Reviewerly/exHarmony huggingface dataset:
Extracting Randomness from Extractor-Dependent Sources

We revisit the well-studied problem of extracting nearly uniform randomness from an arbitrary source of sufficient min-entropy. Strong seeded extractors solve this problem by relying on a public random seed, which is unknown to the source. Here, we consider a setting where the seed is reused over time and the source may depend on prior calls to the extractor with the same seed. Can we still extract nearly uniform randomness? In more detail, we assume the seed is chosen randomly, but the source can make arbitrary oracle queries to the extractor with the given seed before outputting a sample. We require that the sample has entropy and differs from any of the previously queried values. The extracted output should look uniform even to a distinguisher that gets the seed. We consider two variants of the problem, depending on whether the source only outputs the sample, or whether it can also output some correlated public auxiliary information that preserves the sample's entropy. Our results are: Without Auxiliary Information: We show that every pseudo-random function (PRF) with a sufficiently high security level is a good extractor in this setting, even if the distinguisher is computationally unbounded. We further show that the source necessarily needs to be computationally bounded and that such extractors imply one-way functions. With Auxiliary Information: We construct secure extractors in this setting, as long as both the source and the distinguisher are computationally bounded. We give several constructions based on different intermediate primitives, yielding instantiations based on the DDH, DLIN, LWE or DCR assumptions. On the negative side, we show that one cannot prove security against computationally unbounded distinguishers in this setting under any standard assumption via a black-box reduction. Furthermore, even when restricting to computationally bounded distinguishers, we show that there exist PRFs that are insecure as extractors in this setting and that a large class of constructions cannot be proven secure via a black-box reduction from standard assumptions.
Shear-unlimited common-path speckle interferometer

A single-aperture common-path speckle interferometer with an unlimited shear amount is developed. This unlimited shear amount is introduced when a Wollaston prism is placed near the Fourier plane of a common-path interferometer, which is built by using a quasi-${4f}$4f imaging system. The fundamentals of the shear amount and the spatial carrier frequency generation are analyzed mathematically, and the theoretical predictions are validated by a static experiment. Mode-I fracture experiments through the three-point bending are conducted to prove the feasibility and the capability of this method in full-field strain measurement with various shear amounts. A remarkable feature of this setup is that no tilt is required between the optical components to produce the unlimited shear amount in off-axis holography.
Knowledge Graphs for an Automated Information Provision in the Factory Planning

Today's factory planning is characterized by a multitude of different IT systems. This results in data silos that lead to problems with the provision of information within factory planning. This paper addresses this problem by integrating data from different data silos into a knowledge graph based on the Semantic Web Stack. To improve the information provision a function is developed, which automatically identifies data changes and makes the data available in line with the demands of the planners on the basis of a role-based concept. The developed concept is implemented and validated in the factory planning of a german automobile manufacturer.
A Computational Approach of Recognizing Emotion from Bengali Texts

Emotion recognition is the task of determining distinct emotion exhibited in text. In recent year, due to the availability of enormous amount of textual data, specially dogmatic and self expressiveness of text played a significant role to lead focus in this area. This paper presents an emotion recognition technique that can identify six basic emotions from Bengali texts such as happy, sad, anger, fear, surprise, and disgust respectively. We develop a corpus consisting of 1200 emotive words that are used to train the SVM classifier for identifying different emotions. Experimental result shows that the proposed system can recognize emotions with 73% accuracy which is higher than the Naive based approach (60%).
Timed-Elastic Smooth Curve Optimization for Mobile-Base Motion Planning

This paper proposes the use of piecewise C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> smooth curve for mobile-base motion planning and control, coined Timed-Elastic Smooth Curve (TESC) planner. Based on a Timed-Elastic Band, the problem is defined so that the trajectory lies on a spline in SE(2) with non-vanishing n-th derivatives at every point. Formulated as a multi-objective nonlinear optimization problem, it allows imposing soft constraints such as collision-avoidance, velocity, acceleration and jerk limits, and more. The planning process is realtime-capable allowing the robot to navigate in dynamic complex scenarios. The proposed method is compared against the state-of-the-art in various scenarios. Results show that trajectories generated by the TESC planner have smaller average acceleration and are more efficient in terms of total curvature and pseudo-kinetic energy while being produced with more consistency than state-of-the-art planners do.
A Modified Inception-ResNet Network with Discriminant Weighting Loss for Handwritten Chinese Character Recognition

Handwritten Chinese character recognition (HCCR) is a representative large character set pattern classification task. Recently, convolutional neural networks have provided promising solutions for this challenging task. This paper adopts the modified Inception-ResNet network for handwritten Chinese character recognition, and proposes a discriminant weighting method for cross-entropy loss calculation which focuses on recognition errors in the training stage. Sparse training technique is also incorporated. Under the specific condition of utilizing the testing mini-batch mean and variance for batch normalization, the proposed method achieves improved performance on the ICDAR-2013 offline handwritten Chinese character competition dataset.
Woodblock-Printing Mongolian Words Recognition by Bi-LSTM with Attention Mechanism

Woodblock-printing Mongolian documents are seriously degraded due to aging. Therefore, it is difficult to segment woodblock-printing Mongolian words are into individual glyphs. In this paper, a holistic recognition approach based on sequence to sequence model has been proposed for the woodblock-printing Mongolian words. The input of the proposed model is the sequence of frames of a wood-block printing Mongolian word. In order to generating the corresponding sequence of frames, each word image should be normalized into the same sizes in advance. And then, each word image is segmented into several fragments with equal size along writing direction. The output of the proposed model is a sequence of letters. To be specific, the proposed model contains three parts: an encoder, a decoder and an attention network. The encoder consists of a deep neural network and a bi-directional Long Short-Term Memory (Bi-LSTM). The decoder consists of a Long Short-Term Memory (LSTM) with a softmax layer. The encoder and decoder are connected by an attention network, which can map multiple frames to one letter. Experimental results demonstrate that the proposed approach outperforms the segmentation based method.
Improvement of Word Bag Model based on Image Classification

Bag of words was first used in the field of text categorization. In recent years, with the development of image processing technology, Bag of words has also been widely used in image classification and image retrieval. The use of Bag of words has simple and effective features, but in the image. There are still many defects in the classification. After researching and analyzing the SVM image classification model based on traditional Bag of words, it is found that there are two shortcomings in the traditional Bag of words in image classification: (1) the accuracy of visual word representation is not high; (2) the flexibility is poor. According to these two defects, combined with the contrast words and Hamming embedding technology, the traditional word bag model is improved, and a high discriminative force of Bag of words is applied and combined into the SVM image classification model. A SVM image classification model based on high discriminative Bag of words is presented. The experimental results show that the accuracy and applicability of the classification are improved.
Enhancing Trust in Business Ecosystems With Blockchain Technology

Trust is the foundation of business and it plays an increasingly important role in complex business ecosystems. Establishing and maintaining trust is time-consuming, expensive, and fraught with risk. Examining trust from the perspectives of people, process, and technology helps in understanding the characteristics of business ecosystems. Using the global private capital markets as an example, we describe the process of designing a blockchain architecture that enhances trust in a specific domain. Based on the process and results of implementation, we share lessons learned, pitfalls to avoid, and best practices. This helps practitioners reduce technology risk and employ blockchain to enhance trust in complex business ecosystems.
Tabular Cell Classification Using Pre-Trained Cell Embeddings

There is a large amount of data on the web in tabular form, such as excel sheets, CSVs, and web tables. Often, tabular data is meant for human consumption, using data layouts that are difficult for machines to interpret automatically. Previous work uses the stylistic features of tabular cells (e.g. font size, border type, background color) to classify tabular cells by their role in the data layout of the document (top attribute, data, metadata, etc.). In this paper, we propose a method to embed the semantic and contextual information about tabular cells in a low dimension cell embedding space. We then propose an RNN-based classification technique to use these cell vector representations, combining them with stylistic features introduced in previous work, in order to improve the performance of cell type classification in complex documents. We evaluate the performance of our system on three datasets containing documents with various data layouts, in two settings, in-domain, and cross-domain training. Our evaluation result shows that our proposed cell vector representations in combination with our RNN-based classification technique significantly improves cell type classification performance.
Towards Automated Evaluation of Handwritten Assessments

Automated evaluation of handwritten answers has been a challenging problem for scaling the education system for many years. Speeding up the evaluation remains as the major bottleneck for enhancing the throughput of instructors. This paper describes an effective method for automatically evaluating the short descriptive handwritten answers from the digitized images. Our goal is to evaluate a student's handwritten answer by assigning an evaluation score that is comparable to the human-assigned scores. Existing works in this domain mainly focused on evaluating handwritten essays with handcrafted, non-semantic features. Our contribution is two-fold: 1) we model this problem as a self-supervised, feature-based classification problem, which can fine-tune itself for each question without any explicit supervision. 2) We introduce the usage of semantic analysis for auto-evaluation in handwritten text space using the combination of Information Retrieval and Extraction (IRE) and, Natural Language Processing (NLP) methods to derive a set of useful features. We tested our method on three datasets created from various domains, using the help of students of different age groups. Experiments show that our method performs comparably to that of human evaluators.
Citizen Evaluations of Legislator–Constituent Communication

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Smart e-stick for Visually Impaired using Video Intelligence API

The visually impaired need to confront several problems while performing their daily activities. Independent navigation becomes one of the major problems in their social life. This issue further deteriorates when they travel across new unknown environments. This paper provides an efficient solution for the visually impaired in the form of a hardware automated stick based on Google's Cloud Video Intelligence API. This system uses real time video processing to analyze the obstacles or objects coming in the path of the blind and provides feedback in the form of voice messages. Hence the system facilitates real time navigation in both indoor and outdoor environments. Also the cost effectiveness and the versatility of the system make it more adaptive and easy to use.
Towards Understanding Attention-Based Speech Recognition Models

Although the attention-based speech recognition has achieved promising performances, the specific explanation of the intermediate representations remains a black box theory.In this paper, we use the method to visually show and explain continuous encoder outputs.We propose a human-intervened force alignment method to obtain labels for t-distributed stochastic neighbor embedding (t-SNE), and use them to better understand the attention mechanism and the recurrent representations.In addition, we combine t-SNE and canonical correlation analysis (CCA) to analyze the training dynamics of phones in the attentionbased model.Experiments are carried on TIMIT and WSJ respectively.The aligned embeddings of the encoder outputs could form sequence manifolds of the ground truth labels.Figures of t-SNE embeddings visually show what representations the encoder shaped into and how the attention mechanism works for the speech recognition.The comparisons between different models, different layers, and different lengths of the utterance show that manifolds are clearer in the shape when outputs are from the deeper layer of the encoder, the shorter utterance, and models with better performances.We also observe that the same symbols from different utterances tend to gather at similar positions, which proves the consistency of our method.Further comparisons are taken between different epochs of the model using t-SNE and CCA.The results show that both the plosive and the nasal/flap phones converge quickly, while the long vowel phone converge slowly.
Public hash signature for mobile network devices

In this work we have developed a digital signature protocol using hash functions that once implemented on mobile devices have demonstrated to be secure and efficient. It has been incorporated a model for a Certification Authority to exchange public keys between users. This work constitutes an experimental research, which bears a certain resemblance to theoretical research, but is not intended to propose a new theory, but to establish the behavior of a system to know its characteristics, in order to improve its knowledge and/or its performance. The hash signature system was tested on mobile communication devices. The experimental results show that the hash signature improves the efficiency to generate the cryptographic keys and the signing and verification processes when compared to ECC. Likewise, when generating 2048 keys, the hash signature is faster than RSA. In addition, the larger RSA keys consume a significative time, while the hash does not require to increase the size of the keys. Although we have not included here a formal analysis about the protocol, we highlight some points that improve the security of the proposed protocol. Finally, this work constitutes a new approach to public key cryptography based on hash functions that could be used to make digital signatures in electronic commerce. This method is suitable for mobile network devices due to the high speed and low hardware requirements of the hash functions. The method described here, which is compatible with hash functions, belongs to the field of post-quantum cryptography. The security of the method is based on the security of the hash cryptography, which is widely known and discussed.
Recognition of Parts of Speech Using the Vector of Bigram Frequencies

This paper describes how to automatically recognize parts of speech and other grammatical categories of a word such as gender and number. Unlike some previous works, the vector of syntactic bigram frequencies (including the considered word) is used as the source data for recognition of parts of speech and the grammatical categories. Data on frequencies of syntactic bigrams were obtained from the Russian subcorpus of Google Books Ngram. We used part–of–speech tags available in Google Books Ngram, as well as data on parts of speech and grammatical categories of words obtained from the electronic dictionary Open Corpora. To train the model, we selected words from the list of 100.000 most frequent words that don't have homonyms and are found in both Google Books Ngram and Open Corpora. A multilayer perceptron with an output layer of the softmax type was used as a recognizer. The vector of frequencies of syntactic bigrams including the test word and one of the 10.000 most frequent words was at the inputs of the network. The neural network was trained by the criterion of minimum cross–entropy. When recognizing parts of speech on the test sample, the average recognition accuracy was 99.1%. Nouns and verbs were recognized best of all (with the accuracy of 99.77% and 99.62%, respectively). The recognition accuracy of the word number was 99.61%. The achieved recognition accuracy of the word gender was substantially lower, it was just 91.9%.
See, Feel, Move

Playtesting of games often relies on a mixed-methods approach to obtain more holistic insights about and, in turn, improve the player experience. However, triangulating the different data sources and visualizing them in an integrated manner such that they contextualize each other still proves challenging. Despite its potential value for gauging player behaviour, this area of research continues to be underexplored. In this paper, we propose a visualization approach that combines commonly tracked movement data with - from a visualization perspective rarely considered - gaze behaviour and emotional responses. We evaluated our approach through a qualitative expert study with five professional game developers. Our results show that both the individual visualization of gaze, emotions, and movement but especially their combination are valuable to understand and form hypotheses about player behaviour. At the same time, our results stress that careful attention needs to be paid to ensure that the visualization remains legible and does not obfuscate information.
Lexical category bias across interpreting types: Implications for synergy between cognitive constraints and language representations

While previous results based on quantitative approaches to interpreting output have evidenced distinctive lexical, syntactic and language sequence features across interpreting types as a result of processing differences, none of them has probed into the lexical category realm. In this paper, the activity index, a normalized ratio between verb and adjective occurrences in the text, was used to capture the lexical-category-related patterns across output texts of three interpreting types, namely, simultaneous interpreting (SI), consecutive interpreting (CI) and read-out translated speech (TR). The results showed that the CI outputs yielded greater activity than the SI outputs due to lower frequencies of adjectives in the former. The discrepancy remained significant when differences in activity of input, text size and average sentence length were controlled for, suggesting processing differences as the chief factor underlying this lexical category bias. Given the syntactic priority of verbs over adjectives, the striking loss of adjectives from CI processing probably derives from "underestimated" cognitive demands in CI, which may exceed those in SI. Our research captures a lexical category bias in language processing under extreme cognitive pressure, and this biased language behaviour may reflect a dynamic adaptive mechanism of language representations to accommodate cognitive constraints.
Mixed-integer optimization approach to learning association rules for unplanned ICU transfer

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Most existing prediction studies were based on univariate analysis or multiple logistic regression to provide one-size-fit-all results. However, patient condition varying from case to case may not be accurately examined by such a simplistic judgment. In this study, we present a new decision tool using a mathematical optimization approach aiming to automatically discover rules associating diagnostic features with high-risk outcome (i.e., unplanned transfers) in different deterioration scenarios. We consider four mutually exclusive patient subgroups based on the principal reasons of ED visits: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases at a suburban teaching hospital. The analysis results demonstrate significant rules associated with unplanned transfer outcome for each subgroups and also show comparable prediction accuracy (>70%) compared to state-of-the-art machine learning methods while providing easy-to-interpret symptom-outcome information.
Zero Shot Learning Based Script Identification in the Wild

The text recognition system for natural images or video frames containing multilingual text needs a method to first identify the written script and then recognize the word in the identified script. However, the occurrence of some scripts is rare as compared to others. Due to the availability of a few samples of the rare script, the supervised learning of the deep neural networks is difficult. To overcome this problem, we have proposed a zero-shot learning based method for script identification. We have also proposed architecture for script identification which fuses the global feature vector and the semantic embedding vector. The semantic embedding of the script is obtained by using the spatial dependency of the stroke's sequence via the recurrent neural network. The proposed architecture shows superior results as compared to the baseline approaches.