nils-herrmann/clean_scirepeval huggingface dataset:
QSplat: a multiresolution point rendering system for large meshes

Advances in 3D scanning technologies have enabled the practical creation of meshes with hundreds of millions of polygons. Traditional algorithms for display, simplification, and progressive transmission of meshes are impractical for data sets of this size. We describe a system for representing and progressively displaying these meshes that combines a multiresolution hierarchy based on bounding spheres with a rendering system based on points. A single data structure is used for view frustum culling, backface culling, level-of-detail selection, and rendering. The representation is compact and can be computed quickly, making it suitable for large data sets. Our implementation, written for use in a large-scale 3D digitization project, launches quickly, maintains a user-settable interactive frame rate regardless of object complexity or camera position, yields reasonable image quality during motion, and refines progressively when idle to a high final image quality. We have demonstrated the system on scanned models containing hundreds of millions of samples.
Cause and analysis of stator and rotor failures in 3-phase squirrel cage induction motors

The authors attempt to identify the various causes of stator and rotor failures in three-phase squirrel cage induction motors. A specific methodology is proposed to facilitate an accurate analysis of these failures. It is noted that, due to the destructive nature of most failures, it is not easy, and is sometimes impossible, to determine the primary cause of failure. By a process of elimination, one can usually be assured of properly identifying the most likely cause of the failure. It is pointed out that the key point in going through this process of elimination is to use the basic steps of analyzing the failure class and pattern, noting the general motor appearance, identifying the operating condition at the time of failure, and gaining knowledge of the past history of the motor and application.<<ETX>>
The INTERSPEECH 2011 Speaker State Challenge

While the first open comparative challenges in the field of paralinguistics targeted more ‘conventional’ phenomena such as emotion, age, and gender, there still exists a multiplicity of not yet covered, but highly relevant speaker states and traits. The INTERSPEECH 2011 Speaker State Challenge thus addresses two new sub-challenges to overcome the usually low compatibility of results: In the Intoxication Sub-Challenge, alcoholisation of speakers has to be determined in two classes; in the Sleepiness Sub-Challenge, another two-class classification task has to be solved. This paper introduces the conditions, the Challenge corpora “Alcohol Language Corpus” and “Sleepy Language Corpus”, and a standard feature set that may be used. Further, baseline results are given.
SentiPers: A Sentiment Analysis Corpus for Persian

Sentiment Analysis (SA) is a major field of study in natural language processing, computational linguistics and information retrieval. Interest in SA has been constantly growing in both academia and industry over the recent years. Moreover, there is an increasing need for generating appropriate resources and datasets in particular for low resource languages including Persian. These datasets play an important role in designing and developing appropriate opinion mining platforms using supervised, semi-supervised or unsupervised methods. In this paper, we outline the entire process of developing a manually annotated sentiment corpus, SentiPers, which covers formal and informal written contemporary Persian. To the best of our knowledge, SentiPers is a unique sentiment corpus with such a rich annotation in three different levels including document-level, sentence-level, and entity/aspect-level for Persian. The corpus contains more than 26,000 sentences of users’ opinions from digital product domain and benefits from special characteristics such as quantifying the positiveness or negativity of an opinion through assigning a number within a specific range to any given sentence. Furthermore, we present statistics on various components of our corpus as well as studying the inter-annotator agreement among the annotators. Finally, some of the challenges that we faced during the annotation process will be discussed as well.
Mobile health technology evaluation: the mHealth evidence workshop.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

Network traffic classification has become significantly important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization, in which the network traffic is categorized into major classes (e.g., FTP and P2P), and application identification in which identification of end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to the most of current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder and convolution neural network (CNN) in order to classify network traffic. Deep packet with CNN as its classification model achieved F1 score of 0.95 in application identification task and it also accomplished F1 score of 0.97 in traffic characterization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.
Graph stream classification using labeled and unlabeled graphs

Graph classification is becoming increasingly popular due to the rapidly rising applications involving data with structural dependency. The wide spread of the graph applications and the inherent complex relationships between graph objects have made the labels of the graph data expensive and/or difficult to obtain, especially for applications involving dynamic changing graph records. While labeled graphs are limited, the copious amounts of unlabeled graphs are often easy to obtain with trivial efforts. In this paper, we propose a framework to build a stream based graph classification model by combining both labeled and unlabeled graphs. Our method, called gSLU, employs an ensemble based framework to partition graph streams into a number of graph chunks each containing some labeled and unlabeled graphs. For each individual chunk, we propose a minimum-redundancy subgraph feature selection module to select a set of informative subgraph features to build a classifier. To tackle the concept drifting in graph streams, an instance level weighting mechanism is used to dynamically adjust the instance weight, through which the subgraph feature selection can emphasize on difficult graph samples. The classifiers built from different graph chunks form an ensemble for graph stream classification. Experiments on real-world graph streams demonstrate clear benefits of using minimum-redundancy subgraph features to build accurate classifiers. By employing instance level weighting, our graph ensemble model can effectively adapt to the concept drifting in the graph stream for classification.
Reengineering Aircraft Structural Life Prediction Using a Digital Twin

Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail.
Flattening curved documents in images

Compared to scanned images, document pictures captured by camera can suffer from distortions due to perspective and page warping. It is necessary to restore a frontal planar view of the page before other OCR techniques can be applied. In this paper we describe a novel approach for flattening a curved document in a single picture captured by an uncalibrated camera. To our knowledge this is the first reported method able to process general curved documents in images without camera calibration. We propose to model the page surface by a developable surface, and exploit the properties (parallelism and equal line spacing) of the printed textual content on the page to recover the surface shape. Experiments show that the output images are much more OCR friendly than the original ones. While our method is designed to work with any general developable surfaces, it can be adapted for typical special cases including planar pages, scans of thick books, and opened books.
Increasing Advertising Value of Mobile Marketing - An Empirical Study of Antecedents

Mobile marketing offers great opportunities for businesses. Marketing activities supported by mobile devices allow companies to directly communicate with their consumers without time or location barriers. Possibilities for marketers are numerous, but many aspects of mobile marketing still need further investigation. Especially, the topic of mobile advertising (m-advertising) is of major interest. M-advertising addresses consumers with individualized advertising messages via mobile devices. The underlying paper discusses the relevance of m-advertising and investigates how perceived advertising value of mobile marketing can be increased. The analysis is based on a study among consumers. All together a quota sample of 815 mobile phone users was interviewed. The results indicate that the message content is of greatest relevance for the perceived advertising value, while a high frequency of message exposure has a negative impact on it.
A multimodule parallelable series-connected PWM voltage regulator

This paper presents the analysis and design of a single-phase voltage regulator (VR) and its multimodule parallel control. The VR employs the pulsewidth modulation three-arm rectifier–inverter topology. The inverter side adjusts the load voltage with the series regulating structure aiming to minimize converter capacity and attain higher efficiency. The rectifier side regenerates the load power and executes the active power filter function to achieve unity power factor. Based on such high-performance VR, a resistive droop method combined with the – droop and – shift scheme is then proposed to control the current sharing such that multiple VRs can be paralleled directly without any control interconnection. The proposed parallel control technique possesses the features of fast response, precise voltage regulation, equal fundamental and harmonic current sharing, tolerance for parameter mismatch, and so on. Two prototype 1-KVA VRs are implemented, and the effectiveness is demonstrated by some simulation and experimental results.
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents

We introduce a hierarchical architecture for machine reading capable of extracting precise information from long documents. The model divides the document into small, overlapping windows and encodes all windows in parallel with an RNN. It then attends over these window encodings, reducing them to a single encoding, which is decoded into an answer using a sequence decoder. This hierarchical approach allows the model to scale to longer documents without increasing the number of sequential steps. In a supervised setting, our model achieves state of the art accuracy of 76.8 on the WikiReading dataset. We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows. We evaluate models that can reuse autoencoder states and outputs without finetuning their weights, allowing for more efficient training and inference.
VideoStory: A New Multimedia Embedding for Few-Example Recognition and Translation of Events

This paper proposes a new video representation for few-example event recognition and translation. Different from existing representations, which rely on either low-level features, or pre-specified attributes, we propose to learn an embedding from videos and their descriptions. In our embedding, which we call VideoStory, correlated term labels are combined if their combination improves the video classifier prediction. Our proposed algorithm prevents the combination of correlated terms which are visually dissimilar by optimizing a joint-objective balancing descriptiveness and predictability. The algorithm learns from textual descriptions of video content, which we obtain for free from the web by a simple spidering procedure. We use our VideoStory representation for few-example recognition of events on more than 65K challenging web videos from the NIST TRECVID event detection task and the Columbia Consumer Video collection. Our experiments establish that i) VideoStory outperforms an embedding without joint-objective and alternatives without any embedding, ii) The varying quality of input video descriptions from the web is compensated by harvesting more data, iii) VideoStory sets a new state-of-the-art for few-example event recognition, outperforming very recent attribute and low-level motion encodings. What is more, VideoStory translates a previously unseen video to its most likely description from visual content only.
A flexible capacitive tactile sensor array for prosthetic hand real-time contact force measurement

This paper develops a novel flexible capacitive tactile sensor array for prosthetic hand gripping force measurement. The sensor array has 8 × 8 (= 64) sensing units, each sensing unit has a four-layered structure: two thick PET layers with embedded copper electrodes generates a capacitor, a PDMS film with line-structure used as an insulation layer, and a top PDMS bump layer to concentrate external force. The structural design, working principle, and fabrication process of this sensor array are presented. The fabricated tactile sensor array features high flexibility has a spatial resolution of 2 mm. This is followed by the characterization of the sensing unit for normal force measurement and found that the sensing unit has two sensitivities: 4.82 0/00/mN for small contact force and 0.23 0/00/mN for large gripping force measurements. Finally, the tactile sensor array is integrated into a prosthetic hand for gripping force measurement. Results showed that the developed flexible capacitive tactile sensor array could be utilized for tactile sensing and real-time contact force visualization for prosthetic hand gripping applications.
Rating Prediction Based on Social Sentiment From Textual Reviews

In recent years, we have witnessed a flourish of review websites. It presents a great opportunity to share our viewpoints for various products we purchase. However, we face an information overloading problem. How to mine valuable information from reviews to understand a user's preferences and make an accurate recommendation is crucial. Traditional recommender systems (RS) consider some factors, such as user's purchase records, product category, and geographic location. In this work, we propose a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems. Firstly, we propose a social user sentimental measurement approach and calculate each user's sentiment on items/products. Secondly, we not only consider a user's own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, we consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers' comprehensive evaluation. At last, we fuse three factors-user sentiment similarity, interpersonal sentimental influence, and item's reputation similarity-into our recommender system to make an accurate rating prediction. We conduct a performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp. Our experimental results show the sentiment can well characterize user preferences, which helps to improve the recommendation performance.
Alternative strategies of categorization

Psychological studies of categorization often assume that all concepts are of the same general kind, and are operated on by the same kind of categorization process. In this paper, we argue against this unitary view, and for the existence of qualitatively different categorization processes. In particular, we focus on the distinction between categorizing an item by: (a) applying a category-defining rule to the item vs. (b) determining the similarity of that item to remembered exemplars of a category. We begin by characterizing rule application and similarity computations as strategies of categorization. Next, we review experimental studies that have used artificial categories and shown that differences in instructions or time pressure can lead to either rule-based categorization or similarity-based categorization. Then we consider studies that have used natural concepts and again demonstrated that categorization can be done by either rule application or similarity calculations. Lastly, we take up evidence from cognitive neuroscience relevant to the rule vs. similarity issue. There is some indirect evidence from brain-damaged patients for neurological differences between categorization based on rules vs. that based on similarity (with the former involving frontal regions, and the latter relying more on posterior areas). For more direct evidence, we present the results of a recent neuroimaging experiment, which indicates that different neural circuits are involved when people categorize items on the basis of a rule as compared with when they categorize the same items on the basis of similarity.
A comparison of vertex ordering algorithms for large graph visualization

In this study, we examine the use of graph ordering algorithms for visual analysis of data sets using visual similarity matrices. Visual similarity matrices display the relationships between data items in a dot-matrix plot format, with the axes labeled with the data items and points drawn if there is a relationship between two data items. The biggest challenge for displaying data using this representation is finding an ordering of the data items that reveals the internal structure of the data set. Poor orderings are indistinguishable from noise whereas a good ordering can reveal complex and subtle features of the data. We consider three general classes of algorithms for generating orderings: simple graph theoretic algorithms, symbolic sparse matrix reordering algorithms, and spectral decomposition algorithms. We apply each algorithm to synthetic and real world data sets and evaluate each algorithm for interpretability (i.e., does the algorithm lead to images with usable visual features?) and stability (i.e., does the algorithm consistently produce similar results?). We also provide a detailed discussion of the results for each algorithm across the different graph types and include a discussion of some strategies for using ordering algorithms for data analysis based on these results.
Load shedding for aggregation queries over data streams

Systems for processing continuous monitoring queries over data streams must be adaptive because data streams are often bursty and data characteristics may vary over time. We focus on one particular type of adaptivity: the ability to gracefully degrade performance via "load shedding" (dropping unprocessed tuples to reduce system load) when the demands placed on the system cannot be met in full given available resources. Focusing on aggregation queries, we present algorithms that determine at what points in a query plan should load shedding be performed and what amount of load should be shed at each point in order to minimize the degree of inaccuracy introduced into query answers. We report the results of experiments that validate our analytical conclusions.
On Monte Carlo Tree Search and Reinforcement Learning

Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search.
Robust place recognition for 3D range data based on point features

The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Whereas this problem has been studied intensively in the context of robot vision, relatively few approaches are available for three-dimensional range data. In this paper, we present a novel and robust method for place recognition based on range images. Our algorithm matches a given 3D scan against a database using point features and scores potential transformations by comparing significant points in the scans. A further advantage of our approach is that the features allow for a computation of the relative transformations between scans which is relevant for registration processes. Our approach has been implemented and tested on different 3D data sets obtained outdoors. In several experiments we demonstrate the advantages of our approach also in comparison to existing techniques.