Reviewerly/exHarmony huggingface dataset:
Incremental Online Spoken Language Understanding.

Spoken Language Understanding (SLU) typically comprises of an automatic speech recognition (ASR) followed by a natural language understanding (NLU) module. The two modules process signals in a blocking sequential fashion, i.e., the NLU often has to wait for the ASR to finish processing on an utterance basis, potentially leading to high latencies that render the spoken interaction less natural. In this paper, we propose recurrent neural network (RNN) based incremental processing towards the SLU task of intent detection. The proposed methodology offers lower latencies than a typical SLU system, without any significant reduction in system accuracy. We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems. A lexical End-of-Sentence (EOS) detector is proposed for segmenting the stream of transcript into sentences for intent classification. Intent detection experiments are conducted on benchmark ATIS dataset modified to emulate a continuous incremental stream of words with no utterance demarcation. We also analyze the prospects of early intent detection, before EOS, with our proposed system.
Technical Images and Visual Art in the Era of Artificial Intelligence

Artificial Intelligence (AI) and art share a common past, where artists employed AI algorithms to generate art. This paper explores the early days of AI-generated images, using Harold Cohen's AARON software as a paradigm of symbolic AI creative systems, and contextualizes the use of modern neural network technologies to create visual artworks. It discusses the methodologies and strategies used to make art using AI in the 1960s, comparing them to new AI algorithms. The discussion focuses on GOFAI (Good Old Fashioned Artificial Intelligence) and GANs (Generative Adversarial Networks) as the main technologies used in distinct historical periods to generate images. Vilém Flusser's conception of technical images provides a conceptual framework for examining the qualities and attributes of AI-generated images.
When Is the Number of True Different Permutation Polynomials Equal to 0?

In this paper, we have obtained the prime factorization form of positive integers N for which the number of true different fourth- and fifth-degree permutation polynomials (PPs) modulo N is equal to zero. We have also obtained the prime factorization form of N so that the number of any degree PPs nonreducible at lower degree PPs, fulfilling Zhao and Fan (ZF) sufficient conditions, is equal to zero. Some conclusions are drawn comparing all fourth- and fifth-degree permutation polynomials with those fulfilling ZF sufficient conditions.
An Improved Randomized Circle Detection Algorithm Using in Printed Circuit Board Locating Mark

This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform.The experimental results denote that this algorithm can locate the circular mark of Printed Circuit Board (PCB).
GazeFCW

Collision warning is essential in Advanced Driver Assistance Systems. However, all the studies focus on accurate assessment of risky situations without considering the driver's attention mechanism, which causes the proportion of valid warning triggers to be extremely low. In this paper, we present GazeFCW --- a novel system that uses the driver's gaze direction to filter out unnecessary warning triggers. We verified the proposed system against several roads of different conditions. Our evaluation, across different crowded roads, shows a significant enhancement in warning trigger efficiency---compared to the standard system---reflected by an increase in the valid trigger proportion by 42%, a decrease in the invalid trigger proportion from 74.7% down to 32.7%, while adding only 50 ms run time execution overhead and causing negligible missing triggers.
Design and fabrication of the portable artwork preservative device (PAPD)

Traditional artworks not only have the long history, but also offer the high economic value in the auction market. The artwork is commonly used paper or silk paper as raw material, which needs the relative harsh condition to save. However, there are generally three types of conditions to do harm to the valuable artworks, which are water, fire and earthquake or stealing conditions. In order to protect the artworks from irreversible damaged conditions, the portable artwork preservative device is designed to protect the artworks using rolling up the artwork method and keep it inside the artwork box. Three types of sensor modules have been selected in this study, which are used to detect the external conditions. It is concluded that this device shows the valid response performance, and it is automatedly rolled up the artwork around 10 s when it detects the signals from three sensor modules. In addition, the joystick module is used to control the artwork movement manually.
Differential Diagnosis of Heart Disease in Emergency Departments Using Decision Tree and Medical Knowledge

Generating differential diagnosis has been a subjective process primarily relying on a physician’s experience. However, the increased availability of electronic health records (EHRs) means that this process has the potential to benefit from machine learning-based decision support technology. No differential diagnosis models are currently available for heart disease, particularly for physicians in emergency departments (EDs). In this paper, we applied the decision tree method to automatically build a heart disease differential diagnosis model from structured and unstructured ED data. Our results show that the automatically learned model can achieve a classification accuracy of 89%. Our study demonstrates that data-driven differential diagnosis rules can be automatically learned from analyzing EHR data and that this learning can be clinically meaningful when merged with external medical knowledge.
Adversarial ML Attack on Self Organizing Cellular Networks

Deep Neural Networks (DNN) have been widely adopted in self-organizing networks (SON) for automating different networking tasks. Recently, it has been shown that DNN lack robustness against adversarial examples where an adversary can fool the DNN model into incorrect classification by introducing a small imperceptible perturbation to the original example. SON is expected to use DNN for multiple fundamental cellular tasks and many DNN-based solutions for performing SON tasks have been proposed in the literature have not been tested against adversarial examples. In this paper, we have tested and explained the robustness of SON against adversarial example and investigated the performance of an important SON use case in the face of adversarial attacks. We have also generated explanations of incorrect classifications by utilizing an explainable artificial intelligence (AI) technique.
Quantitative Identification Criteria for Secondary Toolmarks on Bullets Shot from AK-74

The paper compares the effectiveness of identifying low-count land marking patterns using the method of consecutively matching striations (CMS method) and the probabilistic method. The study was conducted on 48 pairs of matching toolmarks and 66 pairs of non-matching toolmarks. The criterion for assigning a positive match that was determined using the CMS method yielded one false positive in the analysis of known non-matches. Approximately 50% of matching pairs of toolmarks identified by the expert were omitted in the analysis. The application of the probabilistic method showed that out of the same 16 pairs of matches identified by the expert, no pair will be missed if the probability value of 5 х 10 -3 is taken as the identification criterion. No false positive statement will be made either. Thus it is shown that the probabilistic method of toolmark identification is more effective. It was demonstrated to be practically impossible to justify elimination when comparing low-count toolmark patterns.
McLaurin series as a new technique to improve encryption process

Abstract Now days many institutions and organizations needs to protect their information and secret data from theft and many other attempts to destroy and fabricate these data and information by unauthorized persons who tries to manipulate by the privacy of these institutions and organizations. So, it is necessary to find a secure way to protect such a data and information, therefore in this paper a new method proposed to develop the encryption techniques through using the McLaurin series as a new cryptosystem, and through using different cryptanalysis techniques and tools, the proposed method was inevitable against the different attacks, it is also a one way function technique.
Social Interaction in Spatial Augmented Exertion Interfaces

Exertion interfaces have the potential to facilitate social interaction and physical activity, supporting people as part of a healthy lifestyle. Knowledge about the interrelationship between social interaction and physical activity in exertion interfaces and knowledge of the best technology and interactive features they are facilitated by is currently lacking. Based on existing exertion interfaces and previous research, pilot prototypes, followed by high fidelity prototypes will be designed and developed following a research through design approach.
Strength Improvement and Stress Analysis of E-Glass Laminated Plates with Circular Notches Using Digital Image Correlation

In the current work, the stress concentration and tensile strength degradation of E-glass/epoxy laminates are addressed in the present investigation through a combination of both experimental and numerical studies.The numerical study is performed using finite element method (FEM).The main aim of this work is to improve the ultimate strength of perforated composite plates, by using defense hole system (DHS) technique.The samples are manufactured from commercially available unidirectional (UD) E-glass and clear 1070 resin epoxy.Digital image correlation (DIC) technique is also used to get the full-field surface strain measurements in perforated samples with various open hole diameters and DHS configurations, in order to show their effects on failure strength.Based on the experimental results, the ultimate strength can be improved by introducing two circular auxiliary holes along with the principal stress directions.
New Trend in Fintech: Research on Artificial Intelligence Model Interpretability in Financial Fields

With the development of Fintech, applying artificial intelligence (AI) technologies to the financial field is a general trend.However, there are some inappropriate conditions, for instance, the AI model is always treated as a black box and cannot be interpreted.This paper studies the AI model interpretability when the models are applied in the financial field.We analyze the reasons of black box problem and explore the effective solutions.We propose a new kind of automatic Regtech tool-LIMER, and put forward policy suggestions, thereby continuously promoting the development of Fintech to a higher level.
On the Salience of Adversarial Examples

Adversarial examples are beginning to evolve as rapidly as the deep learning models they are designed to attack. These intentionally-manipulated inputs attempt to mislead the targeted model while maintaining the appearance of innocuous input data. Countermeasures against these attacks that take a global approach tend to be lossy to the original data, or ineffective in removing the perturbations. Localized approaches have proven effective, however it is difficult to identify affected areas in the data in order to apply a targeted cleaning algorithm. For image data, visual saliency estimation models identify important features in an image, and provide a targeting mechanism for countering adversarial examples. In this work, we examine the effectiveness of state-of-the-art saliency models on complex scenes, in their original and perturbed forms. In a thorough range of standard metrics, we compare performance on clean image data with adversarial examples to demonstrate the vulnerability of deep learning-based saliency models to adversarial examples.
Document Specific Supervised Keyphrase Extraction With Strong Semantic Relations

Keyphrase extraction is the task of automatically extracting descriptive phrases or concepts that represent the main topics in a document. Finding good keyphrases in a document can quickly summarize knowledge for information retrieval and decision making. Existing keyphrase extraction methods cannot be customized to each specific document, and cannot capture flexible semantic relations. In this paper, a keyphrase extraction algorithm using maximum sequential pattern mining with one-off and general gaps condition, called Ke-MSMING, is presented. Ke_MSMING first searches all keyphrase candidates from a document using sequential patterns mining and the topic model, and then adopts supervised machine learning to classify each keyphrase candidate as a keyphrase or not. Finally, Ke_MSMING selects top-N keyphrases as the final keyphrases. Ke_MSMING not only uses baseline features and pattern features but also uses centrality features obtained from the cooccurrence semantic network, and the cooccurrence networks can yield powerful semantic relations for keyphrase extraction. Experimental results on two datasets demonstrate that Ke_MSMING has better performance than other state-of-the-art keyphrase extraction approaches.
To the question of the methodic of universities’ scientific potential appreciation

The development issues of the scientific potential of higher education as an important component of the national scientific and technological system of any country has been considered. The main purpose of the study is to develop, substantiate and test methods for assessing the scientific potential of universities. The main method of research is a system-dynamic approach to the study of scientific potential. The concept of “scientific potential of higher education” has been clarified. It seems reasonable, that the modern mission of higher education, both in Russia and in the world, is to create and further develop a unified high-level educational system, capable not only of training personnel for certain sectors of the real sector of the economy, but also to produce new knowledge, i.e. to ensure high-quality scientific and technological progress in these sectors. An original method of assessing the scientific potential of higher education has been developed, and its approbation is the main empirical result of the study. The essence of this method lies in the consistent estimation of factors of formation of the scientific potential (initial assessment) and the forms of its realization (secondary assessment), and also in the comparison of the indices of the secondary and primary evaluations (summative evaluation). The obtained results allow us to develop a strategy for the development of scientific potential, depending on the level of effectiveness of its implementation. The applied value of the research results is determined by the prospects of their application in the process of development of state policy for the development of research activities in higher education. In addition, the proposed methodology can be used by University leaders, who have set themselves the goal of building scientific capacity and integration into the national scientific and technological system and international scientific cooperation.
An Evaluation of Communication Protocol Languages for Engineering Multiagent Systems

Communication protocols are central to engineering decentralized multiagent systems. Modern protocol languages are typically formal and address aspects of decentralization, such as asynchrony. However, modern languages differ in important ways in their basic abstractions and operational assumptions. This diversity makes a comparative evaluation of protocol languages a challenging task. We contribute a rich evaluation of modern protocol languages based on diverse approaches. Among the selected languages, Scribble is based on session types; Trace-C and Trace-F on trace expressions; HAPN on hierarchical state machines, and BSPL on information causality. Our contribution is four-fold. One, we contribute important criteria for evaluating protocol languages. Two, for each criterion, we compare the languages on the basis of whether they are able to specify elementary protocols that go to the heart of the criterion. Three, for each language, we map our findings to a canonical architecture style for multiagent systems, highlighting where the languages depart from the architecture. Four, we identify a few design principles for protocol languages as guidance for future research.
Automatic Camera Path Generation from 360$$^\circ $$ Video

Omnidirectional ($$360^\circ $$) video is a novel media format, rapidly becoming adopted in media production and consumption as part of today’s ongoing virtual reality revolution. The goal of automatic camera path generation is to calculate automatically a visually interesting camera path from a $$360^\circ $$ video in order to provide a traditional, TV-like consumption experience. In this work, we describe our algorithm for automatic camera path generation, based on extraction of the information of the scene objects with deep learning based methods.
Story Quality as a Matter of Perception: Using Word Embeddings to Estimate Cognitive Interest

Storytelling is a capable tool for interactive agents and better stories can enable better interactions. Many existing automated evaluation techniques are either focused on textual features that are not necessarily reflective of perceived interestingness (e.g. coherence), or are domain-specific, relying on a priori semantics models (e.g. in a game). However, the effectiveness of storytelling depends both on its versatility to adapt to new domains and the perceived interestingness of its generated stories. In this paper, drawing from cognitive science literature, we propose and evaluate a method for estimating cognitive interest in stories based on the level of predictive inference they cause during perception.
CISELexia: Computer-Based Method for Improving Self-Awareness in Children with Dyslexia

Dyslexia has a lack of ability which causes other disturbances to social-emotional development.Based on an interview with the Chair of the Indonesian Dyslexia Association, increasing self-awareness is one form of dyslexia therapy to reduce that problem.Therefore there is a need for increasing self-awareness to solve that problem, whereas such technology the closest has not been fit with dyslexic children need.Then this study tries to focus on designed and developed CISELexia (Computer-Based Method for Improving Self-Awareness in Children with Dyslexia) technology using a gamification approach.It aims to help dyslexic children improve their competencies by increasing their self-awareness.Based on the implementation results CISELexia can increase the child's self-awareness state according to the rubric questionnaire given for about 10%.
Contextual Text Denoising with Masked Language Models

Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks. Unfortunately, state-of-the-art models are vulnerable to noisy texts. We propose a new contextual text denoising algorithm based on the ready-to-use masked language model. The proposed algorithm does not require retraining of the model and can be integrated into any NLP system without additional training on paired cleaning training data. We evaluate our method under synthetic noise and natural noise and show that the proposed algorithm can use context information to correct noise text and improve the performance of noisy inputs in several downstream tasks.
Keyword Spotting in Historical Bangla Handwritten Document Image Using CNN

In this paper, we propose a segmentation-free keyword spotting method for historical Bangla Handwritten Document images. For a given query keyword, the proposed method generates a set of candidate text regions within a document image using Histogram of Oriented Gradient (HOG) features. Next, the set of candidate text regions are fed to a trained Convolution Neural Network (CNN) model. The network finds the class label of each candidate text region and the regions whose class labels are same as the class label of the query word, are spotted in the document image. The proposed method is tested on three historical handwritten Bengali datasets and one historical English handwritten dataset. Detailed performance analysis demonstrates the goodness of the proposed method.
An Adaptive Exposure Fusion Method Using fuzzy Logic and Multivariate Normal Conditional Random Fields

High dynamic range (HDR) has wide applications involving intelligent vision sensing which includes enhanced electronic imaging, smart surveillance, self-driving cars, intelligent medical diagnosis, etc. Exposure fusion is an essential HDR technique which fuses different exposures of the same scene into an HDR-like image. However, determining the appropriate fusion weights is difficult because each differently exposed image only contains a subset of the scene's details. When blending, the problem of local color inconsistency is more challenging; thus, it often requires manual tuning to avoid image artifacts. To address this problem, we present an adaptive coarse-to-fine searching approach to find the optimal fusion weights. In the coarse-tuning stage, fuzzy logic is used to efficiently decide the initial weights. In the fine-tuning stage, the multivariate normal conditional random field model is used to adjust the fuzzy-based initial weights which allows us to consider both intra- and inter-image information in the data. Moreover, a multiscale enhanced fusion scheme is proposed to blend input images when maintaining the details in each scale-level. The proposed fuzzy-based MNCRF (Multivariate Normal Conditional Random Fields) fusion method provided a smoother blending result and a more natural look. Meanwhile, the details in the highlighted and dark regions were preserved simultaneously. The experimental results demonstrated that our work outperformed the state-of-the-art methods not only in several objective quality measures but also in a user study analysis.
Energy-Inspired Models: Learning with Sampler-Induced Distributions

Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in EBMs relies on approximate sampling algorithms, leading to a mismatch between the model and inference. Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model. This yields a class of energy-inspired models (EIMs) that incorporate learned energy functions while still providing exact samples and tractable log-likelihood lower bounds. We describe and evaluate three instantiations of such models based on truncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling. These models outperform or perform comparably to the recently proposed Learned Accept/Reject Sampling algorithm and provide new insights on ranking Noise Contrastive Estimation and Contrastive Predictive Coding. Moreover, EIMs allow us to generalize a recent connection between multi-sample variational lower bounds and auxiliary variable variational inference. We show how recent variational bounds can be unified with EIMs as the variational family.