of musical instruments, music expression, and other computer music research. The timbre model is based on a sinusoidal model, and it consists of a spectral envelope, frequencies, a temporal envelope and different irregularity parameters. The paper is divided into four parts: an overview of the research done on the perception of timbre, an overview of the signal processing aspects dealing with sinusoidal modeling, the timbre model, and an introduction of some expressive extensions to the timbre model.
Choking under pressure: multiple routes to skill failure.

Poor performance in pressure-filled situations, or "choking under pressure," has largely been explained by two different classes of theories. Distraction theories propose that choking occurs because attention needed to perform the task at hand is coopted by task-irrelevant thoughts and worries. Explicit monitoring theories claim essentially the opposite-that pressure prompts individuals to attend closely to skill processes in a manner that disrupts execution. Although both mechanisms have been shown to occur in certain contexts, it is unclear when distraction and/or explicit monitoring will ultimately impact performance. The authors propose that aspects of the pressure situation itself can lead to distraction and/or explicit monitoring, differentially harming skills that rely more or less on working memory and attentional control. In Experiments 1-2, it is shown that pressure that induces distraction (involving performance-contingent outcomes) hurts rule-based category learning heavily dependent on attentional control. In contrast, pressure that induces explicit monitoring of performance (monitoring by others) hurts information-integration category learning thought to run best without heavy demands on working memory and attentional control. In Experiment 3, the authors leverage knowledge about how specific types of pressure impact performance to design interventions to eliminate choking. Finally, in Experiment 4, the selective effects of monitoring-pressure are replicated in a different procedural-based task: the serial reaction time task. Skill failure (and success) depends in part on how the performance environment influences attention and the extent to which skill execution depends on explicit attentional control.
Dyspraxia in autism: association with motor, social, and communicative deficits.

Impaired performance of skilled gestures, referred to as dyspraxia, is consistently reported in children with autism; however, its neurological basis is not well understood. Basic motor skill deficits are also observed in children with autism and it is unclear whether dyspraxia observed in children with autism can be accounted for by problems with motor skills. Forty-seven high-functioning children with an autism spectrum disorder (ASD), autism, or Asperger syndrome (43 males, four females; mean age 10y 7m [SD 1y 10m], mean Full-scale IQ (FSIQ) 99.4 [SD 15.9]), and 47 typically developing (TD) controls (41 males, six females; mean age 10y 6m [SD 1y 5m], mean FSIQ 113.8 [SD 12.3], age range 8-4y) completed: (1) the Physical and Neurological Assessment of Subtle Signs, an examination of basic motor skills standardized for children, and (2) a praxis examination that included gestures to command, to imitation, and with tool-use. Hierarchical regression was used to examine the association between basic motor skill performance (i.e. times to complete repetitive limb movements) and praxis performance (total praxis errors). After controlling for age and IQ, basic motor skill was a significant predictor of performance on praxis examination. Nevertheless, the ASD group continued to show significantly poorer praxis than controls after accounting for basic motor skill. Furthermore, praxis performance was a strong predictor of the defining features of autism, measured using the Autism Diagnostic Observation Schedule, and this correlation remained significant after accounting for basic motor skill. Results indicate that dyspraxia in autism cannot be entirely accounted for by impairments in basic motor skills, suggesting the presence of additional contributory factors. Furthermore, praxis in children with autism is strongly correlated with the social, communicative, and behavioral impairments that define the disorder, suggesting that dyspraxia may be a core feature of autism or a marker of the neurological abnormalities underlying the disorder.
How Internet-of-Things (IoT) making the university campuses smart? QA higher education (QAHE) perspective

This paper proposes a model to develop a university smart campus enabled through Internet-of-Thins (IoT) technology. The infusion of IoT in education embraces the internet-based communication between controllers, sensors and the physical objects. This landscape of modern technology has changed the universities education on a massive scale. Various objects are integrated with sensors, wearable technologies are collecting data with augmented reality and intra-communication is taking place through cloud computing. This IoT paradigm defines the diverse range of parameters for universities identifying opportunities to make them smart urging new interaction between objects and people. This research focuses to make smart rooms, smart parking as well as delivering smart education to students.
A Deep Learning Approach to Universal Skin Disease Classification

Skin diseases are very common in people’s daily life. Each year, millions of people in American are affected by all kinds of skin disorders. Diagnosis of skin diseases sometimes requires a high-level of expertise due to the variety of their visual aspects. As human judgment are often subjective and hardly reproducible, to achieve a more objective and reliable diagnosis, a computer aided diagnostic system should be considered. In this paper, we investigate the feasibility of constructing a universal skin disease diagnosis system using deep convolutional neural network (CNN). We train the CNN architecture using the 23,000 skin disease images from the Dermnet dataset and test its performance with both the Dermnet and OLE, another skin disease dataset, images. Our system can achieve as high as 73.1% Top-1 accuracy and 91.0% Top-5 accuracy when testing on the Dermnet dataset. For the test on the OLE dataset, Top-1 and Top-5 accuracies are 31.1% and 69.5%. We show that these accuracies can be further improved if more training images are used.
The Impact of Indoor Lighting on Students ’ Learning Performance in Learning Environments : A knowledge internalization perspective

The purpose of this study is to identify the influence of indoor lighting on students’ learning performance within learning environments from knowledge internalization perspective. This study is a comprehensive review of literatures base on the influence of indoor lighting on people’s productivity and performance especially students’ learning performance. The result that comes from this study shows that it is essential to improve lighting in learning environments to enhance students’ learning performance and also motivate them to learn more. In this study the researchers utilized Pulay (2010) survey and measured the influence of lighting on students’ learning performance. Utilizing survey data collected from 150 students from Alpha course in Malaysia. This study found significant impact between lighting quality and students’ learning performance this finding is also supported by interview from two experts.
An Interval-Based Representation of Temporal Knowledge

This paper describes a method for maintaining the relationships between temporal intervals in a hierarchical manner using constraint propagation techniques. The representation includes a notion of the present moment (i.e., "now") , and allows one to represent intervals that may extend indefinitely into the past/future. This research was supported in part by the National Science Foundation under Grant Number IST-80-12418, and in part by the Office of Naval Research under Grant Number N00014-80-O0197.
A^2-Nets: Double Attention Networks

Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the “double attention block”, a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently. The component is designed with a double attention mechanism in two steps, where the first step gathers features from the entire space into a compact set through second-order attention pooling and the second step adaptively selects and distributes features to each location via another attention. The proposed double attention block is easy to adopt and can be plugged into existing deep neural networks conveniently. We conduct extensive ablation studies and experiments on both image and video recognition tasks for evaluating its performance. On the image recognition task, a ResNet-50 equipped with our double attention blocks outperforms a much larger ResNet-152 architecture on ImageNet-1k dataset with over 40% less the number of parameters and less FLOPs. On the action recognition task, our proposed model achieves the state-of-the-art results on the Kinetics and UCF-101 datasets with significantly higher efficiency than recent works.
Task-Driven Feature Pooling for Image Classification

Feature pooling is an important strategy to achieve high performance in image classification. However, most pooling methods are unsupervised and heuristic. In this paper, we propose a novel task-driven pooling (TDP) model to directly learn the pooled representation from data in a discriminative manner. Different from the traditional methods (e.g., average and max pooling), TDP is an implicit pooling method which elegantly integrates the learning of representations into the given classification task. The optimization of TDP can equalize the similarities between the descriptors and the learned representation, and maximize the classification accuracy. TDP can be combined with the traditional BoW models (coding vectors) or the recent state-of-the-art CNN models (feature maps) to achieve a much better pooled representation. Furthermore, a self-training mechanism is used to generate the TDP representation for a new test image. A multi-task extension of TDP is also proposed to further improve the performance. Experiments on three databases (Flower-17, Indoor-67 and Caltech-101) well validate the effectiveness of our models.
The Role of Search Engine Optimization in Search Marketing

Web sites invest significant resources in trying to influence their visibility among online search results. In addition to paying for sponsored links, they invest in methods known as search engine optimization (SEO) that improve the ranking of a site among the search results without improving its quality. We study the economic incentives of Web sites to invest in SEO and its implications on search engine and advertiser payoffs. We find that the process is equivalent to an all-pay auction with noise and headstarts. Our results show that, under certain conditions, a positive level of search engine optimization improves the search engine’s ranking quality and thus the satisfaction of its visitors. In particular, if the quality of sites coincides with their valuation for visitors then search engine optimization serves as a mechanism that improves the ranking by correcting measurement errors. While this benefits consumers and increases traffic to the search engine, sites participating in search engine optimization could be worse off due to wasteful spending unless their valuation for traffic is very high. We also investigate how search engine optimization affects the revenues from sponsored links. Surprisingly, we find that in many cases search engine revenues are increased by SEO.